Biology subjects

Reese, J.

Publications and source records attributed to Reese, J..

6 recordsLinked to original sources

bioRxiv2023

Effects of pasteurization on osteopontin levels in human breastmilk and pasteurized breastmilk products

BackgroundOsteopontin (OPN) is an important breastmilk protein involved in infant intestinal, immunological, and brain development. However, little is known about how common milk pasteurization and storage techniques affect this important bioactive protein. MethodsHuman milk osteopontin concentration was measured in single-donor fresh or frozen breastmilk, pooled Holder-pasteurized donor breastmilk, and a shelf-stable (retort pasteurized) breastmilk product by ELISA. Breastmilk samples were pasteurized and/or frozen before measuring osteopontin concentrations. ResultsHolder pasteurization of breastmilk resulted in an [~]50% decrease in osteopontin levels within single-donor samples, whereas pooled donor breastmilk had comparable osteopontin levels to non-pasteurized single-donor samples. Breastmilk from mothers of preterm infants trended toward higher osteopontin concentration than mothers of term infants; however, samples from preterm mothers experienced greater osteopontin degradation upon pasteurization. Finally, freezing breastmilk prior to Holder pasteurization resulted in less osteopontin degradation than Holder pasteurization prior to freezing. ConclusionCommonly used breastmilk pasteurization and storage techniques, including freezing, Holder and retort pasteurization, decrease the levels of the bioactive protein osteopontin in human breastmilk. ImpactO_LIPasteurization of human breastmilk significantly decreases the levels of the bioactive protein osteopontin C_LIO_LIUse of both pasteurization and freezing techniques for breastmilk preservation results in greater loss of osteopontin C_LIO_LIThis study presents for the first time an analysis of osteopontin levels in single-donor pasteurized milk samples C_LI

physiology↗

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↗

bioRxiv2022

An algorithmic framework for isoform-specific functional analysis

Gene Ontology (GO) overrepresentation analysis characterizes the biological mechanisms common to sets of differentially expressed genes identified by high-throughput experiments. To date, GO overrepresentation analysis has mainly been used to evaluate differentially expressed genes, but short- and long-read RNA-seq technologies now allow increasingly accurate identification of differential alternative splicing. The function of most splice isoforms remain unknown, but if acccurate predictions could be made, overrepresentation analysis could be applied to differentially spliced isoforms to assess the functional implications of alternative splicing in RNA-seq experiments. We present isopret (Isoform Interpretation), a new paradigm for isoform function prediction based on the expectation-maximization framework. isopret leverages the relationships between sequence and functional isoform similarity to infer isoform specific functions in a highly accurate fashion. This enabled us to adapt GO overrepresentation analysis, which to date has been limited to differential gene expression, to be extended to assess overrepresentation of GO annotations in differentially spliced isoforms. An analysis of 100 RNA-seq studies including investigations of development, cancer, and common disease demonstrated that expression and splicing regulate different sets of biological functions. We make isopret predictions freely available in a desktop application that can be used to analyze differential expression and splicing in any bulk RNA-seq dataset.

bioinformatics↗

bioRxiv2021

SvAnna: efficient and accurate pathogenicity prediction for coding and regulatory structural variants in long-read genome sequencing

Structural variants (SVs) are implicated in the etiology of Mendelian diseases but have been systematically underascertained owing to limitations of existing technology. Recent technological advances such as long-read sequencing (LRS) enable more comprehensive detection of SVs, but approaches for clinical prioritization of candidate SVs are needed. Existing computational approaches do not specifically target LRS data, thereby missing a substantial proportion of candidate SVs, and do not provide a unified computational model for assessing all types of SVs. Structural Variant Annotation and Analysis (SvAnna) assesses all classes of SV and their intersection with transcripts and regulatory sequences in the context of topologically associating domains, relating predicted effects on gene function with clinical phenotype data. We show with a collection of 182 published case reports with pathogenic SVs that SvAnna places over 90% of pathogenic SVs in the top ten ranks. The interpretable prioritizations provided by SvAnna will facilitate the widespread adoption of LRS in diagnostic genomics.

bioinformatics↗

bioRxiv2021

Supervised learning with word embeddings derived from PubMed captures latent knowledge about protein kinases and cancer

Inhibiting protein kinases (PKs) that cause cancers has been an important topic in cancer therapy for years. So far, almost 8% of more than 530 PKs have been targeted by FDA-approved medications and around 150 protein kinase inhibitors (PKIs) have been tested in clinical trials. We present an approach based on natural language processing and machine learning to the relations between PKs and cancers, predicting PKs whose inhibition would be efficacious to treat a certain cancer. Our approach represents PKs and cancers as semantically meaningful 100-dimensional vectors based on co-occurrence patterns in PubMed abstracts. We use information about phase I-IV trials in ClinicalTrials.gov to construct a training set for random forest classification. In historical data, associations between PKs and specific cancers could be predicted years in advance with good accuracy. Our model may be a tool to predict the relevance of inhibiting PKs with specific cancers.

bioinformatics↗

bioRxiv2021

Identification of mundulone and mundulone acetate as natural products with tocolytic efficacy in mono- and combination-therapy with current tocolytics

Currently, there are a lack of FDA-approved tocolytics for the management of preterm labor. We previously observed that the isoflavones mundulone and mundulone acetate (MA) inhibit intracellular Ca2+-regulated myometrial contractility. Here, we further probed the potential of these natural products to be small molecule leads for discovery of novel tocolytics by: (1) examining uterine-selectivity by comparing concentration-response between human primary myometrial cells and a major off-target site, aortic vascular smooth muscle cells (VSMCs), (2) identifying synergistic combinations with current clinical tocolytics to increase efficacy or and reduce off-target side effects, (3) determining cytotoxic effects and (4) investigating the efficacy, potency and tissue-selectivity between myometrial contractility and constriction of fetal ductus arteriosus (DA), a major off-target of current tocolytics. Mundulone displayed significantly greater efficacy (Emax = 80.5% vs. 44.5%, p=0.0005) and potency (IC50 = 27 M and 14 M, p=0.007) compared to MA in the inhibition of intracellular-Ca2+ from myometrial cells. MA showed greater uterine-selectivity, compared to mundulone, based on greater differences in the IC50 (4.3 vs. 2.3 fold) and Emax (70% vs. 0%) between myometrial cells compared to aorta VSMCs. Moreover, MA demonstrated a favorable in vitro therapeutic index of 8.8, compared to TI = 0.8 of mundulone, due to its significantly (p<0.0005) smaller effect on the viability of myometrial (hTERT-HM), liver (HepG2) and kidney (RPTEC) cells. However, mundulone exhibited synergism with two current tocolytics (atosiban and nifedipine), while MA only displayed synergistic efficacy with only nifedipine. Of these synergistic combinations, only mundulone + atosiban demonstrated a favorable TI = 10 compared to TI=0.8 for mundulone alone. While only mundulone showed concentration-dependent inhibition of ex vivo mouse myometrial contractions, neither mundulone or MA affected mouse fetal DA vasoreactivity. The combination of mundulone and atosiban yielded greater tocolytic efficacy and potency on term pregnant mouse and human myometrial tissue compared to single-drugs. Collectively, these data highlight the difference in uterine-selectivity of Ca2+-mobilization, effects on cell viability and tocolytic efficacy between mundulone and MA. These natural products could benefit from medicinal chemistry efforts to study the structural activity relationship for further development into a promising single- and/or combination-tocolytic therapy for management of preterm labor. Chemical compounds studied in this articleatosiban (Pubchem CID: 5311010); indomethacin (Pubchem CID: 3715); mundulone (Pubchem CID: 4587968); mundulone acetate (Pubchem CID: 6857790); nifedipine (Pubchem CID: 4485); oxytocin acetate (Pubchem CID: 5771); U46619 (Pubchem CID: 5311493)

pharmacology and toxicology↗