Yeasts and yeast-based products are nutrient-rich bioresources with broad applications in technologies for the production of food, feed, medicine, and cosmetics. However, traditional processing often results in non-specific lysis and suboptimal product quality. Yeast extract can be used as a flavor enhancer, nutritional supplement, or fermentation substrate, and the other components of the yeast cell wall and nucleic acids can be processed into bioactive materials, including glucans and nucleotides. These materials offer both nutritional and therapeutic benefits. Precision hydrolysis, leveraging the high specificity of tailored enzymes, has emerged as a superior strategy for maximizing the yield and functional quality of high-value yeast-based products. It provides superior outcomes by improving the quality of yeast-based products. Tailored enzymatic strategies, leveraging mechanistically focused core enzymes, including proteases, β-glucanases, and coupled nucleases-deaminases, have demonstrated superior efficiency, nutritional enhancement, and sensory refinement. This review focuses on the mechanistic properties of yeast processing enzymes, emphasizing their functional classification and applications in precision hydrolysis. It details how such enzymes are optimized for the targeted release and modification of high-value components. Additionally, the review highlights recent strategies for tailored biosynthesis of yeast processing enzymes, including enzyme discovery, heterologous expression systems, and machine-learning-guided optimization. This review aims to support future innovations that will promote the development of sustainable, high-value, and diversified yeast-based bioproducts by optimizing the biosynthesis of processing enzymes, thus lowering the overall cost of precision hydrolysis. 

The ability to precisely insert DNA payloads into a genome enables the comprehensive engineering of cellular phenotypes and the creation of new biotechnologies. To achieve such modifications, the most widely used techniques rely on a host cell’s native DNA repair mechanisms like homologous recombination, which hampers their broader use in organisms lacking these capabilities. Here, we explore the current landscape of genome integration systems with a particular focus on those that function in bacteria and are precise, self-contained, and portable, placing minimal requirements on the host cell. Through a historical analysis, we observe long-term use of recombineering technologies, a recent rise in the use of CRISPR-guided systems that consist of associated integrase machinery, and growing efforts to modify non-model organisms. Looking forward, we highlight some of the remaining challenges and how synthetic genomics may offer a way to create bacterial strains optimized for extensive long-term modification. As the field of synthetic biology sets its sights on real-world impact, the effective engineering of genomes will be critical to shaping the robust phenotypes that applications demand.

Building synthetic versions of biological cells from the bottom up offers an unprecedented opportunity to understand the rules of life and harness cellular capabilities in biotechnology. Whereas substantial progress has been made in recapitulating elementary cell functions, we argue that accelerating the engineering of synthetic cells requires a shift in research practices. The dominant approach—rationally designing and integrating functional modules—becomes restrictive when dealing with the massively complex biochemical pathways associated with life, especially when design principles remain unclear. We advocate moving away from theoretical rational design towards a data-driven model that is centred on library generation. Inspired by a systems chemistry perspective, this strategy prioritizes the systematic creation and distribution of composition–function libraries. To enable this, experimental strategies must integrate high-throughput synthetic cell generation, automation and closed-feedback control of workflows. Broad adoption will also require greater emphasis on quantitative benchmarking, and the de-skilling of techniques, supporting effective laboratory-to-laboratory collaboration. Living cells rely on the choreography of multiple simultaneous functions, without clear boundaries between molecular subsystems. Replicating these capabilities in synthetic cells would represent a major advance in understanding life. This Perspective argues that this challenge requires a shift away from modular design concepts, towards a strategy that integrates the theoretical principles of systems chemistry with data-driven high-throughput experimental methods.

Understanding the extrinsic and intrinsic environmental factors that influence lag phase duration is critical for developing strategies to control the growth of foodborne pathogens. Although the exponential growth rate can be predicted as a straightforward response to the growth environment, lag phase duration is difficult to predict because it depends on not only the current growth conditions but also the previous growth environment and physiological status of the bacterial cells. Therefore, this article aims to provide a comprehensive understanding of the dynamic, adaptable, and evolvable nature of the lag phase. It is based on relevant literature (experimental studies, literature summaries, observational data) on the effect of pre- and postgrowth environments. We discuss the modeling strategies employed to incorporate physiological heterogeneity and dynamic food environments into predictive modeling frameworks. Overall, we summarize the empirical and mechanistic modeling strategies for quantifying lag phase duration.

Pulmonary drug delivery offers several advantages over systemic administration, including localized targeting of diseased lung tissue, rapid absorption through the extensive alveolar surface area, and minimized systemic side effects owing to reduced off-target distribution. These benefits make it a promising approach for treating a range of respiratory and systemic conditions. However, mucociliary clearance, immune surveillance and enzymatic degradation pose major challenges to drug retention in the lungs. In this Review, we discuss microrobotic delivery systems for tackling the challenges of pulmonary drug delivery. We first outline key considerations for the design of microrobots for pulmonary delivery, including propulsion systems, targeting, controlled drug release, overcoming of biological barriers, delivery routes and clearance, and we then highlight applications for lung-related conditions. In particular, we outline biohybrid platforms based on green algae and inhalable platforms for the treatment of pneumonia and lung metastasis. Finally, we examine the engineering of microrobotic swarms and survey future opportunities and milestones for microrobotic pulmonary delivery. Delivering drugs directly to the lungs enables targeted treatment and long-term drug retention, while minimizing systemic side effects. This Review explores the design of microrobotic platforms engineered for pulmonary drug delivery.

Chemotaxis is an adaptive mechanism that shapes the behavior of motile bacteria in habitats characterized by fluctuating and often conflicting cues environmental (e.g. stay-or-go). Chemotactic responses are orchestrated by phosphorylation of CheY, which triggers rotational switching of the flagella. In Escherichia coli and similar taxa, CheZ is the principal CheY-P phosphatase, whereas in lineages lacking CheZ, members of the structurally distinct CheC-FliY-CheX family fulfill this role. Intriguingly, some bacteria code for CheX and CheZ, presenting a conundrum regarding their function, and the role of CheX in CheZ-containing organisms is unknown. We imposed a sustained motility constraint under conditions of looming nutrient depletion in Vibrio vulnificus, which possesses both CheX and CheZ, using the c-di-GMP effector PlzD that robustly curtails swimming motility. Our analyses revealed that the activity of CheX, but not CheZ, could be attenuated to mitigate the imposed constraint, assigning CheX a pivotal function in fine-tuning foraging behavior during a “stay-or-go” decision. V. vulnificus CheX maintained CheY-P phosphatase activity despite its conserved dimeric fold structure exhibiting divergence in active-site architecture, suggesting a preserved catalytic mechanism among distantly related homologs. Co-conservation of cheX and cheZ across disparate bacterial phyla suggests their adaptative retention confers robustness and versatility to chemotactic control. In a bacterium where they coexist, CheX tuned CheY-P–dependent motility under looming nutrient depletion, while CheZ did not, and their co-conservation likely provide robust, versatile control of stay-or-go foraging decisions across diverse bacteria.

Marine diazotrophs are microscopic planktonic organisms ubiquitous in the ocean, that play a major ecological role: they supply nitrogen to the surface ocean biosphere, an essential but scarce nutrient in ~60% of the global ocean. Over the past decades, they have attracted considerable attention, with numerous studies providing key insights into their diversity, lifestyle, biogeographical distribution, and biogeochemical role in planktonic ecosystems. An increasing number of studies show that these microbes regulate marine productivity and shape the food web by alleviating nitrogen limitation, thereby contributing to carbon sequestration to the deep ocean. Yet, the diazotroph-derived organic carbon exported to the deep ocean is still poorly quantified, limiting robust estimates of the ocean’s contribution to CO₂ sequestration and climate change mitigation under present and future conditions. This knowledge gap reflects the complexity of diazotroph export pathways to the deep ocean, whose quantification and variability drivers remain difficult to resolve with current methods. This review aims to synthesize current knowledge on the role of diazotrophs in their interactions with the food web and the biological carbon pump, reanalyze existing datasets, identify key knowledge gaps, and propose future research directions.

Cancer-associated fibroblasts (CAFs) construct a protective stromal barrier that promotes tumor growth and resistance to therapy. To dismantle this, we developed CAT-BLAST, an engineered bacterial platform designed to potently eliminate these cells. We engineered a safe, high-expression E. coli chassis, arming it with a FAP-specific synthetic adhesin for precise CAF targeting and the ability to secrete the ClyA cytotoxin to induce apoptosis in both CAFs and adjacent tumor cells. This platform demonstrated robust, FAP-specific targeting across diverse human tumor xenograft models and achieved significant tumor suppression in both murine colorectal cancer and melanoma.

Continuous directed evolution is a powerful Synthetic Biology tool to engineer proteins with desired functions in vivo. Mimicking natural evolution, it involves repeated cycles of high-frequency mutagenesis, selection, and replication within platform cells, where the function of the target gene is tightly linked to the host cell’s fitness. However, cells might escape the selection pressure due to the inherent flexibility of their metabolism, which allows for adaptation. Whole-proteome analysis as well as targeted proteomics offer valuable insights into global and specific cellular changes. They can identify modifications in the target protein and its interactors to help understand its evolution and network integration. Using the continuous evolution of the Arabidopsis thaliana methionine synthases AtMS1 and AtMS2 as an example, we show how mass spectrometry-based proteomics was able to assess the abundance of target enzymes, identify flaws in population construction, measure methionine metabolic adaptation, and allow informed decision-making in the evolution campaign.

Proteases, enzymes that play critical roles in health and disease, exert their function through the cleavage of peptide bonds. Identifying substrates that are efficiently and selectively cleaved by target proteases is essential for studying protease activity and for harnessing it in protease-activated diagnostics and therapeutics. However, the vast design space of possible substrates (c.a. 2010 amino acid combinations for a 10-mer peptide) and the limited accessibility of high-throughput activity profiling tools hinder the speed and success of substrate design. We present CleaveNet, an end-to-end AI pipeline for the design of protease substrates. Applied to matrix metalloproteinases, CleaveNet enhances the scale, tunability, and efficiency of substrate design. CleaveNet generates peptide substrates that exhibit sound biophysical properties and capture not only well-established but also previously-uncharacterized cleavage motifs. To control substrate design, CleaveNet incorporates a conditioning tag that steers peptide generation towards desired cleavage profiles, enabling targeted design of efficient and selective substrates. CleaveNet-generated substrates were validated experimentally through a large-scale in vitro screen, even in the challenging case of designing highly selective substrates for MMP13. We envision that CleaveNet will accelerate our ability to study and capitalize on protease activity, paving the way for in silico design tools across enzyme classes. Effective substrates are key to probing and harnessing protease activity. This work presents CleaveNet, an AI tool that generates efficient, selective substrates, revealing known and distinct cleavage motifs and tuning designs to target activity profiles.

Sequence alignment is essential for genomic research and clinical diagnostics, yet detecting complex rearrangements such as inversions, duplications, and gene conversions remains challenging due to allele complexity and limitations of current methods. We introduce VACmap, a non-linear mapping approach to enhance the detection and representation of all genetic variations. VACmap improves duplication detection from 20% to 90% in the Challenging Medically-Relevant Genes (CMRG) benchmark and improves characterization of complex inversions in repetitive regions and gene conversion events. It improves resolving clinically significant loci, including the LPA gene (with repetitive KIV-2 units linked to coronary heart disease), GBA1 and STRC genes (risk factors for Parkinson’s disease and hearing loss, respectively, affected by pseudogene recombination with GBAP1 and STRCP1). Here, we show that VACmap delivers better alignment accuracy and SV detection, providing a robust tool for genomic analysis and clinical insights, with potential to advance understanding of genetic diversity and disease mechanisms. Here the authors introduce VACmap, a nonlinear long-read aligner that improves detection of complex structural variations like duplications, inversions, and gene conversions. It enhances SV callers’ performance on benchmarks and resolves clinically relevant loci in LPA, GBA1, and STRC genes.