G protein-coupled receptor endocytosis generates spatiotemporal bias in β-arrestin signaling.

The stabilization of different active conformations of G protein-coupled receptors is thought to underlie the varying efficacies of biased and balanced agonists. Here, profiling the activation of signal transducers by angiotensin II type 1 receptor (ATR) agonists revealed that the extent and kinetics of β-arrestin binding exhibited substantial ligand-dependent differences, which were lost when receptor internalization was inhibited. When ATR endocytosis was prevented, even weak partial agonists of the β-arrestin pathway acted as full or near-full agonists, suggesting that receptor conformation did not exclusively determine β-arrestin recruitment.

Missing data in amortized simulation-based neural posterior estimation.

Amortized simulation-based neural posterior estimation provides a novel machine learning based approach for solving parameter estimation problems. It has been shown to be computationally efficient and able to handle complex models and data sets. Yet, the available approach cannot handle the in experimental studies ubiquitous case of missing data, and might provide incorrect posterior estimates.

From Planning Stage Towards FAIR Data: A Practical Metadatasheet For Biomedical Scientists.

Datasets consist of measurement data and metadata. Metadata provides context, essential for understanding and (re-)using data. Various metadata standards exist for different methods, systems and contexts.

Deoxyguanosine kinase deficiency: natural history and liver transplant outcome.

Autosomal recessive pathogenetic variants in the gene cause deficiency of deoxyguanosine kinase activity and mitochondrial deoxynucleotides pool imbalance, consequently, leading to quantitative and/or qualitative impairment of mitochondrial DNA synthesis. Typically, patients present early-onset liver failure with or without neurological involvement and a clinical course rapidly progressing to death. This is an international multicentre study aiming to provide a retrospective natural history of deoxyguanosine kinase deficient patients.

Digenic Inheritance in Rare Disorders and Mitochondrial Disease-Crossing the Frontier to a More Comprehensive Understanding of Etiology.

Our understanding of rare disease genetics has been shaped by a monogenic disease model. While the traditional monogenic disease model has been successful in identifying numerous disease-associated genes and significantly enlarged our knowledge in the field of human genetics, it has limitations in explaining phenomena like phenotypic variability and reduced penetrance. Widening the perspective beyond Mendelian inheritance has the potential to enable a better understanding of disease complexity in rare disorders.

MISATO: machine learning dataset of protein-ligand complexes for structure-based drug discovery.

Large language models have greatly enhanced our ability to understand biology and chemistry, yet robust methods for structure-based drug discovery, quantum chemistry and structural biology are still sparse. Precise biomolecule-ligand interaction datasets are urgently needed for large language models. To address this, we present MISATO, a dataset that combines quantum mechanical properties of small molecules and associated molecular dynamics simulations of ~20,000 experimental protein-ligand complexes with extensive validation of experimental data.

Systematic analysis of NDUFAF6 in complex I assembly and mitochondrial disease.

Isolated complex I (CI) deficiencies are a major cause of primary mitochondrial disease. A substantial proportion of CI deficiencies are believed to arise from defects in CI assembly factors (CIAFs) that are not part of the CI holoenzyme. The biochemistry of these CIAFs is poorly defined, making their role in CI assembly unclear, and confounding interpretation of potential disease-causing genetic variants.

Combinatorial microRNA activity is essential for the transition of pluripotent cells from proliferation into dormancy.

Dormancy is a key feature of stem cell function in adult tissues as well as in embryonic cells in the context of diapause. The establishment of dormancy is an active process that involves extensive transcriptional, epigenetic, and metabolic rewiring. How these processes are coordinated to successfully transition cells to the resting dormant state remains unclear.

Loss of NEDD8 in cancer cells causes vulnerability to immune checkpoint blockade in triple-negative breast cancer.

Immune checkpoint blockade therapy aims to activate the immune system to eliminate cancer cells. However, clinical benefits are only recorded in a subset of patients. Here, we leverage genome-wide CRISPR/Cas9 screens in a Tumor-Immune co-Culture System focusing on triple-negative breast cancer (TNBC).

hadge: a comprehensive pipeline for donor deconvolution in single-cell studies.

Single-cell multiplexing techniques (cell hashing and genetic multiplexing) combine multiple samples, optimizing sample processing and reducing costs. Cell hashing conjugates antibody-tags or chemical-oligonucleotides to cell membranes, while genetic multiplexing allows to mix genetically diverse samples and relies on aggregation of RNA reads at known genomic coordinates. We develop hadge (hashing deconvolution combined with genotype information), a Nextflow pipeline that combines 12 methods to perform both hashing- and genotype-based deconvolution.

Species-aware DNA language models capture regulatory elements and their evolution.

Background: The rise of large-scale multi-species genome sequencing projects promises to shed new light on how genomes encode gene regulatory instructions. To this end, new algorithms are needed that can leverage conservation to capture regulatory elements while accounting for their evolution.

Results: Here, we introduce species-aware DNA language models, which we trained on more than 800 species spanning over 500 million years of evolution.

Cellular energy regulates mRNA degradation in a codon-specific manner.

Codon optimality is a major determinant of mRNA translation and degradation rates. However, whether and through which mechanisms its effects are regulated remains poorly understood. Here we show that codon optimality associates with up to 2-fold change in mRNA stability variations between human tissues, and that its effect is attenuated in tissues with high energy metabolism and amplifies with age.

Deep learning in spatially resolved transcriptfomics: a comprehensive technical view.

Spatially resolved transcriptomics (SRT) is a pioneering method for simultaneously studying morphological contexts and gene expression at single-cell precision. Data emerging from SRT are multifaceted, presenting researchers with intricate gene expression matrices, precise spatial details and comprehensive histology visuals. Such rich and intricate datasets, unfortunately, render many conventional methods like traditional machine learning and statistical models ineffective.

Digenic Leigh syndrome on the background of the m.11778G>A Leber hereditary optic neuropathy variant.

Leigh syndrome spectrum (LSS) is a primary mitochondrial disorder defined neuropathologically by a subacute necrotizing encephalomyelopathy and characterized by bilateral basal ganglia and/or brainstem lesions. LSS is associated with variants in several mitochondrial DNA genes and more than 100 nuclear genes, most often related to mitochondrial complex I (CI) dysfunction. Rarely, LSS has been reported in association with primary Leber hereditary optic neuropathy (LHON) variants of the mitochondrial DNA, coding for CI subunits (m.

Viral genome sequencing to decipher in-hospital SARS-CoV-2 transmission events.

The SARS-CoV-2 pandemic has highlighted the need to better define in-hospital transmissions, a need that extends to all other common infectious diseases encountered in clinical settings. To evaluate how whole viral genome sequencing can contribute to deciphering nosocomial SARS-CoV-2 transmission 926 SARS-CoV-2 viral genomes from 622 staff members and patients were collected between February 2020 and January 2021 at a university hospital in Munich, Germany, and analysed along with the place of work, duration of hospital stay, and ward transfers. Bioinformatically defined transmission clusters inferred from viral genome sequencing were compared to those inferred from interview-based contact tracing.

Processivity and specificity of histone acetylation by the male-specific lethal complex.

Acetylation of lysine 16 of histone H4 (H4K16ac) stands out among the histone modifications, because it decompacts the chromatin fiber. The metazoan acetyltransferase MOF (KAT8) regulates transcription through H4K16 acetylation. Antibody-based studies had yielded inconclusive results about the selectivity of MOF to acetylate the H4 N-terminus.

DeepLocRNA: an interpretable deep learning model for predicting RNA subcellular localization with domain-specific transfer-learning.

Motivation: Accurate prediction of RNA subcellular localization plays an important role in understanding cellular processes and functions. Although post-transcriptional processes are governed by trans-acting RNA binding proteins (RBPs) through interaction with cis-regulatory RNA motifs, current methods do not incorporate RBP-binding information.

Results: In this article, we propose DeepLocRNA, an interpretable deep-learning model that leverages a pre-trained multi-task RBP-binding prediction model to predict the subcellular localization of RNA molecules via fine-tuning.

Genetic variants affecting NQO1 protein levels impact the efficacy of idebenone treatment in Leber hereditary optic neuropathy.

Idebenone, the only approved treatment for Leber hereditary optic neuropathy (LHON), promotes recovery of visual function in up to 50% of patients, but we can neither predict nor understand the non-responders. Idebenone is reduced by the cytosolic NAD(P)H oxidoreductase I (NQO1) and directly shuttles electrons to respiratory complex III, bypassing complex I affected in LHON. We show here that two polymorphic variants drastically reduce NQO1 protein levels when homozygous or compound heterozygous.

Impaired biogenesis of basic proteins impacts multiple hallmarks of the aging brain.

Aging and neurodegeneration entail diverse cellular and molecular hallmarks. Here, we studied the effects of aging on the transcriptome, translatome, and multiple layers of the proteome in the brain of a short-lived killifish. We reveal that aging causes widespread reduction of proteins enriched in basic amino acids that is independent of mRNA regulation, and it is not due to impaired proteasome activity.

Reusable rule-based cell cycle model explains compartment-resolved dynamics of 16 observables in RPE-1 cells.

The mammalian cell cycle is regulated by a well-studied but complex biochemical reaction system. Computational models provide a particularly systematic and systemic description of the mechanisms governing mammalian cell cycle control. By combining both state-of-the-art multiplexed experimental methods and powerful computational tools, this work aims at improving on these models along four dimensions: model structure, validation data, validation methodology and model reusability.

Interaction molecular QTL mapping discovers cellular and environmental modifiers of genetic regulatory effects.

Bulk-tissue molecular quantitative trait loci (QTLs) have been the starting point for interpreting disease-associated variants, and context-specific QTLs show particular relevance for disease. Here, we present the results of mapping interaction QTLs (iQTLs) for cell type, age, and other phenotypic variables in multi-omic, longitudinal data from the blood of individuals of diverse ancestries. By modeling the interaction between genotype and estimated cell-type proportions, we demonstrate that cell-type iQTLs could be considered as proxies for cell-type-specific QTL effects, particularly for the most abundant cell type in the tissue.

Deep learning-driven fragment ion series classification enables highly precise and sensitive de novo peptide sequencing.

Unlike for DNA and RNA, accurate and high-throughput sequencing methods for proteins are lacking, hindering the utility of proteomics in applications where the sequences are unknown including variant calling, neoepitope identification, and metaproteomics. We introduce Spectralis, a de novo peptide sequencing method for tandem mass spectrometry. Spectralis leverages several innovations including a convolutional neural network layer connecting peaks in spectra spaced by amino acid masses, proposing fragment ion series classification as a pivotal task for de novo peptide sequencing, and a peptide-spectrum confidence score.

Automated MRI Lung Segmentation and 3D Morphologic Features for Quantification of Neonatal Lung Disease.

Purpose: To analyze the performance of deep learning (DL) models for segmentation of the neonatal lung in MRI and investigate the use of automated MRI-based features for assessment of neonatal lung disease.

Materials And Methods: Quiet-breathing MRI was prospectively performed in two independent cohorts of preterm infants (median gestational age, 26.57 weeks; IQR, 25.