Biology subjects

Caufield, J. H.

Publications and source records attributed to Caufield, J. H..

4 recordsLinked to original sources

bioRxiv2025

KG-Microbe - Building Modular and Scalable Knowledge Graphs for Microbiome and Microbial Sciences

The integration of many disparate forms of data is essential for understanding the microbial world and its interaction with the environment and human health. Doing so is particularly challenging in the context of microbe-host and microbe-microbe interactions that contribute to health or environmental outcomes. There are often thousands of relevant microbial species, and millions of interactions among those microbes and with their environment or host. Some experimental observations only distinguish coarser taxonomic resolutions such as family or phylum-level. Integrated information (e.g., about host and microbial physiology, genetics, and metabolism) facilitates deeper understanding of complex interactions and helps interpret correlative results. The KG-Microbe construction framework is a novel approach to harmonizing bacterial and archaeal data in the form of a knowledge graph (KG). Starting from a core KG with organismal traits, environments and growth preferences, the framework generates a hierarchy of related KGs targeting specific conceptual use cases, including the human host-associated microbiome in the context of disease. KG-Microbe is a standardized and interoperable framework that integrates microbial organismal and genomic traits, represented ontologically, for biomedical, environmental, and other applications. The framework supports customizable taxa subsets representing microbial lineages or communities of interest. Evaluations of the KG-Microbe knowledge graphs through a series of competency questions demonstrate the accuracy and effectiveness of the data harmonization, and the utility of the resulting KGs in inflammatory bowel and Parkinsons diseases. Finally, the predictive and environmental capabilities of the KGs are demonstrated by explaining growth preferences through training a model using graph features. KG-Microbe is a flexible, modular enabling technology for humans and machine learning methods to uncover mechanistic explanations of microbial associations.

bioinformatics↗

bioRxiv2022

Node-degree aware edge sampling mitigates inflated classification performance in biomedical graph representation learning

Graph representation learning is a family of related approaches that learn low-dimensional vector representations of nodes and other graph elements called embeddings. Embeddings approximate characteristics of the graph and can be used for a variety of machine-learning tasks such as novel edge prediction. For many biomedical applications, partial knowledge exists about positive edges that represent relationships between pairs of entities, but little to no knowledge is available about negative edges that represent the explicit lack of a relationship between two nodes. For this reason, classification procedures are forced to assume that the vast majority of unlabeled edges are negative. Existing approaches to sampling negative edges for training and evaluating classifiers do so by uniformly sampling pairs of nodes. We show here that this sampling strategy typically leads to sets of positive and negative edges with imbalanced edge degree distributions. Using representative homogeneous and heterogeneous biomedical knowledge graphs, we show that this strategy artificially inflates measured classification performance. We present a degree-aware node sampling approach for sampling negative edge examples that mitigates this effect and is simple to implement.

bioinformatics↗

bioRxiv2021

YhcB (DUF1043), a novel cell division protein conserved across gamma-proteobacteria

YhcB, an uncharacterized protein conserved across gamma-proteobacteria, is composed predominantly of a single Domain of Unknown Function (DUF 1043) with an N-terminal transmembrane -helix. Here, we show that E. coli YhcB is a conditionally essential protein that interacts with the proteins of the cell divisome (e.g., FtsI, FtsQ) and elongasome (e.g., RodZ, RodA). We found 7 interactions of YhcB that are conserved in Yersinia pestis and/or Vibrio cholerae. Furthermore, we identified several point mutations that abolished interactions of YhcB with FtsI and RodZ. The yhcB knock-out strain does not grow at 45{degrees}C and is hypersensitive to cell-wall acting antibiotics even in stationary phase. The deletion of yhcB leads to filamentation, abnormal FtsZ ring formation, and aberrant septa development. The 2.8 [A] crystal structure for the cytosolic domain from Haemophilus ducreyi YhcB shows a unique tetrameric -helical coiled-coil structure that combines parallel and anti-parallel coiled-coil intersubunit interactions. This structure is likely to organize interprotein oligomeric interactions on the inner surface of the cytoplasmic membrane, possibly involved in regulation of cell division and/or envelope biogenesis/integrity in proteobacteria. In summary, YhcB is a conserved and conditionally essential protein that is predicted to play a role in cell division and consequently or in addition affects envelope biogenesis. ImportanceOnly 0.8 % of the protein annotations in the UniProt are based on experimental evidence and thus, functional characterization of unknown proteins remains a rate-limiting step in molecular biology. Herein, the functional properties of YhcB (DUF1043) were investigated using an integrated approach combining X-ray crystallography with genetics and molecular biology. YhcB is a conserved protein that appears to be needed for the transition from exponential to stationary growth and is involved in cell division and/or envelope biogenesis/integrity. This study will serve as a starting point for future studies on this protein family and on how cells transit from exponential to stationary survival.

microbiology↗

bioRxiv2017

Uncovering Medical Insights from Vast Amounts of Biomedical Data in Clinical Case Reports

Clinical case reports (CCRs) have a time-honored tradition in serving as an important means of sharing clinical experiences on patients presenting with atypical disease phenotypes or receiving new therapies. However, the huge amount of accumulated case reports are isolated, unstructured, and heterogeneous clinical data, posing a great challenge to clinicians and researchers in mining relevant information through existing indexing tools. In this investigation, in order to render CCRs more findable, accessible, interoperable, and reusable (FAIR) by the biomedical community, we created a resource platform, including the construction of a test dataset consisting of 1000 CCRs spanning 14 disease phenotypes, a standardized metadata template and metrics, and a set of computational tools to automatically retrieve relevant medical information and to analyze all published PubMed clinical case reports with respect to trends in publication journals, citations impact, MeSH Terms, drug use, distributions of patient demographics, and relationships with other case reports and databases. Our standardized metadata template and CCR test dataset may be valuable resources to advance medical science and improve patient care for researchers who are using machine learning approaches with a high-quality dataset to train and validate their algorithms. In the future, our analytical tools may be applied towards other large clinical data sources as well.

bioinformatics↗