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Abstract The challenge of transforming organized DNA structures into their metallized counterparts persists in the scientific field. In this context, utilizing DNA molecules modified with 7-deazapurine, provides a transformative solution. In this study, we present the solution structure of a DNA duplex that can be transformed into its metallized equivalent while retaining the natural base pairing arrangement through the creation of silver-modified Watson-Crick base pairs. Unlike previously documented X-ray structures, our research demonstrates the feasibility of preserving the intrinsic DNA self-assembly while incorporating AgI into the double helix, illustrating that the binding of silver does not disrupt the canonical base-pairing organization. Moreover, in our case, the uninterrupted AgI chain deviates from forming conventional straight linear chains; instead, it adheres to a helical arrangement dictated by the underlying DNA structure. This research challenges conventional assumptions and opens the door to precisely design structures based on the organization of highly stable Ag-DNA assemblies.

Abstract Accessing the terahertz (THz) spectral domain through surface-enhanced Raman spectroscopy (SERS) is challenging and opens up the study of low-frequency molecular and electronic excitations. Compared to direct THz probing of heterogenous ensembles, the extreme plasmonic confinement of visible light to deep sub-wavelength scales allows the study of hundreds or even single molecules. We show that self-assembled molecular monolayers of a set of simple aromatic thiols confined inside single-particle plasmonic nanocavities can be distinguished by their low-wavenumber spectral peaks below 200 cm−1, after removal of a bosonic inelastic contribution and an exponential background from the spectrum. Developing environment-dependent density-functional-theory simulations of the metal-molecule configuration enables the assignment and classification of their THz vibrations as well as the identification of intermolecular coupling effects and of the influence of the gold surface configuration. Furthermore, we show dramatically narrower THz SERS spectra from individual molecules at picocavities, which indicates the possibility to study intrinsic vibrational properties beyond inhomogeneous broadening, further supporting the key role of local environment.

Abstract Herein, an enantioselective desymmetrization of cyclic keto sulfonium salts through enantioselective deprotonation/ring opening process by anion-binding catalysis is presented. We report a squaramide/HCO3 - complex as catalytic active species which is able to stereo-differentiate two enantiomeric protons, triggering the ring opening event taking advantage of the great tendency of sulfonium salts to act as leaving groups. Thus, this desymmetrization methodology give rise to β-methylsulfenylated sulfa-Michael addition type products with excellent yields and very good enantioselectivities. The bifunctional organocatalyst has been demonstrated to be capable of activating simultaneously the base and the keto sulfonium salt by DFT calculations and experimental proofs.

Abstract Machine-learned computational chemistry has led to a paradoxical situation in which molecular properties can be accurately predicted, but they are difficult to interpret. Explainable AI (XAI) tools can be used to analyze complex models, but they are highly dependent on the AI technique and the origin of the reference data. Alternatively, interpretable real-space tools can be employed directly, but they are often expensive to compute. To address this dilemma between explainability and accuracy, we developed SchNet4AIM, a SchNet-based architecture capable of dealing with local one-body (atomic) and two-body (interatomic) descriptors. The performance of SchNet4AIM is tested by predicting a wide collection of real-space quantities ranging from atomic charges and delocalization indices to pairwise interaction energies. The accuracy and speed of SchNet4AIM breaks the bottleneck that has prevented the use of real-space chemical descriptors in complex systems. We show that the group delocalization indices, arising from our physically rigorous atomistic predictions, provide reliable indicators of supramolecular binding events, thus contributing to the development of Explainable Chemical Artificial Intelligence (XCAI) models.

Abstract The 2023 monkeypox (mpox) epidemic was caused by a subclade IIb descendant of a monkeypox virus (MPXV) lineage traced back to Nigeria in 1971. Person-to-person transmission appears higher than for clade I or subclade IIa MPXV, possibly caused by genomic changes in subclade IIb MPXV. Key genomic changes could occur in the genome’s low-complexity regions (LCRs), which are challenging to sequence and are often dismissed as uninformative. Here, using a combination of highly sensitive techniques, we determine a high-quality MPXV genome sequence of a representative of the current epidemic with LCRs resolved at unprecedented accuracy. This reveals significant variation in short tandem repeats within LCRs. We demonstrate that LCR entropy in the MPXV genome is significantly higher than that of single-nucleotide polymorphisms (SNPs) and that LCRs are not randomly distributed. In silico analyses indicate that expression, translation, stability, or function of MPXV orthologous poxvirus genes (OPGs), including OPG153, OPG204, and OPG208, could be affected in a manner consistent with the established “genomic accordion” evolutionary strategies of orthopoxviruses. We posit that genomic studies focusing on phenotypic MPXV differences should consider LCR variability.

Abstract Many genes that drive normal cellular development also contribute to oncogenesis. Medulloblastoma (MB) tumors likely arise from neuronal progenitors in the cerebellum, and we hypothesized that the heterogeneity observed in MBs with sonic hedgehog (SHH) activation could be due to differences in developmental pathways. To investigate this question, here we perform single-nucleus RNA sequencing on highly differentiated SHH MBs with extensively nodular histology and observed malignant cells resembling each stage of canonical granule neuron development. Through innovative computational approaches, we connect these results to published datasets and find that some established molecular subtypes of SHH MB appear arrested at different developmental stages. Additionally, using multiplexed proteomic imaging and MALDI imaging mass spectrometry, we identify distinct histological and metabolic profiles for highly differentiated tumors. Our approaches are applicable to understanding the interplay between heterogeneity and differentiation in other cancers and can provide important insights for the design of targeted therapies.

Abstract Machine learning force fields (MLFFs) are gradually evolving towards enabling molecular dynamics simulations of molecules and materials with ab initio accuracy but at a small fraction of the computational cost. However, several challenges remain to be addressed to enable predictive MLFF simulations of realistic molecules, including: (1) developing efficient descriptors for non-local interatomic interactions, which are essential to capture long-range molecular fluctuations, and (2) reducing the dimensionality of the descriptors to enhance the applicability and interpretability of MLFFs. Here we propose an automatized approach to substantially reduce the number of interatomic descriptor features while preserving the accuracy and increasing the efficiency of MLFFs. To simultaneously address the two stated challenges, we illustrate our approach on the example of the global GDML MLFF. We found that non-local features (atoms separated by as far as 15 Å in studied systems) are crucial to retain the overall accuracy of the MLFF for peptides, DNA base pairs, fatty acids, and supramolecular complexes. Interestingly, the number of required non-local features in the reduced descriptors becomes comparable to the number of local interatomic features (those below 5 Å). These results pave the way to constructing global molecular MLFFs whose cost increases linearly, instead of quadratically, with system size.

Abstract Reversible and sub-lethal stresses to the mitochondria elicit a program of compensatory responses that ultimately improve mitochondrial function, a conserved anti-aging mechanism termed mitohormesis. Here, we show that harmol, a member of the beta-carbolines family with anti-depressant properties, improves mitochondrial function and metabolic parameters, and extends healthspan. Treatment with harmol induces a transient mitochondrial depolarization, a strong mitophagy response, and the AMPK compensatory pathway both in cultured C2C12 myotubes and in male mouse liver, brown adipose tissue and muscle, even though harmol crosses poorly the blood–brain barrier. Mechanistically, simultaneous modulation of the targets of harmol monoamine-oxidase B and GABA-A receptor reproduces harmol-induced mitochondrial improvements. Diet-induced pre-diabetic male mice improve their glucose tolerance, liver steatosis and insulin sensitivity after treatment with harmol. Harmol or a combination of monoamine oxidase B and GABA-A receptor modulators extend the lifespan of hermaphrodite Caenorhabditis elegans or female Drosophila melanogaster. Finally, two-year-old male and female mice treated with harmol exhibit delayed frailty onset with improved glycemia, exercise performance and strength. Our results reveal that peripheral targeting of monoamine oxidase B and GABA-A receptor, common antidepressant targets, extends healthspan through mitohormesis.

The presented Mean-Shift Super Resolution (MSSR) algorithm can extend spatial resolution within a single microscopy image. Its applicability extends across a wide range of experimental and instrumental configurations and it is compatible with other super-resolution microscopy approaches.

Fibrosis of the skin plays an important role in scleroderma. Here the authors demonstrate genetic and epigenetic abnormalities at the centromere that affect the replication of the chromosomes, resulting in activation of pathways involved in inflammation and fibrosis

Crop wild relatives’ genetic diversity is usually not considered in conservation planning. Here, the authors introduce an approach to identify conservation areas based on evolutionary and threat processes, by developing proxies of genetic differentiation, and including taxa’s habitat preferences.

Guided by miRNA, Argonaute proteins silence mRNA in multiple ways that are not well understood. Here, the authors develop live-cell biosensors to image the impact tethered regulatory factors, such as Argonaute, have on single-mRNA translation dynamics.

Unicellular fungi with free-living flagellated stages (zoospores) remain poorly known. Here, Galindo et al. sequence single-cell genomes for two atypical parasitic fungi with amoeboid zoospores, and re-evaluate the branching order of early-diverging fungi and the evolution of fungal multicellularity and flagellum-mediated motility.

The inclusion of nuclear quantum effects (NQE) in atomistic simulations of chemical systems is of key importance. Here the authors use machine learned force fields trained on coupled cluster reference data to show the dynamical strengthening of covalent and non-covalent molecular interactions induced by NQE.

Evidence that somatic mutation rates in introns exceed those in exons challenges the molecular evolution tenet that mutation rate and sequence function are independent. Here, authors analyze germline de novo mutations and reveal no evidence for mutation rate differences between exons and introns.

Interference patterns in photoexcited dynamics of many materials have historically been attributed to electronic and vibrational coherences. Here, the authors demonstrate a simple model based on wavefunction symmetry suggesting these coherences originate from non-adiabatic transitions for optically active molecules.

Peroxisome proliferator-activated receptor gamma (PPARγ) is a nuclear receptor. Here the authors provide insights into PPARγ activation by combining fluorine (19F) NMR and molecular dynamics simulations to characterize the nuclear receptor conformational ensemble in solution and the response of this ensemble to ligand and coregulatory peptide binding.

A wealth of gene expression data is publicly available, yet is little use without additional human curation. Ma’ayan and colleagues report a crowdsourcing project involving over 70 participants to annotate and analyse thousands of human disease-related gene expression datasets.

Identifying therapeutic targets in rare cancers is challenging due to the lack of relevant pre-clinical models. Here, the authors generate a cancer cell line from a paediatric patient with a rare undifferentiated sarcoma and through functional genomics and chemical screens identified CDK4 and XPO1 as potential therapeutic targets in this cancer.