Biology subjects

Roper, R.

Publications and source records attributed to Roper, R..

4 recordsLinked to original sources

bioRxiv2026

Sex-specific developmental phenotypes and their response to neonatal Dyrk1a reduction in the Ts65Dn Down syndrome mouse model

Children with Down syndrome (DS) experience delays in cognitive, physical, and motor development. Overexpression of Dual-specificity tyrosine phosphorylation-regulated kinase-1A (DYRK1A), a gene on human chromosome 21 (Hsa21) and triplicated in individuals with Trisomy 21, contributes to neurodevelopmental delays associated with DS, and is a candidate for therapies to improve neurodevelopmental phenotypes. Male and female Ts65Dn DS model pups are trisomic for [~]100 Hsa21 orthologs including Dyrk1a, and both sexes show significant DYRK1A overexpression on postnatal day 6 (P6) in the hippocampus, cerebral cortex, and cerebellum. This study tested the hypothesis that normalization of Dyrk1a copy number in Ts65Dn pups prior to P6 would diminish physical and behavioral developmental outcomes in Ts65Dn mice, thus providing a standard of comparison for success of interventions targeting Dyrk1a. At P3-P21, Ts65Dn compared to euploid pups showed sex-specific deficits in physical, motor, and behavioral development. Male Ts,Dyrk1a+/+/Dox-Cre mice showed improved emergence to running on P19, and both sexes of Ts,Dyrk1a+/+/Dox-Cre mice exhibited reduced isolation-induced ultrasonic vocalizations during the second postnatal week. Dyrk1a normalization in Ts65Dn pups did not improve all abnormal phenotypes, perhaps because of developmental dysregulation between Dyrk1a RNA and DYRK1A protein levels, involvement of other trisomic genes, or improvements in only adult mice.

genetics↗

bioRxiv2024

Generating Biomedical Knowledge Graphs from Knowledge Bases, Registries, and Multiomic Data

As large clinical and multiomics datasets and knowledge resources accumulate, they need to be transformed into computable and actionable information to support automated reasoning. These datasets range from laboratory experiment results to electronic health records (EHRs). Barriers to accessibility and sharing of such datasets include diversity of content, size and privacy. Effective transformation of data into information requires harmonization of stakeholder goals, implementation, enforcement of standards regarding quality and completeness, and availability of resources for maintenance and updates. Systems such as the Biomedical Data Translator leverage knowledge graphs (KGs), structured and machine learning readable knowledge representation, to encode knowledge extracted through inference. We focus here on the transformation of data from multiomics datasets and EHRs into compact knowledge, represented in a KG data structure. We demonstrate this data transformation in the context of the Translator ecosystem, including clinical trials, drug approvals, cancer, wellness, and EHR data. These transformations preserve individual privacy. We provide access to the five resulting KGs through the Translator framework. We show examples of biomedical research questions supported by our KGs, and discuss issues arising from extracting biomedical knowledge from multiomics data.

bioinformatics↗

bioRxiv2022

A Genome Scale Transcriptional Regulatory Model of the Human Placenta

Gene regulation is essential to placental function and fetal development. We report a genome-scale transcriptional regulatory network (TRN) of the human placenta built using digital genomic footprinting and transcriptomic data. We integrated 475 transcriptomes and 12 DNase hypersensitivity datasets from placental samples to globally and quantitatively map transcription factor (TF)-target gene interactions. In an independent dataset, the TRN model predicted target gene expression with an out of sample R2 value greater than 0.25 for 74% of target genes. We performed siRNA knockdowns of 4 TFs and achieved concordance between the predicted gene targets in our TRN and differences in expression of knockdowns with an accuracy of >0.7 for 3 of the 4 TFs. Our final model contained 113,158 interactions across 391 TFs and 7,712 target genes and is publicly available. We identified six TFs which were significantly enriched as regulators for genes previously associated with preterm birth.

systems biology↗

bioRxiv2020

Multiomic Immunophenotyping of COVID-19 Patients Reveals Early Infection Trajectories

Host immune responses play central roles in controlling SARS-CoV2 infection, yet remain incompletely characterized and understood. Here, we present a comprehensive immune response map spanning 454 proteins and 847 metabolites in plasma integrated with single-cell multi-omic assays of PBMCs in which whole transcriptome, 192 surface proteins, and T and B cell receptor sequence were co-analyzed within the context of clinical measures from 50 COVID19 patient samples. Our study reveals novel cellular subpopulations, such as proliferative exhausted CD8+ and CD4+ T cells, and cytotoxic CD4+ T cells, that may be features of severe COVID-19 infection. We condensed over 1 million immune features into a single immune response axis that independently aligns with many clinical features and is also strongly associated with disease severity. Our study represents an important resource towards understanding the heterogeneous immune responses of COVID-19 patients and may provide key information for informing therapeutic development.

immunology↗