A statistical method for image-mediated association studies discovers genes and pathways associated with four brain disorders.

Brain imaging and genomics are critical tools enabling characterization of the genetic basis of brain disorders. However, imaging large cohorts is expensive and may be unavailable for legacy datasets used for genome-wide association studies (GWASs). Using an integrated feature selection/aggregation model, we developed an image-mediated association study (IMAS), which utilizes borrowed imaging/genomics data to conduct association mapping in legacy GWAS cohorts. By leveraging the UK Biobank image-derived phenotypes (IDPs), the IMAS discovered genetic bases underlying four neuropsychiatric disorders and verified them by analyzing annotations, pathways, and expression quantitative trait loci (eQTLs). A cerebellar-mediated mechanism was identified to be common to the four disorders. Simulations show that, if the goal is identifying genetic risk, our IMAS is more powerful than a hypothetical protocol in which the imaging results were available in the GWAS dataset. This implies the feasibility of reanalyzing legacy GWAS datasets without conducting additional imaging, yielding cost savings for integrated analysis of genetics and imaging.

Genomics in Clinical trials for Breast Cancer.

Breast cancer (B.C.) still has increasing incidences and mortality rates globally. It is known that B.C. and other cancers have a very high rate of genetic heterogeneity and genomic mutations. Traditional oncology approaches have not been able to provide a lasting solution. Targeted therapeutics have been instrumental in handling the complexity and resistance associated with B.C. However, the progress of genomic technology has transformed our understanding of the genetic landscape of breast cancer, opening new avenues for improved anti-cancer therapeutics. Genomics is critical in developing tailored therapeutics and identifying patients most benefit from these treatments. The next generation of breast cancer clinical trials has incorporated next-generation sequencing technologies into the process, and we have seen benefits. These innovations have led to the approval of better-targeted therapies for patients with breast cancer. Genomics has a role to play in clinical trials, including genomic tests that have been approved, patient selection and prediction of therapeutic response. Multiple clinical trials in breast cancer have been done and are still ongoing, which have applied genomics technology. Precision medicine can be achieved in breast cancer therapy with increased efforts and advanced genomic studies in this domain. Genomics studies assist with patient outcomes improvement and oncology advancement by providing a deeper understanding of the biology behind breast cancer. This article will examine the present state of genomics in breast cancer clinical trials.

P53 Gene Expression and Nitric Oxide Levels after Artemisinin-Caffeine Treatment in Breast, Lungs and Liver of DMBA-Induced Tumorigenesis.

OBJECTIVE: With increasing incidence of cancers globally and limited resources in some affected countries, repurposing existing drugs for reducing tumorigenesis is highly important. Artemisinin and caffeine have potent anti-oxidative and anti-tumor properties but are therapies for other diseases. This study evaluated the biochemical and p53 gene modulatory effects of doses of artemisinin-caffeine combination on breast, lungs and liver tissues in rats induced with DMBA. METHODS: After due ethical approval, 30 animals were treated with 40mg/kg single dose of 7,12-dimethylbenzene anthracene (DMBA) as a model for DNA damage and induction of carcinogenesis. Five animals each received normal saline (normal), low dose artemisinin (Art; 4mg/kg), low dose caffeine (Caff; 25mg/kg), low dose combination of caff + art (25+4mg/kg), high dose combination of caff + art (50+8mg/kg) or no treatment (DMBA). All treatment doses were orally administered daily for two weeks post DMBA treatment. Nitric oxide levels and p53 relative gene expression was carried out using primer-specific RT-PCR, GAPDH was used as loading control and amplicons were resolved by gel electrophoresis. RESULTS: DMBA induced lesions in breast, liver, and lung tissues evident from histology analysis, compared to normal group. In all 3 tissues, caffeine (25mg/kg) and combination of caff + art (25+4mg/kg) significantly reduced p53 gene expression (p < 0.05), but there was significant increase in the group treated with low dose art (4mg/kg) and high dose caff + art, which were similar to DMBA group (p<0.05). In lungs, nitric oxide (NO) increased in all groups but not in caffeine, in the liver NO decreased with caffeine or its combination with art, compared to DMBA group. CONCLUSIONS: This study shows a dose-dependent synergistic anticancer effects of caffeine and artemisinin combination on p53 gene and nitric oxide regulation hence can mitigate tumor development.

iCn3D: From Web-Based 3D Viewer to Structural Analysis Tool in Batch Mode.

iCn3D was initially developed as a web-based 3D molecular viewer. It then evolved from visualization into a full-featured interactive structural analysis software. It became a collaborative research instrument through the sharing of permanent, shortened URLs that encapsulate not only annotated visual molecular scenes, but also all underlying data and analysis scripts in a FAIR manner. More recently, with the growth of structural databases, the need to analyze large structural datasets systematically led us to use Python scripts and convert the code to be used in Node. js scripts. We showed a few examples of Python scripts at https://github.com/ncbi/icn3d/tree/master/icn3dpython to export secondary structures or PNG images from iCn3D. Users just need to replace the URL in the Python scripts to export other annotations from iCn3D. Furthermore, any interactive iCn3D feature can be converted into a Node. js script to be run in batch mode, enabling an interactive analysis performed on one or a handful of protein complexes to be scaled up to analysis features of large ensembles of structures. Currently available Node. js analysis scripts examples are available at https://github.com/ncbi/icn3d/tree/master/icn3dnode. This development will enable ensemble analyses on growing structural databases such as AlphaFold or RoseTTAFold on one hand and Electron Microscopy on the other. In this paper, we also review new features such as DelPhi electrostatic potential, 3D view of mutations, alignment of multiple chains, assembly of multiple structures by realignment, dynamic symmetry calculation, 2D cartoons at different levels, interactive contact maps, and use of iCn3D in Jupyter Notebook as described at https://pypi.org/project/icn3dpy.