Skip to Content
MDPIMDPI

Advanced

Journal of FungiJournal of Fungi
PDF
  • Review
  • Open Access

19 February 2026

26 Pages

Mining Genetically Encoded Biosensors from Filamentous Fungi

,
,
,
and
1
School of Medicine & Health Sciences, Wuxi Taihu University, Wuxi 214064, China
2
State Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
3
Wuxi School of Medicine, Jiangnan University, Wuxi 214122, China
*
Authors to whom correspondence should be addressed.
J. Fungi2026, 12(2), 150;https://doi.org/10.3390/jof12020150
This article belongs to the Section Fungal Genomics, Genetics and Molecular Biology
Version Notes
Order Reprints

Abstract

Genetically encoded biosensors represent cutting-edge biosensors due to their capabilities in real-time monitoring and precise control in living cells. However, the development of eukaryotic genetically encoded biosensors for new analytes is constrained by the shortage of signal–receptor pairs. Bacterial biosensors have been transferred to eukaryotes to expand the signal detection space, which has achieved remarkable success. However, due to the significant differences between eukaryotic and prokaryotic gene expression systems, optimizing bacterial biosensors has proven challenging. Successful cases indicate that developing orthogonal signal–receptor pairs directly from eukaryotic systems may offer a viable solution. Indeed, the potential of filamentous fungi—a highly diverse group of organisms that share conserved as well as specific signaling and metabolic pathways with yeast and mammalian cells—has been largely overlooked in biosensor development. In this review, we systematically examine biosensing systems in filamentous fungi, summarize their signal recognition receptors, signal transduction pathways, responsive transcription factors, and provide an overview of the biosensors and synthetic tools developed from them. Finally, we highlight the promise and challenges of biosensor development from filamentous fungi and discuss their potential applications.

1. Introduction

Genetically encoded biosensors are a class of biomolecular devices capable of transducing biological recognition events into detectable signals such as fluorescence or gene expression [1,2]. Traditional physical/chemical probes (measuring metabolites, temperature, pH, and dissolved oxygen) cannot directly convert detected signals into downstream biological events and are thus limited to monitoring and cannot achieve automatic regulation. As integral components of living cells, genetically encoded biosensors can enable not only real-time and noninvasive monitoring of intracellular physiological and biochemical states but also precisely control and program higher-order biological processes to confer cellular intelligence [1,3,4,5,6]. Moreover, through subcellular localization design, high spatial resolution within cells can be achieved [7]. Genetically encoded biosensors can be stably inherited, ensuring sustainability at low costs once successfully designed and constructed [8]. Ideally, a versatile biosensor should display a large dynamic range (ON/OFF ratio), minimal leakiness, high inducibility, high host orthogonality (i.e., it does not interfere with host endogenous systems and vice versa), sustainable expression, rapid inducer response, and nontoxicity to the host [9].
Generally, a genetically encoded biosensor primarily comprises a signal recognition module, a transduction module and a signal output module [10]. Signal recognition elements are critical for the development of biosensors. In prokaryotes, signal recognition is typically mediated by allosteric transcription factors (TFs) [11], aptamers [12,13] and Two-Component Systems (TCSs) [14]. In fungi, signal recognition elements include membrane-bound G protein-coupled receptors (GPCRs), ion channels, TCSs and zinc cluster TFs [15,16,17]. Currently, genetically encoded biosensors are mainly constructed based on TFs, as TF-based biosensors offer the versatility to regulate diverse downstream target genes (Figure 1). Indeed, TF-based biosensors serve as core components in synthetic biology, with broad applications in high-throughput screening and selection, directed evolution, dynamic regulation of metabolic pathways, and construction of synthetic genetic circuits [1,18,19]. Recently, synthetic design and construction based on filamentous fungal GPCRs and photoreceptors have also been reported [20,21,22].
Figure 1. Diverse output genes and their corresponding applications by a TF-based biosensor.
With the increasing complexity and refinement of biomanufacturing processes, coupled with the growing demand for point-of-care diagnostics in biomedical applications, there is an urgent need for more effective process control strategies and an expanded detectable signal space. The rapid development of biosensors offers powerful tools for advancing the frontiers in these areas.

2. Limitations of Existing Yeast Biosensors for Synthetic Design

The commonly used yeast biosensors can be classified into endogenous induction systems and heterologous bacterial-derived biosensors. Each category has inherent limitations and developmental bottlenecks for synthetic design.
The intensively used induction systems in metabolic engineering are dominated by endogenous yeast biosensors, including the galactose-responsive GAL system, the copper-inducible PCUP1 system, the phosphate-responsive PPHO5 system and the methionine-regulated PMET3 system [9]. The advantages of these systems are evident, including ease of molecular operation and high inducibility. However, the portability and application scenarios of these native systems are constrained by complex signal transduction cascades, their coupling with host growth requirements and off-target effects (non-orthogonality) [23,24,25] (Figure 2). In the past decades, to overcome the limitations of endogenous biosensors, bacterial-derived biosensors have been transferred to yeast and optimized through directed evolution [26,27]. Engineering prokaryotic regulators is an effective strategy to enrich the yeast biosensor toolbox. However, progress is constrained by the limited number of identified bacterial biosensors and host compatibility issues such as growth toxicity, low inducibility and slow induction speed [20,28,29]. Although fusing strong Activation Domains (ADs) to bacterial TFs may offer a solution, these synthetic systems often exhibit high leakiness, especially when multiple binding sites are present in tandem in the promoter [30]. No rational strategy has been developed beyond directed evolution to optimize bacterial transcriptional activator-based biosensors, as the activation mechanism is different from that of eukaryotes.
Figure 2. Limitations of endogenous and bacterial-derived biosensors.
In summary, non-orthogonal endogenous induction systems, underperforming bacterial-derived biosensors and the shortage of characterized signal-receptor pairs have hindered the development of genetically encoded biosensors for synthetic design.

3. Emerging Opportunity: Mining Biosensor from Filamentous Fungi

Fungi serve as important eukaryotic 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 [15].
Filamentous fungi have evolved to possess a rich repertoire of signal receptors, including GPCRs, zinc cluster TFs, photoreceptors and TCSs. First, GPCRs mediate the detection of nutrients, pheromones, and various types of stress. There are over 15,000 GPCRs predicted in Ascomycota, while only three GPCRs—Ste2, Ste3 and Gpr1—have been characterized in yeast [31]. In other words, species in Ascomycota have an average of over 30 GPCRs. Second, it has been reported that fungi lack nuclear receptors, while zinc cluster TFs may represent their functional analogs [17]. Filamentous fungi possess a unique capacity to utilize some nutrients, like pentoses, and biosynthesize secondary metabolites when compared to yeast. Like nuclear receptors, increasing evidence shows that fungal zinc cluster TFs are direct small-molecule sensors [32,33,34,35]. 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 [32,36]. Moreover, filamentous fungi are renowned for drug discovery due to the rich genetic resource they provide for secondary metabolite biosynthesis [37]. Their pathway genes are clustered and often include a specialized transcriptional activator that drives high-level expression of key metabolic enzymes [38]. Most of these metabolic TFs belong to the Zn(II)2Cys6 family, and their DNA-binding domains (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 [39]) and signal-induced homo- or heterodimerization [34]. Moreover, Saccharomyces cerevisiae lacks native metabolic pathways and regulatory networks for utilizing certain carbon sources like pentoses (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 blind yeast, filamentous fungi, especially Ascomycota species, are capable of perceiving a broad spectrum of light. Taking Botrytis cinerea as an example, there are at least 11 potential photoreceptors capable of perceiving near-UV, blue, green, red and far-red light [40]. Filamentous fungi hence provide prolific genetic components for 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 HK genes are predicted in filamentous fungi such as Neurospora crassa, Cochliobolus heterostrophus, Gibberella moniliformis and Botryotinia fuckeliana [41].
As mentioned above, compared to yeast, filamentous fungi possess unique capabilities in broader nutrient degradation, secondary metabolite synthesis and light sensing. It is reasonable to assume that the sensing receptors and regulators involved in these processes are unique to filamentous fungi and hence may exhibit orthogonality when introduced into yeast. On one hand, this orthogonality ensures that yeast growth is not significantly impaired by the introduced biosensor. On the other hand, the output behavior of the introduced biosensor would be predictable, as it would not be affected by the yeast endogenous system. Indeed, this hypothesis has been preliminarily validated [20]. Beyond orthogonality, mining biosensors from filamentous fungi offers the following advantages. First, many zinc cluster TFs act as transcriptional activators. Activator-based biosensors typically achieve higher induction levels and larger dynamic ranges than those based on bacterial repressors [20]. Furthermore, they exhibit a more predictable positive correlation with TF abundance [42]. Second, molecular genetics between filamentous fungi and budding yeast are conserved [43,44], including gene expression machinery, transporters, signaling transduction and primary metabolic pathways. In other words, when transferring a response system from filamentous fungi to S. cerevisiae, the signal-specific components are sufficient to confer functionality, as evidenced by the xylose and arabinose sensors XlnR and AraR [20]. Third, there is ease of molecular manipulation. In our experience, codon optimization is not required for heterologous expression of filamentous fungal-derived genes 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. Therefore, further work is needed to first determine whether interference exists. If interference is present, it should be quantified to define the potential scope of application.

4. Native Biosensors in Filamentous Fungi

As key decomposers, filamentous fungi possess the ability to detect and degrade complex plant biomass, whereby they sense breakdown products (monomeric nutrients) and activate intricate signaling cascades to induce downstream gene expression for further catabolism. Moreover, to adapt to 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.

4.1. Small-Molecule Induction Systems

In fungi, small-molecule sensors function either as membrane receptors or directly as zinc cluster TFs. Examples in S. cerevisiae include glucose sensors (Snf3, Rgt2 [45] and Gpr1 [46]), galactose sensor Gal3 [47], phosphatidate sensor Opi1 [48], amino acid sensors (Ssy1 [49] and Gap1 [50]), cysteine sensor Pib2 [51], and α-isopropylmalate (α-IPM) sensor Leu3 [52]. In filamentous fungi, responsive TFs and GPCRs for small molecules are summarized in Table 1 and Table 2. In the sections below, we focus on the cAMP-PKA signaling pathway and the reported TFs that respond to specific small molecules.

4.1.1. Small-Molecule Signaling Pathways

Small molecules sensed by filamentous fungi include carbon sources, amino acids, volatile organic compounds (VOCs), quorum-sensing molecules and pheromones. The specific signal transduction pathways and the resulting biological functions 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) [53]. When extracellular small molecules exceed a concentration threshold, membrane-localized GPCRs detect these ligands, triggering dissociation of the Gα subunit (e.g., Aspergillus GpaB [54]) or Gβγ. The free Gα-GTP then diffuses to adenylate cyclase (ACYA [55]), catalyzing production of cAMP and subsequent activation of protein kinase A (PKA). On the other hand, pheromone peptide-triggered Gβγ dimer release 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 provide a foundation for simplified cross-species biosensor transfer across fungal species.
Figure 3. Small-molecule signaling pathway in filamentous fungi.

4.1.2. Small-Molecule-Responsive TFs

Many small-molecule-responsive TFs have been identified in filamentous fungi (Table 1). Most of them are involved in nutrient utilization [36] and community communication, for example, different types of carbon sources, including monomers of plant biomass degradation and amino acids; fungal species-specific GPCR-mediated pheromones for sexual reproduction; volatile compounds and small molecules for inter- or intra-species communication (i.e., quorum sensing). Based on these identified sequences of TF proteins and DNA-binding motifs, they are expected to be applied in biosensor development in other eukaryotic species.
Table 1. Small-molecule-responsive TF summary.

4.2. Environmental Cue-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 and amplified via signaling 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.

4.2.1. GPCRs Responsive 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. These receptors are involved in sensing nutrients, hormones, light, pH, temperature, and inter-population signaling. Environmental cue-related GPCRs, along with their potential ligands, physiological functions, and species distribution, are summarized in Table 2.
Table 2. Environmental cue-responsive GPCR summary.

4.2.2. Signaling Pathway of Environmental Cues

Filamentous fungi have evolved diverse survival strategies and signaling pathways to sense these perturbations and adapt to fluctuating environments (Figure 4) [15]. The core signaling pathways responding to environmental cues in filamentous fungi include the TOR-MAPK, HOG-MAPK, cell wall integrity (CWI)-MAPK, Ca2+ signaling pathway, and HK-MAPK pathways [43]. These MAPK pathways are conserved between filamentous fungi and yeasts [44]. MAPK pathways are interconnected with other signaling pathways, particularly the cAMP-PKA one [114]. Given the complexity of signal networks involved in environmental sensing, this review focuses on well-characterized signaling pathway components in filamentous fungi.
Figure 4. Signaling pathway for environmental cues in filamentous fungi.
The Rim/Pal pathway is a fungal-specific and well-studied signaling pathway that responds to extracellular pH changes [115]. 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 the Aspergillus species serves as the central TF mediating pH signaling and requires two-step proteolytic processing to become functional [116].
Environmental cues such as oxygen levels, mechanical damage, and different nutrient types and abundances trigger intracellular oxidative stress responses. Current research indicates that oxidative stress signaling in S. cerevisiae and Aspergillus species is highly conserved and involves both the canonical HK-Hog1-AtfA (AP-1 ortholog) pathway and non-canonical pathways mediated by SrrA (Skn7 ortholog) and NapA (Yap1 ortholog) [117]. These pathways differ in their reactive oxygen species (ROS, e.g., H2O2, O•−) sensors and downstream TFs. Moreover, these TFs are functional across species of yeast and filamentous fungi [118].
Unlike S. cerevisiae, which lacks light-sensing capability, filamentous fungi are generally 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 [119]. These photoreceptors exhibit distinct subcellular localizations (e.g., opsin on the plasma membrane, WC-1 in the nucleus, and FphA in the cytoplasm). Except for WCC complexes (WC-1 and WC-2), the downstream signal transduction mechanisms of these photoreceptors remain poorly understood. Triggered by blue light, the flavin-cysteinyl adduct is formed in the LOV domain to expose AD and stabilize the WC-1/WC-2 dimer, thereby translocating to the nucleus and functioning as a TF. The Hog1-Atf1 pathway has been reported to participate in FphA-mediated red light signaling [119]. 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 [120]. This example may provide insights into 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 activates coupled pump activity. The guanylyl cyclase effector domain in rhodopsin may couple light sensing with the production of the second messenger cGMP [121].
Microorganisms inherently exist in mechanically dynamic environments, where 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 [122]. By contrast, research on fungal sensors for temperature stimuli—both heat and the cold—remains notably limited.
Table 3. Photoreceptors in fungi.

4.2.3. TFs Responsive to Environmental Cues

A set of TFs responsive to specific environmental cues has 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 [159]. 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 [160]. 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 [161]. SrrA, a Skn7-type response regulator containing an HSF-like DNA-binding domain and a receiver domain, is essential for H2O2 tolerance [162]. 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 [163]. 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 [164].
PacC is a well-studied TF responsive to alkaline pH, and its DNA-binding sequence has been biochemically validated [116]. 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 [165]. 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 [166].
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 [167]. 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 filamentous fungi. Mac1 is activated under copper-limiting conditions, whereas trans-activator AceA is induced by high copper concentrations [168,169]. 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 mechanical sensing and cell wall integrity include Rlm1 and CrzA. Rlm1 is 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 Slt2 (Mpk1), which enhances its transcriptional activity [170]. 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) [171]. 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 [172].
Table 4. Environmental cue-responsive TF summary.

5. Biosensors and Synthetic Tools Developed from Filamentous Fungi

A set of biosensors and synthetic tools has been developed from filamentous fungi. Some examples have been illustrated in Figure 5. These successful cases of cross-species transfer from filamentous fungi have all relied on small-molecule-responsive TFs, pheromone-responsive GPCRs and photoreceptors.

5.1. TF-Based Biosensors

Several small-molecule-responsive TFs from filamentous fungi have been demonstrated to be functional in mammalian cells or yeast. In 2004, the ethanol-inducible AlcR-PalcA system from A. nidulans was successfully transferred into mammalian cells and was demonstrated not to cross-talk with established antibiotics-induced systems [75]. Furthermore, the Q system from N. crassa was introduced in yeast to develop a pair of orthogonal tools, OptoQ-INVRT and OptoQ-AMP, which enable simultaneous optogenetic signal amplification and inversion [82]. Recently, the XlnR-based xylose induction system from A. nidulans has been shown to be functional in S. cerevisiae, exhibiting superior performance compared to bacterial XylR-based biosensors in terms of inducibility, dynamic range, induction kinetics and impact on host growth [20]. In some cases, although the binding motifs of certain natural responsive transcription factors have never been identified, synthetic TFs can serve as a solution. Synthetic TFs are constructed through modular assembly of DBDs, ligand-binding domains (LBDs) and ADs. For example, since the DNA-binding motif of AraR from A. niger was unknown, fusing AraR to the well-characterized LexA DBD enabled the output promoter to be dose-responsive to arabinose [20].
As the mechanisms for sensing and transducing most small molecules have not been fully understood yet, the cross-species transfer of induction systems from eukaryotic cells has long been considered challenging. The functionality of these two-component systems in yeast or mammalian cells suggests that some fungal Zn(II)2Cys6 activators are direct sensors, aligning with a previous study [33], which would greatly facilitate their cross-species transfer.

5.2. GPCR-Based Biosensors

The endogenous α-factor pheromone GPCR signaling pathway in S. cerevisiae is the most extensively studied one, with its key components’ dose–response relationships having been explored through computational and experimental reconstruction [181]. The yeast α-factor GPCR pathway has become a rational model for heterologous GPCR research. For instance, heterologous expression of orthogonal fungal pathogen GPCRs in yeast enables rapid and sensitive detection of fungal pathogens [182,183]. Furthermore, 32 of 45 GPCRs from Ascomycota have been demonstrated to be functional in yeast, with 17 GPCR–peptide pairs displaying high orthogonality. Using eight orthogonal GPCR–peptide pairs, basic two-cell communication links, complex multicellular communication network topologies and three-member interdependent communities were constructed [184]. Additionally, in a recent study, a yeast toolkit named MARS (Anchored Peptide Response System) was developed based on GPCR-peptide pairs from Ascomycota fungi, including Beauveria bassiana, Trichoderma reesei and Saitoella japonica [21]. These studies show that GPCRs derived from filamentous fungi can be functionally implemented in yeast and applied to pathogenic fungal detection, as well as in complex synthetic topology construction.

5.3. Photoreceptor-Based Optogenetic Tools

Optogenetic tools have attracted sustained research interest in neuroscience, immunology and gene expression control due to their operational reversibility and tissue penetrability. Photosensitive proteins derived from filamentous fungi have been engineered to develop synthetic optogenetic tools in mammalian cells and yeast. For instance, in 2012, Yi Yang et al. first constructed the LightOn system by fusing the LOV domain of the N. crassa Vivid protein with the Gal4 DBD and the p65 AD. The blue-light-responsive functionality of this system was validated in multiple mammalian cell lines and in mouse livers [185]. The LOV domain was further optimized through mutagenesis to develop a high-performance mutant termed LVADO, providing yeast cells with a powerful and convenient optogenetic tool [186]. In 2015, Moritoshi Sato et al. developed the Magnets system based on Vivid (VVD). By mutating the N-cap domain, they constructed a pair of variants carrying opposite charges, thereby selectively promoting heterodimerization while suppressing homodimerization to achieve ultrafast OFF kinetics [187] (Figure 5c). Furthermore, in 2018, Luis F. Larrondo et al. developed the FUN-LOV optogenetic switch by exploiting the LOV domain interactions between two blue-light photoreceptors from N. crassa, WC-1 and VVD [188]. This system exhibits a 1300-fold dynamic range and enables spatiotemporally precise control. Moreover, it was employed to control the expression of the MFα1 gene, thereby achieving optogenetic control of intercellular communication [189].
Beyond N. crassa, BcWCL1 from B. cinerea requires blue-light stimulation for interaction with BcWCL2 upon deletion of its PAS domain, suggesting their potential as novel optogenetic switch elements [190]. Additionally, another photoreceptor from this species, BcLOV4, was found to translocate from the cytoplasm to the plasma membrane within approximately one second upon blue-light (455 nm) illumination, and this process is fully reversible [191]. Interestingly, in 2023, this protein was further reported to not only translocate to the membrane but also to form clusters on the membrane upon blue-light stimulation. BcLOV4 thus represents the second photoreceptor reported, following Arabidopsis Cry2, whose light-induced clustering on the membrane could be exploited for extensive use [192]. Recently, Haifeng Ye et al. engineered a chimeric photoreceptor, FnBphP, by fusing the N-terminal extension of the A. nidulans phytochrome FphA with the photosensory core module of Deinococcus radiodurans BphP, thereby developing a sensitive red-light-inducible dimerization system. Compared to DrBphP, FnBphP containing the FphA-NTE significantly enhanced activation levels at 660 nm [22].
Taken together, a growing number of photoreceptors derived from filamentous fungi are being characterized and applied in optogenetic tool development, highlighting their importance in optogenetics.
Figure 5. Examples of filamentous fungi-derived genetically encoded biosensors applied in synthetic biology. (a) XlnR-, AraR-, and Q system-based biosensors were developed in yeast [20,82] and an AlcR-based biosensor was developed in mammalian cells [75]. (b) Fungal orthogonal peptide–GPCR pairs were functional in yeast and applied for the construction of Mating peptide-Anchored Response System (MARS) [21]. (c) VVD from N. crassa and VVD derivate, Magnets, were engineered as light switches to control protein homodimerization and heterodimerization, respectively [193]. (d) The N-terminal extension of FphA from A. nidulans was used to improve the sensitivity of the chimeric photosensory proteins [22].

6. Concluding Remark and Future Perspectives

As critical components for signal sensing, GPCRs, TFs and photoreceptors represent fundamental genetic parts in biosensor development. Due to the inherent limitations of prokaryotic components, the development of eukaryotic parts derived from filamentous fungi that are orthogonal to yeast should receive greater attention. Compared to yeast, genetic components involved in unique phenotypes of filamentous fungi—such as extensive nutrient degradation, secondary metabolite synthesis and light perception—may provide diversity to the yeast toolkit. Indeed, signal transduction pathways for both small molecules and environmental factors are considerably more complex in eukaryotes than in bacteria; however, the growing number of successful cases for cross-species transfer of one- or two-component elements from filamentous fungi suggests that mining biosensors from these organisms is indeed feasible and more accessible than previously recognized. Biosensors developed from these components are expected to address the limitations commonly faced by bacterial biosensors, including low-level induction, host cell toxicity, and limited space for performance optimization.
Despite the promising prospects of developing biosensors from filamentous fungi, several challenges limit their development. First, the vast majority of filamentous fungal biosensing elements remain uncharacterized, representing a critical bottleneck for scalable biosensor development from these organisms. Therefore, it is essential to systematically dissect ligand–GPCR and ligand–TF interactions, as well as elucidate the functional domain of photoreceptors in filamentous fungi. For characterization of ligand–GPCR pairs, established high-throughput platforms are available for reference, such as DCyFIR technology [194] and yWS677 and yWS1922 model yeast strains [181]. Regarding the dissection of ligand–TF pairs, recent mapping of the TF–ligand interaction landscape in E. coli, employing a methodology interweaving experimental validation and Artificial Intelligence-assisted computational prediction, may serve as a reference [195]. Second, developing specific environmental-factor-responsive biosensors could prove challenging due to complex transduction pathways and crosstalk. Nevertheless, designing these biosensors is important for at least two reasons: (1) precision metabolic engineering requires not only monitoring but also automatic regulation driven by intracellular physiological signals; (2) the predominant environment-factor biosensors are developed based on optical transduction mechanisms, which suffer from challenges in field applications [196]. Therefore, it is essential to enhance research on signaling mechanisms of environmental cues to circumvent current bottlenecks.
The application scenarios for biosensors are extremely broad, spanning the biomedical sciences and material sciences. Biosensors 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 is necessary due to potential issues with dosage and toxic side effects. In the long term, drug-responsive genetically encoded biosensors developed from filamentous fungi could facilitate in vivo monitoring and personalized therapy.

Author Contributions

Conceptualization, S.G. and Y.C.; writing—original draft preparation, S.G.; writing—review and editing, S.G., Y.G., S.S., Z.L. and Y.C. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by a start-up fund from Wuxi Taihu University [2025THQD024]. The funders had no role in study design, data collection and analysis, publication decisions, or manuscript preparation.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Qin, L.; Liu, X.; Xu, K.; Li, C. Mining and design of biosensors for engineering microbial cell factory. Curr. Opin. Biotechnol. 2022, 75, 102694. [Google Scholar] [CrossRef] [Scilit]
  2. Yu, W.; Xu, X.; Jin, K.; Liu, Y.; Li, J.; Du, G.; Lv, X.; Liu, L. Genetically encoded biosensors for microbial synthetic biology: From conceptual frameworks to practical applications. Biotechnol. Adv. 2023, 62, 108077. [Google Scholar] [CrossRef] [Scilit]
  3. Newman, R.H.; Fosbrink, M.D.; Zhang, J. Genetically encodable fluorescent biosensors for tracking signaling dynamics in living cells. Chem. Rev. 2011, 111, 3614–3666. [Google Scholar] [CrossRef] [Scilit]
  4. Rogers, J.K.; Guzman, C.D.; Taylor, N.D.; Raman, S.; Anderson, K.; Church, G.M. Synthetic biosensors for precise gene control and real-time monitoring of metabolites. Nucleic Acids Res. 2015, 43, 7648–7660. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  5. Xiang, Y.; Dalchau, N.; Wang, B. Scaling up genetic circuit design for cellular computing: Advances and prospects. Nat. Comput. 2018, 17, 833–853. [Google Scholar] [CrossRef] [Scilit]
  6. Marsafari, M.; Ma, J.; Koffas, M.; Xu, P. Genetically-encoded biosensors for analyzing and controlling cellular process in yeast. Curr. Opin. Biotechnol. 2020, 64, 175–182. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  7. Du, Y.; Hu, H.; Pei, X.; Du, K.; Wei, T. Genetically Encoded FapR-NLuc as a Biosensor to Determine Malonyl-CoA in Situ at Subcellular Scales. Bioconjug Chem. 2019, 30, 826–832. [Google Scholar] [CrossRef] [Scilit]
  8. Huang, C.W.; Lin, C.; Nguyen, M.K.; Hussain, A.; Bui, X.T.; Ngo, H.H. A review of biosensor for environmental monitoring: Principle, application, and corresponding achievement of sustainable development goals. Bioengineered 2023, 14, 58–80. [Google Scholar] [CrossRef] [Scilit]
  9. Gligorovski, V.; Sadeghi, A.; Rahi, S.J. Multidimensional characterization of inducible promoters and a highly light-sensitive LOV-transcription factor. Nat. Commun. 2023, 14, 3810. [Google Scholar] [CrossRef] [Scilit]
  10. Wang, B.; Barahona, M.; Buck, M. A modular cell-based biosensor using engineered genetic logic circuits to detect and integrate multiple environmental signals. Biosens. Bioelectron. 2013, 40, 368–376. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  11. Taylor, N.D.; Garruss, A.S.; Moretti, R.; Chan, S.; Arbing, M.A.; Cascio, D.; Rogers, J.K.; Isaacs, F.J.; Kosuri, S.; Baker, D.; et al. Engineering an allosteric transcription factor to respond to new ligands. Nat. Methods 2016, 13, 177–183. [Google Scholar] [CrossRef] [Scilit]
  12. Tombelli, S.; Minunni, M.; Mascini, M. Analytical applications of aptamers. Biosens. Bioelectron. 2005, 20, 2424–2434. [Google Scholar] [CrossRef] [Scilit]
  13. Sefah, K.; Phillips, J.A.; Xiong, X.; Meng, L.; Van Simaeys, D.; Chen, H.; Martin, J.; Tan, W. Nucleic acid aptamers for biosensors and bio-analytical applications. Analyst 2009, 134, 1765–1775. [Google Scholar] [CrossRef] [Scilit]
  14. Beier, D.; Gross, R. Regulation of bacterial virulence by two-component systems. Curr. Opin. Microbiol. 2006, 9, 143–152. [Google Scholar] [CrossRef] [Scilit]
  15. Bahn, Y.S.; Xue, C.; Idnurm, A.; Rutherford, J.C.; Heitman, J.; Cardenas, M.E. Sensing the environment: Lessons from fungi. Nat. Rev. Microbiol. 2007, 5, 57–69. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  16. Prole, D.L.; Taylor, C.W. Identification and analysis of cation channel homologues in human pathogenic fungi. PLoS ONE 2012, 7, e42404. [Google Scholar] [CrossRef] [Scilit]
  17. Naar, A.M.; Thakur, J.K. Nuclear receptor-like transcription factors in fungi. Genes. Dev. 2009, 23, 419–432. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  18. D’Ambrosio, V.; Jensen, M.K. Lighting up yeast cell factories by transcription factor-based biosensors. FEMS Yeast Res. 2017, 17, fox076. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  19. Tominaga, M.; Kondo, A.; Ishii, J. Engineering of Synthetic Transcriptional Switches in Yeast. Life 2022, 12, 557. [Google Scholar] [CrossRef] [Scilit]
  20. Guo, S.; Du, J.; Li, D.; Xiong, J.; Chen, Y. Versatile xylose and arabinose genetic switches development for yeasts. Metab. Eng. 2025, 87, 21–36. [Google Scholar] [CrossRef] [Scilit]
  21. Meng, F.; Shaw, W.M.; Kam, Y.K.K.; Ellis, T. Engineering yeast multicellular behaviors via synthetic adhesion and contact signaling. Cell 2025, 188, 4936–4949. [Google Scholar] [CrossRef] [Scilit]
  22. Qiao, L.; Niu, L.; Wang, Z.; Dai, D.; Tang, S.; Ma, X.; Deng, Z.; Yu, G.; Zhou, Y.; Yan, T.; et al. Optogenetic-Controlled iPSC-Based Vaccines for Prophylactic and Therapeutic Tumor Suppression in Mice. Adv. Sci. 2025, 12, e16115. [Google Scholar] [CrossRef] [Scilit]
  23. Acar, M.; Becskei, A.; van Oudenaarden, A. Enhancement of cellular memory by reducing stochastic transitions. Nature 2005, 435, 228–232. [Google Scholar] [CrossRef] [Scilit]
  24. Endalur Gopinarayanan, V.; Nair, N.U. A semi-synthetic regulon enables rapid growth of yeast on xylose. Nat. Commun. 2018, 9, 1233. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  25. Wang, M.; Li, S.; Zhao, H. Design and engineering of intracellular-metabolite-sensing/regulation gene circuits in Saccharomyces cerevisiae. Biotechnol. Bioeng. 2016, 113, 206–215. [Google Scholar] [CrossRef] [Scilit]
  26. Skjoedt, M.L.; Snoek, T.; Kildegaard, K.R.; Arsovska, D.; Eichenberger, M.; Goedecke, T.J.; Rajkumar, A.S.; Zhang, J.; Kristensen, M.; Lehka, B.J.; et al. Engineering prokaryotic transcriptional activators as metabolite biosensors in yeast. Nat. Chem. Biol. 2016, 12, 951–958. [Google Scholar] [CrossRef] [Scilit]
  27. Snoek, T.; Chaberski, E.K.; Ambri, F.; Kol, S.; Bjorn, S.P.; Pang, B.; Barajas, J.F.; Welner, D.H.; Jensen, M.K.; Keasling, J.D. Evolution-guided engineering of small-molecule biosensors. Nucleic Acids Res. 2020, 48, e3. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  28. Stanton, B.C.; Nielsen, A.A.; Tamsir, A.; Clancy, K.; Peterson, T.; Voigt, C.A. Genomic mining of prokaryotic repressors for orthogonal logic gates. Nat. Chem. Biol. 2014, 10, 99–105. [Google Scholar] [CrossRef] [Scilit]
  29. Naseri, G.; Koffas, M.A.G. Application of combinatorial optimization strategies in synthetic biology. Nat. Commun. 2020, 11, 2446. [Google Scholar] [CrossRef] [Scilit]
  30. Belli, G.; Gari, E.; Piedrafita, L.; Aldea, M.; Herrero, E. An activator/repressor dual system allows tight tetracycline-regulated gene expression in budding yeast. Nucleic Acids Res. 1998, 26, 942–947. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  31. Liu, Z.; Salamov, A.; Grigoriev, I.V. Identification and classification of fungal GPCR gene families. J. Fungi 2025, 12, 30. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  32. Pardee, K.; Necakov, A.S.; Krause, H. Nuclear Receptors: Small Molecule Sensors that Coordinate Growth, Metabolism and Reproduction. Subcell. Biochem. 2011, 52, 123–153. [Google Scholar] [CrossRef] [Scilit]
  33. Sellick, C.A.; Reece, R.J. Eukaryotic transcription factors as direct nutrient sensors. Trends Biochem. Sci. 2005, 30, 405–412. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  34. Wang, C.; Zhang, X.; Wu, K.; Liu, S.; Li, X.; Zhu, C.; Xiao, Y.; Fang, Z.; Liu, J. Two Zn(2)Cys(6)-type transcription factors respond to aromatic compounds and regulate the expression of laccases in the white-rot fungus Trametes hirsuta. Appl. Environ. Microbiol. 2024, 90, e0054524. [Google Scholar] [CrossRef] [Scilit]
  35. Zhang, Y.; Cortez, J.D.; Hammer, S.K.; Carrasco-Lopez, C.; Garcia Echauri, S.A.; Wiggins, J.B.; Wang, W.; Avalos, J.L. Biosensor for branched-chain amino acid metabolism in yeast and applications in isobutanol and isopentanol production. Nat. Commun. 2022, 13, 270. [Google Scholar] [CrossRef] [Scilit]
  36. Kerkaert, J.D.; Huberman, L.B. Regulation of nutrient utilization in filamentous fungi. Appl. Microbiol. Biotechnol. 2023, 107, 5873–5898. [Google Scholar] [CrossRef] [Scilit]
  37. Keller, N.P. Fungal secondary metabolism: Regulation, function and drug discovery. Nat. Rev. Microbiol. 2019, 17, 167–180. [Google Scholar] [CrossRef] [Scilit]
  38. Mozsik, L.; Iacovelli, R.; Bovenberg, R.A.L.; Driessen, A.J.M. Transcriptional Activation of Biosynthetic Gene Clusters in Filamentous Fungi. Front. Bioeng. Biotechnol. 2022, 10, 901037. [Google Scholar] [CrossRef] [Scilit]
  39. Gomez, D.; Cubero, B.; Cecchetto, G.; Scazzocchio, C. PrnA, a Zn2Cys6 activator with a unique DNA recognition mode, requires inducer for in vivo binding. Mol. Microbiol. 2002, 44, 585–597. [Google Scholar] [CrossRef] [Scilit]
  40. Schumacher, J. How light affects the life of Botrytis. Fungal Genet. Biol. 2017, 106, 26–41. [Google Scholar] [CrossRef] [Scilit]
  41. Catlett, N.L.; Yoder, O.C.; Turgeon, B.G. Whole-genome analysis of two-component signal transduction genes in fungal pathogens. Eukaryot. Cell 2003, 2, 1151–1161. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  42. Wang, B.; Barahona, M.; Buck, M. Amplification of small molecule-inducible gene expression via tuning of intracellular receptor densities. Nucleic Acids Res. 2015, 43, 1955–1964. [Google Scholar] [CrossRef] [Scilit]
  43. Macheleidt, J.; Mattern, D.J.; Fischer, J.; Netzker, T.; Weber, J.; Schroeckh, V.; Valiante, V.; Brakhage, A.A. Regulation and Role of Fungal Secondary Metabolites. Annu. Rev. Genet. 2016, 50, 371–392. [Google Scholar] [CrossRef] [Scilit]
  44. Banuett, F. Signalling in the yeasts: An informational cascade with links to the filamentous fungi. Microbiol. Mol. Biol. Rev. 1998, 62, 249–274. [Google Scholar] [CrossRef] [Scilit]
  45. Kim, J.H.; Mailloux, L.; Bloor, D.; Tae, H.; Nguyen, H.; McDowell, M.; Padilla, J.; DeWaard, A. Multiple roles for the cytoplasmic C-terminal domains of the yeast cell surface receptors Rgt2 and Snf3 in glucose sensing and signaling. Sci. Rep. 2024, 14, 4055. [Google Scholar] [CrossRef] [Scilit]
  46. Kraakman, L.; Lemaire, K.; Ma, P.; Teunissen, A.W.; Donaton, M.C.; Van Dijck, P.; Winderickx, J.; de Winde, J.H.; Thevelein, J.M. A Saccharomyces cerevisiae G-protein coupled receptor, Gpr1, is specifically required for glucose activation of the cAMP pathway during the transition to growth on glucose. Mol. Microbiol. 1999, 32, 1002–1012. [Google Scholar] [CrossRef] [Scilit]
  47. Yano, K.; Fukasawa, T. Galactose-dependent reversible interaction of Gal3p with Gal80p in the induction pathway of Gal4p-activated genes of Saccharomyces cerevisiae. Proc. Natl. Acad. Sci. USA 1997, 94, 1721–1726. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  48. Loewen, C.J.; Gaspar, M.L.; Jesch, S.A.; Delon, C.; Ktistakis, N.T.; Henry, S.A.; Levine, T.P. Phospholipid metabolism regulated by a transcription factor sensing phosphatidic acid. Science 2004, 304, 1644–1647. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  49. Gaber, R.F.; Ottow, K.; Andersen, H.A.; Kielland-Brandt, M.C. Constitutive and hyperresponsive signaling by mutant forms of Saccharomyces cerevisiae amino acid sensor Ssy1. Eukaryot. Cell 2003, 2, 922–929. [Google Scholar] [CrossRef] [Scilit]
  50. Donaton, M.C.; Holsbeeks, I.; Lagatie, O.; Van Zeebroeck, G.; Crauwels, M.; Winderickx, J.; Thevelein, J.M. The Gap1 general amino acid permease acts as an amino acid sensor for activation of protein kinase A targets in the yeast Saccharomyces cerevisiae. Mol. Microbiol. 2003, 50, 911–929. [Google Scholar] [CrossRef] [Scilit]
  51. Zeng, Q.; Araki, Y.; Noda, T. Pib2 is a cysteine sensor involved in TORC1 activation in Saccharomyces cerevisiae. Cell Rep. 2024, 43, 113599. [Google Scholar] [CrossRef] [Scilit]
  52. Sze, J.Y.; Woontner, M.; Jaehning, J.A.; Kohlhaw, G.B. In vitro transcriptional activation by a metabolic intermediate: Activation by Leu3 depends on alpha-isopropylmalate. Science 1992, 258, 1143–1145. [Google Scholar] [CrossRef] [Scilit]
  53. Lengeler, K.B.; Davidson, R.C.; D’Souza, C.; Harashima, T.; Shen, W.C.; Wang, P.; Pan, X.; Waugh, M.; Heitman, J. Signal transduction cascades regulating fungal development and virulence. Microbiol. Mol. Biol. Rev. 2000, 64, 746–785. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  54. Liu, Y.; Yang, K.; Qin, Q.; Lin, G.; Hu, T.; Xu, Z.; Wang, S. G Protein alpha Subunit GpaB is Required for Asexual Development, Aflatoxin Biosynthesis and Pathogenicity by Regulating cAMP Signaling in Aspergillus flavus. Toxins 2018, 10, 117. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  55. Yang, K.; Qin, Q.; Liu, Y.; Zhang, L.; Liang, L.; Lan, H.; Chen, C.; You, Y.; Zhang, F.; Wang, S. Adenylate Cyclase AcyA Regulates Development, Aflatoxin Biosynthesis and Fungal Virulence in Aspergillus flavus. Front. Cell Infect. Microbiol. 2016, 6, 190. [Google Scholar] [CrossRef] [Scilit]
  56. van Peij, N.N.; Visser, J.; de Graaff, L.H. Isolation and analysis of xlnR, encoding a transcriptional activator co-ordinating xylanolytic expression in Aspergillus niger. Mol. Microbiol. 1998, 27, 131–142. [Google Scholar] [CrossRef] [Scilit]
  57. Battaglia, E.; Hansen, S.F.; Leendertse, A.; Madrid, S.; Mulder, H.; Nikolaev, I.; de Vries, R.P. Regulation of pentose utilisation by AraR, but not XlnR, differs in Aspergillus nidulans and Aspergillus niger. Appl. Microbiol. Biotechnol. 2011, 91, 387–397. [Google Scholar] [CrossRef] [Scilit]
  58. Ogawa, M.; Kobayashi, T.; Koyama, Y. ManR, a novel Zn(II)2Cys6 transcriptional activator, controls the beta-mannan utilization system in Aspergillus oryzae. Fungal Genet. Biol. 2012, 49, 987–995. [Google Scholar] [CrossRef] [Scilit]
  59. Yuan, X.L.; Roubos, J.A.; van den Hondel, C.A.; Ram, A.F. Identification of InuR, a new Zn(II)2Cys6 transcriptional activator involved in the regulation of inulinolytic genes in Aspergillus niger. Mol. Genet. Genom. 2008, 279, 11–26. [Google Scholar] [CrossRef] [Scilit]
  60. Pardo, E.; Orejas, M. The Aspergillus nidulans Zn (II) 2Cys6transcription factor AN5673/RhaR mediates L-rhamnose utilization and the production of α-L-rhamnosidases. Microb. Cell Factories 2014, 13, 161. [Google Scholar] [CrossRef] [Scilit]
  61. Kunitake, E.; Tani, S.; Sumitani, J.; Kawaguchi, T. A novel transcriptional regulator, ClbR, controls the cellobiose- and cellulose-responsive induction of cellulase and xylanase genes regulated by two distinct signaling pathways in Aspergillus aculeatus. Appl. Microbiol. Biotechnol. 2013, 97, 2017–2028. [Google Scholar] [CrossRef] [Scilit]
  62. Alazi, E.; Niu, J.; Kowalczyk, J.E.; Peng, M.; Aguilar Pontes, M.V.; van Kan, J.A.; Visser, J.; de Vries, R.P.; Ram, A.F. The transcriptional activator GaaR of Aspergillus niger is required for release and utilization of d-galacturonic acid from pectin. FEBS Lett. 2016, 590, 1804–1815. [Google Scholar] [CrossRef] [Scilit]
  63. Makita, T.; Katsuyama, Y.; Tani, S.; Suzuki, H.; Kato, N.; Todd, R.B.; Hynes, M.J.; Tsukagoshi, N.; Kato, M.; Kobayashi, T. Inducer-dependent nuclear localization of a Zn(II)(2)Cys(6) transcriptional activator, AmyR, in Aspergillus nidulans. Biosci. Biotechnol. Biochem. 2009, 73, 391–399. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  64. Zhao, Q.; Gao, L.; Xu, N.; Zhang, X.; Qin, Y.; Qu, Y.; Liu, G. An l-fucose-responsive transcription factor cross-regulates the expression of a diverse array of carbohydrate-active enzymes in Trichoderma reesei. PLoS Genet. 2025, 21, e1011815. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  65. Arentshorst, M.; Reijngoud, J.; van Tol, D.J.C.; Reid, I.D.; Arendsen, Y.; Pel, H.J.; van Peij, N.; Visser, J.; Punt, P.J.; Tsang, A.; et al. Utilization of ferulic acid in Aspergillus niger requires the transcription factor FarA and a newly identified Far-like protein (FarD) that lacks the canonical Zn(II)(2)Cys(6) domain. Front. Fungal Biol. 2022, 3, 978845. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  66. Choi, H.; Cho, S.H.; Park, J.H.; Seok, Y.J. Fructose-responsive regulation by FruR in Faecalibacterium prausnitzii for its intestinal colonization. Commun. Biol. 2025, 8, 426. [Google Scholar] [CrossRef] [Scilit]
  67. Mira, N.P.; Henriques, S.F.; Keller, G.; Teixeira, M.C.; Matos, R.G.; Arraiano, C.M.; Winge, D.R.; Sa-Correia, I. Identification of a DNA-binding site for the transcription factor Haa1, required for Saccharomyces cerevisiae response to acetic acid stress. Nucleic Acids Res. 2011, 39, 6896–6907. [Google Scholar] [CrossRef] [Scilit]
  68. Punt, P.J.; Strauss, J.; Smit, R.; Kinghorn, J.R.; van den Hondel, C.A.; Scazzocchio, C. The intergenic region between the divergently transcribed niiA and niaD genes of Aspergillus nidulans contains multiple NirA binding sites which act bidirectionally. Mol. Cell Biol. 1995, 15, 5688–5699. [Google Scholar] [CrossRef] [Scilit]
  69. Keller, S.; Macheleidt, J.; Scherlach, K.; Schmaler-Ripcke, J.; Jacobsen, I.D.; Heinekamp, T.; Brakhage, A.A. Pyomelanin formation in Aspergillus fumigatus requires HmgX and the transcriptional activator HmgR but is dispensable for virulence. PLoS ONE 2011, 6, e26604. [Google Scholar] [CrossRef] [Scilit]
  70. Long, N.; Orasch, T.; Zhang, S.; Gao, L.; Xu, X.; Hortschansky, P.; Ye, J.; Zhang, F.; Xu, K.; Gsaller, F.; et al. The Zn2Cys6-type transcription factor LeuB cross-links regulation of leucine biosynthesis and iron acquisition in Aspergillus fumigatus. PLoS Genet. 2018, 14, e1007762. [Google Scholar] [CrossRef] [Scilit]
  71. Dzikowska, A.; Kacprzak, M.; Tomecki, R.; Koper, M.; Scazzocchio, C.; Weglenski, P. Specific induction and carbon/nitrogen repression of arginine catabolism gene of Aspergillus nidulans—Functional in vivo analysis of the otaA promoter. Fungal Genet. Biol. 2003, 38, 175–186. [Google Scholar] [CrossRef] [Scilit]
  72. Natorff, R.; Sienko, M.; Brzywczy, J.; Paszewski, A. The Aspergillus nidulans metR gene encodes a bZIP protein which activates transcription of sulphur metabolism genes. Mol. Microbiol. 2003, 49, 1081–1094. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  73. Wong Sak Hoi, J.; Dumas, B. Ste12 and Ste12-like proteins, fungal transcription factors regulating development and pathogenicity. Eukaryot. Cell 2010, 9, 480–485. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  74. Kulmburg, P.; Mathieu, M.; Dowzer, C.; Kelly, J.; Felenbok, B. Specific binding sites in the alcR and alcA promoters of the ethanol regulon for the CREA repressor mediating carbon catabolite repression in Aspergillus nidulans. Mol. Microbiol. 1993, 7, 847–857. [Google Scholar] [CrossRef] [Scilit]
  75. Weber, W.; Rimann, M.; Spielmann, M.; Keller, B.; Daoud-El Baba, M.; Aubel, D.; Weber, C.C.; Fussenegger, M. Gas-inducible transgene expression in mammalian cells and mice. Nat. Biotechnol. 2004, 22, 1440–1444. [Google Scholar] [CrossRef] [Scilit]
  76. Ahn, C.H.; Lee, S.; Cho, E.; Kim, H.; Chung, B.; Park, W.; Shin, J.; Oh, K.B. A farnesoic acid-responsive transcription factor, Hot1, regulates yeast-hypha morphogenesis in Candida albicans. FEBS Lett. 2017, 591, 1225–1235. [Google Scholar] [CrossRef] [Scilit]
  77. Hernday, A.D.; Lohse, M.B.; Fordyce, P.M.; Nobile, C.J.; DeRisi, J.L.; Johnson, A.D. Structure of the transcriptional network controlling white-opaque switching in Candida albicans. Mol. Microbiol. 2013, 90, 22–35. [Google Scholar] [CrossRef] [Scilit]
  78. Lee, K.; Hahn, J.S. Interplay of Aro80 and GATA activators in regulation of genes for catabolism of aromatic amino acids in Saccharomyces cerevisiae. Mol. Microbiol. 2013, 88, 1120–1134. [Google Scholar] [CrossRef] [Scilit]
  79. Hall, R.A.; Turner, K.J.; Chaloupka, J.; Cottier, F.; De Sordi, L.; Sanglard, D.; Levin, L.R.; Buck, J.; Muhlschlegel, F.A. The quorum-sensing molecules farnesol/homoserine lactone and dodecanol operate via distinct modes of action in Candida albicans. Eukaryot. Cell 2011, 10, 1034–1042. [Google Scholar] [CrossRef] [Scilit]
  80. Tian, X.; He, G.J.; Hu, P.; Chen, L.; Tao, C.; Cui, Y.L.; Shen, L.; Ke, W.; Xu, H.; Zhao, Y.; et al. Cryptococcus neoformans sexual reproduction is controlled by a quorum sensing peptide. Nat. Microbiol. 2018, 3, 698–707. [Google Scholar] [CrossRef] [Scilit]
  81. Potter, C.J.; Tasic, B.; Russler, E.V.; Liang, L.; Luo, L. The Q system: A repressible binary system for transgene expression, lineage tracing, and mosaic analysis. Cell 2010, 141, 536–548. [Google Scholar] [CrossRef] [Scilit]
  82. Lalwani, M.A.; Zhao, E.M.; Wegner, S.A.; Avalos, J.L. The Neurospora crassa Inducible Q System Enables Simultaneous Optogenetic Amplification and Inversion in Saccharomyces cerevisiae for Bidirectional Control of Gene Expression. ACS Synth. Biol. 2021, 10, 2060–2075. [Google Scholar] [CrossRef] [Scilit]
  83. Li, L.; Borkovich, K.A. GPR-4 is a predicted G-protein-coupled receptor required for carbon source-dependent asexual growth and development in Neurospora crassa. Eukaryot. Cell 2006, 5, 1287–1300. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  84. de Souza, W.R.; Morais, E.R.; Krohn, N.G.; Savoldi, M.; Goldman, M.H.; Rodrigues, F.; Caldana, C.; Semelka, C.T.; Tikunov, A.P.; Macdonald, J.M.; et al. Identification of metabolic pathways influenced by the G-protein coupled receptors GprB and GprD in Aspergillus nidulans. PLoS ONE 2013, 8, e62088. [Google Scholar] [CrossRef] [Scilit]
  85. Welton, R.M.; Hoffman, C.S. Glucose monitoring in fission yeast via the Gpa2 galpha, the git5 Gbeta and the git3 putative glucose receptor. Genetics 2000, 156, 513–521. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  86. Xue, C.; Bahn, Y.S.; Cox, G.M.; Heitman, J. G protein-coupled receptor Gpr4 senses amino acids and activates the cAMP-PKA pathway in Cryptococcus neoformans. Mol. Biol. Cell 2006, 17, 667–679. [Google Scholar] [CrossRef] [Scilit]
  87. Jung, M.G.; Kim, S.S.; Yu, J.H.; Shin, K.S. Characterization of gprK Encoding a Putative Hybrid G-Protein-Coupled Receptor in Aspergillus fumigatus. PLoS ONE 2016, 11, e0161312. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  88. Cabrera, I.E.; Pacentine, I.V.; Lim, A.; Guerrero, N.; Krystofova, S.; Li, L.; Michkov, A.V.; Servin, J.A.; Ahrendt, S.R.; Carrillo, A.J.; et al. Global Analysis of Predicted G Protein-Coupled Receptor Genes in the Filamentous Fungus, Neurospora crassa. G3 Genes Genomes Genet. 2015, 5, 2729–2743. [Google Scholar] [CrossRef] [Scilit]
  89. Brown, N.A.; Dos Reis, T.F.; Ries, L.N.; Caldana, C.; Mah, J.H.; Yu, J.H.; Macdonald, J.M.; Goldman, G.H. G-protein coupled receptor-mediated nutrient sensing and developmental control in Aspergillus nidulans. Mol. Microbiol. 2015, 98, 420–439. [Google Scholar] [CrossRef] [Scilit]
  90. Chung, K.S.; Won, M.; Lee, S.B.; Jang, Y.J.; Hoe, K.L.; Kim, D.U.; Lee, J.W.; Kim, K.W.; Yoo, H.S. Isolation of a novel gene from Schizosaccharomyces pombe: Stm1+ encoding a seven-transmembrane loop protein that may couple with the heterotrimeric Galpha 2 protein, Gpa2. J. Biol. Chem. 2001, 276, 40190–40201. [Google Scholar] [CrossRef] [Scilit]
  91. Affeldt, K.J.; Carrig, J.; Amare, M.; Keller, N.P. Global survey of canonical Aspergillus flavus G protein-coupled receptors. mBio 2014, 5, e01501–e01514. [Google Scholar] [CrossRef] [Scilit]
  92. Blumer, K.; Reneke, J.; Thorner, J. The STE2 gene product is the ligand-binding component of the alpha-factor receptor of Saccharomyces cerevisiae. J. Biol. Chem. 1988, 263, 10836–10842. [Google Scholar] [CrossRef] [Scilit]
  93. Seo, J.A.; Han, K.H.; Yu, J.H. The gprA and gprB genes encode putative G protein-coupled receptors required for self-fertilization in Aspergillus nidulans. Mol. Microbiol. 2004, 53, 1611–1623. [Google Scholar] [CrossRef] [Scilit]
  94. Kim, H.; Borkovich, K.A. A pheromone receptor gene, pre-1, is essential for mating type-specific directional growth and fusion of trichogynes and female fertility in Neurospora crassa. Mol. Microbiol. 2004, 52, 1781–1798. [Google Scholar] [CrossRef] [Scilit]
  95. Xue, C.; Wang, Y.; Hsueh, Y.P. Assessment of constitutive activity of a G protein-coupled receptor, CPR2, in Cryptococcus neoformans by heterologous and homologous methods. Methods Enzymol. 2010, 484, 397–412. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  96. McClelland, C.M.; Fu, J.; Woodlee, G.L.; Seymour, T.S.; Wickes, B.L. Isolation and characterization of the Cryptococcus neoformans MATa pheromone gene. Genetics 2002, 160, 935–947. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  97. Nakayama, N.; Miyajima, A.; Arai, K. Nucleotide sequences of STE2 and STE3, cell type-specific sterile genes from Saccharomyces cerevisiae. EMBO J. 1985, 4, 2643–2648. [Google Scholar] [CrossRef] [Scilit]
  98. Kim, H.; Borkovich, K.A. Pheromones are essential for male fertility and sufficient to direct chemotropic polarized growth of trichogynes during mating in Neurospora crassa. Eukaryot. Cell 2006, 5, 544–554. [Google Scholar] [CrossRef] [Scilit]
  99. Moore, T.D.; Edman, J.C. The alpha-mating type locus of Cryptococcus neoformans contains a peptide pheromone gene. Mol. Cell Biol. 1993, 13, 1962–1970. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  100. Lara-Martinez, D.; Tristan-Flores, F.E.; Cervantes-Montelongo, J.A.; Silva-Martinez, G.A. Fungal Stress Responses and the Importance of GPCRs. J. Fungi 2025, 11, 213. [Google Scholar] [CrossRef] [Scilit]
  101. Wang, Z.; Wang, J.; Li, N.; Li, J.; Trail, F.; Dunlap, J.C.; Townsend, J.P. Light sensing by opsins and fungal ecology: NOP-1 modulates entry into sexual reproduction in response to environmental cues. Mol. Ecol. 2018, 27, 216–232. [Google Scholar] [CrossRef] [Scilit]
  102. Garcia-Martinez, J.; Brunk, M.; Avalos, J.; Terpitz, U. The CarO rhodopsin of the fungus Fusarium fujikuroi is a light-driven proton pump that retards spore germination. Sci. Rep. 2015, 5, 7798. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  103. Bieszke, J.A. Characterization of the NOP-1 Opsin Photoreceptor in the Filamentous Fungus Neurospora crassa. Ph.D. Thesis, The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, TX, USA, 2002. [Google Scholar]
  104. Herranz, S.; Rodriguez, J.M.; Bussink, H.J.; Sanchez-Ferrero, J.C.; Arst, H.N., Jr.; Penalva, M.A.; Vincent, O. Arrestin-related proteins mediate pH signaling in fungi. Proc. Natl. Acad. Sci. USA 2005, 102, 12141–12146. [Google Scholar] [CrossRef] [Scilit]
  105. Gao, J.; Xu, X.; Huang, K.; Liang, Z. Fungal G-Protein-Coupled Receptors: A Promising Mediator of the Impact of Extracellular Signals on Biosynthesis of Ochratoxin A. Front. Microbiol. 2021, 12, 631392. [Google Scholar] [CrossRef] [Scilit]
  106. Gehrke, A.; Heinekamp, T.; Jacobsen, I.D.; Brakhage, A.A. Heptahelical receptors GprC and GprD of Aspergillus fumigatus Are essential regulators of colony growth, hyphal morphogenesis, and virulence. Appl. Environ. Microbiol. 2010, 76, 3989–3998. [Google Scholar] [CrossRef] [Scilit]
  107. Kuo, C.Y.; Tay, R.J.; Lin, H.C.; Juan, S.C.; Vidal-Diez de Ulzurrun, G.; Chang, Y.C.; Hoki, J.; Schroeder, F.C.; Hsueh, Y.P. The nematode-trapping fungus Arthrobotrys oligospora detects prey pheromones via G protein-coupled receptors. Nat. Microbiol. 2024, 9, 1738–1751. [Google Scholar] [CrossRef] [Scilit]
  108. Hu, X.; Hoffmann, D.S.; Wang, M.; Schuhmacher, L.; Stroe, M.C.; Schreckenberger, B.; Elstner, M.; Fischer, R. GprC of the nematode-trapping fungus Arthrobotrys flagrans activates mitochondria and reprograms fungal cells for nematode hunting. Nat. Microbiol. 2024, 9, 1752–1763. [Google Scholar] [CrossRef] [Scilit]
  109. Ballou, E.R.; Avelar, G.M.; Childers, D.S.; Mackie, J.; Bain, J.M.; Wagener, J.; Kastora, S.L.; Panea, M.D.; Hardison, S.E.; Walker, L.A.; et al. Lactate signalling regulates fungal beta-glucan masking and immune evasion. Nat. Microbiol. 2016, 2, 16238. [Google Scholar] [CrossRef] [Scilit]
  110. Affeldt, K.J.; Brodhagen, M.; Keller, N.P. Aspergillus oxylipin signaling and quorum sensing pathways depend on g protein-coupled receptors. Toxins 2012, 4, 695–717. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  111. Kulkarni, R.D.; Thon, M.R.; Pan, H.; Dean, R.A. Novel G-protein-coupled receptor-like proteins in the plant pathogenic fungus Magnaporthe grisea. Genome Biol. 2005, 6, R24. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  112. Brown, N.A.; Schrevens, S.; van Dijck, P.; Goldman, G.H. Fungal G-protein-coupled receptors: Mediators of pathogenesis and targets for disease control. Nat. Microbiol. 2018, 3, 402–414. [Google Scholar] [CrossRef] [Scilit]
  113. Shang, J.; Shang, Y.; Tang, G.; Wang, C. Identification of a key G-protein coupled receptor in mediating appressorium formation and fungal virulence against insects. Sci. China Life Sci. 2021, 64, 466–477. [Google Scholar] [CrossRef] [Scilit]
  114. Martinez-Soto, D.; Ruiz-Herrera, J. Functional analysis of the MAPK pathways in fungi. Rev. Iberoam. Micol. 2017, 34, 192–202. [Google Scholar] [CrossRef] [Scilit]
  115. Cornet, M.; Gaillardin, C. pH signaling in human fungal pathogens: A new target for antifungal strategies. Eukaryot. Cell 2014, 13, 342–352. [Google Scholar] [CrossRef] [Scilit]
  116. Tilburn, J.; Sarkar, S.; Widdick, D.A.; Espeso, E.A.; Orejas, M.; Mungroo, J.; Penalva, M.A.; Arst, H.N., Jr. The Aspergillus PacC zinc finger transcription factor mediates regulation of both acid- and alkaline-expressed genes by ambient pH. EMBO J. 1995, 14, 779–790. [Google Scholar] [CrossRef] [Scilit]
  117. Zadrąg-Tęcza, R.; Maślanka, R.; Bednarska, S.; Kwolek-Mirek, M. Response mechanisms to oxidative stress in yeast and filamentous fungi. Stress Response Mech. Fungi Theor. Pract. Asp. 2018, 1–34. [Google Scholar] [CrossRef] [Scilit]
  118. Yaakoub, H.; Mina, S.; Calenda, A.; Bouchara, J.P.; Papon, N. Oxidative stress response pathways in fungi. Cell Mol. Life Sci. 2022, 79, 333. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  119. Yu, Z.; Fischer, R. Light sensing and responses in fungi. Nat. Rev. Microbiol. 2019, 17, 25–36. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  120. Liu, H.; Yu, X.; Li, K.; Klejnot, J.; Yang, H.; Lisiero, D.; Lin, C. Photoexcited CRY2 interacts with CIB1 to regulate transcription and floral initiation in Arabidopsis. Science 2008, 322, 1535–1539. [Google Scholar] [CrossRef] [Scilit]
  121. Avelar, G.M.; Schumacher, R.I.; Zaini, P.A.; Leonard, G.; Richards, T.A.; Gomes, S.L. A rhodopsin-guanylyl cyclase gene fusion functions in visual perception in a fungus. Curr. Biol. 2014, 24, 1234–1240. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  122. Mishra, R.; Minc, N.; Peter, M. Cells under pressure: How yeast cells respond to mechanical forces. Trends Microbiol. 2022, 30, 495–510. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  123. Froehlich, A.C.; Liu, Y.; Loros, J.J.; Dunlap, J.C. White Collar-1, a circadian blue light photoreceptor, binding to the frequency promoter. Science 2002, 297, 815–819. [Google Scholar] [CrossRef] [Scilit]
  124. Guerrero, M.; Ruiz, C.; Romero, A.; Robeson, L.; Ruiz, D.; Salinas, F. The N-Terminal Region of the BcWCL1 Photoreceptor Is Necessary for Self-Dimerization and Transcriptional Activation upon Light Stimulation in Yeast. Int. J. Mol. Sci. 2023, 24, 11874. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  125. Tang, Y.; Zhu, P.; Lu, Z.; Qu, Y.; Huang, L.; Zheng, N.; Wang, Y.; Nie, H.; Jiang, Y.; Xu, L. The Photoreceptor Components FaWC1 and FaWC2 of Fusarium asiaticum Cooperatively Regulate Light Responses but Play Independent Roles in Virulence Expression. Microorganisms 2020, 8, 365. [Google Scholar] [CrossRef] [Scilit]
  126. Qi, Y.; Sun, X.; Ma, L.; Wen, Q.; Qiu, L.; Shen, J. Identification of two Pleurotus ostreatus blue light receptor genes (PoWC-1 and PoWC-2) and in vivo confirmation of complex PoWC-12 formation through yeast two hybrid system. Fungal Biol. 2020, 124, 8–14. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  127. Krobanan, K.; Liang, S.W.; Chiu, H.C.; Shen, W.C. The Blue-Light Photoreceptor Sfwc-1 Gene Regulates the Phototropic Response and Fruiting-Body Development in the Homothallic Ascomycete Sordaria fimicola. Appl. Environ. Microbiol. 2019, 85, e02206-18. [Google Scholar] [CrossRef] [Scilit]
  128. Casas-Flores, S.; Rios-Momberg, M.; Bibbins, M.; Ponce-Noyola, P.; Herrera-Estrella, A. BLR-1 and BLR-2, key regulatory elements of photoconidiation and mycelial growth in Trichoderma atroviride. Microbiology 2004, 150, 3561–3569. [Google Scholar] [CrossRef] [Scilit]
  129. Purschwitz, J.; Muller, S.; Kastner, C.; Schoser, M.; Haas, H.; Espeso, E.A.; Atoui, A.; Calvo, A.M.; Fischer, R. Functional and physical interaction of blue- and red-light sensors in Aspergillus nidulans. Curr. Biol. 2008, 18, 255–259. [Google Scholar] [CrossRef] [Scilit]
  130. Schuhmacher, L.; Heck, S.; Pitz, M.; Mathey, E.; Lamparter, T.; Blumhofer, A.; Leister, K.; Fischer, R. The LOV-domain blue-light receptor LreA of the fungus Alternaria alternata binds predominantly FAD as chromophore and acts as a light and temperature sensor. J. Biol. Chem. 2024, 300, 107238. [Google Scholar] [CrossRef] [Scilit]
  131. Sanz, C.; Rodriguez-Romero, J.; Idnurm, A.; Christie, J.M.; Heitman, J.; Corrochano, L.M.; Eslava, A.P. Phycomyces MADB interacts with MADA to form the primary photoreceptor complex for fungal phototropism. Proc. Natl. Acad. Sci. USA 2009, 106, 7095–7100. [Google Scholar] [CrossRef] [Scilit]
  132. Schwerdtfeger, C.; Linden, H. VIVID is a flavoprotein and serves as a fungal blue light photoreceptor for photoadaptation. EMBO J. 2003, 22, 4846–4855. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  133. Foley, B.J.; Stutts, H.; Schmitt, S.L.; Lokhandwala, J.; Nagar, A.; Zoltowski, B.D. Characterization of a Vivid Homolog in Botrytis cinerea. Photochem. Photobiol. 2018, 94, 985–993. [Google Scholar] [CrossRef] [Scilit]
  134. Schmoll, M.; Franchi, L.; Kubicek, C.P. Envoy, a PAS/LOV domain protein of Hypocrea jecorina (Anamorph Trichoderma reesei), modulates cellulase gene transcription in response to light. Eukaryot. Cell 2005, 4, 1998–2007. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  135. Losi, A.; Gartner, W. A light life together: Photosensing in the plant microbiota. Photochem. Photobiol. Sci. 2021, 20, 451–473. [Google Scholar] [CrossRef] [Scilit]
  136. Bieszke, J.A.; Braun, E.L.; Bean, L.E.; Kang, S.; Natvig, D.O.; Borkovich, K.A. The nop-1 gene of Neurospora crassa encodes a seven transmembrane helix retinal-binding protein homologous to archaeal rhodopsins. Proc. Natl. Acad. Sci. USA 1999, 96, 8034–8039. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  137. Panzer, S.; Brych, A.; Batschauer, A.; Terpitz, U. Opsin 1 and Opsin 2 of the Corn Smut Fungus Ustilago maydis Are Green Light-Driven Proton Pumps. Front. Microbiol. 2019, 10, 735. [Google Scholar] [CrossRef] [Scilit]
  138. Ahrendt, S.R.; Medina, E.M.; Chang, C.A.; Stajich, J.E. Exploring the binding properties and structural stability of an opsin in the chytrid Spizellomyces punctatus using comparative and molecular modeling. PeerJ 2017, 5, e3206. [Google Scholar] [CrossRef] [Scilit]
  139. Kihara, J.; Tanaka, N.; Ueno, M.; Arase, S. Cloning and expression analysis of two opsin-like genes in the phytopathogenic fungus Bipolaris oryzae. FEMS Microbiol. Lett. 2009, 295, 289–294. [Google Scholar] [CrossRef] [Scilit]
  140. Fan, Y.; Solomon, P.; Oliver, R.P.; Brown, L.S. Photochemical characterization of a novel fungal rhodopsin from Phaeosphaeria nodorum. Biochim. Biophys. Acta 2011, 1807, 1457–1466. [Google Scholar] [CrossRef] [Scilit]
  141. Panzer, S.; Zhang, C.; Konte, T.; Brauer, C.; Diemar, A.; Yogendran, P.; Yu-Strzelczyk, J.; Nagel, G.; Gao, S.; Terpitz, U. Modified Rhodopsins From Aureobasidium pullulans Excel With Very High Proton-Transport Rates. Front. Mol. Biosci. 2021, 8, 750528. [Google Scholar] [CrossRef] [Scilit]
  142. Adam, A.; Deimel, S.; Pardo-Medina, J.; García-Martínez, J.; Konte, T.; Limón, M.C.; Avalos, J.; Terpitz, U. Protein activity of the Fusarium fujikuroi rhodopsins CarO and OpsA and their relation to fungus–plant interaction. Int. J. Mol. Sci. 2018, 19, 215. [Google Scholar] [CrossRef] [Scilit]
  143. Avelar, G.M.; Glaser, T.; Leonard, G.; Richards, T.A.; Ulrich, H.; Gomes, S.L. A Cyclic GMP-Dependent K+ Channel in the Blastocladiomycete Fungus Blastocladiella emersonii. Eukaryot. Cell 2015, 14, 958–963. [Google Scholar] [CrossRef] [Scilit]
  144. Broser, M.; Spreen, A.; Konold, P.E.; Schiewer, E.; Adam, S.; Borin, V.; Schapiro, I.; Seifert, R.; Kennis, J.T.M.; Bernal Sierra, Y.A.; et al. NeoR, a near-infrared absorbing rhodopsin. Nat. Commun. 2020, 11, 5682. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  145. Froehlich, A.C.; Chen, C.H.; Belden, W.J.; Madeti, C.; Roenneberg, T.; Merrow, M.; Loros, J.J.; Dunlap, J.C. Genetic and molecular characterization of a cryptochrome from the filamentous fungus Neurospora crassa. Eukaryot. Cell 2010, 9, 738–750. [Google Scholar] [CrossRef] [Scilit]
  146. Garcia-Esquivel, M.; Esquivel-Naranjo, E.U.; Hernandez-Onate, M.A.; Ibarra-Laclette, E.; Herrera-Estrella, A. The Trichoderma atroviride cryptochrome/photolyase genes regulate the expression of blr1-independent genes both in red and blue light. Fungal Biol. 2016, 120, 500–512. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  147. Guzman-Moreno, J.; Flores-Martinez, A.; Brieba, L.G.; Herrera-Estrella, A. The Trichoderma reesei Cry1 protein is a member of the cryptochrome/photolyase family with 6–4 photoproduct repair activity. PLoS ONE 2014, 9, e100625. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  148. Bayram, O.; Biesemann, C.; Krappmann, S.; Galland, P.; Braus, G.H. More than a repair enzyme: Aspergillus nidulans photolyase-like CryA is a regulator of sexual development. Mol. Biol. Cell 2008, 19, 3254–3262. [Google Scholar] [CrossRef] [Scilit]
  149. Castrillo, M.; Garcia-Martinez, J.; Avalos, J. Light-dependent functions of the Fusarium fujikuroi CryD DASH cryptochrome in development and secondary metabolism. Appl. Environ. Microbiol. 2013, 79, 2777–2788. [Google Scholar] [CrossRef] [Scilit]
  150. Cohrs, K.C.; Schumacher, J. The Two Cryptochrome/Photolyase Family Proteins Fulfill Distinct Roles in DNA Photorepair and Regulation of Conidiation in the Gray Mold Fungus Botrytis cinerea. Appl. Environ. Microbiol. 2017, 83, e00812-17. [Google Scholar] [CrossRef] [Scilit]
  151. Tagua, V.G.; Pausch, M.; Eckel, M.; Gutierrez, G.; Miralles-Duran, A.; Sanz, C.; Eslava, A.P.; Pokorny, R.; Corrochano, L.M.; Batschauer, A. Fungal cryptochrome with DNA repair activity reveals an early stage in cryptochrome evolution. Proc. Natl. Acad. Sci. USA 2015, 112, 15130–15135. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  152. Bonomi, H.R.; Toum, L.; Sycz, G.; Sieira, R.; Toscani, A.M.; Gudesblat, G.E.; Leskow, F.C.; Goldbaum, F.A.; Vojnov, A.A.; Malamud, F. Xanthomonas campestris attenuates virulence by sensing light through a bacteriophytochrome photoreceptor. EMBO Rep. 2016, 17, 1565–1577. [Google Scholar] [CrossRef] [Scilit]
  153. Chen, H.; Li, D.; Cai, Y.; Wu, L.F.; Song, T. Bacteriophytochrome from Magnetospirillum magneticum affects phototactic behavior in response to light. FEMS Microbiol. Lett. 2020, 367, fnaa142. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  154. Xue, P.; El Kurdi, A.; Kohler, A.; Ma, H.; Kaeser, G.; Ali, A.; Fischer, R.; Krauss, N.; Lamparter, T. Evidence for weak interaction between phytochromes Agp1 and Agp2 from Agrobacterium fabrum. FEBS Lett. 2019, 593, 926–941. [Google Scholar] [CrossRef] [Scilit]
  155. Blumenstein, A.; Vienken, K.; Tasler, R.; Purschwitz, J.; Veith, D.; Frankenberg-Dinkel, N.; Fischer, R. The Aspergillus nidulans phytochrome FphA represses sexual development in red light. Curr. Biol. 2005, 15, 1833–1838. [Google Scholar] [CrossRef] [Scilit]
  156. Igbalajobi, O.; Yu, Z.; Fischer, R. Red- and Blue-Light Sensing in the Plant Pathogen Alternaria alternata Depends on Phytochrome and the White-Collar Protein LreA. mBio 2019, 10, e00371-19. [Google Scholar] [CrossRef] [Scilit]
  157. Sanchez-Arreguin, J.A.; Cabrera-Ponce, J.L.; Leon-Ramirez, C.G.; Camargo-Escalante, M.O.; Ruiz-Herrera, J. Analysis of the photoreceptors involved in the light-depending basidiocarp formation in Ustilago maydis. Arch. Microbiol. 2020, 202, 93–103. [Google Scholar] [CrossRef] [Scilit]
  158. Froehlich, A.C.; Noh, B.; Vierstra, R.D.; Loros, J.; Dunlap, J.C. Genetic and molecular analysis of phytochromes from the filamentous fungus Neurospora crassa. Eukaryot. Cell 2005, 4, 2140–2152. [Google Scholar] [CrossRef] [Scilit]
  159. Liu, F.; Guo, L.; Luo, Y.; Li, J.; Zhou, Y.; Wang, J.; Huang, X.; Tan, X.; Fu, M.; Yu, B.; et al. Peroxiredoxin PrxA and thioredoxin TrxA mediate the redox signal to the transcription factor NapA in the fungus Aspergillus nidulans. Int. J. Biol. Macromol. 2025, 310, 143434. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  160. Balazs, A.; Pocsi, I.; Hamari, Z.; Leiter, E.; Emri, T.; Miskei, M.; Olah, J.; Toth, V.; Hegedus, N.; Prade, R.A.; et al. AtfA bZIP-type transcription factor regulates oxidative and osmotic stress responses in Aspergillus nidulans. Mol. Genet. Genom. 2010, 283, 289–303. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  161. Bastakis, E.; Gerke, J.; Ozkan, S.; Harting, R.; Lienard, T.; Sasse, C.; Xylakis, E.S.; Aden, M.; Strohdiek, A.; Heinrich, G.; et al. Molecular circuit between Aspergillus nidulans transcription factors MsnA and VelB to coordinate fungal stress and developmental responses. PLoS Genet. 2025, 21, e1011578. [Google Scholar] [CrossRef] [Scilit]
  162. Hagiwara, D.; Asano, Y.; Marui, J.; Furukawa, K.; Kanamaru, K.; Kato, M.; Abe, K.; Kobayashi, T.; Yamashino, T.; Mizuno, T. The SskA and SrrA response regulators are implicated in oxidative stress responses of hyphae and asexual spores in the phosphorelay signaling network of Aspergillus nidulans. Biosci. Biotechnol. Biochem. 2007, 71, 1003–1014. [Google Scholar] [CrossRef] [Scilit]
  163. Chung, D.; Barker, B.M.; Carey, C.C.; Merriman, B.; Werner, E.R.; Lechner, B.E.; Dhingra, S.; Cheng, C.; Xu, W.; Blosser, S.J.; et al. ChIP-seq and in vivo transcriptome analyses of the Aspergillus fumigatus SREBP SrbA reveals a new regulator of the fungal hypoxia response and virulence. PLoS Pathog. 2014, 10, e1004487. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  164. Vaknin, Y.; Hillmann, F.; Iannitti, R.; Ben Baruch, N.; Sandovsky-Losica, H.; Shadkchan, Y.; Romani, L.; Brakhage, A.; Kniemeyer, O.; Osherov, N. Identification and Characterization of a Novel Aspergillus fumigatus Rhomboid Family Putative Protease, RbdA, Involved in Hypoxia Sensing and Virulence. Infect. Immun. 2016, 84, 1866–1878. [Google Scholar] [CrossRef] [Scilit]
  165. Veri, A.O.; Robbins, N.; Cowen, L.E. Regulation of the heat shock transcription factor Hsf1 in fungi: Implications for temperature-dependent virulence traits. FEMS Yeast Res. 2018, 18, foy041. [Google Scholar] [CrossRef] [Scilit]
  166. Zhou, J.; Long, H.; Guo, Y.; Lu, J.; Wang, N.; Liu, H.; Zhou, X.; Cai, M. Glutamate-related nitrogen metabolism regulates cold-adaptive synthesis of red pigment in polar fungus Geomyces sp. WNF-15A. J. Biotechnol. 2025, 404, 121–131. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  167. Hagiwara, D.; Kondo, A.; Fujioka, T.; Abe, K. Functional analysis of C2H2 zinc finger transcription factor CrzA involved in calcium signaling in Aspergillus nidulans. Curr. Genet. 2008, 54, 325–338. [Google Scholar] [CrossRef] [Scilit]
  168. Cai, Z.; Du, W.; Zeng, Q.; Long, N.; Dai, C.; Lu, L. Cu-sensing transcription factor Mac1 coordinates with the Ctr transporter family to regulate Cu acquisition and virulence in Aspergillus fumigatus. Fungal Genet. Biol. 2017, 107, 31–43. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  169. Antsotegi-Uskola, M.; Markina-Inarrairaegui, A.; Ugalde, U. Copper Resistance in Aspergillus nidulans Relies on the P(I)-Type ATPase CrpA, Regulated by the Transcription Factor AceA. Front. Microbiol. 2017, 8, 912. [Google Scholar] [CrossRef] [Scilit]
  170. Jung, U.S.; Sobering, A.K.; Romeo, M.J.; Levin, D.E. Regulation of the yeast Rlm1 transcription factor by the Mpk1 cell wall integrity MAP kinase. Mol. Microbiol. 2002, 46, 781–789. [Google Scholar] [CrossRef] [Scilit]
  171. Xu, C.; Zhang, Y.; Zhang, C.; Chen, L.; Yin, Y.; Chen, Y.; Liu, Z.; Ma, Z. The Mgv1-Rlm1 axis orchestrates SAGA and SWI/SNF complexes at target promoters. Nucleic Acids Res. 2025, 53, gkaf653. [Google Scholar] [CrossRef] [Scilit]
  172. Ries, L.N.A.; Rocha, M.C.; de Castro, P.A.; Silva-Rocha, R.; Silva, R.N.; Freitas, F.Z.; de Assis, L.J.; Bertolini, M.C.; Malavazi, I.; Goldman, G.H. The Aspergillus fumigatus CrzA Transcription Factor Activates Chitin Synthase Gene Expression during the Caspofungin Paradoxical Effect. mBio 2017, 8, e00705-17. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  173. Mendoza-Martinez, A.E.; Cano-Dominguez, N.; Aguirre, J. Yap1 homologs mediate more than the redox regulation of the antioxidant response in filamentous fungi. Fungal Biol. 2020, 124, 253–262. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  174. Peng, S.; Hu, L.; Ge, W.; Deng, J.; Yao, L.; Li, H.; Xu, D.; Mo, H. ChIP-Seq Analysis of AtfA Interactions in Aspergillus flavus Reveals Its Involvement in Aflatoxin Metabolism and Virulence Under Oxidative Stress. Int. J. Mol. Sci. 2024, 25, 12213. [Google Scholar] [CrossRef] [Scilit]
  175. Leach, M.D.; Farrer, R.A.; Tan, K.; Miao, Z.; Walker, L.A.; Cuomo, C.A.; Wheeler, R.T.; Brown, A.J.; Wong, K.H.; Cowen, L.E. Hsf1 and Hsp90 orchestrate temperature-dependent global transcriptional remodelling and chromatin architecture in Candida albicans. Nat. Commun. 2016, 7, 11704. [Google Scholar] [CrossRef] [Scilit]
  176. Martins-Santana, L.; Paula, R.G.; Silva, A.G.; Lopes, D.C.B.; Silva, R.D.N.; Silva-Rocha, R. CRZ1 regulator and calcium cooperatively modulate holocellulases gene expression in Trichoderma reesei QM6a. Genet. Mol. Biol. 2020, 43, e20190244. [Google Scholar] [CrossRef] [Scilit]
  177. de Castro, P.A.; Chen, C.; de Almeida, R.S.; Freitas, F.Z.; Bertolini, M.C.; Morais, E.R.; Brown, N.A.; Ramalho, L.N.; Hagiwara, D.; Mitchell, T.K.; et al. ChIP-seq reveals a role for CrzA in the Aspergillus fumigatus high-osmolarity glycerol response (HOG) signalling pathway. Mol. Microbiol. 2014, 94, 655–674. [Google Scholar] [CrossRef] [Scilit]
  178. Xu, H.; Fang, T.; Omran, R.P.; Whiteway, M.; Jiang, L. RNA sequencing reveals an additional Crz1-binding motif in promoters of its target genes in the human fungal pathogen Candida albicans. Cell Commun. Signal 2020, 18, 1. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  179. Moreno, M.A.; Ibrahim-Granet, O.; Vicentefranqueira, R.; Amich, J.; Ave, P.; Leal, F.; Latge, J.P.; Calera, J.A. The regulation of zinc homeostasis by the ZafA transcriptional activator is essential for Aspergillus fumigatus virulence. Mol. Microbiol. 2007, 64, 1182–1197. [Google Scholar] [CrossRef] [Scilit]
  180. Singhal, V.; Rathore, V. Effects of Zn2+ and Cu2+ on growth, lignin degradation and ligninolytic enzymes in Phanerochaete chrysosporium. World J. Microbiol. Biotechnol. 2001, 17, 235–240. [Google Scholar] [CrossRef] [Scilit]
  181. Shaw, W.M.; Yamauchi, H.; Mead, J.; Gowers, G.F.; Bell, D.J.; Oling, D.; Larsson, N.; Wigglesworth, M.; Ladds, G.; Ellis, T. Engineering a Model Cell for Rational Tuning of GPCR Signaling. Cell 2019, 177, 782–796.e27. [Google Scholar] [CrossRef] [Scilit]
  182. Ostrov, N.; Jimenez, M.; Billerbeck, S.; Brisbois, J.; Matragrano, J.; Ager, A.; Cornish, V.W. A modular yeast biosensor for low-cost point-of-care pathogen detection. Sci. Adv. 2017, 3, e1603221. [Google Scholar] [CrossRef] [Scilit]
  183. Fan, C.; He, N.; Yuan, J. Cascaded amplifying circuit enables sensitive detection of fungal pathogens. Biosens. Bioelectron. 2024, 250, 116058. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  184. Billerbeck, S.; Brisbois, J.; Agmon, N.; Jimenez, M.; Temple, J.; Shen, M.; Boeke, J.D.; Cornish, V.W. A scalable peptide-GPCR language for engineering multicellular communication. Nat. Commun. 2018, 9, 5057. [Google Scholar] [CrossRef] [Scilit]
  185. Wang, X.; Chen, X.; Yang, Y. Spatiotemporal control of gene expression by a light-switchable transgene system. Nat. Methods 2012, 9, 266–269. [Google Scholar] [CrossRef] [Scilit]
  186. Xu, X.; Du, Z.; Liu, R.; Li, T.; Zhao, Y.; Chen, X.; Yang, Y. A Single-Component Optogenetic System Allows Stringent Switch of Gene Expression in Yeast Cells. ACS Synth. Biol. 2018, 7, 2045–2053. [Google Scholar] [CrossRef] [Scilit]
  187. Kawano, F.; Suzuki, H.; Furuya, A.; Sato, M. Engineered pairs of distinct photoswitches for optogenetic control of cellular proteins. Nat. Commun. 2015, 6, 6256. [Google Scholar] [CrossRef] [Scilit]
  188. Salinas, F.; Rojas, V.; Delgado, V.; Lopez, J.; Agosin, E.; Larrondo, L.F. Fungal Light-Oxygen-Voltage Domains for Optogenetic Control of Gene Expression and Flocculation in Yeast. mBio 2018, 9, e00626-18. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  189. Rojas, V.; Larrondo, L.F. Coupling Cell Communication and Optogenetics: Implementation of a Light-Inducible Intercellular System in Yeast. ACS Synth. Biol. 2023, 12, 71–82. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  190. Rojas, V.; Salinas, F.; Romero, A.; Larrondo, L.F.; Canessa, P. Interactions between Core Elements of the Botrytis cinerea Circadian Clock Are Modulated by Light and Different Protein Domains. J. Fungi 2022, 8, 486. [Google Scholar] [CrossRef] [Scilit]
  191. Glantz, S.T.; Berlew, E.E.; Jaber, Z.; Schuster, B.S.; Gardner, K.H.; Chow, B.Y. Directly light-regulated binding of RGS-LOV photoreceptors to anionic membrane phospholipids. Proc. Natl. Acad. Sci. USA 2018, 115, E7720–E7727. [Google Scholar] [CrossRef] [Scilit]
  192. Pal, A.A.; Benman, W.; Mumford, T.R.; Huang, Z.; Chow, B.Y.; Bugaj, L.J. Optogenetic clustering and membrane translocation of the BcLOV4 photoreceptor. Proc. Natl. Acad. Sci. USA 2023, 120, e2221615120. [Google Scholar] [CrossRef] [Scilit]
  193. Krueger, D.; Izquierdo, E.; Viswanathan, R.; Hartmann, J.; Pallares Cartes, C.; De Renzis, S. Principles and applications of optogenetics in developmental biology. Development 2019, 146, dev175067. [Google Scholar] [CrossRef] [Scilit]
  194. Kapolka, N.J.; Taghon, G.J.; Rowe, J.B.; Morgan, W.M.; Enten, J.F.; Lambert, N.A.; Isom, D.G. DCyFIR: A high-throughput CRISPR platform for multiplexed G protein-coupled receptor profiling and ligand discovery. Proc. Natl. Acad. Sci. USA 2020, 117, 13117–13126. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  195. Peng, H.; Kotelnikov, S.; Egbert, M.E.; Ofaim, S.; Stevens, G.C.; Phanse, S.; Saccon, T.; Ignatov, M.; Dutta, S.; Istace, Z.; et al. Ligand interaction landscape of transcription factors and essential enzymes in E. coli. Cell 2025, 188, 1441–1455.e15. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  196. Wahid, E.; Ocheja, O.B.; Marsili, E.; Guaragnella, C.; Guaragnella, N. Biological and technical challenges for implementation of yeast-based biosensors. Microb. Biotechnol. 2023, 16, 54–66. [Google Scholar] [CrossRef] [Scilit] [PubMed]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Article Metrics

Citations

Article Access Statistics

Journal Statistics
Multiple requests from the same IP address are counted as one view.
Transcriptomic and Physiological Profiling Reveals Metabolic Determinants and Key Regulatory Hubs of Fruiting Body Degeneration in Lentinula edodes
Previous
Effects of Isaria cateniannulata on Enzyme Activities and Chitinase Genes in Tetranychus urticae
Next