Biology subjects

Cox, B. J.

Publications and source records attributed to Cox, B. J..

6 recordsLinked to original sources

bioRxiv2026

Persistent Japanese encephalitis virus infection in fetuses of an amplifying pig host affects virus genetic heterogeneity and drives transcriptional footprints in offspring

Japanese encephalitis virus (JEV) poses a global threat to public health. This study demonstrates that during fetal infection in natural amplifying hosts, pigs, the in utero environment is conducive to the emergence of new intra-host JEV variants. Transplacental and fetal infection in pigs with JEV resulted in the emergence of virus variants that at least partially resembled variants reported in field samples from JEV endemic regions. The JEV genetic heterogeneity acquired during fetal infection persisted in expelled fetal membranes and offspring. Like in fetuses, JEV mutations identified in afterbirth fetal membranes and offspring were previously reported in the field. Several selected fetal- and offspring-specific mutations that emerged during persistent in utero infection were stable during passage in cell culture. This comparative analysis with field-reported mutations, together with the stability of emerged mutations during serial passages, may suggest the potential for onward transmission of in utero-emerged JEV variants from infected fetal membranes or offspring. Exposure to JEV during fetal life also induced transcriptional footprints in blood cells and tonsils of live offspring, including alterations in pathways associated with epigenetic regulation and memory. Altogether, we generated new knowledge on JEV biology and pathogenesis in its natural amplifying hosts, pigs, during pregnancy. Our findings provide directions for future studies investigating pathways linking fetal infection to offspring and subsequent environmental dissemination, fetal infection as an additional source of JEV genetic diversification, and the mechanisms underlying molecular footprints and their long-term sequelae in JEV-affected offspring, including increased offspring susceptibility to zoonotic pathogens.

microbiology↗

bioRxiv2024

Decidual natural killer cells promote extravillous trophoblast developmental pathways: evidence from trophoblast organoid co-cultures

The placenta an essential extraembryonic organ that supports the fetus throughout gestation. The interactions between the placenta and the maternal immune system during the first trimester have not been wholly characterized despite their close physical association and hemi-allogeneic relationship. The most abundant type of immune cell in the uterus in the first trimester is the decidual natural killer cell (dNK). Despite their name, dNKs play supportive roles during pregnancy by remodelling uterine spiral arteries. We present evidence suggesting that the matrix metalloproteinases (MMPs) that dNKs secrete to promote this remodelling also drive placental development. This study used a novel co-culture system of dNKs and trophoblast organoids, which are mini-organs representing two to three different cell types of the human placenta. We found that co-cultures for one week led to significant (p=0.020) increases in the organoid area. We also observed significant decreases in trophoblast stemness markers and upregulation of gene sets associated with extravillous trophoblast (EVT) development through bulk RNA sequencing and immunohistochemical examinations. These changes were accompanied by significant (p<0.001) increases in collagen subunit gene expression in the organoids, with simultaneous significant decreases (p<0.001) in the proportion of organoid area occupied by collagen as determined through Massons Trichrome. Cultures containing dNKs also contained significantly higher MMP1, 3, 9, and 10 levels in their culture media, each of which can break down collagen. These findings demonstrate that dNKs promote changes concordant with trophoblast differentiation towards EVTs and villous branching morphogenesis.

developmental biology↗

bioRxiv2022

metGWAS 1.0: An R workflow for network-driven over-representation analysis between independent metabolomic and meta-genome wide association studies

BackgroundMany diseases may result from disrupted metabolic regulation. Metabolite-GWAS studies assess the association of polymorphic variants with metabolite levels in body fluids. While these studies are successful, they have a high cost and technical expertise burden due to combining the analytical biochemistry of metabolomics with the computational genetics of GWAS. Currently, there are 100s of standalone metabolomics and GWAS studies related to similar diseases or phenotypes. A method that could statically evaluate these independent studies to find novel metabolites-genes association is of high interest. Although such an analysis is limited to genes with known metabolite interactions due to the unpaired nature of the data sets, any discovered associations may represent biomarkers and druggable targets for treatment and prevention. MethodsWe developed a bioinformatics tool, metGWAS 1.0, that generates and statistically compares metabolic and genomic gene sets using a hypergeometric test. Metabolic gene sets are generated by mapping disease-associated metabolites to interacting proteins (genes) via online databases. Genomic gene sets are identified from a network representation of the GWAS Catalog comprising 100s of studies. ResultsThe metGWAS 1.0 tool was evaluated using standalone metabolomics datasets extracted from two metabolomics-GWAS case studies. In case-study 1, a cardiovascular disease association study, we identified nine genes (APOA5, PLA2G5, PLA2G2D, PLA2G2E, PLA2G2F, LRAT, PLA2G2A, PLB1, and PLA2G7) that interact with metabolites in the KEGG glycerophospholipid metabolism pathway and contain polymorphic variants associated with cardiovascular disease (P < 0.005). The gene APOA5 was matched from the original metabolomics-GWAS study. In case study 2, a urine metabolome study of kidney metabolism in healthy subjects, we found marginal significance (P = 0.10 and P = 0.13) for glycine, serine, and threonine metabolism and alanine, aspartate, and glutamate metabolism pathways to GWAS data relating to kidney disease. ConclusionThe metGWAS 1.0 platform provides insight into developing methods that bridge standalone metabolomics and disease and phenotype GWAS data. We show the potential to reproduce findings of paired metabolomics-GWAS data and provide novel associations of gene variation and metabolite expression.

bioinformatics↗

bioRxiv2021

A Poor Research Landscape Hinders the Progression of Knowledge and Treatment of Reproductive Diseases

Reproductive diseases have gone under the radar for many years, resulting in insufficient diagnostics and treatments. Infertility rates are rising, preeclampsia claims over 70 000 maternal and 500 000 neonatal lives globally per year, and endometriosis affects 10% of all reproductive-aged women but is often undiagnosed for many years. Changes in policy have been enacted to mitigate the gender inequality in research investigators and subjects of medical research. However, the disparities in reproductive research advancement still exist. Here, we analyzed the reproductive science research landscape in attempt to quantify the gravity of the current situation. We find that non-reproductive organs are annually researched 5-20 times more than reproductive organs leading to an exponentially increasing relative knowledge gap in reproductive sciences. Additionally, reproductive organs (breast and prostate) are mainly researched when there is a disease-focus, leading to a lack of basic understanding of the reproductive organs. This gap in knowledge affects reproductive syndromes, as well as other bodily systems and research areas, such as cancer biology and regenerative medicine. Action must be taken by current researchers, funding organizations, and educators to combat this longstanding disregard of reproductive science.

physiology↗

bioRxiv2020

Integration of viral transcriptome sequencing with structure and sequence motifs predicts novel regulatory elements in SARS-CoV-2

In the last twenty years, three separate coronaviruses have left their typical animal hosts and became human pathogens. An area of research interest is coronavirus transcription regulation that uses an RNA-RNA mediated template-switching mechanism. It is not known how different transcriptional stoichiometries of each viral gene are generated. Analysis of SARS-CoV-2 RNA sequencing data from whole RNA transcriptomes identified TRS dependent and independent transcripts. Integration of transcripts and 5-UTR sequence motifs identified that the pentaloop and the stem-loop 3 were also located upstream of spliced genes. TRS independent transcripts were detected as likely non-polyadenylated. Additionally, a novel conserved sequence motif was discovered at either end of the TRS independent splice junctions. While similar both SARS viruses generated similar TRS independent transcripts they were more abundant in SARS-CoV-2. TRS independent gene regulation requires investigation to determine its relationship to viral pathogenicity.

genetics↗

bioRxiv2018

Extracting human data from published figures: implications for data science and bioethics

The advent of text mining and natural text reading artificial intelligence has opened new research opportunities on the large collections of research publications available through journal and other resources. These systems have begun to identify novel connections or hypotheses due to an ability to read and extract information from more literature than a single individual could in their lifetime. Most research publications contain figures where data is represented in a graph. Modern publication guidelines are strongly encouraging publication of graphs where all data is displayed as apposed to summary figures such as bar charts. Figures are often encoded in a graphing language that is interpreted and displayed as a graphics. Conversion figures in publications to the underlying code should enable text-based mining to extract the underlying raw data of the graph. Here I show that data from publications greater than 15 years old that contain time series data on human patients is extractable from the original publication and can be reassessed using modern tools. This could benefit cases where data sets are not available due to file loss or corruption. This may also create and issue for the publication of human data as sharing of human data often requires research ethics approval.\n\nAuthor summaryFigures embedded in published research manuscripts are a minable resource similar to text mining. Figures are text based code that draws the image, as such the underlying text of the code can be used to reassemble the original data set.

bioinformatics↗