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Allosteric transcription factors (aTFs) are promising tools for environmental and human health monitoring. Here the authors develop a multi-objective, machine learning-guided method to engineer an aTF-based portable diagnostic for environment sensing of lead in drinking water at the legal limit.

Genomic analyses applied to 14 childhood- and adult-onset psychiatric disorders identifies five underlying genomic factors that explain the majority of the genetic variance of the individual disorders.

Mu opioid receptor agonists with a preference for the non-GTP-bound state of the G protein promote GTP release, thereby potentiating antinociceptive effects of drugs such as morphine and fentanyl without also increasing their respiratory or cardiac effects.

In two independent studies, Song et al. and Jiang, Dalgarno et al. present computational frameworks, perturbation-response score and Mixscale, respectively, to determine individual cellular variation in response to perturbation.

Single molecule nanopore analysis is used to reveal adenylate kinase global dynamics in real time. It was found that allosteric interactions guide domain motions and substrate affinity, with 10% of enzymes displaying alternative active forms, suggesting evolutionary paths for enzyme regulation.

Glioblastoma (GBM) is an aggressive tumor characterized by an immunosuppressive microenvironment. Here, the authors present a therapeutic approach based on the implantation of a biodegradable scaffold for the sustained, local release of a TLR7/8 agonist at the time of tumor resection, to achieve anti-tumor immunity and protection from future tumor challenge.

A global network of researchers was formed to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity; this paper reports 13 genome-wide significant loci and potentially actionable mechanisms in response to infection.

In this study, Jirsaraie et al. analyze data from the Adolescent Brain Cognitive Developmental study and use machine learning to predict both current and future psychological symptoms and to determine rates of change in symptom severity over time.

In a phase 1 trial, intramuscular injection of synthetic plasmid DNA encoding monoclonal antibodies against SARS-CoV-2 was safe and well tolerated and did not elicit antidrug antibodies.

Mitochondrial DNA heteroplasmy associates with context-specific nuclear CpG methylation, supported by a cell model. Heteroplasmy–CpG scores relate to mortality and cardiovascular risk, though direction and mechanisms remain unclear.

Consuming the milk of other species is a unique adaptation of Homo sapiens. Here, the authors carry out proteomic analysis of dental calculus of 41 ancient individuals from Sudan and Kenya, indicating milk consumption occurred as soon as herding spread into eastern Africa.

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.

Streptococcus pyogenes is a deadly bacteria without a vaccine. Here, researchers measured antibodies in serum and saliva from a strep throat human challenge trial. Baseline antibodies led to variable responses and affected susceptibility to strep throat.

Polygenic risk scores can help identify individuals at higher risk of type 2 diabetes. Here, the authors characterise a multi-ancestry score across nearly 900,000 people, showing that its predictive value depends on demographic and clinical context and extends to related traits and complications.

Approaches making virtual and experimental screening more resource-efficient are vital for identifying effective inhibitors from a vast pool of potential drugs but remain elusive. Here, the authors address this issue by developing an active learning framework leveraging high-throughput molecular dynamics simulations to identify potential inhibitors for therapeutic applications.

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.

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.

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.

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.

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.

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.

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.

In this work, authors utilise a prefractionation natural product extract screening platform, which uncovers coniotins, lipopeptaibiotics with broad antifungal activity and a mechanism of action that targets fungal cell wall β-glucan.

Spatial transcriptomic analysis of cells in intestinal fistulae of patients with Crohn’s disease reveals the existence of specialized fistula-associated cell states with distinct signalling profiles and extracellular matrix architecture.

The development of broadly neutralizing monoclonal antibodies is crucial in the fight against viral infections. Here, using combinatorial library technology, the authors identify 3D1, a monoclonal antibody with potential pan-inhibitory activity against a range of viral families, and provide structural analysis to reveal the basis of the broad cross-reactivity.

Here the authors provide an explanation for 95% of examined predicted loss of function variants found in disease-associated haploinsufficient genes in the Genome Aggregation Database (gnomAD), underscoring the power of the presented analysis to minimize false assignments of disease risk.

The authors identify thousands of genetic variants affecting RNA splicing in primary human chondrocytes and link several of them to osteoarthritis risk using genome editing and computational approaches.

Genome-wide analyses identify 30 independent loci associated with obsessive–compulsive disorder, highlighting genetic overlap with other psychiatric disorders and implicating putative effector genes and cell types contributing to its etiology.

In this study, the authors report the small molecule inhibitor EDP-235 as a potent inhibitor of SARS-CoV-2 and show that it is effective against a range of variants and other coronaviruses and that it suppresses virus replication, reduces lung damage, and prevents transmission in small animal models.

Crystal structures of the linker region of TRPML1 reveal that the luminal domain forms a tetrameric pore. Along with electrophysiology studies, this work provides insight into the mechanism of channel regulation by Ca₂₊ and H₊.

A study of several longitudinal birth cohorts and cross-sectional cohorts finds only moderate overlap in genetic variants between autism that is diagnosed earlier and that diagnosed later, so they may represent aetiologically different conditions.

Cystic fibrosis is a lethal genetic disorder commonly caused by the F508del mutation which is not amenable to gene therapy. Here, the authors use triplex-forming PNA molecules and donor DNA in biodegradable polymer nanoparticles to correct F508del and achieve clinically relevant levels of gene editing.

Protein nanopores are emerging as sensors for a variety of biomolecules. Here the authors develop a nanopore based on the bacterial toxin ClyA, in conjunction with binding proteins for glucose and asparagine, to detect these biomolecules simultaneously from a variety of unprocessed, diluted body fluids.

The paralogs cytidine diphosphate diacylglycerol synthase 1 and 2 form a potentially targetable synthetic lethal relationship in mesenchymal-like cancers that involves disruption of lipid metabolism.