Breaking the paradigm: Dr Insight empowers signature-free, enhanced drug repurposing.

MOTIVATION: Transcriptome-based computational drug repurposing has attracted considerable interest by bringing about faster and more cost-effective drug discovery. Nevertheless, key limitations of the current drug connectivity-mapping paradigm have been long overlooked, including the lack of effective means to determine optimal query gene signatures. RESULTS: The novel approach Dr Insight implements a frame-breaking statistical model for the 'hand-shake' between disease and drug data. The genome-wide screening of concordantly expressed genes (CEGs) eliminates the need for subjective selection of query signatures, added to eliciting better proxy for potential disease-specific drug targets. Extensive comparisons on simulated and real cancer datasets have validated the superior performance of Dr Insight over several popular drug-repurposing methods to detect known cancer drugs and drug-target interactions. A proof-of-concept trial using the TCGA breast cancer dataset demonstrates the application of Dr Insight for a comprehensive analysis, from redirection of drug therapies, to a systematic construction of disease-specific drug-target networks. AVAILABILITY AND IMPLEMENTATION: Dr Insight R package is available at https://cran.r-project.org/web/packages/DrInsight/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Profiling Gene Programs in the Blood During Liver Regeneration in Living Liver Donors.

The human liver's capacity to rapidly regenerate to a full-sized functional organ after resection has allowed successful outcomes for living donor liver transplantation (LDLT) procedures. However, the ability to detect and track physiological changes occurring during liver regeneration after resection and throughout the restoration process is still lacking. We performed a comprehensive whole-transcriptome RNA sequencing analysis of liver and circulating blood tissue from 12 healthy LDLT donors to define biomarker signatures for monitoring physiological activities during liver regeneration at 14 time points for up to a 1-year procedural follow-up. LDLT donor liver tissue differentially expressed 1238 coding and noncoding genes after resection, and an additional 1260 genes were selectively regulated after LDLT. A total of 15,011 RNA transcript species were identified in the blood in response to liver resection. The transcripts most highly regulated were sequentially expressed within 3 distinct peaks that correlated with sets of functional genes involved in the induction of liver resection-specific innate immune response (peak 1), activation of the complement system (peak 2), and platelet activation and erythropoiesis (peak 3). Each peak corresponded with progressive phases of extracellular matrix degradation, remodeling, and organization during liver restoration. These processes could be tracked by distinct molecular signatures of up-regulated and down-regulated gene profiles in the blood during phases of liver repair and regeneration. In conclusion, the results establish temporal and dynamic transcriptional patterns of gene expression following surgical liver resection that can be detected in the blood and potentially used as biomarker signatures for monitoring phases of liver regeneration.

Phantom: investigating heterogeneous gene sets in time-course data.

MOTIVATION: Gene set analysis is a powerful tool to study the coordinative change of time-course data. However, most existing methods only model the overall change of a gene set, yet completely overlooked heterogeneous time-dependent changes within sub-sets of genes. RESULTS: We have developed a novel statistical method, Phantom, to investigate gene set heterogeneity. Phantom employs the principle of multi-objective optimization to assess the heterogeneity inside a gene set, which also accounts for the temporal dependency in time-course data. Phantom improves the performance of gene set based methods to detect biological changes across time. AVAILABILITY AND IMPLEMENTATION: Phantom webpage can be accessed at: http://www.baylorhealth.edu/Phantom . R package of Phantom is available at https://cran.r-project.org/web/packages/phantom/index.html . CONTACT: jinghua.gu@bswhealth.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Molecular Dissection of Somatic Skeletal Disease in Neurofibromatosis Type 1.

Neurofibromatosis type 1 (NF1) is a tumor predisposition syndrome caused by heterozygous NF1 gene mutations. Patients with NF1 present with pleiotropic somatic secondary manifestations, including development of bone pseudarthrosis after fracture. Somatic NF1 gene mutations were reproducibly identified in patient-derived pseudarthrosis specimens, suggesting a local mosaic cell population including somatic pathologic cells. The somatic cellular pathogenesis of NF1 pseudarthroses remains unclear, though defects in osteogenesis have been posited. Here, we applied time-series single-cell RNA-sequencing (scRNA-seq) to patient-matched control and pseudarthrosis-derived primary bone stromal cells (BSCs). We show that osteogenic specification to an osteoblast progenitor cell population was evident for control bone-derived cells and haploinsufficient pseudarthrosis-derived cells. Similar results were observed for somatic patient fracture-derived NF1-/- cells; however, expression of genetic pathways associated with skeletal mineralization were significantly reduced in NF1-/- cells compared with fracture-derived NF1+/- cells. In mice, we show that Nf1 expressed in bone marrow osteoprogenitors is required for the maintenance of the adult skeleton. Results from our study implicate impaired Clec11a-Itga11-Wnt signaling in the pathogenesis of NF1-associated skeletal disease. © 2022 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).