A global federated real-world data and analytics platform for research.

Objective: This article describes a scalable, performant, sustainable global network of electronic health record data for biomedical and clinical research. Materials and Methods: TriNetX has created a technology platform characterized by a conservative security and governance model that facilitates collaboration and cooperation between industry participants, such as pharmaceutical companies and contract research organizations, and academic and community-based healthcare organizations (HCOs). HCOs participate on the network in return for access to a suite of analytics capabilities, large networks of de-identified data, and more sponsored trial opportunities. Industry participants provide the financial resources to support, expand, and improve the technology platform in return for access to network data, which provides increased efficiencies in clinical trial design and deployment. Results: TriNetX is a growing global network, expanding from 55 HCOs and 7 countries in 2017 to over 220 HCOs and 30 countries in 2022. Over 19 000 sponsored clinical trial opportunities have been initiated through the TriNetX network. There have been over 350 peer-reviewed scientific publications based on the network's data. Conclusions: The continued growth of the TriNetX network and its yield of clinical trial collaborations and published studies indicates that this academic-industry structure is a safe, proven, sustainable path for building and maintaining research-centric data networks.

Compound A, a selective glucocorticoid receptor modulator, enhances heat shock protein Hsp70 gene promoter activation.

Compound A possesses glucocorticoid receptor (GR)-dependent anti-inflammatory properties. Just like classical GR ligands, Compound A can repress NF-κB-mediated gene expression. However, the monomeric Compound A-activated GR is unable to trigger glucocorticoid response element-regulated gene expression. The heat shock response potently activates heat shock factor 1 (HSF1), upregulates Hsp70, a known GR chaperone, and also modulates various aspects of inflammation. We found that the selective GR modulator Compound A and heat shock trigger similar cellular effects in A549 lung epithelial cells. With regard to their anti-inflammatory mechanism, heat shock and Compound A are both able to reduce TNF-stimulated IκBα degradation and NF-κB p65 nuclear translocation. We established an interaction between Compound A-activated GR and Hsp70, but remarkably, although the presence of the Hsp70 chaperone as such appears pivotal for the Compound A-mediated inflammatory gene repression, subsequent novel Hsp70 protein synthesis is uncoupled from an observed CpdA-induced Hsp70 mRNA upregulation and hence obsolete in mediating CpdA's anti-inflammatory effect. The lack of a Compound A-induced increase in Hsp70 protein levels in A549 cells is not mediated by a rapid proteasomal degradation of Hsp70 or by a Compound A-induced general block on translation. Similar to heat shock, Compound A can upregulate transcription of Hsp70 genes in various cell lines and BALB/c mice. Interestingly, whereas Compound A-dependent Hsp70 promoter activation is GR-dependent but HSF1-independent, heat shock-induced Hsp70 expression alternatively occurs in a GR-independent and HSF1-dependent manner in A549 lung epithelial cells.

The potential of text mining in data integration and network biology for plant research: a case study on Arabidopsis.

Despite the availability of various data repositories for plant research, a wealth of information currently remains hidden within the biomolecular literature. Text mining provides the necessary means to retrieve these data through automated processing of texts. However, only recently has advanced text mining methodology been implemented with sufficient computational power to process texts at a large scale. In this study, we assess the potential of large-scale text mining for plant biology research in general and for network biology in particular using a state-of-the-art text mining system applied to all PubMed abstracts and PubMed Central full texts. We present extensive evaluation of the textual data for Arabidopsis thaliana, assessing the overall accuracy of this new resource for usage in plant network analyses. Furthermore, we combine text mining information with both protein-protein and regulatory interactions from experimental databases. Clusters of tightly connected genes are delineated from the resulting network, illustrating how such an integrative approach is essential to grasp the current knowledge available for Arabidopsis and to uncover gene information through guilt by association. All large-scale data sets, as well as the manually curated textual data, are made publicly available, hereby stimulating the application of text mining data in future plant biology studies.

Drug resistance mutations in drug-naive HIV type 1 subtype C-infected individuals from rural Malawi.

In this preliminary study we show that in 2008, 3 years after antiretroviral therapy was introduced into the Karonga District, Malawi, a greater than expected number of drug-naive individuals have been infected with HIV-1 subtype C virus harboring major and minor drug resistance mutations (DRMs). From a sample size of 40 reverse transcriptase (RT) consensus sequences from drug-naive individuals we found five showing NRTI and four showing NNRTI mutations with one individual showing both. From 29 protease consensus sequences, again from drug-naive individuals, we found evidence of minor DRMs in three. Additional major and minor DRMs were found in clonal sequences from a number of individuals that were not present in the original consensus sequences. This clearly illustrates the importance of sequencing multiple HIV-1 variants from individuals to fully assess drug resistance.