Documenting genome diversity is important for the local biomedical communities and instrumental in developing precision and personalized medicine. Currently, tens of thousands of whole-genome sequences from Europe are publicly available, but most of these represent populations of developed countries of Europe. The uneven distribution of the available data is further impaired by the lack of data sharing. Recent whole-genome studies in Eastern Europe, one in Ukraine and one in Russia, demonstrated that local genome diversity and population structure from Eastern Europe historically had not been fully represented. An unexpected wealth of genomic variation uncovered in these studies was not so much a consequence of high variation within their population, but rather due to the "pioneer advantage." We discovered more variants because we were the first to prospect in the Eastern European genome pool. This simple comparison underscores the importance of removing the remaining geographic genome deserts from the rest of the world map of the human genome diversity.

BACKGROUND: Maternal proteins have important roles during early embryonic development. However, our understanding of maternal proteins is still very limited. The integrated analysis of mouse uniparental (parthenogenetic) and biparental (fertilized) embryos at the protein level creates a protein expression landscape that can be used to explore preimplantation mouse development. RESULTS: Using label-free quantitative mass spectrometry (MS) analysis, we report on the maternal proteome of mouse parthenogenetic embryos at pronucleus, 2-cell, 4-cell, 8-cell, morula, and blastocyst stages and highlight dynamic changes in protein expression. In addition, comparison of proteomic profiles of parthenogenotes and fertilized embryos highlights the different fates of maternal proteins. Enrichment analysis uncovered a set of maternal proteins that are strongly correlated with the subcortical maternal complex, and we report that in parthenogenotes, some of these maternal proteins escape the fate of protein degradation. Moreover, we identified a new maternal factor-Fbxw24, and highlight its importance in early embryonic development. We report that Fbxw24 interacts with Ddb1-Cul4b and may regulate maternal protein degradation in mouse. CONCLUSIONS: Our study provides an invaluable resource for mechanistic analysis of maternal proteins and highlights the role of the novel maternal factor Fbw24 in regulating maternal protein degradation during preimplantation embryo development.

An individualized cancer therapy is ideally chosen to target the cancer's driving biological pathways, but identifying such pathways is challenging because of their underlying heterogeneity and there is no guarantee that they are druggable. We hypothesize that a cancer with an activated druggable cancer-specific pathway (DCSP) is more likely to respond to the relevant drug. Here we develop and validate a systematic method to search for such DCSPs, by (i) introducing a pathway activation score (PAS) that integrates cancer-specific driver mutations and gene expression profile and drug-specific gene targets, (ii) applying the method to identify DCSPs from pan-cancer datasets, and (iii) analyzing the correlation between PAS and the response to relevant drugs. In total, 4,794 DCSPs from 23 different cancers have been discovered in the Genomics of Drug Sensitivity in Cancer database and validated in The Cancer Genome Atlas database. Supporting the hypothesis, for the DCSPs in acute myeloid leukemia, cancers with higher PASs are shown to have stronger drug response, and this is validated in the BeatAML cohort. All DCSPs are publicly available at https://www.meb.ki.se/shiny/truvu/DCSP/.

BACKGROUND: In cancer research, high-throughput screening technologies produce large amounts of multiomics data from different populations and cell types. However, analysis of such data encounters difficulties due to disease heterogeneity, further exacerbated by human biological complexity and genomic variability. The specific profile of cancer as a disease (or, more realistically, a set of diseases) urges the development of approaches that maximize the effect while minimizing the dosage of drugs. Now is the time to redefine the approach to drug discovery, bringing an artificial intelligence (AI)-powered informational view that integrates the relevant scientific fields and explores new territories. RESULTS: Here, we show SYNPRED, an interdisciplinary approach that leverages specifically designed ensembles of AI algorithms, as well as links omics and biophysical traits to predict anticancer drug synergy. It uses 5 reference models (Bliss, Highest Single Agent, Loewe, Zero Interaction Potency, and Combination Sensitivity Score), which, coupled with AI algorithms, allowed us to attain the ones with the best predictive performance and pinpoint the most appropriate reference model for synergy prediction, often overlooked in similar studies. By using an independent test set, SYNPRED exhibits state-of-the-art performance metrics either in the classification (accuracy, 0.85; precision, 0.91; recall, 0.90; area under the receiver operating characteristic, 0.80; and F1-score, 0.91) or in the regression models, mainly when using the Combination Sensitivity Score synergy reference model (root mean square error, 11.07; mean squared error, 122.61; Pearson, 0.86; mean absolute error, 7.43; Spearman, 0.87). Moreover, data interpretability was achieved by deploying the most current and robust feature importance approaches. A simple web-based application was constructed, allowing easy access by nonexpert researchers. CONCLUSIONS: The performance of SYNPRED rivals that of the existing methods that tackle the same problem, yielding unbiased results trained with one of the most comprehensive datasets available (NCI ALMANAC). The leveraging of different reference models allowed deeper insights into which of them can be more appropriately used for synergy prediction. The Combination Sensitivity Score clearly stood out with improved performance among the full scope of surveyed approaches and synergy reference models. Furthermore, SYNPRED takes a particular focus on data interpretability, which has been in the spotlight lately when using the most advanced AI techniques.

BACKGROUND: Structural annotation of genetic variants in the context of intermolecular interactions and protein stability can shed light onto mechanisms of disease-related phenotypes. Three-dimensional structures of related proteins in complexes with other proteins, nucleic acids, or ligands enrich such functional interpretation, since intermolecular interactions are well conserved in evolution. RESULTS: We present d-StructMAn, a novel computational method that enables structural annotation of local genetic variants, such as single-nucleotide variants and in-frame indels, and implements it in a highly efficient and user-friendly tool provided as a Docker container. Using d-StructMAn, we annotated several very large sets of human genetic variants, including all variants from ClinVar and all amino acid positions in the human proteome. We were able to provide annotation for more than 46% of positions in the human proteome representing over 60% proteins. CONCLUSIONS: d-StructMAn is the first of its kind and a highly efficient tool for structural annotation of protein-coding genetic variation in the context of observed and potential intermolecular interactions. d-StructMAn is readily applicable to proteome-scale datasets and can be an instrumental building machine-learning tool for predicting genotype-to-phenotype relationships.

BACKGROUND: Asian arowana, Scleropages formosus, is one of the most expensive aquarium fish species worldwide. Its sex, however, cannot be distinguished clearly at any development stage, which impedes captive breeding and species protection for this endangered aquarium fish. RESULTS: To discover molecular clues to the sex of Asian arowana, we sequenced 26.5 Gb of PacBio HiFi reads and 179.2 Gb of Hi-C reads for 1 male fish and also sequenced 106.5 Gb of Illumina reads, 36.0 Gb of PacBio Sequel reads, and 80.7 Gb of Hi-C reads for 1 female individual. The final male and female genome assemblies were approximately 756.8 Mb and 781.5 Mb in length and contained 25,262 and 25,328 protein-coding genes, respectively. We also resequenced the genomes of 15 male and 15 female individuals with approximately 722.1 Gb of Illumina reads. A genome-wide association study identified several potentially divergent regions between male and female individuals. In these regions, cd48 and cfap52 could be candidate genes for sex determination of Asian arowana. We also found some structural variations in few chromosomes between male and female individuals. CONCLUSION: We provided an improved reference genome assembly of female arowana and generated the first sequenced genome of 1 male individual. These valuable genetic resources and resequencing data may improve global aquarium fish research.