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Induced proximity by molecular glues is a strategy that leverages the recruitment of proteins to facilitate their modification or degradation. Here the authors present unbiased quantitative proteomic, biochemical and computational workflows that uncover hundreds of CRBN molecular glue targets using recombinant protein and cell lysate.

Integrative analyses of transcriptome and whole-genome sequencing data for 1,188 tumours across 27 types of cancer are used to provide a comprehensive catalogue of RNA-level alterations in cancer.

Multisystem inflammatory syndrome in children (MIS-C) onsets in COVID-19 patients with manifestations similar to Kawasaki disease (KD). Here the author probe the peripheral blood transcriptome of MIS-C patients to find signatures related to natural killer (NK) cell activation and CD8+ T cell exhaustion that are shared with KD patients.

The flagship paper of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium describes the generation of the integrative analyses of 2,658 cancer whole genomes and their matching normal tissues across 38 tumour types, the structures for international data sharing and standardized analyses, and the main scientific findings from across the consortium studies.

Some cancer patients first present with metastases where the location of the primary is unidentified; these are difficult to treat. In this study, using machine learning, the authors develop a method to determine the tissue of origin of a cancer based on whole sequencing data.

In this study the authors consider the structural variants (SVs) present within cancer cases of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. They report hundreds of genes, including known cancer-associated genes for which the nearby presence of a SV breakpoint is associated with altered expression.

Comprehensive integration of gene expression with epigenetic features is needed to understand the transition of kidney cells from health to injury. Here, the authors integrate dual single nucleus RNA expression and chromatin accessibility, DNA methylation, and histone modifications to decipher the chromatin landscape of the kidney in reference and adaptive injury cell states, identifying a transcription factor network of ELF3, KLF6, and KLF10 which regulates adaptive repair and maladaptive failed repair.

There’s an emerging body of evidence to show how biological sex impacts cancer incidence, treatment and underlying biology. Here, using a large pan-cancer dataset, the authors further highlight how sex differences shape the cancer genome.

Nature evolves diverse pathways for production of acetyl-CoA, a principal biosynthetic building block. Now, through in silico thermodynamic and kinetic analyses, this study proposes an acetyl-CoA biosynthetic route from C1 compounds and implements it in gas-fermenting bacteria for effective production of C2 metabolites.

With the generation of large pan-cancer whole-exome and whole-genome sequencing projects, a question remains about how comparable these datasets are. Here, using The Cancer Genome Atlas samples analysed as part of the Pan-Cancer Analysis of Whole Genomes project, the authors explore the concordance of mutations called by whole exome sequencing and whole genome sequencing techniques.

Understanding deregulation of biological pathways in cancer can provide insight into disease etiology and potential therapies. Here, as part of the PanCancer Analysis of Whole Genomes (PCAWG) consortium, the authors present pathway and network analysis of 2583 whole cancer genomes from 27 tumour types.

Analyses of 2,658 whole genomes across 38 types of cancer identify the contribution of non-coding point mutations and structural variants to driving cancer.

In somatic cells the mechanisms maintaining the chromosome ends are normally inactivated; however, cancer cells can re-activate these pathways to support continuous growth. Here, the authors characterize the telomeric landscapes across tumour types and identify genomic alterations associated with different telomere maintenance mechanisms.

Systematic comparison of SARS-CoV-2 spike proteins from pre- and post-Omicron variants in cell lines, primary respiratory epithelial cells and Syrian hamsters show distinct phenotypic trajectories in replication kinetics, cell tropism, cell-to-cell fusion, spike processing, cell entry routes and virulence.

Whole-genome sequencing data from more than 2,500 cancers of 38 tumour types reveal 16 signatures that can be used to classify somatic structural variants, highlighting the diversity of genomic rearrangements in cancer.

Viral pathogen load in cancer genomes is estimated through analysis of sequencing data from 2,656 tumors across 35 cancer types using multiple pathogen-detection pipelines, identifying viruses in 382 genomic and 68 transcriptome datasets.

Analysis of cancer genome sequencing data has enabled the discovery of driver mutations. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium the authors present DriverPower, a software package that identifies coding and non-coding driver mutations within cancer whole genomes via consideration of mutational burden and functional impact evidence.

A pan-cancer genomic analysis reports the effects of structural variations on chromatin domains (TADs). Most TAD disruptions do not result in appreciable changes in expression of nearby genes.

An analysis of 2,954 genomes from 38 cancer subtypes identified 19,166 retrotransposition events in 35% of samples. Aberrant LINE-1 retrotranspositions can lead to the deletion of tumor-suppressor genes as well as the amplification of oncogenes.

Whole-genome sequencing data for 2,778 cancer samples from 2,658 unique donors across 38 cancer types is used to reconstruct the evolutionary history of cancer, revealing that driver mutations can precede diagnosis by several years to decades.

The authors present SVclone, a computational method for inferring the cancer cell fraction of structural variants from whole-genome sequencing data.

Many tumours exhibit hypoxia (low oxygen) and hypoxic tumours often respond poorly to therapy. Here, the authors quantify hypoxia in 1188 tumours from 27 cancer types, showing elevated hypoxia links to increased mutational load, directing evolutionary trajectories.

Multi-omics datasets pose major challenges to data interpretation and hypothesis generation owing to their high-dimensional molecular profiles. Here, the authors develop ActivePathways method, which uses data fusion techniques for integrative pathway analysis of multi-omics data and candidate gene discovery.

The characterization of 4,645 whole-genome and 19,184 exome sequences, covering most types of cancer, identifies 81 single-base substitution, doublet-base substitution and small-insertion-and-deletion mutational signatures, providing a systematic overview of the mutational processes that contribute to cancer development.

Cancers evolve as they progress under differing selective pressures. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, the authors present the method TrackSig the estimates evolutionary trajectories of somatic mutational processes from single bulk tumour data.

Type I diabetes is characterized by autoantibodies directed against protein or non-protein self-antigens. Here the authors profile glycan reactive anti-carbohydrate antibodies (ACA) in a longitudinal and cross-sectional childhood diabetes cohort and associate clusters of ACA with disease progression.

Huang et al. developed E3-substrate tagging by ubiquitin biotinylation (E-STUB), a proximity labeling-based method for direct identification of ubiquitylated substrates for a given E3 ligase, providing a useful tool for substrate discovery of targeted protein degradation and the understanding of E3 ligase function.

A streamlined sequencing process enables identification of disease-causing variants in the clinic within 8 hours.

Using structural, biochemical, and functional assays, the authors demonstrate that the E3 ligase KLHDC2, via newly developed small-molecule ligands, can be co-opted to target critical targets for degradation.

Directing the position, orientation, and long-range lateral order of block copolymer domains to produce technologically-useful, sublithographic patterns is a challenge. Here, the authors present a promising approach to overcome the challenge by directing assembly using spatial boundaries between planar, low-resolution regions on a surface with different composition.

Stimulator of interferon genes (STING) is known to be involved in defence against DNA viruses, but its role in the control of RNA viruses remains poorly explored. Here the authors show that STING participates in an innate immune response to RNA virus infection in a cGAS-independent manner.

Sequencing and de novo assembly of the maize W22 reference genome enable accurate placement of Mutator (Mu) and Dissociation (Ds) transposable element insertions, providing a foundation for maize functional genomics and transposon biology.

The BRAIN Initiative Cell Census Network has constructed a multimodal cell census and atlas of the mammalian primary motor cortex in a landmark effort towards understanding brain cell-type diversity, neural circuit organization and brain function.

A high-resolution kidney cellular atlas of 51 main cell types, including rare and previously undescribed cell populations, represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.

Analysis of whole-genome sequencing data across 2,658 tumors spanning 38 cancer types shows that chromothripsis is pervasive, with a frequency of more than 50% in several cancer types, contributing to oncogene amplification, gene inactivation and cancer genome evolution.

Analysis of mitochondrial genomes (mtDNA) by using whole-genome sequencing data from 2,658 cancer samples across 38 cancer types identifies hypermutated mtDNA cases, frequent somatic nuclear transfer of mtDNA and high variability of mtDNA copy number in many cancers.

Efficient, large-scale genomic analysis is facilitated on the cloud by a computational tool with error-diagnosing and self-healing capabilities.

In a study of children with high genetic risk aged 2 years or older, a risk score integrating pancreatic islet autoantibodies, genetic factors and family history is highly predictive of type 1 diabetes in the subsequent 8 years.