Mining Genetically Encoded Biosensors from Filamentous Fungi

1. Introduction

Genetically encoded biosensors are a class of biomolecular devices capable of transducing biological recognition events into detectable signals [1,2]. When compared to traditional biosensors like electrochemical biosensors, they display unique advantages. As an intrinsic part of living cells, genetically encoded biosensors enable real-time and noninvasive detection of small molecules or signals within cells, thereby facilitating continuous and dynamic monitoring [3,4]. Moreover, through subcellular localization design, high spatial resolution within cells can be achieved [5]. Genetically encoded biosensors can be stably inherited, ensuring sustainability at low cost once successfully designed and constructed [6]. Generally, a genetically encoded biosensor primarily comprises a signal recognition and transduction module, along with a signal output module [7]. Signal recognition elements are critical for the development of biosensors. In prokaryotes, signal recognition is typically mediated by allosteric transcription factors (TFs) [8], aptamers [9,10] and Two-Component Systems (TCS)[11]. In fungi, signal recognition elements include membrane-bound G-protein coupled receptors (GPCRs) and ion channels, TCS as well as zinc cluster TFs [12,13,14]. Currently, genetically encoded biosensors are majorly constructed based on TFs, as TF-based biosensors offer the versatility to regulate diverse downstream target genes. Indeed, TF-based biosensors serve as a core component in synthetic biology, with broad applications in high-throughput screening and selection, directed evolution, dynamic regulation of metabolic pathways, and the construction of synthetic genetic circuits [1,15,16]. Recently, synthetic design and construction based on filamentous fungal GPCRs and photoreceptors have been reported [17,18] (Figure 1). These strategies are fundamental to application scenarios in biomanufacturing, medical diagnostics, living material and environmental monitoring. With the increasing complexity and refinement of biomanufacturing processes, coupled with the growing demand for point-of-care diagnostics in the biomedical applications, there is an urgent need for more effective process control strategies and an expanded detectable signal space. The rapid development of biosensors represents a powerful tool for advancing the frontiers in these areas.

1. Limitations of Existing Yeast Biosensors for Synthetic Design

The commonly used yeast biosensors can be classified into endogenous induction system and heterologous bacterial-derived biosensors. Each category has inherent limitations and developmental bottlenecks for synthetic design.

The intensive used induction systems in metabolic engineering are dominated by endogenous biosensors in yeast, including the galactose-responsive GAL system, the copper-inducible P_CUP1 system, the phosphate-responsive P_PHO5 system and the methionine-regulated P_MET3 system [20]. The advantages of these systems are evident, including ease of molecular operation and/or high inducibility. However, the portability and application scenarios of these native systems are constrained by complex signal transduction cascades, coupling with host growth requirements and off-target effects (non-orthogonality) [21,22,23] (Figure 2). In past decades, to overcome the limitation of endogenous biosensors, bacteria-derived biosensors were continually transferred to yeast and optimized through directed evolution [24,25]. Engineering prokaryotic regulators is an effective strategy to enrich the toolbox of yeast biosensors. However, progress is constrained by the limited number of identified bacterial biosensors and host compatibility issues such as growth toxicity, lowly inducibility and slow induction speed [26,27,28]. Although the strategy that fusing strong ADs to bacterial TFs may offer a solution, they may exhibit high leakiness, especially when multiple binding sites are present in tandem in the promoter [29]. Except the directed evolution, no rational strategy has been developed for optimization of bacterial transcriptional activators-derived biosensors, as the activation mechanism is different to that of eukaryotes.

In summary, the non-orthogonal endogenous induction systems, the underperformed bacterial-derived biosensors and the shortage of characterized signal-receptor pairs hindered the development of genetically encoded biosensor for synthetic design.

2. Emerging Opportunity: Mining Response Elements from Filamentous Fungi

Fungi serve as important models in eukaryotes for studying environmental perception and signal transduction. As ecological decomposers, filamentous fungi exhibit remarkable adaptability and the ability to sense a wide array of signals, including pheromones, nutrients, gases, light, host organisms, and mechanical stress [12].

Filamentous fungi have evolved to possess a rich repertoire of signal receptors, including GPCRs, Zinc clusters TF, photoreceptors and TCSs. First, GPCRs mediate the detection of nutrients, pheromones, and various types of stress. There are over 15,000 GPCRs have been predicted in Ascomycota, while only three GPCRs- Ste2, Ste3 and Gpr1, have been characterized in yeast [30]. In other words, species in Ascomycota have an average of over 30 GPCR. Second, it has been reported that fungi lack nuclear receptors, while zinc cluster TFs may represent their functional analogs [14]. Filamentous fungi possess a unique capacity of utilizing some nutrients like pentoses and biosynthesizing secondary metabolites when compared to yeast. Like nuclear receptor, increasing evidences showed that fungal zinc cluster TFs are small-molecule sensors [31,32,33]. For example, in ascomycetous fungi, a network of regulators has been reported to control biomass degradation and catabolic pathways for the resulting monomers, which are often the inducers of these regulators activity[31,34]. Moreover, filamentous fungi are renowned for drug discovery due to rich genetic resource of secondary metabolites biosynthesis [35]. Their pathway genes are clustered and found to often include a specialized transcriptional activator that drives high-level expression of key metabolic enzymes [36]. Most of these metabolic TFs belong to the Zn(II)₂Cys₆ family and their DBDs exhibit remarkable sequence recognition specificity. This specificity is dictated by the following molecular mechanisms: small molecule-induced activation (e.g., proline induced DNA-binding of PrnA[37]); signal-induced homo- or heterodimerization[32]. Moreover, S. cerevisiae natively lacks metabolic pathways and regulatory networks for utilizing certain carbon sources like pentose (e.g., xylose，arabinose and rhamnose) or synthesizing secondary metabolites, which indicates that heterologous reconstruction of these pathway-specific TF-mediated induction systems could be suitable for synthetic design. Third, unlike yeast are blind, filamentous fungi especially Ascomycota species are capable of perceiving a broad spectrum of light. Taken Botrytis cinerea as an example, there are at least eleven potential photoreceptors to perceive near-UV, blue, green, red and far-red light [38]. Filamentous fungi hence provide prolific genetic components for synthetic optogenetic tools. Lastly, filamentous fungi have been reported to possess far more histidine kinases (HKs) than yeasts. Only one HK-Sln1, has been identified in S. cerevisiae, while at least 10 HKs genes are predicted in filamentous fungus like Neurospora crassa, Cochliobolus heterostrophus, Gibberella moniliformis and Botryotinia fuckeliana[39].

Apart from the rich repertoire of signal receptors and potential orthogonality from unique phenotypes, mining biosensors from filamentous fungi has following advantages. First, many Zinc clusters TFs act as transcriptional activators. Beyond larger dynamic range, gene circuits built with activators are more concise and compact than those constructed with repressors. Second, molecular genetics between filamentous fungi and budding yeast are conserved [40,41], including gene expression machinery, environmental response and stress adaptation mechanisms, transporter, signaling transduction and primary metabolic pathways. In other words, when transfer a response system from filamentous fungi to S. cerevisiae, the signal-specific components would be enough to implement functionality, as evidenced by xylose and arabinose sensor XlnR and AraR[27]. Second, ease of molecular manipulation. Based on our experiences, there is no requirement for codon optimization to heterogeneously express a filamentous fungal derived gene in S. cerevisiae. However, this conservation in signaling pathways would be a double-edged sword, as it may also interfere with the endogenous systems of yeast, thereby undermining the orthogonality required for synthetic design. Therefore, more works need to be done in future to quantify this interference.

3. Native Biosensor in Filamentous Fungi

As key decomposers, these fungi possess the ability to detect and degrade complex plant biomass, where they sense breakdown products (monomeric nutrients) and activate intricate signaling cascades to induce downstream gene expression for further catabolism. Moreover, to adapt with fluctuations in pH, temperature, light, moisture, and oxygen availability, filamentous fungi have evolved specialized sensors—including transmembrane receptors, TFs and phytochromes—that detect these environmental cues and initiate adaptive survival strategies. Consequently, filamentous fungi naturally harbor a rich repertoire of small-molecule and environmental-factor biosensors. A thorough understanding of these signaling mechanisms provides the foundation for cross-species transfer and the rational engineering of these native components into synthetic biological devices.

3.1. Small Molecule-Induction Systems

Small molecules are sensed either through cell membrane receptors or after being transported into the cell via transporters, where they may also directly bind to intracellular receptors like zinc cluster TFs[32,42,43]. For the former, this recognition primarily relies on the cAMP-PKA signaling pathway to transmit the signal to specific TFs, thereby completing the signal output. Since receptors for most small molecules remain unidentified, they will not be discussed in detail here. We focus on the cAMP-PKA signaling pathway and the reported TFs that respond to specific small molecules.

3.1.1. Signaling Pathway of Small Molecules

Small molecules sensed by filamentous fungi include carbon sources, amino acids, volatile organic compounds (VOCs), quorum-sensing molecules and pheromone. The specific signal transduction pathways and the resulting biological function vary depending on the type of signal molecules and organisms, though their core upstream components share evolutionary conservation with yeast—most notably the cAMP-PKA pathway (Figure 3)[44]. When extracellular small molecules exceed a concentration threshold, membrane-localized GPCRs detect these ligands, triggering dissociation of the Gα subunit (e.g., Aspergillus GpaB[45]) or Gβγ. The free Gα-GTP then diffuses to adenylate cyclase (ACYA[46]), catalyzing production of the cAMP and subsequent activation of protein kinase A (PKA). On the other hand, pheromone peptides-triggered Gβγ dimer releasing initiates the MAPK cascade. These complex regulatory networks ultimately converge on the phosphorylation of specific effector TFs (Figure 3). Presumably, the conservation of cAMP-PKA signaling could provides a foundation for simplified cross-species biosensor transfer across fungal species.

3.1.2. Small-Molecule Responsive TFs

Many small-molecule responsive TFs have been identified in filamentous fungi (Table 1). Most of them are involved in nutrition utilization [34] and community communication. For example, different type of carbon sources including monomer of plant biomass degradation and amino acids; fungal species-specific GPCR-mediated pheromone for sexual reproduction; volatile compounds and small molecules for inter- or intra-species communication (i.e., quorum sensing). Based on information of these identified sequences of TF protein and DNA binding motif, they are expected to be applied for biosensor development in other eukaryotic species.

3.2. Environmental Cues Responsive Systems

Environmental signals are initially perceived by receptor proteins—such as GPCRs, ion channels or HKs—located on the cell membrane. These signals are then transduced through distinct signaling pathways like MAPK pathways, amplified via cascades, and ultimately lead to alterations in the activity of TFs, thereby driving physiological adaptations to environmental perturbations. Accordingly, the following sections will discuss key components—including GPCRs, signal transduction pathways, and TFs—in the context of different environmental stimuli.

3.2.1. Responsive GPCR to Environmental Cues

GPCRs represent the largest class of cell surface receptors in eukaryotes, and GPCR signaling serves as a primary mechanism for eukaryotic cells to perceive and respond to environmental cues. In filamentous fungi, GPCRs exhibit remarkable diversity and can be classified into 17 major categories encompassing over 15,000 members based on predicted structural similarity and homology[30]. These receptors are involved in sensing nutrients, hormones, light, pH, temperature, and inter-population signaling. Selected GPCRs, along with their potential ligands, physiological functions, and species distribution, are summarized in Table 2.

Despite their abundance, the vast majority of fungal GPCRs remain poorly characterized. For example, while GPCRs have been systematically predicted in several model filamentous fungi[72,73], critical information regarding their activating ligands, activation mechanisms, and associated signaling components remains largely unknown. Moreover, structural studies on fungal GPCRs are even more scarce. A notable exception is the recent resolution of the yeast Ste2 receptor, which revealed significant structural differences compared to mammalian GPCRs—particularly in the position of transmembrane helix H₄ and the conformation of the G protein-binding site. This discovery highlights the potential of targeting fungal-specific GPCRs for the development of antifungal agents[74]. Furthermore, structural studies on GPCR-ligand interactions will facilitate the engineering of filamentous fungal GPCRs, thereby enriching the synthetic biology toolbox.

3.2.2. Signaling Pathway of Environmental Cues

Environmental signals such as oxidative stress, pH, temperature, light, water availability (or osmolarity) and even electromagnetic fields collectively shape the living conditions of organisms. Environmental perturbations influence growth, development and secondary metabolism biosynthesis. Filamentous fungi have evolved diverse survival strategies and signaling pathways to sense these perturbations and adapt to fluctuating environments (Figure 4)[12]. The core signaling pathways responding to environmental cues in filamentous fungi include the TOR-MAPK, HOG-MAPK, CWI-MAPK, Ca²⁺ signaling pathway, and HK-MAPK pathways[40]. These MAPK pathways are highly interconnected. For example, the HOG/MAPK signaling pathway is involved in sensing water availability, mechanical force, oxidative stress, pH, thermal, or even light [104,105]. On the other hand, a single environmental signal is able to trigger multiple signaling pathways. For instance, osmotic stress can trigger the HOG-MAPK, CWI-MAPK, Ca²⁺pathway, and HK-MAPK pathway simultaneously[41]. In account of the complexity of signaling networks involved in environmental cues response, the discussion focuses only on the well-characterized pathways in filamentous fungi, including pH, oxidative stress, light, and temperature.

The Rim/Pal pathway is a fungal-specific and well-studied signaling pathway that responds to extracellular pH changes[106]. This pathway is conserved in filamentous fungi, at least within the Ascomycota phylum, and shares similarities with S.cerevisiae. As the Rim101 ortholog in yeast, PacC in Aspergillus species serves as the central TF mediating pH signaling and requires a two-step proteolytic processing to become functional [107].

Environmental cues such as oxygen levels, mechanical damage, and different nutrient types and abundance trigger intracellular oxidative stress responses. Current research indicates that oxidative stress signaling in S. cerevisia and Aspergillus species are highly conserved and involve both the canonical HK-Hog1 pathway and non-canonical pathways mediated by SrrA (Skn7 ortholog) and NapA (Yap1 ortholog) [108]. These pathways differ in their reactive oxygen species (ROS, e.g., H₂O₂, O^•−) sensors and downstream TFs. The effector transcription factor for the HK-Hog1 pathway is AtfA (AP-1 ortholog). Hence, these TFs are functional across species of yeast and filamentous fungi[109].

Unlike S. cerevisiae, which lacks light-sensing capability, filamentous fungi generally are capable of perceiving light and can distinguish different wavelengths. Several photoreceptors for specific light spectra have been characterized (Table 3). These include cryptochrome CryA for near-UV light, WC-1 and Vivid for blue light，opsin for green light and phytochrome FphA for red light[110]. These photoreceptors exhibit distinct subcellular localizations (e.g., opsin on the plasma membrane, WC-1 in the nucleus, and FphA in the cytoplasm). Except WCC complex (WC-1 and WC-2), the downstream signal transduction mechanisms of these photoreceptors remain poorly understood. Triggered by blue light, the flavin-cystine adduct was formed in LOV domain to expose activation domain and stabilize WC-1 and WC-2 dimer, which thereby translocates to the nucleus and functions as a TF. The Hog1-Atf1 pathway has been reported to participate in FphA-mediated red light signaling[110]. The signaling mechanisms of cryptochromes (e.g., CryA) in response to blue light remain largely unexplored in fungi. In Arabidopsis thaliana, cryptochrome CryA directly binds the TF- CIB1 upon blue light stimulation to activate transcription initiation[111]. This example may provide insights for studying CryA signaling mechanisms in fungi. Green light receptors like NOP-1 have been identified in filamentous fungi. Green light induces photoisomerization of retinal from all-trans to 13-cis, which activate the coupled pump activity. The guanylyl cyclase effector domain in rhodopsin may couple light sensing with the production of the second messenger cGMP [112].

Microorganisms inherently exist in mechanically dynamic environments, where cell wall integrity (CWI) is crucial for maintaining osmotic homeostasis and resisting external mechanical forces. Membrane proteins Wsc1 and Mid2 function as mechanosensors; their extracellular domains undergo spring-like extension and compression, thereby initiating intracellular signaling. Additionally, the calcium channel proteins Mid1 and Cch1 are capable of perceiving mechanical stress and membrane damage. Downstream signaling pathways—including the CrzA-Ca²⁺ pathway, the CWI-MAPK cascade, and the SmuSH pathway—act in coordination to mediate responses to sustained mechanical pressure[113]. By contrast, research on fungal sensors for temperature stimuli—both heat and cold—remains notably limited.

3.2.3. Responsive TFs to Environmental Cues

A set of transcription factors (TFs) responsive to specific environmental cues have been identified (Table 4). How do these TFs perceive their cognate signals? What are their regulons or DNA-binding motifs? Most importantly, do they hold potential for development as biosensors? This review examines the identified TFs in light of these questions.

NapA, AtfA, MsnA (ortholog of yeast Msn2/4), and SrrA (ortholog of yeast Skn7) are TFs involved in the oxidative stress response. Upon H₂O₂ stimulation, NapA is oxidized by the peroxiredoxin PrxA in the cytoplasm, leading to its translocation into the nucleus to initiate transcription. NapA can be inactivated by thioredoxin TrxA reduction in both the cytoplasm and nucleus [150]. AtfA, a bZIP-type TF, interacts with SakA, the terminal kinase of the high-osmolarity glycerol (HOG) mitogen-activated protein kinase (MAPK) pathway, thereby serving as a key regulator in response to osmotic and oxidative stress [151]. MsnA, involved in various stress responses including oxidative stress, may interact with VelB; however, its upstream signaling components remain unclear. It contains multiple potential phosphorylation and ubiquitination sites, and its DNA-binding motif has recently been resolved [152]. SrrA, a Skn7-type response regulator containing an HSF-like DNA-binding domain and a receiver domain, is essential for H₂O₂ tolerance [153]. Nevertheless, information regarding its upstream regulators, downstream DNA-binding motifs, and target genes remains limited. SrbA, a basic helix-loop-helix (bHLH) TF belonging to the sterol regulatory element-binding protein (SREBP) family, has a well-defined DNA-binding motif [154]. The ΔsrbA mutant exhibits impaired growth under hypoxic conditions. Under low oxygen, full-length SrbA located in the endoplasmic reticulum undergoes proteolytic cleavage in a multi-factor dependent process, releasing its N-terminal bHLH domain into the nucleus to function as a transcriptional activator [155].

PacC is a well-studied TF responsive to alkaline pH, and its DNA-binding sequence has been biochemically validated [156]. The upstream regulation of PacC involves the membrane-associated proteins PalI, PalH, and PalF.

HsfA is the master regulator of thermal stress adaptation. Its activity is modulated by phosphorylation, although the specific upstream regulatory kinases remain unidentified. Despite functional conservation across eukaryotes, HsfA homologs exhibit variations in structure, post-translational modifications, and interacting partners [157]. In contrast to the extensive research on heat-responsive TFs, regulators of cold adaptation in fungi are poorly understood. Recently, the transcription factor Scaffold5.t61 was demonstrated to respond to low temperatures and regulate the synthesis of high-quality red pigment via modulation of glutamate metabolism [158].

Deciphering metal ion regulatory networks provides valuable insights for designing biosensors to monitor physiological or environmental metal ions and for developing bioremediation strategies. Ca²⁺ and Zn²⁺ are essential divalent cations involved in diverse biological processes. The primary regulator of calcium homeostasis is CrzA, which has been identified in Aspergillus [159]. In A. fumigatus, ZafA is the zinc-responsive TF responsible for zinc homeostasis and can functionally complement Zap1 in S. cerevisiae. Notably, Cd²⁺ can mimic the repressive effect of Zn²⁺ on ZafA. Cu²⁺-responsive TFs are widely reported in fungi including filamentous fungi. Mac1 is activated under copper-limiting conditions, whereas trans-activator AceA is induced by high copper concentrations [160,161]. However, specific responsive TFs for other biologically relevant divalent ions such as Mn²⁺, Co²⁺, and Ni²⁺ remain unidentified, although Ca²⁺-CrzA signaling has been implicated in Mn²⁺ sensing.

In fungi, key TFs involved in mechanosensing and cell wall integrity include Rlm1 and CrzA. Rlm1is a critical component of the CWI MAPK pathway. Upon mechanical stress or cell wall damage, the CWI pathway is activated, leading to phosphorylation of Rlm1 at specific sites (e.g., Ser427 and Thr435) by the Slt2 (Mpk1), which enhances its transcriptional activity [162]. A recent study revealed the DNA binding sequence of Rlm1 and showed that Rlm1 in F. graminearum can be activated by mycotoxins such as deoxynivalenol (DON) [163]. CrzA, a key effector of Ca²⁺-calcineurin signaling, translocates from the cytoplasm to the nucleus upon dephosphorylation in response to calcium stress, promoting the expression of cell wall biosynthesis-related genes. This nuclear translocation can be inhibited by cyclosporine A [164].

4. Mining Strategies of Sensing Elements in Filamentous Fungi

4.1. Genome Mining

(1)

Sequence-Based Genome Mining

The availability of rich genomic resources from diverse species provide an opportunity to discover natural sensing elements. For identified sensing proteins, conserved-domain alignment can be used to retrieve homologues with potentially superior performance. For example, XylR from Bacillus licheniformis have the largest dynamic range than other 5 homologues [173]. Moreover, the numerous predicted [174] and identified gene clusters, including biosynthetic gene clusters (BGCs) or catabolic gene clusters, from filamentous fungi could provide a platform for biosensor mining. In prokaryotes, it is common that the TF in a gene cluster bind the primary substrate degraded by the co-clustered enzymes, which implement feedforward activation regulation to switch on the catabolic pathway. Indeed, by leveraging this principle，16 metabolite-inducible systems, a new resveratrol-responsive biosensor and the erythritol induction systems were discovered in Cupriavidus necator H16[175], Novosphingobium aromaticivorans [176] and E. coli [177], respectively. Likewise, in white-rot fungi, aromatic compounds (e.g, guaiacol) induce the heterodimerization of TFs- TH8421 and TH4300, activating laccase genes to degrade these compounds [32]. Therefore, it is reasonable that there are more likewise examples showing the feedback or feedforward regulatory systems in biosynthetic or catabolic metabolism, thereby many metabolite-responsive systems are expected to exist in filamentous fungi. In future, a high-throughput mining strategy could be envisioned, involving in silico prediction of TF-ligand (substrate/product) pairs based on fungal gene clusters, and modular construction of native or synthetic TF-promoter pair for prototype test in desired chassis (Figure 5).

(2)

Structure-guided genome mining

As protein structure is often more evolutionarily conserved than sequence[178]. Ligand-binding-pocket-centric search can uncover remote homologues. Prior to DALI [179] and TM-align [180] , Foldseek [181] is presently the most widely used tool for rapid structural comparison and serve as a powerful complement to sequence-based mining.

4.2. Transcriptome-Guided Mining

Transcriptome-guided mining has been described [182]. In this way, stress and 1-butanol biosensor have been developed [182,183]. Briefly, this strategy involves defining the specific conditions of interest under a signal stimulus. Relevant transcriptome data under these conditions are systematically retrieved from databases or generated experimentally. Condition-specific and upregulated TFs are then identified as candidates. The information of TF binding sites (TFBS) can be retrieved from published literature or mapped from ChIP-seq datasets. When TFBSs are unavailable, fusing the TF to a well-characterized DBD [27] or clone the native, signal-responsive promoter as a putative output promoter can be alternative strategies. Finally, the TF and its cognate promoter are modularly assembled and engineered in selected host.

4.3. Deciphering Protein-Ligand Interaction Landscapes

Recently, the TF-ligand interaction landscape has been mapped in E.coli[184]. This methodology can be adapted for filamentous fungi species. It involves identifying candidate binding ligands for TFs/enzymes through affinity purification coupled with precision mass spectrometry. The pool of candidate ligands is then refined using AI-driven molecular docking. Finally, the binding affinity and functional induction are validated using standard compounds. This approach is anticipated to enable the batch development of a number of small-molecule inducible systems from filamentous fungi.

4.4. AI-Assisted Ligand Binding Site (LBS) Prediction

Various AI-driven tools have been developed for LBS prediction [185]. These tools can perform an initial screening against proteomes. By incorporating prior knowledge, the range of potential ligands can be progressively narrowed. Subsequently, AI tools can be employed to predict ligand binding pose[185]. This workflow aims to significantly reduce the experimental burden prior to validation.

5. Concluding Remark and Future Perspectives

As key components of signal sensing, GPCRs, TFs, and photoreceptors serve as fundamental genetic parts in biosensor development. Due to the inherent limitations of prokaryotic components, the development of fungal-derived components orthogonal to yeast from filamentous fungi should receive greater attention, as it can not only compensate for the shortcomings of bacterial components but also leverage the advantages of eukaryotic systems.

The application scenarios of biosensors are extremely broad, such as in biomedical and materials science. Biosensor can be integrated to facilitate drug discovery and screening, intelligent drug delivery, therapeutic drug monitoring，point-of-care drug/pathogen diagnostics and living material fabrication. For example, filamentous fungi are well known for producing key pharmaceuticals, including penicillin, griseofulvin, lovastatin, cyclosporine, and ergometrine. Monitoring their pharmacokinetics become necessary due to potential issues with dosage and toxic side effects. The native molecular responsive elements-based living biosensors could provide a safter and faster solution for monitoring.

Despite the growing demand of biosensors, research on identification of signal-receptor pairs lag far behind. Particularly, in filamentous fungi, most of genetic resources are still “dark matter”. Therefore, the pressing challenge is to identify the ligands of a large number of orphan GPCRs, zinc cluster TFs and photoreceptors. The established modular construction tools, AI tools and yeast synthetic GPCR system could provide effective and efficient solutions to deorphanize these sensing elements, which are poised to significantly accelerate advancement in synthetic biology.