Biology subjects

Phillips, N.

Publications and source records attributed to Phillips, N..

9 recordsLinked to original sources

bioRxiv2026

Integrated inference of cancer gene expression from cell-free plasma chromatin

Gene expression is a defining determinant of tumor identity, behavior, and therapeutic response, yet remains challenging to measure noninvasively. Here, we introduce APEX (Associating Plasma Epigenomic features with eXpression), a framework for inferring expression from circulating cell-free chromatin. Trained on [~]270,000 gene-sample pairs from matched tumor RNA-seq and plasma cfChIP-seq across multiple cancers and validated on >15 unseen cancer subtypes, APEX accurately infers cancer gene expression across a range of tumor fractions and outperforms existing plasma-based approaches by integrating positional histone mark and DNA fragmentation patterns across promoters and gene bodies. Using plasma alone, APEX enables classification of prognostically relevant basal and classical pancreatic cancer subtypes and identifies plasma-inferred NECTIN4 expression as a biomarker of response to enfortumab vedotin in metastatic bladder cancer. Together, these findings establish APEX as a biopsy-free approach for profiling tumor transcriptional states and extend liquid biopsy beyond genomic alterations to clinically relevant gene expression programs.

genomics↗

bioRxiv2025

SNAP: Streamlined Nextflow Analysis Pipeline for Immunoprecipitation-Based Epigenomic Profiling of Circulating Chromatin

Epigenomic profiling of circulating chromatin is a powerful and minimally invasive approach for detecting and monitoring disease, but there are no bioinformatics pipelines tailored to the unique characteristics of cell-free chromatin. We present SNAP (Streamlined Nextflow Analysis Pipeline), a reproducible, scalable, and modular workflow specifically designed for immunoprecipitation-based methods for profiling cell-free chromatin. SNAP incorporates quality control metrics optimized for circulating chromatin, including enrichment score and fragment count thresholds, as well as direct estimation of circulating tumor DNA (ctDNA) content from fragment length distributions. It also includes SNP fingerprinting to enable sample identity verification. When applied to cfChIP-seq and cfMeDIP-seq data across multiple cancer types, SNAPs quality filters significantly improved classification performance while maintaining high data retention. Independent validation using plasma from patients with osteosarcoma confirmed the detection of tumor-associated epigenomic signatures that correlated with ctDNA levels and reflected disease biology. SNAPs modular architecture enables straightforward extension to additional cell-free immunoprecipitation-based assays, providing a robust framework to support studies of circulating chromatin broadly. SNAP is compatible with cloud and high-performance computing environments and is publicly available at https://github.com/prc992/SNAP/. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/694452v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@47c734org.highwire.dtl.DTLVardef@674af8org.highwire.dtl.DTLVardef@16ae938org.highwire.dtl.DTLVardef@1f57a01_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Detection and monitoring of translocation renal cell carcinoma via plasma cell-free epigenomic profiling

TFE3 translocation renal cell carcinoma (tRCC), an aggressive kidney cancer driven by TFE3 gene fusions, is frequently misdiagnosed owing to morphologic overlap with other kidney cancer subtypes. Conventional liquid biopsy assays that detect tumor DNA via somatic mutations or copy number alterations are unsuitable for tRCC, since it often lacks recurrent genetic alterations and because fusion breakpoints are highly variable between patients. We reasoned that epigenomic profiling could more effectively detect tRCC, because the driver fusion constitutes an oncogenic transcription factor that alters gene regulation. By defining a TFE3-driven epigenomic signature in tRCC cell lines and detecting it in patient plasma using chromatin immunoprecipitation and sequencing, we distinguished tRCC from clear cell RCC (AUC=0.87) and healthy controls (AUC=0.91) at low tumor fractions (<1%). This work establishes a framework for non-invasive epigenomic detection, diagnosis and monitoring of tRCC, with implications for other mutationally quiet, fusion-driven cancers. SIGNIFICANCETranslocation renal cell carcinoma (tRCC) is an aggressive fusion-driven subtype of kidney cancer that is frequently misdiagnosed due to morphologic overlap with other kidney cancer subtypes. Conventional liquid biopsy assays targeting DNA alterations are suboptimal for use in tRCC due to its paucity of genomic changes. We demonstrate the utility of cell-free chromatin profiling to noninvasively detect and monitor tRCC with high accuracy, a method that could have applicability to other genomically quiet cancers.

genomics↗

bioRxiv2024

The level of HAND1 controls the specification of multipotent cardiac and extraembryonic progenitors

Diverse sets of progenitors contribute to the development of the embryonic heart, but the mechanisms of their specification have remained elusive. Here, using a human pluripotent stem cell (hPSC) model, we deciphered cardiac and non-cardiac lineage trajectories in differentiation and identified transcription factors underpinning cell specification, identity and function. We discovered a concentration-dependent, fate determining function in mesodermal progenitors for the basic helix-loop-helix transcription factor HAND1 and uncovered its gene regulatory network. At low level, HAND1 directs differentiation towards multipotent juxta-cardiac field progenitors able to make cardiomyocytes and epicardial cells, whereas at high level it promotes the development of extraembryonic mesoderm. Importantly, HAND1-low progenitors can be propagated in their multipotent state. This detailed mechanistic insight into human development will accelerate the delivery of effective disease modelling, including for congenital heart disease, and cell therapy-based regenerative medicine.

developmental biology↗

bioRxiv2024

The evolutionary ancient MEIS transcription factors actuate lineage-specific transcription to establish cardiac fate

Control of gene expression is commonly mediated by distinct combinations of transcription factors (TFs). This cooperative action allows multiple biological signals to be integrated at specific regulatory elements, resulting in highly specific gene expression patterns in space and time. It is unclear whether combinatorial binding is also necessary to bring together TFs with distinct biochemical functions, which collaborate to effectively recruit and activate RNA polymerase II. Using a cardiac differentiation model, we find that the largely ubiquitous, evolutionary ancient homeodomain proteins MEIS are essential for activating a cardiac-specific gene expression program. MEIS TFs act as actuators, fully activating transcriptional programs selected by lineage-restricted TFs to drive the dynamic progression of cardiac differentiation. Combinatorial binding of MEIS with lineage-enriched TFs, GATA and HOX, provides selectivity, guiding MEIS to function at cardiac-specific enhancers. In turn, MEIS TFs promote accumulation of the methyltransferase KMT2D to initiate lineage-specific enhancer commissioning. MEIS combinatorial binding dynamics, dictated by the changing dosage of its partners, drive cells into progressive stages of cardiac differentiation. Our results uncover tissue-specific transcriptional activation as the result of ubiquitous actuator TFs harnessing general transcriptional coactivators at tissue-specific enhancers, to which they are directed by binding with lineage- and domain-specific TFs.

developmental biology↗

bioRxiv2024

How does perception of zoo animal welfare influence public attitudes, experiences, and behavioural intentions? A mixed-methods systematic review

The public expects zoos to provide high standards of animal care. Failing to meet public expectations can have detrimental impacts on public experiences and behaviour, which in turn can compromise zoos organisational goals relative to conservation and public education. Despite increased research interest in understanding how the public perceives animal welfare in zoo settings, to date the factors that influence such perceptions are still unclear. To address this gap in knowledge, we conducted a mixed methods systematic review using a PRISMA approach to identify the factors that influence public perceptions of zoo animal welfare and the potential ramifications of these perceptions on public attitudes, experiences, and behaviours. A total of 115 peer reviewed journal articles were analysed: 43 provided qualitative data for thematic synthesis and 85 reported quantitative data for content analysis. Three main groupings were identified that impacted public perception of animal welfare in zoos: human, animal, and environmental factors. Within the human factors, ethical justifications, direct interactions, and inappropriate visitor behaviours were important. For the animal factors, animals behaviour, apparent health status, and the suitability of certain taxa for captivity were found to be key. Finally, several aspects of the environment -- conditions of the facility, the exhibit, and welfare-related educational material --were influential. Overall, negative perceptions of animal welfare resulted in negative visitor attitudes towards zoos, detrimentally impacted experiences, and lowered likelihood to visit zoos and engagement in conservation efforts. The articles in this review provided valuable insights into the factors affecting public perception of zoo animal welfare; however, future research may benefit from a more structured approach to increase comparability and validity of results across studies. We conclude by proposing seven recommendations to increase the robustness and validity of future research in this area.

zoology↗

bioRxiv2024

Predicting Alu exonization in the human genome with a deep learning model

Alu exonization, or the recruitment of intronic Alu elements into gene sequences, has contributed to functional diversification; however, its extent and the ways in which it influences gene regulation are not fully understood. We developed an unbiased approach to predict Alu exonization events from genomic sequences implemented in a deep learning model, eXAlu, that overcomes the limitations of tissue or condition specificity and the computational burden of RNA-seq analysis. The model captures previously reported characteristics of exonized Alu sequences and can predict sequence elements important for Alu exonization. Using eXAlu, we estimate the number of Alu elements in the human genome undergoing exonization to be between 55-110K, 11-21 fold more than represented in the GENCODE gene database. Using RT-PCR we were able to validate selected predicted Alu exonization events, supporting the accuracy of our method. Lastly, we highlight a potential application of our method to identify polymorphic Alu insertion exonizations in individuals and in the population from whole genome sequencing data.

bioinformatics↗

bioRxiv2022

NeuroCI: Continuous Integration of Neuroimaging Results Across Software Pipelines and Datasets

--Neuroimaging study results can vary significantly depending on the datasets and processing pipelines utilized by researchers to run their analyses, contributing to reproducibility issues. These issues are compounded by the fact that there are a large variety of seemingly equivalent tools and methodologies available to researchers for processing neuroimaging data. Here we present NeuroCI, a novel software framework that allows users to evaluate the variability of their results across multiple pipelines and datasets. NeuroCI makes use of Continuous Integration (CI), a software engineering technique, to facilitate the reproducibility of computational experiments by launching a series of automated tests when code or data is added to a repository. However, unlike regular CI services, our CI-based framework uses distributed computation and storage to meet the large memory and storage requirements of neuroimaging pipelines and datasets. Moreover, the frameworks modular design enables it to continuously ingest pipelines and datasets provided by the user, and to compute and visualize results across the multiple different pipelines and datasets. This allows researchers and practitioners to quantify the variability and reliability of results in their domain across a large range of computational methods.

bioinformatics↗

bioRxiv2017

Programmable transport of micro- and nanoparticles by Paramecium caudatum

We exploit chemo- and galvanotactic behaviour of Paramecium caudatum to design a hybrid device that allows for controlled uptake, transport and deposition of environmental micro- and nanoparticulates in an aqueous medium. Manipulation of these objects is specific, programmable and parallel. We demonstrate how device operation and output interpretation may be automated via a DIY low-cost fluorescence spectrometer, driven by a microprocessor board. The applications of the device presented range from collection and detoxification of environmental contaminants (e.g. nanoparticles), to micromixing, to natural expressions of computer logic.

microbiology↗