WEE1 and CHK1 jointly regulate Cdk activity to prevent DNA damage during replication.

Experimental design: Sixteen patients received fludarabine/cyclophosphamide conditioning 68 combined with total lymphoid irradiation followed by adoptive immunotherapy with IL-2-69 activated haploidentical NK cells. 70

The immense increase in the generation of genomic scale data poses an unmet analytical challenge, due to a lack of established methodology with the required flexibility and power. We propose a first principled approach to statistical analysis of sequence-level genomic information. We provide a growing collection of generic biological investigations that query pairwise relations between tracks, represented as mathematical objects, along the genome. The Genomic HyperBrowser implements the approach and is available at http://hyperbrowser.uio.no.

Inhibitory signaling during natural killer (NK) cell education translates into increased responsiveness to activation; however, the intracellular mechanism for functional tuning by inhibitory receptors remains unclear. Secretory lysosomes are part of the acidic lysosomal compartment that mediates intracellular signalling in several cell types. Here we show that educated NK cells expressing self-MHC specific inhibitory killer cell immunoglobulin-like receptors (KIR) accumulate granzyme B in dense-core secretory lysosomes that converge close to the centrosome. This discrete morphological phenotype is independent of transcriptional programs that regulate effector function, metabolism and lysosomal biogenesis. Meanwhile, interference of signaling from acidic Ca2+ stores in primary NK cells reduces target-specific Ca2+-flux, degranulation and cytokine production. Furthermore, inhibition of PI(3,5)P2 synthesis, or genetic silencing of the PI(3,5)P2-regulated lysosomal Ca2+-channel TRPML1, leads to increased granzyme B and enhanced functional potential, thereby mimicking the educated state. These results indicate an intrinsic role for lysosomal remodeling in NK cell education.

The global population is at present suffering from a pandemic of Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The goal of this study was to use artificial intelligence (AI) to predict blueprints for designing universal vaccines against SARS-CoV-2, that contain a sufficiently broad repertoire of T-cell epitopes capable of providing coverage and protection across the global population. To help achieve these aims, we profiled the entire SARS-CoV-2 proteome across the most frequent 100 HLA-A, HLA-B and HLA-DR alleles in the human population, using host-infected cell surface antigen presentation and immunogenicity predictors from the NEC Immune Profiler suite of tools, and generated comprehensive epitope maps. We then used these epitope maps as input for a Monte Carlo simulation designed to identify statistically significant “epitope hotspot” regions in the virus that are most likely to be immunogenic across a broad spectrum of HLA types. We then removed epitope hotspots that shared significant homology with proteins in the human proteome to reduce the chance of inducing off-target autoimmune responses. We also analyzed the antigen presentation and immunogenic landscape of all the nonsynonymous mutations across 3,400 different sequences of the virus, to identify a trend whereby SARS-COV-2 mutations are predicted to have reduced potential to be presented by host-infected cells, and consequently detected by the host immune system. A sequence conservation analysis then removed epitope hotspots that occurred in less-conserved regions of the viral proteome. Finally, we used a database of the HLA haplotypes of approximately 22,000 individuals to develop a “digital twin” type simulation to model how effective different combinations of hotspots would work in a diverse human population; the approach identified an optimal constellation of epitope hotspots that could provide maximum coverage in the global population. By combining the antigen presentation to the infected-host cell surface and immunogenicity predictions of the NEC Immune Profiler with a robust Monte Carlo and digital twin simulation, we have profiled the entire SARS-CoV-2 proteome and identified a subset of epitope hotspots that could be harnessed in a vaccine formulation to provide a broad coverage across the global population.

Tumor cells have the ability to exploit stromal cells to facilitate metastasis. By using malignant melanoma as a model, we show that the stroma adjacent to metastatic lesions is enriched in the known metastasis-promoting protein S100A4. S100A4 stimulates cancer cells to secrete paracrine factors, such as inflammatory cytokines IL8, CCL2 and SAA, which activate stromal cells (endothelial cells and monocytes) so that they acquire tumor-supportive properties. Our data establishes S100A4 as an inducer of a cytokine network enabling tumor cells to engage angiogenic and inflammatory stromal cells, which might contribute to pro-metastatic activity of S100A4.

The pathogenetic role, including its target genes, of the recurrent 3p12-p14 loss in cervical cancer has remained unclear. To determine the onset of the event during carcinogenesis, we used microarray techniques and found that the loss was the most frequent 3p event, occurring in 61% of 92 invasive carcinomas, in only 2% of 43 high-grade intraepithelial lesions (CIN2/3), and in 33% of 6 CIN3 lesions adjacent to invasive carcinomas, suggesting a role in acquisition of invasiveness or early during the invasive phase. We performed an integrative DNA copy number and expression analysis of 77 invasive carcinomas, where all genes within the recurrent region were included. We selected eight genes, THOC7, PSMD6, SLC25A26, TMF1, RYBP, SHQ1, EBLN2, and GBE1, which were highly down-regulated in cases with loss, as confirmed at the protein level for RYBP and TMF1 by immunohistochemistry. The eight genes were subjected to network analysis based on the expression profiles, revealing interaction partners of proteins encoded by the genes that were coordinately regulated in tumours with loss. Several partners were shared among the eight genes, indicating crosstalk in their signalling. Gene ontology analysis showed enrichment of biological processes such as apoptosis, proliferation, and stress response in the network and suggested a relationship between down-regulation of the eight genes and activation of tumourigenic pathways. Survival analysis showed prognostic impact of the eight-gene signature that was confirmed in a validation cohort of 74 patients and was independent of clinical parameters. These results support the role of the eight candidate genes as targets of the 3p12-p14 loss in cervical cancer and suggest that the strong selection advantage of the loss during carcinogenesis might be caused by a synergetic effect of several tumourigenic processes controlled by these targets.

The immense increase in availability of genomic scale datasets, such as those provided by the ENCODE and Roadmap Epigenomics projects, presents unprecedented opportunities for individual researchers to pose novel falsifiable biological questions. With this opportunity, however, researchers are faced with the challenge of how to best analyze and interpret their genome-scale datasets. A powerful way of representing genome-scale data is as feature-specific coordinates relative to reference genome assemblies, i.e. as genomic tracks. The Genomic HyperBrowser (http://hyperbrowser.uio.no) is an open-ended web server for the analysis of genomic track data. Through the provision of several highly customizable components for processing and statistical analysis of genomic tracks, the HyperBrowser opens for a range of genomic investigations, related to, e.g., gene regulation, disease association or epigenetic modifications of the genome.