Metabolism of halophilic archaea

In spite of their common hypersaline environment, halophilic archaea are surprisingly different in their nutritional demands and metabolic pathways. The metabolic diversity of halophilic archaea was investigated at the genomic level through systematic metabolic reconstruction and comparative analysis of four completely sequenced species: Halobacterium salinarum, Haloarcula marismortui, Haloquadratum walsbyi, and the haloalkaliphile Natronomonas pharaonis. The comparative study reveals different sets of enzyme genes amongst halophilic archaea, e.g. in glycerol degradation, pentose metabolism, and folate synthesis. The carefully assessed metabolic data represent a reliable resource for future system biology approaches as it also links to current experimental data on (halo)archaea from the literature

A foundation model for atomistic materials chemistry

\ua9 2025 Author(s).Atomistic simulations of matter, especially those that leverage first-principles (ab initio) electronic structure theory, provide a microscopic view of the world, underpinning much of our understanding of chemistry and materials science. Over the last decade or so, machine-learned force fields have transformed atomistic modeling by enabling simulations of ab initio quality over unprecedented time and length scales. However, early machine-learning (ML) force fields have largely been limited by (i) the substantial computational and human effort required to develop and validate potentials for each particular system of interest and (ii) a general lack of transferability from one chemical system to the next. Here, we show that it is possible to create a general-purpose atomistic ML model, trained on a public dataset of moderate size, that is capable of running stable molecular dynamics for a wide range of molecules and materials. We demonstrate the power of the MACE-MP-0 model—and its qualitative and at times quantitative accuracy—on a diverse set of problems in the physical sciences, including properties of solids, liquids, gases, chemical reactions, interfaces, and even the dynamics of a small protein. The model can be applied out of the box as a starting or “foundation” model for any atomistic system of interest and, when desired, can be fine-tuned on just a handful of application-specific data points to reach ab initio accuracy. Establishing that a stable force-field model can cover almost all materials changes atomistic modeling in a fundamental way: experienced users obtain reliable results much faster, and beginners face a lower barrier to entry. Foundation models thus represent a step toward democratizing the revolution in atomic-scale modeling that has been brought about by ML force fields

Towards an atomistic understanding of disordered carbon electrode materials

Disordered nanoporous and "hard" carbons are widely used in batteries and supercapacitors, but their atomic structures are poorly determined. Here, we combine machine learning and DFT to obtain new atomistic insight into carbonaceous energy materials. We study structural models of porous and graphitic carbons, and Na intercalation as relevant for sodium-ion batteries

Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics

Amorphous silicon (a-Si) is a widely studied non-crystalline material, and yet the subtle details of its atomistic structure are still unclear. Here, we show that accurate structural mod-els of a-Si can be obtained using a machine-learning-based interatomic potential. Our best a-Si network is obtained by simulated cooling from the melt at a rate of 10^11 K/s (that is, on the 10 ns timescale), contains less than 2% defects, and agrees with experiments regarding excess energies, diffraction data, and 29Si NMR chemical shifts. We show that this level of quality is impossible to achieve with faster quench simulations. We then generate a 4,096-atom system which correctly reproduces the magnitude of the first sharp diffraction peak (FSDP) in the structure factor, achieving the closest agreement with experiments to date. Our study demonstrates the broader impact of machine-learning potentials for elucidating structures and properties of technologically important amorphous materials

The secretogranin II-derived peptide secretoneurin stimulates luteinizing hormone secretion from gonadotrophs

Secretoneurin (SN) isa 33-to34-aminoacid neuropeptide derived from secretogranin-II, a member of the chromogranin family. We previously synthesized a putative goldfish (gf) SN and demonstrated its ability to stimulate LH release in vivo. However, it was not known whether goldfish actually produced the free SN peptide or whether SN directly stimulates LH release from isolated pituitary cells. Using a combination of reverse-phase HPLC and mass spectrometry analysis, we isolated for the first time a 34-amino acid free gfSN peptide from the whole brain. Moreover, Western blot analysis indicated the existence of this peptide in goldfish pituitary. Immunocyto- chemical localization studies revealed the presence of SN immunoreactivity in prolactin cells of rostral pars distalis of the anterior pituitary. Additionally, we found that magnocellular cells of the goldfish preoptic region are highly immunoreactive for SN. These neurons send heavily labeled projections that pass through the pituitary stalk and innervate the neurointermediateand anterior lobes. In static 12-h incubation of dispersed pituitary cells, application of SN antiserum reduced LH levels, whereas 1 and 10 nM gfSN, respectively, induced 2.5-fold (P < 0.001) and 1.9-fold (P < 0.01) increments of LH release into the medium, increases similar to those elicited by 100 nM concentrations of GnRH. LikeGnRH, gfSN elevated intracellular Ca2+ in identified gonadotrophs. Whereas we do not yet know the relative contribution of neural SN or pituitary SN to LH release, we propose that SN could act as a neuroendocrine and/or paracrine factor to regulate LH release from the anterior pituitary. Copyright © 2009 by The Endocrine Society.link_to_subscribed_fulltex