Biology subjects

Thomas, N.

Publications and source records attributed to Thomas, N..

At least 19 recordsLinked to original sources

bioRxiv2026

Estradiol Modulates Cytotoxic Responses to Topoisomerase II Targeting Agents in Estrogen Receptor Positive Breast Cancer Cells

Endocrine therapy is the primary treatment for estrogen receptor-positive (ER+) breast cancer. Paradoxically high dose estradiol can induce ER+ breast cancer cell death in an ER dependent manner. Although a well-established phenomenon, the mechanism of estradiol (E2)-induced breast cancer cell death has remained elusive. More recent evidence has revealed that E2 drives estrogen receptor alpha (ERa)-dependent transcription through TOP2 mediated DNA double-strand breaks. This raises two important questions. Firstly, is E2-dependent breast cancer cell death dependent on TOP2 mediated DNA double strand cleavage, and, secondly, does E2 signaling alter sensitivity of ER+ breast cancer cells to TOP2 poisons. Two ER+ breast cancer cell lines T47D and MCF-7 cells were treated with E2, doxorubicin (TOP2 poison) or T638 (catalytic TOP2 inhibitor), alone, and with each drug combined with E2 for 72 h. Viability was measured and normalized to untreated controls and IC50 values were calculated from dose-response curve. E2 enhanced doxorubicin potency in T47D and MCF7 ER+ breast cancer cell lines. E2 was toxic to T47D and MCF7 cells in a concentration dependent manner. The TOP2 catalytic inhibitor, T638, which blocks TOP2-mediated DNA cleavage reduced viability of both cell lines when used alone but decreased the cytotoxic effects of E2 about 7.6-fold, consistent with a role for TOP2-mediated DNA cleavage in the cytotoxic effects of E2. Thus, E2 sensitizes ER+ breast cancer cells to the TOP2 poison, whereas T638, the catalytic inhibitor, diminishes the cytotoxic response to E2. These opposing effects are consistent with E2 acting through TOP2-dependent DNA breaks.

cancer biology↗

bioRxiv2026

Back to basics: Observed statistics are sufficient to predict drug responses

Predicting how cells, tissues, and patients will respond to a drug, cytokine, or genetic perturbation is central to biological and clinical reasoning. The practical goal is to estimate the analysis-ready readouts that support this reasoning: which cellular responses are context-dependent, which perturbations reveal shared or divergent mechanisms, and which observations should become the basis for the next experiment. Here we introduce Rhaister, a perturbation-response predictor that operates directly on screen-level summary statistics. By measuring just a few perturbations in a new biological context, Rhaister predicts the unmeasured perturbations by learning how response patterns vary across reference contexts. This formulation applies to both fine-grained molecular readouts, such as transcriptional responses from Tahoe-100M or other large perturbation screen, or on phenotypic endpoints. To train and apply Rhaister on pheno-typic endpoints we created Emerald Bay, a purpose-built Tahoe dataset that unifies multi-day cancer drug perturbation, pooled Mosaic tumor contexts, and paired transcriptomic response measurements*. Across these settings, Rhaister matches or exceeds substantially more expensive virtual-cell models, often achieving the highest values possible in evaluation metrics, while training in seconds and running predictions in milliseconds. On Emerald Bay, Rhaister predicts context-specific drug phenotypes from sensitivity measurements alone and improves further when including transcriptomic information. We also introduce Rhaister-O, predicting drug responses in new contexts from baseline expression alone and, to our knowledge, provides the first zero-shot model for this task. Rhaister establishes summary-statistic perturbation modeling as a fast, interpretable framework for predicting biological response across new contexts.

genomics↗

bioRxiv2026

Language Modeling Materializes a World Model of Protein Biology

Proteins are fundamental to life. The full extent of their biology is beyond our ability to characterize with experimental approaches in the physical laboratory. Accurate digital representations could accelerate the discovery of protein biology through virtual experiments. We propose language modeling to learn unified and general representations that can be scaled to all of protein biology. Building on these representations, we develop a structure prediction model that exceeds the performance of established methods for biomolecular complex prediction across benchmarks, including for the interactions of antibodies with their targets. A simple search procedure yields high experimental success rates for the discovery of proteins with nanomolar binding affinities for both miniproteins and single-chain antibodies, a modality critical for therapeutic design. Study of the concepts in the language models representation space reveals a systematic organization aligned with the reductionist understanding of proteins developed through empirical science. Leveraging this organization, we generate a comprehensive map of protein biology encompassing over 6.8 billion sequences and 1.1 billion predicted structures, identifying connections across known and unknown biology. As a whole, this shows language modeling as a powerful substrate for representing the biology of proteins, operating across scales from the prediction and design of protein interactions at the atomic level, to identifying properties of proteins at different levels of granularity and abstraction, to the scale of mapping connections between proteins across billions of years of evolution.

bioinformatics↗

bioRxiv2026

Tracing cell communication programs across conditions at single cell resolution with CCC-RISE

Cell-cell communication (CCC) mediates coordinated cellular activities that vary dynamically across time, location, and biological context. While various tools exist to infer CCC, they typically aggregate data according to pre-defined cell types, obscuring critical single-cell heterogeneity. Furthermore, because signaling pathways and cell populations operate in a coordinated manner, an integrative analytical approach is essential. To address these challenges, we developed CCC-RISE, an extension of the tensor-based method Reduction and Insight in Single-cell Exploration (RISE). CCC-RISE identifies integrative patterns of single-cell variation by deconvolving communication into interpretable modules defined by unique sender cells, receiver cells, ligands, and condition associations. We applied this framework to a COVID-19 cohort with varying disease severity and a lung transplant cohort with acute allograft dysfunction. In both contexts, CCC-RISE successfully identified disease-relevant communication programs and traced them to specific cellular subpopulations, often crossing conventional cell-type boundaries. This approach offers a robust pipeline enabling the identification of disease-relevant signaling subpopulations that are invisible to aggregate methods. HighlightsO_LICCC-RISE enables integrative analysis of cell-cell communication across multiple conditions at single-cell resolution C_LIO_LICCC-RISE deconvolves signaling patterns into modules defined by their sender cells, receiver cells, LR pairs, and experimental conditions/samples C_LIO_LIAnalysis at single-cell resolution uncovers signaling activity within and across conventional cell types C_LI

systems biology↗

bioRxiv2026

Identifying and ranking species that need urgent management action to achieve Target 4 of the Global Biodiversity Framework

Target 4 of the Kunming-Montreal Global Biodiversity Framework (KMGBF) calls for urgent management actions to halt humaninduced extinctions and enable species recovery. However, most Parties face substantial challenges in determining which species require urgent management actions. Here, we present a transparent, standardised protocol that identifies and ranks species most likely to need urgent management actions at the national level, using globally available data from the IUCN Red List of Threatened Species. The protocol integrates four criteria aligned with Target 4: global extinction risk, rate of decline, population or range restriction, and endemism, to generate a national ranked list of species. Species scoring highly on these four criteria, and therefore most in need of urgent management action, are ranked most highly. We applied this method to all 250 countries and territories listed in the IUCN Red List and pilottested national rankings with participants from eight diverse countries. Across pilots, participants reported that the ranked lists were scientifically robust, timesaving, and valuable starting points for national prioritysetting, while stating the importance of national context, and the need for additional technical and financial support for implementation. Our results demonstrate that a sciencebased approach can meaningfully support Parties in identifying species requiring urgent action under Target 4, in a standardised way. With 2030 approaching rapidly, this protocol provides an immediate, practical tool to accelerate progress toward halting extinctions and advancing species recovery.

ecology↗

bioRxiv2025

LCR-modules: a collection of workflows for cancer genome analysis

The surge of genomic data from advanced sequencing technologies is outpacing current analytical pipelines. We introduce LCR-modules, an open-source suite of bioinformatics tools designed for flexible and automated cancer genome data analysis. LCR-modules enables reproducible analysis of diverse cancer genomics data at scale. The suite comprises 49 Snakemake-based workflows organized into three levels, facilitating tasks from low-level quality control to complex cohort-level analyses. LCR-modules supports various sequencing types and integrates pipelines such as mutation calling, expression quantification, and cohort-level aggregation, ensuring flexibility and reproducibility. LCR-modules represents a significant advancement in genomic data analysis, reducing barriers in reproducibility and scalability and has already been applied to a combination of exomes and genomes from over 10,800 samples.

bioinformatics↗

bioRxiv2025

Treatment Resistant Persister Cells Exploit Macrophage Lipid Metabolism to Sustain Glioblastoma Growth

Glioblastoma (GBM) displays pronounced intratumoral heterogeneity, posing significant challenges to understanding its biology and developing effective treatments. Using spatial multi-omics, in vivo functional assays, and systems-level analysis, we delineate the diverse metabolic and immune architecture of GBM. We identify a lipid-dependent lineage of treatment-resistant persister cells (TRPCs) that engage tumor-associated macrophages (TAMs) in a spatially organized, metabolically specialized crosstalk. TRPCs co-opt CCR2, CSF1R, CD163+ TAMs for lipid scavenging and acquisition, promoting a pro-tumorigenic and immunosuppressive microenvironment. This cooperative axis is critically dependent on lipid chaperones like FABPs, whose targeting disrupts TAM recruitment, remodels immune composition, and suppresses tumor growth. Retrospective clinical analyses reveal that elevated TRPC-associated transcriptome may serve as stratification criteria to identify patients benefiting from lipid-lowering therapies like statins. Our findings uncover a targetable immunometabolic circuit between TRPCs and TAMs and support the development of precision therapies that disrupt lipid-fueled tumor-immune cooperation in GBM. In briefTreatment-resistant persister cells (TRPCs) in glioblastoma spatially engage TAMs to facilitate lipid transfer, thereby sustaining tumor growth and promoting immune evasion. Targeting this TRPC-TAM metabolic axis reprograms the immunosuppressive microenvironment and improves therapeutic outcomes, revealing a clinically actionable metabolic vulnerability with potential for precision immune-metabolic interventions in GBM. HighlightsO_LIGBM exhibits spatially resolved heterogeneity revealing correlative metabolic and immune micro-niches C_LIO_LITRPC lineage promotes a pro-tumorigenic and immunosuppressive microenvironment by recruiting lipid-specialized TAMs C_LIO_LITRPC lineage hijacks TAMs for metabolic support via stimulating lipid transfer and acquisition C_LIO_LIDisruption of the TRPC-TAM lipid axis, including through FABP3 targeting, reprograms the immune landscape, limits TAM recruitment, and impairs tumor progression C_LIO_LITRPC lineage and associated micro-niche transcriptomic profiles can serve as criteria for patient stratification and identification of responders to lipid-lowering therapies, such as statins C_LI

cancer biology↗

bioRxiv2025

Tahoe-100M: A Giga-Scale Single-Cell Perturbation Atlas for Context-Dependent Gene Function and Cellular Modeling

Building predictive models of the cell requires systematically mapping how perturbations reshape each cells state, function, and behavior. Here, we present Tahoe-100M, a giga-scale single-cell atlas of 100 million transcriptomic profiles measuring how each of 1,100 small-molecule perturbations impact cells across 50 cancer cell lines. Our high-throughput Mosaic platform, composed of a highly diverse and optimally balanced "cell village", reduces batch effects and enables parallel profiling of thousands of conditions at single-cell resolution at an unprecedented scale. As the largest single-cell dataset to date, Tahoe-100M enables artificial-intelligence (AI)-driven models to learn context-dependent functions, capturing fundamental principles of gene regulation and network dynamics. Although we leverage cancer models and pharmacological compounds to create this resource, Tahoe-100M is fundamentally designed as a broadly applicable perturbation atlas and supports deeper insights into cell biology across multiple tissues and contexts. By publicly releasing this atlas, we aim to accelerate the creation and development of robust AI frameworks for systems biology, ultimately improving our ability to predict and manipulate cellular behaviors across a wide range of applications.

genomics↗

bioRxiv2024

The C-terminus of the multi-drug efflux pump EmrE prevents proton leak by gating transport

The model multi-drug efflux pump from Escherichia coli, EmrE, can perform multiple types of transport leading to different biological outcomes, conferring resistance to some drug substrates and enhancing susceptibility to others. While transporters have traditionally been classified as antiporters, symporters, or uniporters, there is growing recognition that some transporters may exhibit mixed modalities. This raises new questions about the regulation and mechanisms of these transporters. Here we show that the C-terminal tail of EmrE acts as a secondary gate, preventing proton leak in the absence of drug. Substrate binding unlocks this gate, allowing transport to proceed. Truncation of the C-terminal tail ({Delta}107-EmrE) leads to altered pH regulation of alternating access, an important kinetic step in the transport cycle, as measured by NMR. {Delta}107-EmrE has increased proton leak in proteoliposome assays and bacteria expressing this mutant have reduced growth. MD simulations of {Delta}107-EmrE show formation of a water wire from the open face of the transporter to the primary binding site in the core, facilitating proton leak. In WT-EmrE, the C-terminal tail forms specific interactions that block formation of the water wire. Together these data strongly support the C-terminus of EmrE acting as a secondary gate that regulates access to the primary binding site in the core of the transporter.

biophysics↗

bioRxiv2024

Integrative, high-resolution analysis of single cells across experimental conditions with PARAFAC2

Effective and scalable exploration and analysis tools are vital for the extraction of insights from large-scale single-cell data. However, current techniques for modeling single-cell studies performed across experimental conditions (e.g., samples, perturbations, or patients) require restrictive assumptions, lack flexibility, or do not adequately deconvolute condition-to-condition variation from cell-to-cell variation. Here, we report that Reduction and Insight in Single-cell Exploration (RISE), an adaptation of the tensor decomposition method PARAFAC2, enables the dimensionality reduction and analysis of single-cell data across conditions. We demonstrate the benefits of RISE across two distinct examples of single-cell RNA-sequencing experiments of peripheral immune cells: pharmacologic drug perturbations and systemic lupus erythematosus (SLE) patient samples. RISE enables straightforward associations of gene variation patterns with specific patients or perturbations, while connecting each coordinated change to single cells without requiring cell type annotations. The theoretical grounding of RISE suggests a unified framework for many single-cell data modeling tasks. Thus, RISE provides an intuitive universal dimensionality reduction approach for multi-sample single-cell studies across diverse biological contexts. HighlightsO_LIRISE enables tensor-based analysis of single-cell experiments across conditions. C_LIO_LIRISE separates condition-specific effects from cell-to-cell variation. C_LIO_LIRISE provides intuitive isolation of patterns into condition-, cell-, and gene-specific patterns. C_LI

systems biology↗

bioRxiv2024

Simulating 500 million years of evolution with a language model

More than three billion years of evolution have produced an image of biology encoded into the space of natural proteins. Here we show that language models trained on tokens generated by evolution can act as evolutionary simulators to generate functional proteins that are far away from known proteins. We present ESM3, a frontier multimodal generative language model that reasons over the sequence, structure, and function of proteins. ESM3 can follow complex prompts combining its modalities and is highly responsive to biological alignment. We have prompted ESM3 to generate fluorescent proteins with a chain of thought. Among the generations that we synthesized, we found a bright fluorescent protein at far distance (58% identity) from known fluorescent proteins. Similarly distant natural fluorescent proteins are separated by over five hundred million years of evolution.

synthetic biology↗

bioRxiv2024

Engineering of highly active and diverse nuclease enzymes by combining machine learning and ultra-high-throughput screening

Optimizing enzymes to function in novel chemical environments is a central goal of synthetic biology, but optimization is often hindered by a rugged, expansive protein search space and costly experiments. In this work, we present TeleProt, an ML framework that blends evolutionary and experimental data to design diverse protein variant libraries, and employ it to improve the catalytic activity of a nuclease enzyme that degrades biofilms that accumulate on chronic wounds. After multiple rounds of high-throughput experiments using both TeleProt and standard directed evolution (DE) approaches in parallel, we find that our approach found a significantly better top-performing enzyme variant than DE, had a better hit rate at finding diverse, high-activity variants, and was even able to design a high-performance initial library using no prior experimental data. We have released a dataset of 55K nuclease variants, one of the most extensive genotype-phenotype enzyme activity landscapes to date, to drive further progress in ML-guided design.

bioinformatics↗

bioRxiv2024

CRM1 regulates androgen receptor stability and impacts DNA repair pathways in prostate cancer, independent of the androgen receptor.

Among the known nuclear exportins, CRM1 is the most studied prototype. Dysregulation of CRM1 occurs in many cancers, hence, understanding the role of CRM1 in cancer can help in developing synergistic therapeutics. The study investigates how CRM1 affects prostate cancer growth and survival. It examines the role of CRM1 in regulating androgen receptor (AR) and DNA repair in prostate cancer. Our findings reveal that CRM1 influences AR mRNA and protein stability, leading to a loss of AR protein upon CRM1 inhibition. Furthermore, it highlights the involvement of HSP90 alpha, a known AR chaperone, in the CRM1-dependent regulation of AR protein stability. The combination of CRM1 inhibition with an HSP90 inhibitor demonstrates potent effects on decreasing prostate cancer cell growth and survival. The study further explores the influence of CRM1 on DNA repair proteins and proposes a strategy of combining CRM1 inhibitors with DNA repair pathway inhibitors to decrease prostate cancer growth. Overall, the findings suggest that CRM1 plays a crucial role in prostate cancer growth, and a combination of inhibitors targeting CRM1 and DNA repair pathways could be a promising therapeutic strategy.

cancer biology↗

bioRxiv2023

Mapping chromatin state and transcriptional response in CIC-DUX4 undifferentiated round cell sarcoma

CIC-DUX4 is a rare and understudied transcription factor fusion oncoprotein. CIC-DUX4 co-opts native gene targets to drive a lethal form of human sarcoma. The molecular underpinnings that lead to oncogenic reprograming and CIC-DUX4 sarcomagenesis remain largely undefined. Through an integrative ChIP and RNA-Seq analysis using patient-derived CIC-DUX4 cells, we define CIC-DUX4 mediated chromatin states and function. We show that CIC-DUX4 primarily localizes to proximal and distal cis-regulatory elements where it associates with active histone marks. Our findings nominate key signaling pathways and molecular targets that enable CIC-DUX4 to mediate tumor cell survival. Collectively, our data demonstrate how the CIC-DUX4 fusion oncoprotein impacts chromatin state and transcriptional responses to drive an oncogenic program in undifferentiated sarcoma. SignificanceCIC-DUX4 sarcoma is a rare and lethal sarcoma that affects children, adolescent young adults, and adults. CIC-DUX4 sarcoma is associated with rapid metastatic dissemination and relative insensitivity to chemotherapy. There are no current standard-of-care therapies for CIC-DUX4 sarcoma leading to universally poor outcomes for patients. Through a deep mechanistic understanding of how the CIC-DUX4 fusion oncoprotein reprograms chromatin state and function, we aim to improve outcomes for CIC-DUX4 patients.

cancer biology↗

bioRxiv2023

Computational design of N-linked glycans for high throughput epitope profiling

Efficient identification of epitopes is crucial for drug discovery and design as it enables the selection of optimal epitopes, expansion of lead antibody diversity, and verification of binding interface. Although high resolution low throughput methods like X-ray crystallography can determine epitopes or protein-protein interactions accurately, they are time-consuming and can only be applied to a limited number of complexes. To overcome these limitations, we have developed a rapid computational method that incorporates N-linked glycans to mask epitopes or protein interaction surfaces, thereby providing an atomistic mapping of these regions. Using human coagulation factor IXa (fIXa) as a model system, we could rapidly and reliably delineate epitopes through the insertion of N-linked glycans that efficiently disrupt binding in a site-selective manner. To validate the efficacy of our method, we conducted ELISA experiments and high-throughput yeast surface display assays. Furthermore, X-ray crystallography was employed to verify the results, thereby recapitulating through the method of N-linked glycans an atomistic resolution mapping of the epitope.

bioengineering↗

bioRxiv2022

Tuned Fitness Landscapes for Benchmarking Model-Guided Protein Design

Advancements in DNA synthesis and sequencing technologies have enabled a novel paradigm of protein design where machine learning (ML) models trained on experimental data are used to guide exploration of a protein fitness landscape. ML-guided directed evolution (MLDE) builds on the success of traditional directed evolution and unlocks strategies which make more efficient use of experimental data. Building an MLDE pipeline involves many design choices across the design-build-test-learn loop ranging from data collection strategies to modeling, each of which has a large impact on the success of designed sequences. The cost of collecting experimental data makes benchmarking every component of these pipelines on real data prohibitively difficult, necessitating the development of synthetic landscapes where MLDE strategies can be tested. In this work, we develop a framework called SLIP ("Synthetic Landscape Inference for Proteins") for constructing biologically-motivated synthetic landscapes with tunable difficulty based on Potts models. This framework can be extended to any protein family for which there is a sequence alignment. We show that without tuning, Potts models are easy to optimize. In contrast, our tuning framework provides landscapes sufficiently challenging to benchmark MLDE pipelines. SLIP is open-source and is available at https://github.com/google-research/slip.

synthetic biology↗

bioRxiv2022

Multi-scale observations of mangrove blue carbon fluxes; the NASA Carbon Monitoring System BlueFlux field campaign

The BlueFlux field campaign is supported by NASAs Carbon Monitoring System (CMS) and will develop prototype blue carbon products to inform coastal carbon management. Blue carbon is included in carbon-dioxide removal actions proposed to reduce atmospheric CO2 concentrations to mitigate climate change. Due to their high productivity and carbon storage, combined with historic losses and a wide-range of beneficial ecosystem services, the restoration and conservation of mangrove ecosystems features prominently in blue-carbon planning. The goal of BlueFlux is to carry out multi-scale measurements of CO2 and CH4 fluxes using chambers, flux towers, and aircraft and scale these to gridded products using space-based observations of forest structure and surface reflectance. The measurements cover gradients in disturbance, mainly from the history of hurricanes in the region that drive the dieback of mangroves and the formation of ghost forests. The fluxes of CH4 emissions will be contrasted with CO2 uptake to provide a more complete budget of radiative forcing and to understand the net climate benefits of blue carbon. BlueFlux demonstrates that quantifying the removals of CO2 and emissions of CH4 using a multi-scale approach can provide increased confidence in regional greenhouse-gas accounting, contribute to process-understanding, and help inform restoration and conservation efforts in the context of climate mitigation.

ecology↗

bioRxiv2022

Attenuation of a DNA Cruciform by a Conserved Regulator Directs T3SS-1 mediated virulence in Vibrio parahaemolyticus

Pathogenic Vibrio species account for 3-5 million annual life-threatening human infections. Virulence is driven by bacterial hemolysin and toxin gene expression often positively regulated by the winged helix-turn-helix (wHTH) HlyU transcriptional regulator family and silenced by Histone-like nucleoid structural protein (H-NS). In the case of Vibrio parahaemolyticus, HlyU is required for virulence gene expression associated with Type 3 Secretion System-1 (T3SS-1) although its mechanism of action is not understood. Here, we provide evidence for DNA cruciform attenuation mediated by HlyU binding to support concomitant virulence gene expression. Genetic and biochemical experiments revealed that upon HlyU mediated DNA cruciform attenuation, an intergenic cryptic promoter became accessible allowing for exsA mRNA expression and initiation of an ExsA autoactivation feedback loop at a separate ExsA-dependent promoter. Using a heterologous E. coli expression system, we reconstituted the dual promoter elements which revealed that HlyU binding and DNA cruciform attenuation were strictly required to initiate the ExsA autoactivation loop. The data indicate that HlyU acts to attenuate a transcriptional repressive DNA cruciform to support T3SS-1 virulence gene expression and reveals a non-canonical extricating gene regulation mechanism in pathogenic Vibrio species.

microbiology↗