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Membrane-anchored matrix metalloproteinase 14 (MMP14) is involved broadly in organ development through both its proteolytic and signal-transducing functions. Knockout of Mmp14 (KO) in mice results in a dramatic reduction of body size and wasting followed by premature death, the mechanism of which is poorly understood. Since the mammary gland develops after birth and is thus dependent for its functional progression on systemic and local cues, we chose it as an organ model for understanding why KO mice fail to thrive. A global analysis of the mammary glands’ proteome in the wild type (WT) and KO mice provided insight into an unexpected role of MMP14 in maintaining metabolism and homeostasis. We performed mass spectrometry and quantitative proteomics to determine the protein signatures of mammary glands from 7 to 11 days old WT and KO mice and found that KO rudiments had a significantly higher level of rate-limiting enzymes involved in catabolic pathways. Glycogen and lipid levels in KO rudiments were reduced, and the circulating levels of triglycerides and glucose were lower. Analysis of the ultrastructure of mammary glands imaged by electron microscopy revealed a significant increase in autophagy signatures in KO mice. Finally, Mmp14 silenced mammary epithelial cells displayed enhanced autophagy. Applied to a systemic level, these findings indicate that MMP14 is a crucial regulator of tissue homeostasis. If operative on a systemic level, these findings could explain how Mmp14 KO litter fail to thrive due to disorder in metabolism.

An important goal of macrosystems ecology (MSE) research is to advance understanding of ecological systems at both fine and broad temporal and spatial scales. Our premise in this paper is that MSE projects require integrated information management at their inception. Such efforts will lead to improved communication and sharing of knowledge among diverse project participants, better science outcomes, and more transparent and accessible (ie \"open\") science. We encourage researchers to \"complete the data life cycle\" by publishing well-documented datasets, thereby facilitating re-use of the data to answer new and different questions from the ones conceived by those involved in the original projects. The practice of documenting and submitting datasets to data repositories that are publicly accessible ensures that research results and data are available to and use-able by other researchers, thus fostering open science. However, ecologists are often unfamiliar with the requirements and information management tools for effectively preserving data and receive little institutional or professional incentive to do so. Here, we provide recommendations for achieving these ends and give examples from current MSE projects to demonstrate why information management is critical for ensuring that scientific results can be reproduced and that data can be shared for future use.

Background Lignin is an aromatic polymer deposited in secondary cell walls of higher plants to provide strength, rigidity, and hydrophobicity to vascular tissues. Due to its interconnections with cell wall polysaccharides, lignin plays important roles during plant growth and defense, but also has a negative impact on industrial processes aimed at obtaining monosaccharides from plant biomass. Engineering lignin offers a solution to this issue. For example, previous work showed that heterologous expression of a coliphage S -adenosylmethionine hydrolase (AdoMetase) was an effective approach to reduce lignin in the model plant Arabidopsis. The efficacy of this engineering strategy remains to be evaluated in bioenergy crops. Results We studied the impact of expressing AdoMetase on lignin synthesis in sorghum ( Sorghum bicolor L. Moench). Lignin content, monomer composition, and size, as well as biomass saccharification efficiency were determined in transgenic sorghum lines. The transcriptome and metabolome were analyzed in stems at three developmental stages. Plant growth and biomass composition was further evaluated under field conditions. Results evidenced that lignin was reduced by 18% in the best transgenic line, presumably due to reduced activity of the S -adenosylmethionine-dependent O -methyltransferases involved in lignin synthesis. The modified sorghum features altered lignin monomer composition and increased lignin molecular weights. The degree of methylation of glucuronic acid on xylan was reduced. These changes enabled a ~20% increase in glucose yield after biomass pretreatment and saccharification compared to wild type. RNA-seq and untargeted metabolomic analyses evidenced some pleiotropic effects associated with AdoMetase expression. The transgenic sorghum showed developmental delay and reduced biomass yields at harvest, especially under field growing conditions. Conclusions The expression of AdoMetase represents an effective lignin engineering approach in sorghum. However, considering that this strategy potentially impacts multiple S -adenosylmethionine-dependent methyltransferases, adequate promoters for fine-tuning AdoMetase expression will be needed to mitigate yield penalty.

Xylariales is one of the largest and most ecologically diverse fungal orders. Xylarialean fungi are well-known for their chemical diversity, reflecting a hyperdiversity of biosynthetic gene clusters (BCGs), even compared to other bioactive fungi. Enhanced secondary metabolism appears linked to the number of horizontal gene transfer (HGT) events and gene duplications, which is highest in the clade that also has a greater ability to both degrade lignocellulose as saprotrophs and interact with a wider variety of plant and lichen hosts as symbiotic endophytes. Thus, one hypothesis for BGC diversification in this clade is that diverse competitive interactions--in both their free-living and symbiotic life stages--may exert selective pressure for HGT and a diverse metabolic repertoire. Here, we tested this hypothesis using untargeted metabolomics to examine how pairwise co-culture interactions between seven xylarialean fungi influenced their metabolite production. Overall, we detected >9,000 features, including putatively anti-microbial, insecticidal, and medicinal compounds. In total, 6,115 features were over-represented in co-cultures vs. 2,071 in monocultures. No features occurred in all 21 interactions, and only 39% of features occurred in >10 different co-culture combinations. Each additional co-culture interaction resulted in an 11 to 14-fold increase in metabolite richness. Consistent with HGT, metabolite profiles did not reflect phylogenetic relationships. Overall, the diversity and specificity of metabolites support the role of widespread and diverse competitive interactions to drive diversification of xylarialean metabolism. Additionally, as plant hormones were only detected in co-culture, our results also reveal how competition may influence the outcome of endophytic symbioses.Competing Interest StatementThe authors have declared no competing interest.

Accurately predicting the three-dimensional structures of protein-ligand complexes remains a fundamental challenge in computational drug discovery that limits the pace and success of therapeutic design. Deep learning methods have recently shown strong potential as structural prediction tools, achieving promising accuracy across diverse biomolecular systems. However, their performance and utility are constrained by scarce experimental data, inefficient architectures, physically invalid poses, and the limited ability to exploit auxiliary information available at inference. To address these issues, we introduce Pearl (Placing Every Atom in the Right Location), a foundation model for protein-ligand cofolding at scale. Pearl addresses these challenges with three key innovations: (1) training recipes that include large-scale synthetic data to overcome data scarcity; (2) architectures that incorporate an SO(3)-equivariant diffusion module to inherently respect 3D rotational symmetries, improving generalization and sample efficiency, and (3) controllable inference, including a generalized multi-chain templating system supporting both protein and non-polymeric components as well as dual unconditional/conditional modes. Pearl establishes a new state-of-the-art performance in protein-ligand cofolding. On the key metric of generating accurate (RMSD < 2 Å) and physically valid poses, Pearl surpasses AlphaFold 3 and other open source baselines on the public Runs N' Poses and PoseBusters benchmarks, delivering 14.5% and 14.2% improvements, respectively, over the next best model. In the pocket-conditional cofolding regime, Pearl delivers \\(3.6\\) improvement on a proprietary set of challenging, real-world drug targets at the more rigorous RMSD < 1 Å threshold. Finally, we demonstrate that model performance correlates directly with synthetic dataset size used in training.

The U.S. Department of Energy Systems Biology Knowledgebase (KBase) is an open-source software and data platform designed to meet the grand challenge of systems biology - predicting and designing biological function from the biomolecular (small scale) to the ecological (large scale). KBase is available for anyone to use, and enables researchers to collaboratively generate, test, compare, and share hypotheses about biological functions; perform large-scale analyses on scalable computing infrastructure; and combine experimental evidence and conclusions that lead to accurate models of plant and microbial physiology and community dynamics. The KBase platform has (1) extensible analytical capabilities that currently include genome assembly, annotation, ontology assignment, comparative genomics, transcriptomics, and metabolic modeling; (2) a web-browser-based user interface that supports building, sharing, and publishing reproducible and well-annotated analyses with integrated data; (3) access to extensive computational resources; and (4) a software development kit allowing the community to add functionality to the system.