Representing glycophenotypes: semantic unification of glycobiology resources for disease discovery.

While abnormalities related to carbohydrates (glycans) are frequent for patients with rare and undiagnosed diseases as well as in many common diseases, these glycan-related phenotypes (glycophenotypes) are not well represented in knowledge bases (KBs). If glycan-related diseases were more robustly represented and curated with glycophenotypes, these could be used for molecular phenotyping to help to realize the goals of precision medicine. Diagnosis of rare diseases by computational cross-species comparison of genotype-phenotype data has been facilitated by leveraging ontological representations of clinical phenotypes, using Human Phenotype Ontology (HPO), and model organism ontologies such as Mammalian Phenotype Ontology (MP) in the context of the Monarch Initiative. In this article, we discuss the importance and complexity of glycobiology and review the structure of glycan-related content from existing KBs and biological ontologies. We show how semantically structuring knowledge about the annotation of glycophenotypes could enhance disease diagnosis, and propose a solution to integrate glycophenotypes and related diseases into the Unified Phenotype Ontology (uPheno), HPO, Monarch and other KBs. We encourage the community to practice good identifier hygiene for glycans in support of semantic analysis, and clinicians to add glycomics to their diagnostic analyses of rare diseases

The Infectious Disease Ontology in the Age of COVID-19

The Infectious Disease Ontology (IDO) is a suite of interoperable ontology modules that aims to provide coverage of all aspects of the infectious disease domain, including biomedical research, clinical care, and public health. IDO Core is designed to be a disease and pathogen neutral ontology, covering just those types of entities and relations that are relevant to infectious diseases generally. IDO Core is then extended by a collection of ontology modules focusing on specific diseases and pathogens. In this paper we present applications of IDO Core within various areas of infectious disease research, together with an overview of all IDO extension ontologies and the methodology on the basis of which they are built. We also survey recent developments involving IDO, including the creation of IDO Virus; the Coronaviruses Infectious Disease Ontology (CIDO); and an extension of CIDO focused on COVID-19 (IDO-CovID-19).We also discuss how these ontologies might assist in information-driven efforts to deal with the ongoing COVID-19 pandemic, to accelerate data discovery in the early stages of future pandemics, and to promote reproducibility of infectious disease research

What is a hologenomic adaptation? Emergent individuality and inter-identity in multispecies systems

Contemporary biological research has suggested that some host–microbiome multispecies systems (referred to as “holobionts”) can in certain circumstances evolve as unique biological individual, thus being a unit of selection in evolution. If this is so, then it is arguably the case that some biological adaptations have evolved at the level of the multispecies system, what we call hologenomic adaptations. However, no research has yet been devoted to investigating their nature, or how these adaptations can be distinguished from adaptations at the species-level (genomic adaptations). In this paper, we cover this gap by investigating the nature of hologenomic adaptations. By drawing on the case of the evolution of sanguivory diet in vampire bats, we argue that a trait constitutes a hologenomic adaptation when its evolution can only be explained if the holobiont is considered the biological individual that manifests this adaptation, while the bacterial taxa that bear the trait are only opportunistic beneficiaries of it. We then use the philosophical notions of emergence and inter-identity to explain the nature of this form of individuality and argue why it is special of holobionts. Overall, our paper illustrates how the use of philosophical concepts can illuminate scientific discussions, in the trend of what has recently been called metaphysics of biology

From metagenomics to the metagenome: Conceptual change and the rhetoric of translational genomic research

As the international genomic research community moves from the tool-making efforts of the Human Genome Project into biomedical applications of those tools, new metaphors are being suggested as useful to understanding how our genes work – and for understanding who we are as biological organisms. In this essay we focus on the Human Microbiome Project as one such translational initiative. The HMP is a new ‘metagenomic’ research effort to sequence the genomes of human microbiological flora, in order to pursue the interesting hypothesis that our ‘microbiome’ plays a vital and interactive role with our human genome in normal human physiology. Rather than describing the human genome as the ‘blueprint’ for human nature, the promoters of the HMP stress the ways in which our primate lineage DNA is interdependent with the genomes of our microbiological flora. They argue that the human body should be understood as an ecosystem with multiple ecological niches and habitats in which a variety of cellular species collaborate and compete, and that human beings should be understood as ‘superorganisms’ that incorporate multiple symbiotic cell species into a single individual with very blurry boundaries. These metaphors carry interesting philosophical messages, but their inspiration is not entirely ideological. Instead, part of their cachet within genome science stems from the ways in which they are rooted in genomic research techniques, in what philosophers of science have called a ‘tools-to-theory’ heuristic. Their emergence within genome science illustrates the complexity of conceptual change in translational research, by showing how it reflects both aspirational and methodological influences

Term-BLAST-like alignment tool for concept recognition in noisy clinical texts.

MOTIVATION: Methods for concept recognition (CR) in clinical texts have largely been tested on abstracts or articles from the medical literature. However, texts from electronic health records (EHRs) frequently contain spelling errors, abbreviations, and other nonstandard ways of representing clinical concepts. RESULTS: Here, we present a method inspired by the BLAST algorithm for biosequence alignment that screens texts for potential matches on the basis of matching k-mer counts and scores candidates based on conformance to typical patterns of spelling errors derived from 2.9 million clinical notes. Our method, the Term-BLAST-like alignment tool (TBLAT) leverages a gold standard corpus for typographical errors to implement a sequence alignment-inspired method for efficient entity linkage. We present a comprehensive experimental comparison of TBLAT with five widely used tools. Experimental results show an increase of 10% in recall on scientific publications and 20% increase in recall on EHR records (when compared against the next best method), hence supporting a significant enhancement of the entity linking task. The method can be used stand-alone or as a complement to existing approaches. AVAILABILITY AND IMPLEMENTATION: Fenominal is a Java library that implements TBLAT for named CR of Human Phenotype Ontology terms and is available at https://github.com/monarch-initiative/fenominal under the GNU General Public License v3.0

Multidimensional Proteomics Analysis of Amniotic Fluid to Provide Insight into the Mechanisms of Idiopathic Preterm Birth

Though recent advancement in proteomics has provided a novel perspective on several distinct pathogenetic mechanisms leading to preterm birth (inflammation, bleeding), the etiology of most preterm births still remains elusive. We conducted a multidimensional proteomic analysis of the amniotic fluid to identify pathways related to preterm birth in the absence of inflammation or bleeding.A proteomic fingerprint was generated from fresh amniotic fluid using surface-enhanced laser desorbtion ionization time of flight (SELDI-TOF) mass spectrometry in a total of 286 consecutive samples retrieved from women who presented with signs or symptoms of preterm labor or preterm premature rupture of the membranes. Inflammation and/or bleeding proteomic patterns were detected in 32% (92/286) of the SELDI tracings. In the remaining tracings, a hierarchical algorithm was applied based on descriptors quantifying similarity/dissimilarity among proteomic fingerprints. This allowed identification of a novel profile (Q-profile) based on the presence of 5 SELDI peaks in the 10-12.5 kDa mass area. Women displaying the Q-profile (mean+/-SD, gestational age: 25+/-4 weeks, n = 40) were more likely to deliver preterm despite expectant management in the context of intact membranes and normal amniotic fluid clinical results. Utilizing identification-centered proteomics techniques (fluorescence two-dimensional differential gel electrophoresis, robotic tryptic digestion and mass spectrometry) coupled with Protein ANalysis THrough Evolutionary Relationships (PANTHER) ontological classifications, we determined that in amniotic fluids with Q-profile the differentially expressed proteins are primarily involved in non-inflammatory biological processes such as protein metabolism, signal transduction and transport.Proteomic profiling of amniotic fluid coupled with non-hierarchical bioinformatics algorithms identified a subgroup of patients at risk for preterm birth in the absence of intra-amniotic inflammation or bleeding, suggesting a novel pathogenetic pathway leading to preterm birth. The altered proteins may offer opportunities for therapeutical intervention and future drug development to prevent prematurity

Population pharmacokinetics of apramycin from first-in-human plasma and urine data to support prediction of efficacious dose

BACKGROUND: Apramycin is under development for human use as EBL-1003, a crystalline free base of apramycin, in face of increasing incidence of multidrug-resistant bacteria. Both toxicity and cross-resistance, commonly seen for other aminoglycosides, appear relatively low owing to its distinct chemical structure. OBJECTIVES: To perform a population pharmacokinetic (PPK) analysis and predict an efficacious dose based on data from a first-in-human Phase I trial. METHODS: The drug was administered intravenously over 30 min in five ascending-dose groups ranging from 0.3 to 30 mg/kg. Plasma and urine samples were collected from 30 healthy volunteers. PPK model development was performed stepwise and the final model was used for PTA analysis. RESULTS: A mammillary four-compartment PPK model, with linear elimination and a renal fractional excretion of 90%, described the data. Apramycin clearance was proportional to the absolute estimated glomerular filtration rate (eGFR). All fixed effect parameters were allometrically scaled to total body weight (TBW). Clearance and steady-state volume of distribution were estimated to 5.5 L/h and 16 L, respectively, for a typical individual with absolute eGFR of 124 mL/min and TBW of 70 kg. PTA analyses demonstrated that the anticipated efficacious dose (30 mg/kg daily, 30 min intravenous infusion) reaches a probability of 96.4% for a free AUC/MIC target of 40, given an MIC of 8 mg/L, in a virtual Phase II patient population with an absolute eGFR extrapolated to 80 mL/min. CONCLUSIONS: The results support further Phase II clinical trials with apramycin at an anticipated efficacious dose of 30 mg/kg once daily

Data mining the serous ovarian tumor transcriptome

Ovarian cancer is the most lethal gynecologic cancer in the United States. If caught in early stages, patient survival rate is 94%, late stage survival rates drop to 28%. It is because most cases are caught in late stages that high mortality is seen. Correct diagnosis is dependent on the presence of symptoms: ~90% of diagnosed ovar- ian cancers are symptomatic. These symptoms tend to be unfocused and not acute. The goal of this project is to develop a transcript-level data set measuring ovarian tumor expression and associated paracrine signaling for later biomarker research. To this end, laser capture microdissection was used with exon based oligonucleotide ar- rays to measure the transcriptome of benign and malignant (Type II) serous ovarian surface epithelial-stromal tumors. In addition to profiling tumor, surrounding stro- mal tissue expression was measured to examine potential paracrine signaling. In total, ~270 million measurements were performed using 50 microarrays. An initial analysis was performed to measure quality, and to compare our measurements against known ovarian cancer properties as established in the molecular genetics literature. Using ontological annotation and de novo pathway generation methods, major trends were defined in the data set including the following: apical surface and tight junction ac- tivity, mitotic activity, tumor suppression in benign tumors, epithelial-mesenchymal transitioning, known ovarian tumor oncogene activity, and evidence of paracrine sig- naling. A list of differentially expressed transcripts was defined which may be explored as biomarkers. The potential for meaningful future analysis is diverse. This data set will contribute to the capacity of the cancer genetics community to perform high resolution exploration of serous ovarian epithelial-stromal surface tumors, aiding in developing better diagnostics and therapeutics

Anatomic Demarcation by Positional Variation in Fibroblast Gene Expression Programs

Fibroblasts are ubiquitous mesenchymal cells with many vital functions during development, tissue repair, and disease. Fibroblasts from different anatomic sites have distinct and characteristic gene expression patterns, but the principles that govern their molecular specialization are poorly understood. Spatial organization of cellular differentiation may be achieved by unique specification of each cell type; alternatively, organization may arise by cells interpreting their position along a coordinate system. Here we test these models by analyzing the genome-wide gene expression profiles of primary fibroblast populations from 43 unique anatomical sites spanning the human body. Large-scale differences in the gene expression programs were related to three anatomic divisions: anterior-posterior (rostral-caudal), proximal-distal, and dermal versus nondermal. A set of 337 genes that varied according to these positional divisions was able to group all 47 samples by their anatomic sites of origin. Genes involved in pattern formation, cell-cell signaling, and matrix remodeling were enriched among this minimal set of positional identifier genes. Many important features of the embryonic pattern of HOX gene expression were retained in fibroblasts and were confirmed both in vitro and in vivo. Together, these findings suggest that site-specific variations in fibroblast gene expression programs are not idiosyncratic but rather are systematically related to their positional identities relative to major anatomic axes