Microbes frequently encounter fluctuating environments, requiring dynamic energy management strategies for survival. While carbon storage polymers like polyhydroxybutyrate (PHB) are ubiquitous across bacterial taxa, their precise ecological advantage remains poorly understood (1). Here we show that carbon storage drives conditional fitness during environmental transitions. Using a high-throughput single-cell microfluidic platform, we tracked tens of thousands of Cupriavidus necator cells under precisely controlled carbon and nitrogen fluctuations. We found that PHB provides no advantage under nutrient abundance but becomes decisive at starvation boundaries: during carbon starvation, it enables ~30% more progeny before arrest; during recovery from nitrogen starvation, it shortens lag and accelerates regrowth. Strikingly, at the single-cell level, PHB granules are inherited in an asymmetric, all-or-nothing fashion, concentrating resources into specific lineages to overcome the discrete energetic threshold required for cell division. Despite this single-cell variance, at the population level, PHB fractions robustly return to a common setpoint after nutrient shifts - a homeostatic behavior consistent with integral feedback control. These findings reveal that while PHB does not increase the basal exponential growth rate, it confers a distinct fitness advantage by prolonging the proliferative phase during nutrient depletion and facilitating successful recovery from starvation, explaining the evolutionary persistence of carbon storage in environments with pulsed resource availability.

The generation of Metagenome Assembled Genomes (MAG) has become a standard and basic step in the analysis of metagenomic data. This multi-step process, which includes assembly, binning, refinement, and quality control, has many alternative approaches, algorithms, and parameters. Determining the ideal approach for a given ecosystem and study, or highlighting algorithmic gaps in need of additional research and development, requires rigorous benchmarking. We present MAG-E (MAG pipeline Evaluator), a generalizable and expandable framework for end-to-end evaluation of entire MAG pipelines: from assembly, through binning, to quality control and filtering. MAG-E relies on simulations that are built to match an ecosystem of interest and provide a ground truth for accurate evaluation. To demonstrate the capabilities of MAG-E, we benchmark two assemblers, six binning algorithms, three binning modes, and three quality control and refinement methods in the context of the human gut microbiome. Our findings offer multiple insights into optimal MAG generation in this context. We find that metaSPAdes consistently outperforms MEGAHIT in terms of recall (completeness), and that COMEBin overall outperforms alternative binning algorithms, but has lower precision than SemiBin2. While multi-sample binning results in higher precision, as previously shown, single-sample binning has higher recall and leads to better overall performance with modern binners. Binning refinement, which combines bins from multiple different algorithms, leads to reduced performance. We further show that CheckM2 systematically overestimates completeness and underestimates contamination, and that this is partially ameliorated when using GUNC. Finally, we analyze performance at the contig level, and demonstrate that binning algorithms systematically underperform for prophages and fail to bin contigs that are shared between genomes. Overall, MAG-E offers deep insights into successes and gaps in this important analytic process.

Large-scale sequence-to-function deep learning models have demonstrated unparalleled ability to model biological sequences and have revolutionized the field of regulatory genomics. However, the majority of such efforts have centered on human and mammalian systems, leaving plant regulatory genomics comparatively underexplored. To address this gap, we introduce Deep-Plant, a supervised foundation model trained to predict chromatin state directly from genomic sequence. In contrast to large language models, which are trained in a self-supervised manner using sequence alone, our model is trained to predict chromatin state across tissues and conditions. Training the model on a large collection of genome-wide experiments including DNA accessibility, transcription factor binding, and histone modifications, provides it with added biological context beyond the sequence itself. We demonstrate that the resulting model is an effective platform for developing accurate models of regulatory activity relevant to gene expression and active enhancers, exhibiting large improvements in speed, accuracy, and interpretability over the complementary approach of fine-tuning DNA language models. Deep-Plant models are available in Arabidopsis and rice, and work well as a building block for sequence modeling in related species such as corn. Together, these results establish supervised, chromatin-informed foundation models as a practical and effective paradigm for regulatory sequence modeling in plants.

Natural food pigments primarily originate from two sources: chemical synthesis and plant-derived production. With the rapid advancement of society and technology, there is a growing demand for environmentally friendly and healthy food options. Consequently, the demand for safe, nontoxic, and sustainable sources of natural pigments has risen sharply. Natural pigments are biosynthesized during the growth and metabolic processes of plant tissues, and compounds derived from these pigments exhibit a wide range of biological activities that are beneficial to human health and disease treatment. However, due to their inherent instability and low abundance, increasing research efforts have been directed toward the bioengineering of natural pigment production. This review classifies natural pigments into five major structural categories: pyrrole, isoprenoids, quinones, phenols, and betalains. Unlike previous reviews that focused on a single pigment component or specific application fields, this review systematically integrates the biosynthetic pathways, synthetic biology strategies, pharmacological activity mechanisms, and application progress in medicine, health care, and cosmetics of natural pigment-containing medicinal materials. It emphasizes their multiple potentials as “functional pigments” in the development of natural medicines. Additionally, the review combines emerging technologies such as metabolic engineering, artificial intelligence (AI)-assisted screening, and biosensing, proposing a cross-disciplinary development path from basic synthesis to high-value applications and demonstrating strong systematicity and a forward-looking nature. It provides a new integrated perspective for innovative research on natural pigment components.

Photosynthetic cyanobacteria are promising platforms for sustainable chemical production, as they can convert light and CO₂ into valuable compounds. Achieving this often requires engineering cyanobacteria with non-native enzymes with strong promoters to maximize enzyme accumulation. However, despite extensive engineering efforts, the extent to which heterologous proteins misfold and undergo degradation in cyanobacteria remains unknown. Here, we systematically investigate the fate of recombinant proteins in Synechocystis sp. PCC 6803 by quantifying metabolic enzyme degradation. To do this, we developed a quantitative approach that combines split-GFP protein reporting with inducible CRISPRi knockdown of Clp protease system, enabling detection of proteins that would otherwise be degraded. Applying this method to 103 heterologous proteins previously used in cyanobacterial metabolic engineering studies, we find that nearly half undergo significant degradation, with some losing over 95% of their potential expression. Furthermore, we demonstrate that replacing enzymes with homologs is often a more effective strategy to address expression issues than optimizing genetic elements. These findings provide the first quantitative overview of heterologous protein expression in cyanobacteria and identify enzymes that are poorly expressed and suboptimal for their respective pathways, information usable to increase production titers in photosynthetic cell factories.