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Abstract Time-resolved serial crystallography at X-ray Free Electron Lasers offers the opportunity to observe ultrafast photochemical reactions at the atomic level. The technique has yielded exciting molecular insights into various biological processes including light sensing and photochemical energy conversion. However, to achieve sufficient levels of activation within an optically dense crystal, high laser power densities are often used, which has led to an ongoing debate to which extent photodamage may compromise interpretation of the results. Here we compare time-resolved serial crystallographic data of the bacteriorhodopsin K-intermediate collected at laser power densities ranging from 0.04 to 2493 GW/cm2 and follow energy dissipation of the absorbed photons logarithmically from picoseconds to milliseconds. Although the effects of high laser power densities on the overall structure are small, in the upper excitation range we observe significant changes in retinal conformation and increased heating of the functionally critical counterion cluster. We compare light-activation within crystals to that in solution and discuss the impact of the observed changes on bacteriorhodopsin biology.

Abstract Two-dimensional (2D) electron systems are promising for investigating correlated quantum phenomena. In particular, 2D oxides provide a platform that can host various quantum phases such as quantized Hall effect, superconductivity, or magnetism. The realization of such quantum phases in 2D oxides heavily relies on dedicated heterostructure growths. Here we show the integer quantum Hall effect achieved in chemical vapor deposition grown Bi2O2Se - a representative member of a more accessible oxide family. A single or few subband 2D electron system can be prepared in thin films of Bi2O2Se, where the film thickness acts as the key subband design parameter and the occupation is determined by the electric field effect. This oxide platform exhibits characteristic advantages in structural flexibility due to its layered nature, making it suitable for scalable growth. The unique small mass distinguishes Bi2O2Se from other high-mobility oxides, providing a new platform for exploring quantum Hall physics in 2D oxides.

Abstract Decline in mitochondrial function is linked to decreased muscle mass and strength in conditions like sarcopenia and type 2 diabetes. Despite therapeutic opportunities, there is limited and equivocal data regarding molecular cues controlling muscle mitochondrial plasticity. Here we uncovered that the mitochondrial mRNA-stabilizing protein SLIRP, in complex with LRPPRC, is a PGC-1α target that regulates mitochondrial structure, respiration, and mtDNA-encoded-mRNA pools in skeletal muscle. Exercise training effectively counteracts mitochondrial defects caused by genetically-induced LRPPRC/SLIRP loss, despite sustained low mtDNA-encoded-mRNA pools, by increasing mitoribosome translation capacity and mitochondrial quality control. In humans, exercise training robustly increases muscle SLIRP and LRPPRC protein across exercise modalities and sexes, yet less prominently in individuals with type 2 diabetes. SLIRP muscle loss reduces Drosophila lifespan. Our data points to a mechanism of post-transcriptional mitochondrial regulation in muscle via mitochondrial mRNA stabilization, offering insights into how exercise enhances mitoribosome capacity and mitochondrial quality control to alleviate defects.

Abstract The cognitive reserve (CR) hypothesis posits that individuals can differ in how their brain function is disrupted by pathology associated with aging and neurodegeneration. Here, we test this hypothesis in the continuum from cognitively normal to at-risk stages for Alzheimer’s Disease (AD) to AD dementia using longitudinal data from 490 participants of the DELCODE multicentric observational study. Brain function is measured using task fMRI of visual memory encoding. Using a multivariate moderation analysis, we identify a CR-related activity pattern underlying successful memory encoding that moderates the detrimental effect of AD pathological load on cognitive performance. CR is mainly represented by a more pronounced expression of the task-active network encompassing deactivation of the default mode network (DMN) and activation of inferior temporal regions including the fusiform gyrus. We devise personalized fMRI-based CR scores that moderate the impact of AD pathology on cognitive performance and are positively associated with years of education. Furthermore, higher CR scores attenuate the effect of AD pathology on cognitive decline over time. Our findings primarily provide evidence for the maintenance of core cognitive circuits including the DMN as the neural basis of CR. Individual brain activity levels of these areas during memory encoding have prognostic value for future cognitive decline.

Abstract Heart shape captures variation in cardiac structure beyond traditional phenotypes of mass and volume. Although observational studies have demonstrated associations with cardiometabolic risk factors and diseases, its genetic basis is less understood. We utilised cardiovascular magnetic resonance images from 45,683 UK Biobank participants to construct a heart shape atlas from bi-ventricular end-diastolic surface mesh models through principal component (PC) analysis. Genome-wide association studies were performed on the first 11 PCs that captured 83.6% of shape variance. We identified 43 significant loci, 14 were previously unreported for cardiac traits. Genetically predicted PCs were associated with cardiometabolic diseases. In particular two PCs (2 and 3) linked with more spherical ventricles being associated with increased risk of atrial fibrillation. Our study explores the genetic basis of multidimensional bi-ventricular heart shape using PCA, reporting new loci and biology, as well as polygenic risk scores for exploring genetic relationships of heart shape with cardiometabolic diseases.

Abstract Few aerobic hyperthermophilic microorganisms degrade polysaccharides. Here, we describe the genome-enabled enrichment and optical tweezer-based isolation of an aerobic polysaccharide-degrading hyperthermophile, Fervidibacter sacchari, previously ascribed to candidate phylum Fervidibacteria. F. sacchari uses polysaccharides and monosaccharides for growth at 65–87.5 °C and expresses 191 carbohydrate-active enzymes (CAZymes) according to RNA-Seq and proteomics, including 31 with unusual glycoside hydrolase domains (GH109, GH177, GH179). Fluorescence in-situ hybridization and nanoscale secondary ion mass spectrometry confirmed rapid assimilation of 13C-starch in spring sediments. Purified GHs were optimally active at 80–100 °C on ten different polysaccharides. Finally, we propose reassigning Fervidibacteria as a class within phylum Armatimonadota, along with 18 other species, and show that a high number and diversity of CAZymes is a hallmark of the phylum, in both aerobic and anaerobic lineages. Our study establishes Fervidibacteria as hyperthermophilic polysaccharide degraders in terrestrial geothermal springs and suggests a broad role for Armatimonadota in polysaccharide catabolism.

Abstract Microbial formation and oxidation of volatile alkanes in anoxic environments significantly impacts biogeochemical cycles on Earth. The discovery of archaea oxidizing volatile alkanes via deeply branching methyl-coenzyme M reductase variants, dubbed alkyl-CoM reductases (ACR), prompted the hypothesis of archaea-catalysed alkane formation in nature (alkanogenesis). A combination of metabolic modelling, anaerobic physiology assays, and isotope labeling of Candidatus Syntrophoarchaeum archaea catalyzing the anaerobic oxidation of butane (AOB) show a back flux of CO2 to butane, demonstrating reversibility of the entire AOB pathway. Back fluxes correlate with thermodynamics and kinetics of the archaeal catabolic system. AOB reversibility supports a biological formation of butane, and generally of higher volatile alkanes, helping to explain the presence of isotopically light alkanes and deeply branching ACR genes in sedimentary basins isolated from gas reservoirs.

Abstract Macrophages are pleiotropic and diverse cells that populate all tissues of the body. Besides tissue-specific resident macrophages such as alveolar macrophages, Kupffer cells, and microglia, multiple organs harbor at least two subtypes of other resident macrophages at steady state. During certain circumstances, like tissue insult, additional subtypes of macrophages are recruited to the tissue from the monocyte pool. Previously, a recruited macrophage population marked by expression of Spp1, Cd9, Gpnmb, Fabp5, and Trem2, has been described in several models of organ injury and cancer, and has been linked to fibrosis in mice and humans. Here, we show that Notch2 blockade, given systemically or locally, leads to an increase in this putative pro-fibrotic macrophage in the lung and that this macrophage state can only be adopted by monocytically derived cells and not resident alveolar macrophages. Using a bleomycin and COVID-19 model of lung injury and fibrosis, we find that the expansion of these macrophages before lung injury does not promote fibrosis but rather appears to ameliorate it. This suggests that these damage-associated macrophages are not, by themselves, drivers of fibrosis in the lung.

Abstract The generation of antibody-drug conjugates with optimal functionality depends on many parameters. These include binder epitope, antibody format, linker composition, conjugation site(s), drug-to-antibody ratio, and conjugation method. The production of matrices that cover all possible parameters is a major challenge in identifying optimal antibody-drug conjugates. To address this bottleneck, we adapted our Format Chain Exchange technology (FORCE), originally established for bispecific antibodies, toward the generation of binder-format-payload matrices (pair-FORCE). Antibody derivatives with exchange-enabled Fc-heterodimers are combined with payload-conjugated Fc donors, and subsequent chain-exchange transfers payloads to antibody derivatives in different formats. The resulting binder-format-conjugate matrices can be generated with cytotoxic payloads, dyes, haptens, and large molecules, resulting in versatile tools for ADC screening campaigns. We show the relevance of pair-FORCE for identifying optimal HER2-targeting antibody-drug conjugates. Analysis of this matrix reveals that the notion of format-defines-function applies not only to bispecific antibodies, but also to antibody-drug conjugates.

Abstract Biodegradability testing in soil helps to identify safe synthetic organic chemicals but is still obscured by the formation of soil-bound ‘non-extractable’ residues (NERs). Present-day methodologies using radiocarbon or stable (13C, 15N) isotope labeling cannot easily differentiate soil-bound parent chemicals or transformation products (xenoNERs) from harmless soil-bound biomolecules of microbial degraders (bioNERs). Hypothesizing a minimal retention of hydrogen in biomolecules, we here apply stable hydrogen isotope – deuterium (D) – labeling to unravel the origin of NERs. Soil biodegradation tests with D- and 13C-labeled 2,4-D, glyphosate and sulfamethoxazole reveal consistently lower proportions of applied D than 13C in total NERs and in amino acids, a quantitative biomarker for bioNERs. Soil-bound D thus mostly represents xenoNERs and not bioNERs, enabling an efficient quantification of xenoNERs by just measuring the total bound D. D or tritium (T) labeling could thus improve the value of biodegradability testing results for diverse organic chemicals forming soil-bound residues.

Abstract The earliest archaeological evidence from northern Africa dates to ca. 2.44 Ma. Nevertheless, the palaeoenvironmental setting of hominins living in this part of the continent at the Plio-Pleistocene transition remains poorly documented, particularly in comparison to eastern and southern Africa. The Guefaït-4 fossil site in eastern Morocco sheds light on our knowledge of palaeoenvironments in northern Africa. Our study reveals the oldest known presence of C4 plants in the northern part of the continent in a mosaic landscape that includes open grasslands, forested areas, wetlands, and seasonal aridity. This diverse landscape and resource availability likely facilitated the occupation of the region by mammals, including potentially hominins. Our regional-scale study provides a complementary perspective to global-scale studies and highlights the importance of considering the diversity of microhabitats within a given region when studying species-dispersal dynamics.

Abstract Anaerobic microbial fermentations provide high product yields and are a cornerstone of industrial bio-based processes. However, the need for redox balancing limits the array of fermentable substrate-product combinations. To overcome this limitation, here we design an aerobic fermentative metabolism that allows the introduction of selected respiratory modules. These can use oxygen to re-balance otherwise unbalanced fermentations, hence achieving controlled respiro-fermentative growth. Following this design, we engineer and characterize an obligate fermentative Escherichia coli strain that aerobically ferments glucose to stoichiometric amounts of lactate. We then re-integrate the quinone-dependent glycerol 3-phosphate dehydrogenase and demonstrate glycerol fermentation to lactate while selectively transferring the surplus of electrons to the respiratory chain. To showcase the potential of this fermentation mode, we direct fermentative flux from glycerol towards isobutanol production. In summary, our design permits using oxygen to selectively re-balance fermentations. This concept is an advance freeing highly efficient microbial fermentation from the limitations imposed by traditional redox balancing.

Abstract Lithium dendrite growth in inorganic solid-state electrolytes acts as a main stumbling block for the commercial development of all-solid-state lithium batteries. Indeed, Li dendrites often lead to solid-state electrolyte fractures, undermining device integrity and safety. Despite the significance of these issues, the mechanisms driving the solid-state electrolyte fracture process at the microscopic level remain poorly understood. Here, via operando optical and ex situ dark field X-ray microscopy measurements of LiSn∣single-crystal Li6.5La3Zr1.5Ta0.5O12∣LiSn symmetric cells, we provide insights into solid-state electrolyte strain patterns and lattice orientation changes associated with dendrite growth. We report the observation of dislocations in the immediate vicinity of dendrite tips, including one instance where a dislocation is anchored directly to a tip. This latter occurrence in single-crystalline ceramics suggests an interplay between dendrite proliferation and dislocation formation. We speculate that the mechanical stress induced by dendrite expansion triggers dislocation generation. These dislocations seem to influence the fracture process, potentially affecting the directional growth and branching observed in lithium dendrites.

Abstract Inflammasome activation results in the cleavage of gasdermin D (GSDMD) by pro-inflammatory caspases. The N-terminal domains (GSDMDNT) oligomerize and assemble pores penetrating the target membrane. As methods to study pore formation in living cells are insufficient, the order of conformational changes, oligomerization, and membrane insertion remained unclear. We have raised nanobodies (VHHs) against human GSDMD and find that cytosolic expression of VHHGSDMD-1 and VHHGSDMD-2 prevents oligomerization of GSDMDNT and pyroptosis. The nanobody-stabilized GSDMDNT monomers partition into the plasma membrane, suggesting that membrane insertion precedes oligomerization. Inhibition of GSDMD pore formation switches cell death from pyroptosis to apoptosis, likely driven by the enhanced caspase-1 activity required to activate caspase-3. Recombinant antagonistic nanobodies added to the extracellular space prevent pyroptosis and exhibit unexpected therapeutic potential. They may thus be suitable to treat the ever-growing list of diseases caused by activation of (non-) canonical inflammasomes.

Abstract During the epithelial-mesenchymal transition driving mouse embryo gastrulation, cells divide more frequently at the primitive streak, and half of those divisions happen away from the apical pole. These observations suggest that non-apical mitoses might play a role in cell delamination. We aim to uncover and challenge the molecular determinants of mitosis position in different regions of the epiblast through computational modeling and pharmacological treatments of embryos and stem cell-based epiblast spheroids. Blocking basement membrane degradation at the streak has no impact on the asymmetry in mitosis frequency and position. By contrast, disturbance of the actomyosin cytoskeleton or cell cycle dynamics elicits ectopic non-apical mitosis and shows that the streak region is characterized by local relaxation of the actomyosin cytoskeleton and less stringent regulation of cell division. These factors are essential for normal dynamics at the streak and favor cell delamination from the epiblast.

Abstract Activation of caged fluorophores in microscopy has mostly relied on the absorption of a single ultraviolet (UV) photon of ≲400 nm wavelength or on the simultaneous absorption of two near-infrared (NIR) photons >700 nm. Here, we show that two green photons (515 nm) can substitute for a single photon (~260 nm) to activate popular silicon-rhodamine (Si-R) dyes. Activation in the green range eliminates the chromatic aberrations that plague activation by UV or NIR light. Thus, in confocal fluorescence microscopy, the activation focal volume can be matched with that of confocal detection. Besides, detrimental losses of UV and NIR light in the optical system are avoided. We apply two-photon activation (2PA) of three Si-R dyes in different superresolution approaches. STED microscopy of thick samples is improved through optical sectioning and photobleaching reduced by confining active fluorophores to a thin layer. 2PA of individualized fluorophores enables MINSTED nanoscopy with nanometer-resolution.

Abstract Epithelial-Mesenchymal Transition (EMT) is a key process in physiological and pathological settings. EMT is often presented as a linear sequence with (i) disassembly of cell-cell junctions, (ii) loss of epithelial polarity and (iii) reorganization of the cytoskeleton leading to basal extrusion from the epithelium. Once out, cells can adopt a migratory phenotype with a front-rear polarity. While this sequence can occur, in vivo observations have challenged it. It is now accepted that multiple EMT scenarios coexist in heterogeneous cell populations. However, the relative importance of each step as well as that of variability and heterogeneity on the efficiency of cell extrusion has not been assessed. Here we used computational modelling to simulate multiple EMT-like scenarios and confronted these data to the EMT of neural crest cells. Overall, our data point to a key role of nuclear positioning and protrusive activity to generate timely basal extrusion.

Abstract Ageing is the accumulation of changes and decline of function of organisms over time. The concept and biomarkers of biological age have been established, notably DNA methylation-based clocks. The emergence of single-cell DNA methylation profiling methods opens the possibility of studying the biological age of individual cells. Here, we generate a large single-cell DNA methylation and transcriptome dataset from mouse peripheral blood samples, spanning a broad range of ages. The number of genes expressed increases with age, but gene-specific changes are small. We next develop scEpiAge, a single-cell DNA methylation age predictor, which can accurately predict age in (very sparse) publicly available datasets, and also in single cells. DNA methylation age distribution is wider than technically expected, indicating epigenetic age heterogeneity and functional differences. Our work provides a foundation for single-cell and sparse data epigenetic age predictors, validates their functionality and highlights epigenetic heterogeneity during ageing.

Abstract More than half of the world’s rivers dry up periodically, but our understanding of the biological communities in dry riverbeds remains limited. Specifically, the roles of dispersal, environmental filtering and biotic interactions in driving biodiversity in dry rivers are poorly understood. Here, we conduct a large-scale coordinated survey of patterns and drivers of biodiversity in dry riverbeds. We focus on eight major taxa, including microorganisms, invertebrates and plants: Algae, Archaea, Bacteria, Fungi, Protozoa, Arthropods, Nematodes and Streptophyta. We use environmental DNA metabarcoding to assess biodiversity in dry sediments collected over a 1-year period from 84 non-perennial rivers across 19 countries on four continents. Both direct factors, such as nutrient and carbon availability, and indirect factors such as climate influence the local biodiversity of most taxa. Limited resource availability and prolonged dry phases favor oligotrophic microbial taxa. Co-variation among taxa, particularly Bacteria, Fungi, Algae and Protozoa, explain more spatial variation in community composition than dispersal or environmental gradients. This finding suggests that biotic interactions or unmeasured ecological and evolutionary factors may strongly influence communities during dry phases, altering biodiversity responses to global changes.

Abstract Mitochondrial oxidative phosphorylation (OXPHOS) fuels cellular ATP demands. OXPHOS defects lead to severe human disorders with unexplained tissue specific pathologies. Mitochondrial gene expression is essential for OXPHOS biogenesis since core subunits of the complexes are mitochondrial-encoded. COX14 is required for translation of COX1, the central mitochondrial-encoded subunit of complex IV. Here we describe a COX14 mutant mouse corresponding to a patient with complex IV deficiency. COX14M19I mice display broad tissue-specific pathologies. A hallmark phenotype is severe liver inflammation linked to release of mitochondrial RNA into the cytosol sensed by RIG-1 pathway. We find that mitochondrial RNA release is triggered by increased reactive oxygen species production in the deficiency of complex IV. Additionally, we describe a COA3Y72C mouse, affected in an assembly factor that cooperates with COX14 in early COX1 biogenesis, which displays a similar yet milder inflammatory phenotype. Our study provides insight into a link between defective mitochondrial gene expression and tissue-specific inflammation.

Abstract The Structural Genomics Consortium is an international open science research organization with a focus on accelerating early-stage drug discovery, namely hit discovery and optimization. We, as many others, believe that artificial intelligence (AI) is poised to be a main accelerator in the field. The question is then how to best benefit from recent advances in AI and how to generate, format and disseminate data to enable future breakthroughs in AI-guided drug discovery. We present here the recommendations of a working group composed of experts from both the public and private sectors. Robust data management requires precise ontologies and standardized vocabulary while a centralized database architecture across laboratories facilitates data integration into high-value datasets. Lab automation and opening electronic lab notebooks to data mining push the boundaries of data sharing and data modeling. Important considerations for building robust machine-learning models include transparent and reproducible data processing, choosing the most relevant data representation, defining the right training and test sets, and estimating prediction uncertainty. Beyond data-sharing, cloud-based computing can be harnessed to build and disseminate machine-learning models. Important vectors of acceleration for hit and chemical probe discovery will be (1) the real-time integration of experimental data generation and modeling workflows within design-make-test-analyze (DMTA) cycles openly, and at scale and (2) the adoption of a mindset where data scientists and experimentalists work as a unified team, and where data science is incorporated into the experimental design.

Abstract Quantification of behavior is critical in diverse applications from neuroscience, veterinary medicine to animal conservation. A common key step for behavioral analysis is first extracting relevant keypoints on animals, known as pose estimation. However, reliable inference of poses currently requires domain knowledge and manual labeling effort to build supervised models. We present SuperAnimal, a method to develop unified foundation models that can be used on over 45 species, without additional manual labels. These models show excellent performance across six pose estimation benchmarks. We demonstrate how to fine-tune the models (if needed) on differently labeled data and provide tooling for unsupervised video adaptation to boost performance and decrease jitter across frames. If fine-tuned, SuperAnimal models are 10–100× more data efficient than prior transfer-learning-based approaches. We illustrate the utility of our models in behavioral classification and kinematic analysis. Collectively, we present a data-efficient solution for animal pose estimation.

Abstract Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. Their realization through in vitro enzyme characterization and engineering is mostly low throughput and labor-intensive. Therefore, strategies for increasing throughput while diminishing manual labor are gaining momentum, such as in vivo screening and evolution campaigns. Computational tools like machine learning further support enzyme engineering efforts by widening the explorable design space. Here, we propose an integrated solution to enzyme engineering challenges whereby ML-guided, automated workflows (including library generation, implementation of hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection) could be realized to accelerate pipelines towards superior biocatalysts.