Background: Even during long-term combination antiretroviral therapy (cART), people living with HIV (PLHIV) have a dysregulated immune system, characterized by persistent immune activation, accelerated immune ageing and increased risk of non-AIDS comorbidities. A multi-omics approach is applied to a large cohort of PLHIV to understand pathways underlying these dysregulations in order to identify new biomarkers and novel genetically validated therapeutic drugs targets.Methods: The 2000HIV study is a prospective longitudinal cohort study of PLHIV on cART. In addition, untreated HIV spontaneous controllers were recruited. In-depth multi-omics characterization will be performed, including genomics, epigenomics, transcriptomics, proteomics, metabolomics and metagenomics, functional immunological assays and extensive immunophenotyping. Furthermore, the latent viral reservoir will be assessed through cell associated HIV-1 RNA and DNA, and full-length individual proviral sequencing on a subset. Clinical measurements include an ECG, carotid intima-media thickness and plaque measurement, hepatic steatosis and fibrosis measurement as well as psychological symptoms and recreational drug questionnaires. Additionally, considering the developing pandemic, COVID-19 history and vaccination was recorded. Participants return for a two-year follow-up visit. The 2000HIV study consists of a discovery and validation cohort collected at separate sites to immediately validate any finding in an independent cohort.Results: Overall, 1895 PLHIV from four sites were included for analysis, 1559 in the discovery and 336 in the validation cohort. The study population was representative of a Western European HIV population, including 288 (15.2%) cis-women, 463 (24.4%) non-whites, and 1360 (71.8%) MSM (Men who have Sex with Men). Extreme phenotypes included 114 spontaneous controllers, 81 rapid progressors and 162 immunological non-responders. According to the Framingham score 321 (16.9%) had a cardiovascular risk of >20% in the next 10 years. COVID-19 infection was documented in 234 (12.3%) participants and 474 (25.0%) individuals had received a COVID-19 vaccine.Conclusion: The 2000HIV study established a cohort of 1895 PLHIV that employs multi-omics to discover new biological pathways and biomarkers to unravel non-AIDS comorbidities, extreme phenotypes and the latent viral reservoir that impact the health of PLHIV. The ultimate goal is to contribute to a more personalized approach to the best standard of care and a potential cure for PLHIV.

Transfer RNA^[Ser]Sec carries multiple post-transcriptional modifications. The A37G mutation in tRNA^[Ser]Sec abrogates isopentenylation of base 37 and has a profound effect on selenoprotein expression in mice. Patients with a homozygous pathogenic p.R323Q variant in tRNA-isopentenyl-transferase (TRIT1) show a severe neurological disorder, and hence we wondered whether selenoprotein expression was impaired. Patient fibroblasts with the homozygous p.R323Q variant did not show a general decrease in selenoprotein expression. However, recombinant human TRIT1^R323Q had significantly diminished activities towards several tRNA substrates in vitro. We thus engineered mice conditionally deficient in Trit1 in hepatocytes and neurons. Mass-spectrometry revealed that hypermodification of U₃₄ to mcm⁵Um occurs independently of isopentenylation of A₃₇ in tRNA^[Ser]Sec. Western blotting and ⁷⁵Se metabolic labeling showed only moderate effects on selenoprotein levels and ⁷⁵Se incorporation. A detailed analysis of Trit1-deficient liver using ribosomal profiling demonstrated that UGA/Sec re-coding was moderately affected in Selenop, Txnrd1, and Sephs2, but not in Gpx1. 2′O-methylation of U₃₄ in tRNA^[Ser]Sec depends on FTSJ1, but does not affect UGA/Sec re-coding in selenoprotein translation. Taken together, our results show that a lack of isopentenylation of tRNA^[Ser]Sec affects UGA/Sec read-through but differs from a A37G mutation.

Pediatric tumors frequently arise from embryonal cells, often displaying a stem cell-like (“small round blue”) morphology in tissue sections. Because recently “stemness” has been associated with a poor immune response in tumors, we investigated the association of prognostic gene expression, stemness and the immune microenvironment systematically using transcriptomes of 4068 tumors occurring mostly at the pediatric and young adult age. While the prognostic landscape of gene expression (PRECOG) and infiltrating immune cell types (CIBERSORT) is similar to that of tumor entities occurring mainly in adults, the patterns are distinct for each diagnostic entity. A high stemness score (mRNAsi) correlates with clinical and morphologic subtype in Wilms tumors, neuroblastomas, synovial sarcomas, atypical teratoid rhabdoid tumors and germ cell tumors. In neuroblastomas, a high mRNAsi is associated with shortened overall survival. In Wilms tumors a high mRNAsi correlates with blastemal morphology, whereas tumors with predominant epithelial or stromal differentiation have a low mRNAsi and a high percentage of M2 type macrophages. This could be validated in Wilms tumor tissue (n = 78). Here, blastemal areas are low in M2 macrophage infiltrates, while nearby stromal differentiated areas contain abundant M2 macrophages, suggesting local microanatomic regulation of the immune response.

SUMMARY: The SARS-CoV-2 pandemic is currently leading to increasing numbers of COVID-19 patients all over the world. Clinical presentations range from asymptomatic, mild respiratory tract infection, to severe cases with acute respiratory distress syndrome, respiratory failure, and death. Reports on a dysregulated immune system in the severe cases calls for a better characterization and understanding of the changes in the immune system. Here, we profiled whole blood transcriptomes of 39 COVID-19 patients and 10 control donors enabling a data-driven stratification based on molecular phenotype. Neutrophil activation-associated signatures were prominently enriched in severe patient groups, which was corroborated in whole blood transcriptomes from an independent second cohort of 30 as well as in granulocyte samples from a third cohort of 11 COVID-19 patients. Comparison of COVID-19 blood transcriptomes with those of a collection of over 2,800 samples derived from 11 different viral infections, inflammatory diseases and independent control samples revealed highly specific transcriptome signatures for COVID-19. Further, stratified transcriptomes predicted patient subgroup-specific drug candidates targeting the dysregulated systemic immune response of the host.

With progressive digitalization of healthcare systems worldwide, large-scale collection of electronic health records (EHRs) has become commonplace. However, an extensible framework for comprehensive exploratory analysis that accounts for data heterogeneity is missing. Here, we introduce ehrapy, a modular open-source Python framework designed for exploratory end-to-end analysis of heterogeneous epidemiology and electronic health record data. Ehrapy incorporates a series of analytical steps, from data extraction and quality control to the generation of low-dimensional representations. Complemented by rich statistical modules, ehrapy facilitates associating patients with disease states, differential comparison between patient clusters, survival analysis, trajectory inference, causal inference, and more. Leveraging ontologies, ehrapy further enables data sharing and training EHR deep learning models paving the way for foundational models in biomedical research. We demonstrated ehrapy’s features in five distinct examples: We first applied ehrapy to stratify patients affected by unspecified pneumonia into finer-grained phenotypes. Furthermore, we revealed biomarkers for significant differences in survival among these groups. Additionally, we quantify medication-class effects of pneumonia medications on length of stay. We further leveraged ehrapy to analyze cardiovascular risks across different data modalities. Finally, we reconstructed disease state trajectories in SARS-CoV-2 patients based on imaging data. Ehrapy thus provides a framework that we envision will standardize analysis pipelines on EHR data and serve as a cornerstone for the community.