Biology subjects

Harris, N. L.

Publications and source records attributed to Harris, N. L..

9 recordsLinked to original sources

bioRxiv2025

Mast cells interact directly with colorectal cancer cells to promote epithelial-to-mesenchymal transition

Mast cells (MCs), a type of granulocytic immune cell, can be both pro- and anti-tumorigenic in colorectal cancer (CRC). We hypothesized that these contrasting findings may be in part due to di_erential interactions of MCs with CRC subtypes. BRAF mutant CRC uniquely contains intestinal secretory cell types. In this study, we demonstrated that MCs are enriched in BRAF mutant CRC, likely because they are recruited by factors released from cancer secretory cells. To investigate the functional consequences of MC-CRC cell interactions, we performed direct coculture experiments. We demonstrated that MCs promote epithelial-to-mesenchymal transition (EMT) in CRC cells in a calcium- and contact-dependent fashion. Furthermore, inhibiting LFA-1 and ICAM1 integrin binding reduced the coculture-induced EMT-related marker expression in CRC cells. The MC-CRC cell interaction facilitates the transfer of biological materials, including mRNA molecules, from MCs to CRC cells. This study is the first to report a contact-dependent, pro-tumorigenic role of MCs in CRC, as well as the transfer of molecules encoded by MCs to CRC cells. These findings enhance our comprehension of cell-cell communication between immune and cancer cells. Furthermore, this work suggests that targeting MC-CRC interactions, particularly through modulating integrin pathways, could o_er new therapeutic strategies for aggressive CRC subtypes.

cancer biology↗

bioRxiv2024

The Unified Phenotype Ontology (uPheno): A framework for cross-species integrative phenomics

Phenotypic data are critical for understanding biological mechanisms and consequences of genomic variation, and are pivotal for clinical use cases such as disease diagnostics and treatment development. For over a century, vast quantities of phenotype data have been collected in many different contexts covering a variety of organisms. The emerging field of phenomics focuses on integrating and interpreting these data to inform biological hypotheses. A major impediment in phenomics is the wide range of distinct and disconnected approaches to recording the observable characteristics of an organism. Phenotype data are collected and curated using free text, single terms or combinations of terms, using multiple vocabularies, terminologies, or ontologies. Integrating these heterogeneous and often siloed data enables the application of biological knowledge both within and across species. Existing integration efforts are typically limited to mappings between pairs of terminologies; a generic knowledge representation that captures the full range of cross-species phenomics data is much needed. We have developed the Unified Phenotype Ontology (uPheno) framework, a community effort to provide an integration layer over domain-specific phenotype ontologies, as a single, unified, logical representation. uPheno comprises (1) a system for consistent computational definition of phenotype terms using ontology design patterns, maintained as a community library; (2) a hierarchical vocabulary of species-neutral phenotype terms under which their species-specific counterparts are grouped; and (3) mapping tables between species-specific ontologies. This harmonized representation supports use cases such as cross-species integration of genotype-phenotype associations from different organisms and cross-species informed variant prioritization.

bioinformatics↗

bioRxiv2024

Towards a standard benchmark for variant and gene prioritisation algorithms: PhEval - Phenotypic inference Evaluation framework

BackgroundComputational approaches to support rare disease diagnosis are challenging to build, requiring the integration of complex data types such as ontologies, gene-to-phenotype associations, and cross-species data into variant and gene prioritisation algorithms (VGPAs). However, the performance of VGPAs has been difficult to measure and is impacted by many factors, for example, ontology structure, annotation completeness or changes to the underlying algorithm. Assertions of the capabilities of VGPAs are often not reproducible, in part because there is no standardised, empirical framework and openly available patient data to assess the efficacy of VGPAs - ultimately hindering the development of effective prioritisation tools. ResultsIn this paper, we present our benchmarking tool, PhEval, which aims to provide a standardised and empirical framework to evaluate phenotype-driven VGPAs. The inclusion of standardised test corpora and test corpus generation tools in the PhEval suite of tools allows open benchmarking and comparison of methods on standardised data sets. ConclusionsPhEval and the standardised test corpora solve the issues of patient data availability and experimental tooling configuration when benchmarking and comparing rare disease VGPAs. By providing standardised data on patient cohorts from real-world case-reports and controlling the configuration of evaluated VGPAs, PhEval enables transparent, portable, comparable and reproducible benchmarking of VGPAs. As these tools are often a key component of many rare disease diagnostic pipelines, a thorough and standardised method of assessment is essential for improving patient diagnosis and care.

bioinformatics↗

bioRxiv2024

Phenomics Assistant: An Interface for LLM-based Biomedical Knowledge Graph Exploration

We introduce Phenomics Assistant, a prototype chat-based interface for querying the Monarch knowledge graph (KG), a comprehensive biomedical database. While unaided Large Large Language models (LLMs) are prone to mistakes in factual recall, their strong abilities in summarization and tool use suggest new opportunities to help non-expert users query and interact with complex data, while drawing on the KG to improve reliability of the answers. Leveraging the ability of LLMs to interpret queries in natural language, Phenomics Assistant enables a wide range of users to interactively discover relationships between diseases, genes, and phenotypes. To assess the reliability of our approach and compare the accuracy of different LLMs, we evaluated Phenomics Assistant answers on benchmark tasks for gene-disease association and gene alias queries. While comparisons across tested LLMs revealed differences in their ability to interpret KG-provided information, we found that even basic KG access markedly boosts the reliability of standalone LLMs. By enabling users to pose queries in natural language and summarizing results in familiar terms, Phenomics Assistant represents a new approach for navigating the Monarch KG.

bioinformatics↗

bioRxiv2023

The Ontology of Biological Attributes (OBA) - Computational Traits for the Life Sciences

Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focused measurable trait data. Moreover, variations in gene expression in response to environmental disturbances even without any genetic alterations can also be associated with particular biological attributes. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.

bioinformatics↗

bioRxiv2021

OBO Foundry in 2021: Operationalizing Open Data Principles to Evaluate Ontologies

Biological ontologies are used to organize, curate, and interpret the vast quantities of data arising from biological experiments. While this works well when using a single ontology, integrating multiple ontologies can be problematic, as they are developed independently, which can lead to incompatibilities. The Open Biological and Biomedical Ontologies (OBO) Foundry was created to address this by facilitating the development, harmonization, application, and sharing of ontologies, guided by a set of overarching principles. One challenge in reaching these goals was that the OBO principles were not originally encoded in a precise fashion, and interpretation was subjective. Here we show how we have addressed this by formally encoding the OBO principles as operational rules and implementing a suite of automated validation checks and a dashboard for objectively evaluating each ontologys compliance with each principle. This entailed a substantial effort to curate metadata across all ontologies and to coordinate with individual stakeholders. We have applied these checks across the full OBO suite of ontologies, revealing areas where individual ontologies require changes to conform to our principles. Our work demonstrates how a sizable federated community can be organized and evaluated on objective criteria that help improve overall quality and interoperability, which is vital for the sustenance of the OBO project and towards the overall goals of making data FAIR.

bioinformatics↗

bioRxiv2021

Small Intestinal Resident Eosinophils Maintain Gut Homeostasis Following Microbial Colonisation

Intestinal homeostasis following postnatal microbial colonization requires the coordination of multiple processes including the activation of immune cells, cell-cell communication, the controlled deposition of extracellular matrix, and epithelial cell turnover and differentiation. The intestine harbors the largest frequency of resident eosinophils of all homeostatic organs, yet the functional significance of eosinophil recruitment to this organ has long remained enigmatic. Eosinophils are equipped to both respond to, and modify, their local tissue environment and thus are able to regulate the adaption of tissues to environmental changes. We report a critical role for eosinophils in regulating villous structure, barrier integrity and motility in the small intestine. Notably, the microbiota was identified as a key driver of small intestinal eosinophil activation and function. Collectively our findings demonstrate a critical role for eosinophils in facilitating mutualistic interactions between host and microbiota and provide a rationale for the functional significance of their early life recruitment in the small intestine. HIGHLIGHTSO_LIThe microbiota is a critical regulator of eosinophil activation and turnover C_LIO_LIEosinophils uphold intestinal barrier integrity following microbial colonization C_LIO_LILoss of eosinophils at steady-state results in increased villous blunting and altered intestinal motility C_LI

immunology↗

bioRxiv2020

KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 response

Integrated, up-to-date data about SARS-CoV-2 and coronavirus disease 2019 (COVID-19) is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community varies drastically for different tasks - the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates biomedical data to produce knowledge graphs (KGs) for COVID-19 response. This KG framework can also be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics. BIGGER PICTUREAn effective response to the COVID-19 pandemic relies on integration of many different types of data available about SARS-CoV-2 and related viruses. KG-COVID-19 is a framework for producing knowledge graphs that can be customized for downstream applications including machine learning tasks, hypothesis-based querying, and browsable user interface to enable researchers to explore COVID-19 data and discover relationships.

bioinformatics↗

bioRxiv2016

The DOE Systems Biology Knowledgebase (KBase)

The U.S. Department of Energy Systems Biology Knowledgebase (KBase) is an open-source software and data platform designed to meet the grand challenge of systems biology -- predicting and designing biological function from the biomolecular (small scale) to the ecological (large scale). KBase is available for anyone to use, and enables researchers to collaboratively generate, test, compare, and share hypotheses about biological functions; perform large-scale analyses on scalable computing infrastructure; and combine experimental evidence and conclusions that lead to accurate models of plant and microbial physiology and community dynamics. The KBase platform has (1) extensible analytical capabilities that currently include genome assembly, annotation, ontology assignment, comparative genomics, transcriptomics, and metabolic modeling; (2) a web-browser-based user interface that supports building, sharing, and publishing reproducible and well-annotated analyses with integrated data; (3) access to extensive computational resources; and (4) a software development kit allowing the community to add functionality to the system.

bioinformatics↗