Plant intracellular immune receptors are widely deployed in breeding to protect crops from disease. In addition to nucleotide-binding leucine-rich repeat receptors (NLRs), tandem kinase proteins (TKPs) have recently emerged as an important family of immune receptors within staple cereal food crops, but how TKPs recognize effectors and whether they are amenable to engineering is essentially unknown. Here, we show that the barley and wheat TKPs Rmo2 and Rwt7 recognize different blast fungus effectors via their integrated HMA domains using different protein interfaces with nanomolar binding affinity. Structural analysis pinpointed interface residues that dictate effector recognition and enabled engineering of dual-specificity TKPs. These results establish integrated HMA domains as programmable modules within TKPs for designing new specificities in plant immunity for diseases relevant to global agriculture

Biosynthetic gene clusters (BGCs) are responsible the biosynthesis of many natural products, including a multitude of effective therapeutics and their precursors. Advances in genomic data collection as well as computational techniques have made it possible to identify BGCs at scale. However, accurately determining the types of BGC-encoded products from genomic content remains elusive. Here, we introduce BGCat (BGC annotation tool), a machine learning method for fine-grained structural classification of BGC-encoded products, leveraging the NPClassifier natural product nomenclature. Our method leverages a pre-trained protein language model for creating meaningful gene representations and a deep neural network for class label prediction. We show the method outperforms state-of-the-art approaches in coarse-grained product classification and is effective for detailed classification. We implement a clustering-based augmentation strategy for BGC-product relationships, addressing a crucial gap in the available datasets. We then introduce the concept of product class profiles (PCPs) of gene cluster families (GCFs), associating each GCF with a probabilisitc distribution of product types and offering a new perspective on GCF functions. Lastly, we use BGCat to provide new product class labels for over 100k BGCs in antiSMASH DB that presently have minimal information about their products.

Synthetic biology is being widely applied in tumor therapy, ranging from attenuating microbial toxicity to constructing synthetic gene circuits and developing CAR-T cells, all of which are reshaping the landscape of cancer immunotherapy. In this review, we summarize recent advances in microbial-based therapeutics that leverage bacteria’s natural tropism for hypoxic tumor regions to deliver immunomodulatory payloads with high spatial precision. Parallel progress in CAR-T cell engineering has led to the development of armored and logic-gated constructs designed to overcome challenges such as antigen heterogeneity, the immunosuppressive tumor microenvironment, and T cell exhaustion. Synthetic biology further integrates these platforms via programmable genetic circuits capable of performing Boolean logic operations, ensuring therapeutic activation only in the presence of tumor-specific biomarkers. While this convergence offers the unprecedented precision, safety, and potency in reprogramming anti-tumor immunity, the clinical translation of these complex systems faces significant hurdles. Despite challenges in clinical translation-including safety concerns, immune clearance, and manufacturing complexity-the field is advancing toward multifunctional “smart” therapies, synergistic microbial-cell combinations, and personalized treatment strategies. Together, these innovations are defining a new generation of precision-engineered immunotherapies with the potential to transform the treatment of refractory malignancies.

Competition-based immunoassays are a common strategy for detecting small-molecule biomarkers. However, these assays rely on the availability of a custom competitor molecule that can effectively be displaced upon analyte binding, often requiring time-consuming synthesis and conjugation steps. De novo designed protein binders present a compelling alternative, as their binding properties can be tuned and they allow for straightforward genetic-incorporation into existing immunoassays. Here, we leverage the BindCraft pipeline to design competitive binders by targeting antigen-binding sites, followed by in silico filtering to select for steric clashes with the small-molecule analyte. As a proof of concept, we designed digoxin competitors and experimentally screened the binders using a simple bioluminescent assay, identifying 7/10 successful binders directly in bacterial lysate. These binders exhibited low to moderate binding affinities (Kd = 42 nM - 1.1 uM) and were displaced by digoxin. Two de novo binders were encoded into a previously established competition-based immunosensor, enabling sensitive digoxin detection (Kd = 109 nM). These results demonstrate that deep learning-based models can rapidly yield effective competitor binders, enabling straightforward adaptation and optimization of sensing platforms for small-molecule targets.

Characterizing and manipulating cellular behavior requires a mechanistic understanding of the causal interactions between cellular components. We present an approach to detect causal interactions between genes without the need to perturb the physiological state of cells. This approach exploits naturally occurring cell-to-cell variability which is experimentally accessible from static population snapshots of genetically identical cells without the need to follow cells over time. Our main contribution is a simple mathematical relation that constrains the propagation of gene expression noise through biochemical reaction networks. This relation allows us to rigorously interpret fluctuation data even when only a small part of a complex gene regulatory process can be observed. We show how this relation can, in theory, be exploited to detect causal interactions by synthetically engineering a passive reporter of gene expression, akin to the established ‘dual reporter assay’. While the focus of our contribution is theoretical, we also present an experimental proof-of-principle to demonstrate the real-world applicability of our approach in certain circumstances. Our experimental data suggest that the method can detect causal interactions in specific synthetic gene regulatory circuits in Escherichia coli, confirming our theoretical result in a narrow set of controlled experimental settings. Further work is needed to show that the approach is practical on a large scale, with naturally occurring gene regulatory networks, or in organisms other than E. coli.

G protein-coupled receptors (GPCRs) recognize ligands on the cell surface, initiating intracellular signaling pathways that control a variety of biological processes, from neurotransmission and hormone regulation to light detection and smell. As entryways into these pathways, GPCRs are key pharmacological targets, with 30% of FDA-approved drugs targeting them. High-throughput GPCR-based sensors in yeast are proven platforms for the identification of novel GPCR ligands. Most human GPCRs (hGPCRs), however, led to small increases in the signal after activation, hindering the development of high-throughput (HT) assays. To streamline the generation of HT assays for biomedically important hGPCRs, here we analyze five fluorescent reporters in the context of hGPCR-based sensors. Using the serotonin receptor 4 (HTR4)-based sensor as a testbed, we identify YPet, a yellow fluorescent protein previously evolved for improved intracellular fluorescence, as the optimal fluorescent reporter when using flow cytometry, fluorescence-activated cell sorting, or a fluorescent plate reader. YPet increases the dynamic range of hGPCR-based sensors in general, enabling the engineering of HTR4-, MC4R- S1PR2-, HTR1A-, and Mel1A-based sensors with vastly higher increases in signal than previously engineered sensors. YPet even allowed the construction of a functional HTR1D-based sensor, a sensor that had been difficult for the field to construct. Finally, the fast maturation of YPet reduces the time to readout from 4 h to 30 min, unlocking point-of-care diagnostic applications previously inaccessible to hGPCR-based sensors in yeast. Looking ahead, the identification of YPet as the optimal fluorescent reporter for yeast hGPCR-based sensors opens the door to the standardized generation of hGPCR high-throughput assays in this host, and sets the stage for ultrahigh-throughput single-cell experiments toward the identification of new ligands for known GPCRs, GPCR deorphanization, and GPCR engineering to bind designer ligands.

Horizontal gene transfer is a pivotal element in the evolution of microbes, enabling them to acquire novel genes and phenotypes. Integrative and conjugative elements (ICEs) are a type of mobile genetic element that can integrate into the host genome and propagate during chromosome replication and cell division. The induction of ICE gene expression results in the excision of the ICE gene, the production of conserved conjugation machinery, a Type IV Secretion System, and the potential for DNA transfer to appropriate receptors. It has been observed that ICEs frequently contain cargo genes that do not typically align with the ICE life cycle. These genes often result in the manifestation of phenotypes that are of particular interest. The bacterium Sphingopyxis granuli strain TFA is being studied for its ability to degrade the contaminant tetralin present in crude oils. Genomic analysis identified eight possible integrative mobile elements in S. granuli TFA. Most of these regions exhibited a distribution pattern that was restricted to the species, and they lacked some functional modules that are characteristic of a complete ICE. This finding suggests the presence of degenerate structures or limited mobilization capacity. However, only two of the detected elements exhibited the capacity to retain all the modules necessary for transfer, integration, and maintenance. These elements also contained a cargo module that included genes associated with lipid metabolic pathways and resistance mechanisms. Among them, ICE3 was distinguished as the sole complete functional ICE that was also present in other species. Transcriptomic analysis under multiple stress conditions revealed differential and consistent activation of ICE3 genes, demonstrating their direct contribution to bacterial resilience and suggesting a key adaptive role in response to adverse environmental changes.