Introduction

Studies on fungi were previously frowned upon. Alison Pouliot (2023) wrote in The Guardian “Fungi have endured a long history of neglect and disdain. In 1887 the British mycologist William Hay commented that he who studied fungi “must boldly face a good deal of scorn … and is actually regarded as a sort of idiot among the lower orders”. A few years earlier the nature writer Margaret Plues observed how the stranger “blinded by conventionalities” sneered at those seeking fungi. Unlike birders who look upwards for their charismatic avian delights, fungus hunters glance downwards, for what the “father of modern taxonomy”, Swedish biologist Carl Linnaeus, referred to as “thievish and voracious beggars”.” This has certainly changed with studies on fungi now being looked at much more positively, especially with their importance in human, animal and plant disease (Hyde et al. 2018; Mapook et al. 2022; Niego et al. 2023a, b), potential in novel compound discovery (Hyde et al. 2019; Mapook et al. 2022), value in industrial applications (Hyde et al. 2019) and potential in the biocircular economy (Kirchherr et al. 2017; Meyer et al. 2016, 2020).

The effect of metrics on mycology

An often-overlooked topic is the effect of Metrics on the study of Mycology. We can only speak from our experience, but it would be interesting to see how this affected other mycologists. In the 1990s the Web of Science started to influence university assessments (1996 onwards) and promotions. At the time the highest impact mycology journals, Mycologia and Mycological Research, had impact factors of 1–2, while some ecological journals had impact factors of 3–4. Therefore, the peer pressure led some of us to turn towards ecological research, so we published several papers at the end of the Century with an ecological slant (e.g., Hyde and Lee 1998; Taylor et al. 1999; Luo et al. 2004; Tao et al. 2008). At the turn of the century, molecular research was becoming important and journals specialising in this topic also had higher impact factors of 3–4. So again, the trend was to do molecular research and publish in these high impact journals (e.g., Liew et al. 2000, 2002; Guo et al. 2001).

During the early 2000s the ecological and molecular trends continued, whereas after 2010 some mycological journals overtook the ecological journals, so ecological studies in mycology became less important. As time progressed, the impact factors of some mainstream mycological journals (Fungal Diversity, Persoonia, Studies in Mycology) increased and these became the desired journals to publish in. The focus of these journals was mainly diversity, taxonomy, and phylogeny and thus these topics became popular. Thus, our research also focused on this area and we published many important papers during this period (e.g., Shearer et al. 2010; Hyde et al. 2013, 2014a, b). More recently it has become important to publish in Quartile 1 journals and Fungal Biology Reviews, Fungal Diversity, IMAfungus, the Journal of Fungi, Mycosphere and Studies in Mycology have become desirable journals. The future is hard to predict, phylogenomics and functional genomics have become hot topics, however in mycology this is not matched by the high-impact journals where such papers are published, but this may change. At least for the time being biodiversity, taxonomy and phylogenetics will remain important topics as papers can be published in high-impact journals.

The effect of Web of Science metrics on books has been extremely detrimental. During the mid stages of the careers of some of us published more than 20 books. At the time books were needed for promotions. As for now, there is very little kudos from publishing books, as they have no impact factor and scientific books make little profit. This is why very few comprehensive monographs are presently published.

In conclusion, there is no doubt that the Web of Science has had a profound influence on the research and publications in mycology. Whether one considers this a good or bad thing will depend very much on the researcher's interests.

The golden era of mycology

We the senior authors of this paper began their careers in the 1980s, the number of mycologists was declining and places such as CBS, IMI, Kew, and British Universities were shedding these specialists at an alarming rate. In this period, it was hugely difficult to obtain funding and as stated above, the impact factor of mycological journals was low. Thus, it was almost impossible for a young Ph.D. to obtain a university position. Fast forward to the present day, the emerging economies of Asia and South America are recruiting large numbers of mycologists. Even The Royal Botanical Gardens, Kew, has realised the importance of mycology and created several new positions for mycologists. It has become much easier to obtain funding and the impact factors of some mycology journals are some of the highest in specialised science. Thus, it is the golden era of mycology and this should last for some time to come because of the applied potential of the fungi (Hyde et al. 2019).

Below we provide notes on 15 important research trends and discuss their current research, limitations, and future expectations. We use these trends to indicate the expected research in mycology in the future.

Current trends, limitations, and future research in emerging diseases and their control

As an applied area of research that is directly linked to human health, medical mycology is probably one of the most rapidly evolving subdisciplines. Fundamental changes are taking place in all aspects: in the fungi and their evolution, in the hosts and their immunity, in the environmental conditions of human lifestyles, and at a global scale in climate and sustainability.

From a fungal perspective, hosts have become available with dramatically altered susceptibility to infection due to immunological alterations, constitutional diseases, or as a side effect of medical intervention. The second half of the twentieth century is marked by the emergence of patient populations that were immunocompromised in view of the treatment of life-threatening or chronic inflammatory and systemic diseases, while also organ transplants became standard therapy. In the USA, an estimated 2.7% of the population may have evidence of immunosuppression (Harpaz et al. 2016), and this number is growing due to increasing life expectancy and eldering demography in large parts of the world. Immunosuppression generally aims for a temporary or local decrease of innate cellular immune responses. Inevitably, this highly successful intervention has a downside in the emergence of a wide diversity of environmental fungi as opportunistic pathogens (de Hoog et al. 2020), many of which were previously unknown to medical mycology. The ability of these fungi to adapt to the compromised conditions within the debilitated host and potentially tolerate human endothermic conditions are the key factors in the pathogenic processes of environmental fungi (Köhler et al. 2014). At the scale of the individual patient, management and control of fungal disease is dependent on the level of diagnostics, understanding of pathogenic processes, knowledge of fungus-host interaction, and available therapy. This enables precision medicine focused on defined patient groups (Märtson et al. 2021), with antifungal stewardship leading to the design of personalised, tailored therapy preventing inappropriate use of prophylaxis (Singh et al. 2018).

More epidemics are expected

Microbial epidemics have always battered humanity and are expected to increase in frequency. The early pandemics, like plague and cholera, mostly originated from polluted water, as urban hygiene was at a low level until the nineteenth century, but the majority of the recent pandemics are zoonotic, and the intervals between the epidemics are decreasing. We recently witnessed the HIV, MERS, SARS, and COVID-19 pandemics that originated from primates, camels, and possibly from bats and intermediate hosts. Among novel threats are avian and swine flu which already are associated with human infection, having reached stage 2 (in a scale of 5; Piret and Boivin 2021) in adaptation to humans. Large-scale animal breeding, global trade of exotic animals, increased population density, and intercontinental travel enhance the spread, spill-over, and adaptation of microbial pathogens. In fungi, these often concern multiple infections from a single source, e.g., with Sporothrix schenckii from plant material (Dooley et al. 1997; Govender et al. 2015) or Verruconis gallopava from contaminated straw in a chicken coop (Blalock et al. 1973). Others are promoted by viral infections damaging the human cellular immune system. The HIV pandemic led to pronounced infections of true pathogens (Carpouron et al. 2022) in Cryptococcus, Sporothrix, Talaromyces, and Histoplasma. The COVID-19 pandemic enhanced the infection of opportunists in Rhizopus (Singh et al. 2021; Nehara et al. 2021) and Aspergillus (Calderón-Parra et al. 2022). Serious problems are looming in the monoculture of animal farming, where large numbers of animals are packed within limited space. The agent of goat Q-fever, Coxiella burnetii and that of bird flu, H7N9 Influenza-A are already becoming endemic in Europe, and both can be transmitted to humans with serious consequences. Unexpected fungi may follow in their slipstream. As a result, bioindustry plants today are close to implementing similar biosafety measures as microbiological laboratories.

Changes in lifestyle follow a global trend. In the past, endemic infectious diseases and regional epidemics were associated with agricultural labour on the field. Traumatic eye and subcutaneous infections from plant material and dermatophytoses acquired from farm animals were prevalent. Today, the farmer is placed at a distance from infectious agents by modern technology. In addition, rural populations diminish worldwide as a result of urbanization. Diseases like chromoblastomycosis by black fungi or cattle-borne Trichophyton verrucosum infection are disappearing. They are replaced by infections particularly involving from pet animals (Zhan et al. 2015). For example, 44.5% of U.S. households own dogs, and 29% cats (www.forbes.com). The spectrum of animals held is becoming wider, with novel agents from exotic animals appearing, such as the emergence of Trichophyton erinacei from the African pygmy hedgehog (Hsieh et al. 2010).

An interdisciplinary One-health approach is mandatory to prevent unforeseen and expected consequences of intensive agriculture. Evaluation of emerging infectious diseases can be done in a broader sense e.g. by reducing monoculture, regardless of the specific etiologic pathogen, and comparative genomics will allow further investigation of genetic and biological characteristics, especially in terms of adaptation to new habitats. Population translocation is accelerated by human-made climate change (Kimutai et al. 2022), as large areas are likely to become uninhabitable (https://www.un.org/africarenewal/). Human populations will spread disease at an unprecedented pace, conversely, in the wealthy part of the world, global travel is likely to increase. Also, fungal biodiversity patterns and mycobiomes are changing under the pressure of changing environmental conditions (Větrovský et al. 2019; Case et al. 2022), making it harder to predict epidemic expansions. Future pandemics will be very hard to control.

Problematic therapy and future solutions

Fungi are expected to be the infectious health problem of the future, because infections are chronic and recalcitrant, often with poor host response despite in vitro susceptibility (Vinh et al. 2023; Berman et al. 2020). The emerging resistance to commonly used antifungals in some of the major opportunists is a global concern in medical mycology. In addition, the discordance between overall treatment outcome and low levels of clinical resistance may be attributable to antifungal drug tolerance (Berman et al. 2020). Acquired antifungal resistance (Fisher et al. 2022) is a probable result of similar compounds being used in agriculture (Barber et al. 2020). In regions with intense farming, Aspergillus fumigatus comprises up to 20% of azole-resistant strains in natural populations (Chowdhary et al. 2013). The dermatophyte Trichophyton indotineae is often resistant to terbinafine, which is the main antifungal used by the public (Singh et al. 2018). In several orders of the fungal kingdom, such as Hypocreales, Microascales, and Mucorales, resistance is not acquired, but intrinsically present, even in the absence of previous exposure to antifungals (Caramalho et al. 2017). Also, some yeasts, among which is the emerging hospital agent Candida auris have reduced susceptibility, causing problems comparable to those of MRSA. Despite advances in antifungal therapy, there are few drug classes. New generations of antifungals are being developed with novel targets, such as echinocandins and orotomides, which are promising but generally are effective in only a limited number of species, or luliconazole which can only be applied topically. Currently, several promising antifungal agents undergoing clinical investigation, each with unique mechanisms of action. These include novamycin, an antifungal peptide that disrupts the plasma membrane, leading to cell lysis. Another agent, olorofim, belongs to the orotomide class of drugs and inhibits pyrimidine biosynthesis by reversibly inhibiting mitochondrial dihydroorotate dehydrogenase. Additionally, fosmanogepix, a GPI inhibitor, hinders the activity of Gwt1, preventing GPI anchoring. These agents exemplify novel drug targets and showcase their potential in the field of antifungal drug development. There are multiple bottlenecks to the successful development of antifungal drugs due to the unique characteristics of fungal cells. These challenges include the need to establish high-throughput screening methods, optimize target specificity and bioavailability, and minimize host toxicity to ensure efficacy. In silico approaches have the potential to expedite the early stages of drug development; however, their effectiveness relies on the availability of high-resolution protein structures and a strong mechanistic understanding of signaling pathways and the impact of therapeutic intervention on fungal physiology (Velazhahan et al. 2023).

Novel types of therapy have been applied to some of the classical, severe, and previously untreatable diseases that ultimately led to the death of the patient. In the past these were ascribed to supposedly uncommon, highly virulent fungi (Rajendran et al. 2003); particularly the mutilating diseases by black fungi (Bonifaz et al. 2013) and Mucor irregularis (Kang et al. 2014) have been mysterious. Today, these fungi are understood as being opportunists in patients with inherited immune disorders (Lanternier et al. 2015). Mutations in the essential dectin-signalling CARD9 protein increase susceptibility to particular fungal infections (Song et al. 2020). These primary immunodeficiencies include several hundreds of single-gene inborn errors of immunity and occur in close to 1% of the human population (Quinn et al. 2022). Similar to the increase in allergic diseases in the developed world, this problem is likely to grow in the future. Classical antifungal therapy is only temporarily successful in these patients due to emerging resistance. Immunotherapy or stem cell transplantation seems to provide a successful cure (Grumach et al. 2015) and may open new ways of therapy in chronic infections. For pathogens that are controlled by adaptive cellular immunity, the development of preventative tools such as vaccines might be an option (Oliveira et al. 2021).

The need for reliable diagnostics

Medical mycology comprises 778 species in 250 genera (www.atlasclinicalfungi.org, dd 01–04–2023), and this number is increasing nearly every month. General knowledge of the great majority of these species is scant, while a very limited number of prevalent clinical taxa have been thoroughly investigated. The recent emergence of novel agents, such as Candida auris and Sporothrix brasiliensis, demonstrates severe gaps in understanding sources and routes of infection, potential pathology, immune response, and therapy. Numerous taxa are described on the basis of just a few isolates, neglecting intraspecific variability. Without an understanding of clonality, recombination, uniparental sex, and horizontal gene transfer, it is difficult to develop reliable diagnostics for hospital routines (Song et al. 2023). Diversity studies and taxonomy are fundamental but need a stronger integration with the sciences that follow (de Hoog et al. 2023). The focus on molecular phylogeny overshadows the comprehension of the practical significance of the described entities for applied sciences and patient care. Phylogeny provides insight into adaptive trends between species, and nomenclature opens the doors toward existing literature. However, phylogeny-based rearrangement of classical genera leads to fragmentation, such that the coherence between pathogens is lost again. For example, the 52 medically relevant candida-like species are now dispersed over 19 genera and this process is continuing. This underlines the responsibility of the taxonomist to provide a workable system.

One limitation in controlling emerging diseases is the lack of rapid diagnostic tools and surveillance systems. Timely detection and identification of pathogens are essential for implementing effective control measures. Diagnostic methods are reliable only when based on sound taxonomy. The future of diagnostics may include multi-microbial tools that can provide a comprehensive view of microbial diversity and detect bacteria, fungi, and viruses simultaneously in clinical samples. Currently, protein- and DNA-based methods are available such as PCR, gene and genome sequencing, metagenomics, and MALDI-ToF, but these are not suitable for point-of-care diagnosis (POD), particularly in resource-limited settings or during outbreaks. Based on advanced knowledge, simplified techniques can be developed that are applicable in a wide range of settings and require less human implementation (Prattes et al. 2016) and are therefore particularly useful in developing regions (Osaigbovo and Bongomin 2021). An artificial intelligence approach has been evaluated for the autodetection of fungal hyphae from a microscopic image and seems promising (Koo et al. 2021). Additionally, microfluidic approaches such as lab-on-chip (Richter et al. 2022) can be utilized to promote fast detection of host/microbial biomarkers.

Overall, developing and improving diagnostic techniques and surveillance capabilities should be a priority. Future research should focus on enhancing our understanding of the ecological factors contributing to disease emergence. Exploring the complex interactions between pathogens, hosts, and the environment can help identify high-risk areas and predict disease outbreaks. Furthermore, strengthening international collaborations, data sharing, and communication networks is essential for a coordinated global response to emerging fungal infections.

Current trends, limitations, and future research in novel compound and drug discovery

Role of fungi in classical natural product-based drug discovery

Natural products have historically been one of the most important sources for human therapeutics, and they remain indispensable for the discovery and development of new drugs (Atanasov et al. 2021; Newman and Cragg 2020). As summarized by Bills and Gloer (2016), several of the most important pharmaceuticals and agrochemicals are derived from fungi. Aside from the blockbuster cardiovascular drugs like statins and the immunomodulating compounds like cyclosporine and fingolimod (Mapook et al. 2022; Niego et al. 2023b), the beta-lactams are certainly the best-known class of fungi-derived drugs (Fig. 1).

Fig. 1
figure 1

Some of the most prominent fungal natural products-derived drugs currently on the market

Since the discovery of penicillin, scientists have turned to fungi to find cures for mankind's diseases. Out of this arose the blooming age of antibiotics in the late 1940s which lasted until the 1960s (Karwehl and Stadler 2016; da Cunha et al. 2019), followed by a period of neglectful thinking that such drugs would always be available and last for eternity. This has given rise to resistance against these drugs and has slowly become a mass problem (Mitra et al. 2022). Roughly 60 years later, the focus has now turned on having to find new drugs predominantly against superbugs, strains of, e.g. bacteria, which are resistant to multiple classes of antibiotics, if not all, as in the case of some recalcitrant strains of Mycobacterium tuberculosis (Gygli et al. 2017). By far the most dangerous bacterial pathogens, however, are the organisms that are classified in the ESKAPE panel: Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa und Enterobacter spp. often become multi-resistant and are responsible for an increasing number of deaths (Miethke et al. 2021).

The main research focus in the medical field over the last four decades has shifted to different topics, such as finding chemotherapeutics for cancerous diseases (a field, in which natural products also are playing a major role), but in particular for diabetes and other metabolic disorders, cardiovascular drugs and other indications in which higher revenues can be expected. One of the reasons for this shift was the fact that the development costs for drugs have exploded, and regardless of the indication, the development of novel drugs from scratch may now cost up to a billion USD (Wouters et al. 2020). Especially because of the extremely high attrition rates, it became less fashionable to develop totally novel molecules from natural sources to drugs, and the antibiotics and other anti-infective sectors in which the natural products are particularly strong have been heavily affected by this trend. (Atanasov et al. 2021).

With this focal shift, many biomedical research branches are now devoted to drug development for these and other types of diseases of affluence. Therefore, the major contributor to new FDA-approved drugs over the past 30 years has been “biologicals” (macromolecular medicinal products of fermentative origin) (Rader 2008; Newman and Cragg 2020; Butler et al. 2023). Its most prominent representative is of course recombinant human insulin (Falcetta et al. 2022). Generally, their use is currently focused primarily on oncology, rheumatology, gastroenterology, and cardiology (Walsh and Walsh 2022).

Thus, the main problem remains: finding new drugs against superbugs and bringing them to the market as quickly as possible, and counteracting their resistance. Therefore, classical drug discovery research, which is based on natural product chemistry, has been given new attention in the last few years. Natural products have their fair share in the approval of new drugs by the FDA, while in contrast, the approval of new synthetic drugs has almost disappeared (Newman and Cragg 2020; Butler et al. 2023), the proportion of fungal natural products among the newly approved drugs is relatively small.

Yet, at the same time, the last approved new compound class of antibiotics, pleuromutilins, that came onto the market are derived from fungi (Paukner and Riedl 2017; Mapook et al. 2022). With the diverse range of natural products fungi have to offer, they remain one of the most promising sources for new lead structures. In addition, science has dedicated itself to the development of new dosage forms, particularly nanotechnology, which is a cutting-edge research topic at present. One of the most promising fungal metabolites with biomedical use in this regard is chitinosan, a fungal chitin-derivative under investigation as a drug delivery system, as hemostatic, and as medical material for wound healing (Nawawi et al. 2020). This would make natural products more bioavailable and thus a lot more interesting for use as drugs.

One big problem of the pharmaceutical industry is that all large companies have given up or outsourced their capacities for natural product research and development. Not only for the mycological field, but the search for novel compounds from nature also requires the availability of interdisciplinary resources and expertise, ranging from classical microbiology via know-how in biotechnology to analytical and preparative natural product chemistry (Fig. 2). Once a given company has abandoned these R&D activities, which had historically been built up over several decades, it may take decades to get back the know-how.

Fig. 2
figure 2

Overview of current state of fungal drug research (created with www.BioRender.com)

On the other hand, there is hope. A large number of bioactivity screening systems are nowadays available for newly discovered natural products, which often only require small amounts compared to the past. This does not mean that already known natural products cannot be tested in these new assays as well, and stand out as hits. One such example would be rubiginosin C (Quang et al. 2004), which has been known from Hypoxylon rubiginosum for almost 20 years, yet only recently stood out to be a highly promising biofilm inhibitor of pathogenic Candida spp. (Zeng et al. 2023).

Just as bioactivity assays need less and less amount of material, classic natural products drug discovery has changed in this regard. New technologies in the field of structure elucidation, mainly nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) allow for structure elucidation of smaller and smaller amounts. One look into the respective literature shows that natural product isolation and characterization of less than 1 mg has become the norm, with a trend to even lower amounts (< 0.5 mg), such as five unprecedented diketopiperazines from the endophytic fungus Batnamyces globulariicola, cultured from the medicinal plant Globularia alypum, native to Algeria (Noumeur et al. 2020). This opens up drug discovery in completely new territories, which have previously been disclosed. Not only geographical territories, where in recent times fungal natural products research has had and is focusing predominantly on tropical, understudied regions (Sandargo et al. 2019), but also specialized ecological niches, can thus now be investigated for potential drug producers.

A challenge here can be the adherence to all legal procedures, such as the Nagoya protocol (Heinrich and Hesketh 2019). However, the greater limitation in all these cases lies with the cultivation conditions, which in turn can lead to challenges in the reproduction of interesting drug candidates and makes an upscaling of these more difficult, as well as potentially elaborate isolation procedures. After all, a complex upstream process (USP) followed by a lavish downstream process (DSP) leads to a high cost of goods, which makes a drug candidate unappealing (Suresh and Basu 2008).

A frequent solution to this problem is the fermentation of an easier-to-produce precursor molecule which is then chemically modified in further steps, called “semisynthesis”. A majority of fungal drugs on the market have at least one step of semisynthesis, not only to increase production, but largely to improve bioavailability (like micafungin, an antifungal agent semi-synthesized from fermentative echinocandin; Hashimoto 2009), or to improve its (selective) bioactivity (like ceftaroline fosamil (teflaro® / Zinforo®) the last generation semisynthetic cephem to enter the market with improved selectivity for multi-resistant Gram-positive bacteria, Newman and Cragg 2020). In some cases, semisynthesis is also used to lower the actual bioactivity, such as in the case of irofulven, a lesser toxic derivative of illudin S (Chaverra-Muñoz and Hüttel 2022). These chemical modifications, a part of a vast field of medicinal chemistry, are becoming increasingly important and it is impossible to imagine a future without them. The best example of the success of a semisynthetic approach based on fungal metabolites is probably the cephalosporins, where five generations of compound families were synthesized over several decades, resulting in substantial improvements in efficacy against human pathogens (Lin and Kück 2022).

However, the now-established way of producing non-synthesizable natural substances more cost-effectively is synthetic biology, which introduces the BGCs into a new, easier-to-cultivate host via heterologous production or can even allow for total biosynthesis of novel natural products. As shown for strobilurins (Nofiani et al. 2018) and pleuromutilins (Alberti et al. 2017a, b) it has even been possible to transfer the BGC from Basidiomycota into workhorses like Aspergillus (flavus var.) or yzae that belong to the Ascomycota. It has even been possible to synthesize natural products using enzymes, as recently demonstrated for psilocybin by Fricke et al. (2017). The mechanisms of many biosynthetic pathways, including those that encode for rather rare and complicated metabolite classes, have been elucidated over the past years (Feng et al. 2020; Tian et al. 2020; Schotte et al. 2020).

Current trends and future potential

When the first genomes of filamentous fungi became available, the large number of biosynthetic gene clusters in these fungi surprised the researchers, as the number of clusters by far succeeded the diversity of SMs known from these fungi during previous natural product isolation studies. Since then, genome mining has been established as an alternative way for the discovery of new secondary metabolites. Its common workflow is the identification of biosynthetic gene clusters (BGCs), the heterologous expression or activation of interesting BGCs, followed by metabolite characterization. Examples (Fig. 3) yielded in novel carbon skeletons include the discovery of new meroterpenoids from a gene cluster encoding polyketide synthase, prenyltransferase, terpenoid cyclase, and other tailoring enzymes (Zhang et al. 2018); the discovery of flavunoidine, whose gene cluster contains two terpene gene clusters and a nonribosomal peptide synthetase (Yee et al. 2020) and the sesterterpenes schultriene and nigtetraene discovered by heterologous expression of fungal bifunctional terpene syntases (Jiang et al. 2022). Arbusulic acid was isolated besides other compounds after epigenetic activation of the endophytic fungus Calcarisporium arbuscula (Mao et al. 2015), whereas the production of tripyridone in Aspergillus nidulans was induced by co-cultivation with filamentous Streptomycetes bacteria producing antifungal glycopeptide antibiotics (Gerke et al. 2022).

Fig. 3
figure 3

Novel compounds isolated from genome mining approaches

Precondition for the genome mining is the availability of full genome information to be mined. Although fewer fungal genomes have been sequenced compared to the bacterial kingdom, more than 1000 fungal genomes have already been interpreted for secondary metabolite production (Robey et al. 2021). However, most of those studies focused on well-studied genera such as Aspergillus, Fusarium, and Penicillium.

Thus, it is important to expand sequencing efforts to less studied, nevertheless very prolific groups of fungi. Based on high-quality genome sequences for 13 representative species of Hypoxylaceae, Kuhnert et al. (2021) surveyed the diversity of biosynthetic pathways and found 783 biosynthetic pathways across the 14 studied species, the majority of which were arranged in biosynthetic gene clusters. Of the 375 gene cluster families (GCF) found, only ten GCFs were conserved across all of these fungi, indicating that a high degree of speciation is accompanied by changes in secondary metabolism. Besides giving insights in the genetic background that drives the production and diversification of secondary metabolites, this and similar studies will help to systematically access the secondary metabolites of prolific fungal secondary metabolite producers.

A drawback of the genome mining approaches is that often compounds with no obvious bioactivity are isolated. Thus, target-directed genome mining efforts try to increase the odds of finding bioactive compounds. Because antimicrobial producers must be self-resistant to their own metabolites, fungi often encode resistance genes to avoid self-toxicity in the BGCs of corresponding bioactive compounds. FunARTS, the Fungal bioActive compound Resistant Target Seeker, is a recent exploration engine to specifically mine for these resistance genes (Yilmaz et al. 2023).

So far, genome mining has largely focused on unknown metabolites from predicted BGCs with known core enzymes, like PKS, NRPS, and terpene synthases. However, the most spectacular findings are expected in the search for unknown secondary metabolites with so far unidentified types of core enzymes, the so-called “unknown-unknowns”. Yee et al. (2023) identified a new arginine-containing cyclodepsipeptide synthase, which is responsible for a highly modified cyclo-arginine-tyrosine dipeptide. However, since the scaffold of this compound was already known from derivatives, it remains to be seen if this method can meet the high expectations.

Metabolomics is a recently developed, complementary research field. The identification of known compounds, a process known as dereplication, is readily possible with HPLC–MS/MS data from crude extracts if reference data are available. Thus, the integration of genomics and metabolomics has the potential to revolutionize fungal natural product research (Hautbergue et al. 2018). Beyond metabolomics, the integration of transcriptomics and proteomics data may provide an additional layer of information for pathway analysis (“multi-omics”). However, it is very important to reduce the complexity of the vast amount of data in a sensible way (Rinschen et al. 2019). Of particular interest is the systematic connection of pathway data to biological activities. However, it is, of course, not possible to assess the biological effects of single components in mixtures like extracts unless the respective compounds can be isolated to purity. The same holds true for their unambiguous identification, for which techniques like 2D-NMR spectroscopy, crystallization, or derivatization remain indispensable.

Furthermore, synthetic biology, designing and constructing biological modules, biological systems, and biological machines or re-design of existing biological systems for useful purposes, will greatly help to access the chemical diversity uncovered by genome mining (Skellam et al. 2019; Keller 2019). The combination of genome mining with a synthetic biology-based method of heterologous biosynthesis is a promising approach to rationally access NPs with novel structures and biological activities. As an example, this method was used to explore the biological activity-related chemical space of fungal decalin-containing diterpenoid pyrones (Tsukada et al. 2020). Even though these compounds do not have very prominent and selective biological activities, the latter study shows the feasibility of the approach to expand the chemical space. Meroterpenoids (Matsuda and Abe 2016), NRPS-PKS hybrids (Boettger and Hertweck 2013), and other compounds of mixed biosynthetic origin are increasingly being studied, also because their BGC can be made out in the genomes rather easily.

Finally, yet importantly, the application of artificial intelligence (AI) to natural product drug discovery has grown tremendously in recent years, due to ever-increasing computing power, extensive storage, and accessible software (Saldivar-Gonzalez et al. 2022). Thus, we expect the rational application of AI to assist in discovering bioactive natural products and capturing the molecular “patterns” of these privileged structures for combinatorial design or target selectivity. Main help will most likely be the prediction of chemical structures from microbial genomes and automation of the natural product dereplication process, the identification of NP substructures, computer-assisted structure elucidation, and virtual screening approaches. Additionally, the prediction of biological functions of the NPs will be afforded by the recent advances in machine learning having leveraged to accelerate the accurate atomic-resolution structure prediction of proteins (Lin et al. 2023a, b).

Conclusion

Above we have summarized the classical development of natural product-based drug discovery with fungi as well as various modern options that recently became available via the –OMICS technologies. Curiously, the options to quickly improve the production and activities of natural compounds nowadays appear higher than ever before, while the lack of relevant expertise in the pharmaceutical industry steadily contributes to the innovation gap regarding novel drugs that can be brought to the market. A further increase of public funding and incentives for the industry to re-establish capacities and know-how for natural product-based drugs would be highly desirable, especially with respect to indications like anti-infectives, where the greatest need is going to arise in the future.

Current trends, limitations, and future research in fungal classification and phylogenomics

The term "phylogenomics" was introduced by Jonathan Eisen in 1998 for improving functional predictions for uncharacterized genes and to study the evolution of gene families in the genome-scale phylogenetic analysis (Eisen 1998). Compared with multi-locus phylogenetic analysis, genomes contain more species evolution information, and a larger dataset can decrease sampling errors. Reconstruction of phylogeny based on genome-scale data can more realistically reflect the evolutionary relationship between taxa (Virendra and Somnath 2009). In the last 20 years, advances in next-generation sequencing technologies and bioinformatics have made the genomic era now a reality. Phylogenomics has since become a widely used approach in evolutionary biology and genomics (Guttmacher and Collins 2003; McGuire et al. 2020; Eisen and Fraser 2003; Virendra and Somnath 2009). There has been a rapid increase of fungal genome data in public databases, such as NCBI database (https://www.ncbi.nlm.nih.gov/datasets/genome/?taxon=4751) and 1000 Fungal Genomes Project (https://mycocosm.jgi.doe.gov/mycocosm/home/1000-fungal-genomes). Researchers are therefore increasingly using genomic data to infer the evolutionary relationships among fungi, and have obtained remarkable progress in fungal taxonomy. A number of long-standing taxonomic questions have been resolved, and at the same time, the accuracy of fungal classification and the understanding the of fungal evolution have been improved greatly (Zhang et al. 2017). Pizarro et al. (2018) utilized a genome-scale dataset of 2556 single-copy protein-coding genes to reconstruct a phylogeny of the most diverse group of lichen-forming fungi. This study strongly supported the monophyly of major clades and resolved previously unresolved relationships in the family Parmeliaceae. Based on morphological characters and two-locus phylogenetic analysis, the important plant-pathogenic genus Pythium sensu lato was split into five genera: Pythium, Elongisporangium, Globisporangium, Ovatisporangium, and Pilasporangium (Uzuhashi et al. 2010). This revision however, was not generally accepted by the scientific community due to the lack of phylogenetic support until a phylogenomic study of Pythium was conducted by Nguyen et al. (2022).

In higher-level taxonomic studies, phylogenomic analysis helps in resolving several problematic lineages, including the grouping of microsporidia + Rozella with Cryptomycota (James et al. 2013), the formal phylogenetic classification of splitting zygomycete taxa into Mucoromycota and Zoopagomycota (Spatafora et al. 2016), and the systematic placement of the xerotolerant mold Wallemia as the earliest diverging group in Agaricomycotina (Padamsee et al. 2012). In addition, Li et al. (2021b) discovered that ~ 85% of fungal taxonomy ranks used in the dataset were broadly consistent with both genome sequence divergence and divergence times at higher taxonomic levels, suggesting the effectiveness of using the divergence time approach to rank taxonomic lineages.

Limitations

While phylogenomics has brought powerful analytic methods to fungal taxonomy, there are some limitations that should be considered. Current phylogenomic studies suffer from limited taxon sampling, which can lead to incomplete or inaccurate phylogenetic reconstructions. The phylogenomic study by Chen et al. (2023a) included all available genomes of Sordariomycetes from the public database. The final dataset only comprised 156 genera, 50 families, 17 orders, and five subclasses, which is much lower than the number of existing taxa (1619 genera, 184 families, 46 orders, and seven subclasses) summarised in the latest outline (Wijayawardene et al. 2022a, b). Most genomes in the public database are from pathogenic or biotechnologically useful fungi, with whole genome sequencing was conducted by non-fungal specialists for non-taxonomic purposes. This led to poor sampling in many groups, which may be underrepresented in genomic databases. Furthermore, because most genomes are not from ex-type or representative strains, there are no morphological characterizations for these genomes. This impedes the combination of phylogenomic analysis and morphological studies. In fungal species delineation, there have been several attempts to differentiate species using genome data; however, this kind of application has been hampered by the lack of clear and universal criteria (Sepúlveda et al. 2017; Gostinčar 2020; Matute and Sepúlveda 2019). The cost of whole genome sequencing has been decreasing due to advancements in sequencing technologies. However, it remains relatively more expensive as compared to Sanger sequencing, which is used in multigene phylogenetic analysis. Furthermore, whole genome sequencing instruments are not available in underdeveloped countries. The subsequent requirements of extensive computational resources, sophisticated bioinformatic skills and poor collaborations between bioinformaticians and taxonomists also limit the promotion of phylogenomics in fungal taxonomy to some extent.

Future

The main limitation of phylogenomics in fungal taxonomy is an unbalanced genomic sampling. As more fungal genomes are sequenced, including those from understudied and diverse fungal lineages, phylogenomic studies can benefit from increased genomic sampling. This will enable a more comprehensive understanding of fungal diversity and evolution, filling in gaps in the fungal tree of life and improving the resolution of phylogenetic relationships. The vast majority of fungal taxonomic groups do however, not have the ability and conditions to perform phylogenomics analysis Therefore, the use of morphology and multi-locus analysis will be the main methodologies used in fungal taxonomy. Phylogenomic approaches will be supplements to solve some taxonomic problems. The fungal taxonomy community should support and advocate for the implementation of phylogenomics into taxonomy. It is hoped that phylogenomics will provide a more comprehensive and accurate understanding of fungal diversity and evolution in the future.

Current trends, limitations, and future research in genomics and ecology of fungal plant pathogens

A brief history of the origin and evolution of fungi

Some studies have shown that the ability of fungi to colonise plants is ancient, suggesting a correlation between streptophyte algae (embryophytes and their closest green algal relatives) and the lineages of fungi Ascomycota, Basidiomycota, and Chytridiomycota around 1 billion years ago (Jones et al. 2015). The application of genome analysis and comparative studies has supported this hypothesis. For instance, the presence of pectinases and cellulases through millions of years of evolutionary history suggests that most of the ancestors of fungi evolved using plant-based nutrition (Berbee et al. 2017). Moreover, as algae evolved into land plants, and as the polysaccharides in their cell walls differentiated, fungal enzymes accompanied this evolution process (Lange et al. 2019). In this regard, fungi developed strategies to invade complex substrates, making them the principal degraders of biomass, and efficient saprotrophs and pathogens able to infect a wide range of hosts (Bucher et al. 2004; Berbee et al. 2017; Howlett et al. 2015). In natural ecosystems, the interaction between plants and their pathogens drives a co‑evolutionary dynamic (Howlett et al. 2015). However, in managed ecosystems, as crops evolve through artificial selection (e.g., selection of desired traits), pathogens need to rapidly evolve and adapt to new environmental conditions and niches leading to the increase of virulent phenotypes (Möller and Stukenbrock 2017). Consequently, the awareness of mycologists and plant pathologists to study fungal pathogens and to evaluate their evolutionary relationships based on genome data has been considerable in recent studies (Ball et al. 2020; Garcia et al. 2021; Priest et al. 2020).

What does genomics offer?

Genome analysis is pivotal to understanding the mechanisms underlying the infection processes (Schikora-Tamarit and Gabaldón 2022) and adoption patterns involved in fungal lifestyles and ecological niches (Gonçalves et al. 2022; Janusz et al. 2017). Additionally, genome data provides information on gene families and their functional potential for virulence and host infection (Ball et al. 2020). This is of paramount importance for the plant pathology sector to understand the biology of diseases, to improve diagnostic methods, and ultimately to manage and/or prevent disease outbreaks (Aylward et al. 2017; Weisberg et al. 2021). Therefore, current efforts on fungal genome sequencing have been made to cover the above-mentioned purposes by unveiling:

  1. (1)

    carbohydrate-active enzymes responsible for the degradation of plant cell walls, showing the ability of fungi to penetrate and colonise plant tissues.

  2. (2)

    biosynthetic gene clusters for the discovery of bioactive compounds with pharmaceutical, biomedicine, and agricultural applications.

  3. (3)

    transmembrane transporters of ions, sugars, and molecules that contribute to fungal virulence.

  4. (4)

    pathogenicity/virulence genes and candidate effectors that manipulate the host immune defense.

  5. (5)

    candidate genes involved in evolutionary processes that shape fungal pathogens' adaptation to different environments (e.g., increasing temperatures, drought, high levels of salinity).

  6. (6)

    the role of secreted effectors on fungal virulence and lifestyle switching.

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    genes related to morphological, physiological, and reproduction among different isolates.

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    genetic basis for multi-omics analyses to offer a complete overview on plant-pathogen interactions.

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    genomic variants linked to virulence through the analysis of single nucleotide polymorphisms (SNPs) (Constantin et al. 2021; Garcia et al. 2021; Gonçalves et al. 2022; Grandaubert et al. 2019; Xu 2020).

Current limitations on genome analysis

The important roles of fungi and their ubiquity and plurivorous nature, coupled with the advances in next-generation sequencing (NGS) have driven researchers to sequence numerous fungal genomes (Möller and Stukenbrock 2017; Priest et al. 2020). This has led to a greater availability of fungal genomes in public databases (e.g., NCBI, JGI Genome Portal) which significantly expanded our knowledge of the infection processes, fungal ecology, and genome evolution (Aylward et al. 2017; Xu 2020). However, there is a limited understanding of the impact of genome architectures on the upsurge of pathogenicity changes and the adaptation to changing environments mainly due to:

  1. (1)

    increasing numbers of fungal genomes published as genome announcements lacking functional gene annotations.

  2. (2)

    interpretation of genomes based mostly on low-quality assemblies, weak annotations, and homology-based predictions.

  3. (3)

    shortage of sufficient funding to obtain whole genome sequencing and to access publicly available databases and web surfaces for data analysis.

  4. (4)

    wrongly identified fungal species and misidentification of the submitted genomes in databases which introduce errors and deceive further analysis.

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    requirement of experts in data sciences and plant pathology for a correct and careful interpretation of data.

These limitations are challenging for researchers impairing the achievement of high-quality and large-scale comparative analysis (Gabaldón 2020; Stavrou et al. 2018).

The close relationship between bioinformaticians and basic mycology

Mycology is integrative and requires experts with diverse skills in fungal biology, bioinformatics, and molecular biology (Hibbett et al. 2013). Despite the efforts to understand the biology and ecology of fungi, mycologists and plant pathologists need to establish a close collaboration with bioinformaticians. Combining their expertise can be beneficial to deepen our knowledge of fungi and their role in biological processes and lead to several advantages (Aylward et al. 2017; Gautam et al. 2022; Roth et al. 2023; Wijayawardene et al. 2022a, b):

  1. (1)

    Bioinformaticians can assist mycologists in analysing the genomes of several fungal species, thus providing valuable insights into their evolution, physiology, and potential applications.

  2. (2)

    By analysing the gene expression (transcriptomics) and protein profiles (proteomics) of fungi, bioinformaticians can help mycologists understand how genes are regulated and the biological functions of proteins.

  3. (3)

    Comparing the genomes of different fungi can assist in the identification of common features and differences between species. This can shed light on the evolutionary relationships and the genetic basis of unique properties of certain fungi.

  4. (4)

    In environmental mycology, metagenomics allows for the analysis of all the genetic material present in a sample. In this regard, bioinformaticians can handle the vast amounts of data generated from such studies and help in the identification of different fungal species.

  5. (5)

    Bioinformatics tools can aid in predicting the functions of genes and proteins in fungi, even for species where experimental validation may be challenging or time-consuming.

  6. (6)

    Understanding the genetic factors involved in fungal pathogenicity can lead to the discovery of potential drug targets. Bioinformatic analysis can assist in identifying crucial genes and pathways related to fungal virulence.

  7. (7)

    Bioinformaticians can facilitate the sharing of large datasets and help establish collaborative efforts between different research groups and institutions in the field of mycology.

  8. (8)

    Bioinformaticians can create software tools and databases tailored specifically for mycological research, making it easier for mycologists to analyse and interpret their data.

Overall, the collaboration between bioinformaticians and basic mycologists can accelerate research, increase the accuracy of analyses, and provide a better understanding of fungi (Roth et al. 2023). As technologies and computational methods continue to advance, this partnership will play an increasingly critical role in advancing mycological research and its applications in various fields, including medicine, agriculture, and industry (Weisberg et al. 2021; Wijayawardene et al. 2022a, b).

Future perspectives in untangling genome architectures

Despite the above-mentioned challenges, further efforts should be made to continue pursuing knowledge on the mechanisms of virulence, processes of genome evolution, environmental adaptations, and speciation events (Aylward et al. 2017; Ball et al. 2020). This information can be achieved through the analysis of genome compartments that are enriched in effectors and transposable elements, responsible for genomic plasticity and the evolution of new virulence phenotypes (Möller and Stukenbrock 2017). Therefore, comparative whole genome analysis between pathogenic and non-pathogenic isolates will contribute to.

  1. (1)

    identifying those compartments and determining their roles in the rapid evolution of fungi.

  2. (2)

    monitoring the population dynamics of pathogens.

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    revealing fungal ecological adaptations.

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    understanding evolutionary dynamics (Ball et al. 2020; Grandaubert et al. 2019).

Moreover, bearing in mind that fungi can adopt different lifestyles (e.g., endophytes, pathogens, saprotrophs), genome analysis should be considered in future studies for a deep understanding of the molecular traits and mechanisms that determine lifestyle switches (Constantin et al. 2021; Hilário and Gonçalves 2023). As a single reference genome cannot reflect the genetic diversity of a species, it is also vital to increase the number of sequenced genomes of closely related and divergent isolates within a given species to assess evolutionary relationships (Gabaldón 2020; Xia et al. 2022). High-quality genomes are required to obtain more accurate and correct information from a fungal genome and its functional annotation. In this regard, it has been suggested that plant pathology programs must provide their students with more opportunities to deepen their skills in data sciences (Xu 2020). This will surely help them to gain experience and expertise in working with genomic datasets, thus improving the quality of the published data (Weisberg et al. 2021).

The integration of all these approaches provided by genomics will certainly enable new perspectives in untangling fungal-pathogen interactions, shed light on the biology, ecology, and evolution of pathogens, and ultimately in identifying diagnostic markers for agricultural settings (Hilário and Gonçalves 2023; Weisberg et al. 2021; Xia et al. 2022).

Current trends, limitations and future research in functional genomics

Functional genomics is an interdisciplinary field that integrates molecular biology and cell biology, utilizing the massive amount of data generated by genomic and transcriptomic projects. This has the aim of deciphering how genes and their regulatory elements work together to carry out biological processes and determine the functions of specific genes in various biological systems (Smit et al. 2017). These functional genomic approaches to fungi enable researchers to shed light on the molecular mechanisms underlying fungal biology, focusing on their growth, development, metabolism, and response to environmental stimuli (Chethana et al. 2020; Peng et al. 2022; Huang et al. 2023). The significance of fungal functional genomics lies in its ability to elucidate gene function, paving the way for advancements in various fields, including medicine, agriculture, and biotechnology (Huberman 2021). Furthermore, identifying these genes and proteins that are essential for critical processes like fungal survival and pathogenicity provides the knowledge required to develop targeted therapies and antifungal drugs that disrupt vital processes in pathogenic fungi impacting plants, animals, and humans, improving the treatment of fungal infections and reducing the emergence of drug resistance (Sanz et al. 2017; Segal et al. 2018; Chethana et al. 2020; Bruno et al. 2021; Peng et al. 2022; Huang et al. 2023). Moreover, functional genomics provides insights into evolutionary relationships, gene family expansions or contractions, and the acquisition of novel traits that are important to understand the processes that shaped fungal diversity and adaptation to different ecological niches (Kim et al. 2014; Liu et al. 2017; Ball et al. 2020; Garcia et al. 2021; Priest et al. 2020).

As fungi play essential roles in agriculture, both as beneficial symbionts and devastating plant pathogens (Hyde et al. 2019), functional genomics research also provides insights into fungal-plant interactions, including symbiosis, disease resistance, and nutrient uptake (Seo et al. 2015). Understanding these interactions can lead to the development of strategies for crop protection, enhancing plant health, and improving agricultural productivity (Bhardwaj et al. 2014; Yang et al. 2023). Hyde et al. (2019) discussed the immense biotechnological potential of fungi to produce a wide range of enzymes, bioactive compounds, and secondary metabolites for industrial processes or pharmaceutical compounds. This knowledge can be harnessed to optimize fungal strains for biotechnological applications and develop new bioproducts. Overall, fungal functional genomics is essential for expanding our knowledge of fungal biology, enhancing food security, boosting human health, and spurring innovation across various industries (Wijayawardene et al. 2023a, b). By uncovering the functions of fungal genes and their interactions, this field contributes to the development of new strategies, therapies, and biotechnological applications with broad societal impacts.

Current trends in fungal functional genomic studies

The advent of genomics research has revolutionized our understanding of fungi by generating an unprecedented volume of data, serving as the foundation for functional research in this diverse group of organisms. The sequencing of fungal genomes has produced enormous datasets that provide valuable insights into the genetic makeup of various fungal species (Noble and Andrianopoulos 2013; Ma et al. 2014; Yan et al. 2018; Grandaubert et al. 2019; Constantin et al. 2021; Garcia et al. 2021; Gonçalves et al. 2022; Greener et al. 2022). Integrating this massive data with other "omics'' approaches, such as transcriptomics and proteomics, offers a holistic perspective of fungal functional genomics, paving the way for groundbreaking discoveries and innovative applications in industries such as agriculture, medicine, and biotechnology (Muller et al. 2013; Abram 2015; Grandaubert et al. 2019; Chethana et al. 2020; Xu 2020; Constantin et al. 2021; Garcia et al. 2021; Gonçalves et al. 2022; Peng et al. 2022; Huang et al. 2023). In recent years, several prominent trends have emerged in fungal functional genomics. These trends encompass a range of techniques and approaches that are reshaping our understanding of fungal biology and offering new avenues for applications in various fields.

A prominent trend in functional genomic research is the integration of multi-omics datasets, such as genomics, transcriptomics, proteomics, and metabolomics, into functional research, providing a deeper understanding of the functional elements within fungal genomes and their relationships to fungal biology (Chethana et al. 2020; Presley et al. 2020; Constantin et al. 2021; Garcia et al. 2021; Gonçalves et al. 2022; Li et al. 2022a, b, c; Peng et al. 2022; Huang et al. 2023). Such research has the potential to uncover novel insights into the biology, evolution, and biotechnological potential of fungi by correlating genomic information with gene expression profiles, protein interactions, and metabolic pathways. For instance, Presley et al. (2020) integrated functional research with transcriptomics (comparative RNA-Seq) and proteomics to identify various interaction methods and the individual proteins mediating these relationships among two model brown rot fungi of softwood timber. Additionally, the integration of metabolomics and functional genomics in the study by Li et al. (2022a, b, c) elucidated the metabolic adaptations of Trichoderma reesei under cellulase-inducing circumstances, revealing key regulatory mechanisms for efficient cellulase synthesis. Furthermore, comparative genomics research combined with functional genomic research in fungi facilitates the identification of conserved genes, orthologs, regulatory elements, and lineage-specific adaptations, and provides insights into the evolutionary innovations and specialised functions that contribute to the diversity of fungal lifestyles, including pathogenicity, symbiosis, and environmental adaptation (Muller et al. 2013; Abram 2015; Yan et al. 2018; Grandaubert et al. 2019; Xu 2020; Constantin et al. 2021; Garcia et al. 2021; Gonçalves et al. 2022). For example, comparative analysis of the transcriptome and genome data of Aspergillus species identified genes responsible for carbon utilization, secondary metabolism, and stress response, laying the foundation for exploiting them for biotechnological and medical applications (Terabayashi et al. 2010; de Vries et al. 2017). Furthermore, functional studies, in conjunction with comparative analysis, revealed the evolution of the fungal chitin synthase (CHS) gene family, as well as its relationship to fungal morphogenesis and adaptability to ecological niches (Liu et al. 2017). Large-scale comparative analysis of 135 genomes of mycorrhizal species revealed their complex symbiotic traits through gene duplications and diversifications and provided evidence for convergent evolution, and identified key genetic components involved in the nutrient exchange between mycorrhizal fungi and their plant hosts (Miyauchi et al. 2020). Similar research can aid in understanding the origin and development of plant-fungal relationships and their impact on ecosystem function.

Another emerging trend in the study of pathogenic fungi is using functional genomic techniques in conjunction with transcriptomics, proteomics, and gene editing to uncover the genetic mechanisms underlying their pathogenicity and host interactions. These studies lay the groundwork for identifying virulence factors, comprehending host–pathogen interactions, and developing tailored antifungal therapies (Chethana et al. 2020; Chakraborty et al. 2021; Duan et al. 2021; Kowalski et al. 2021; Peng et al. 2022; Przybyla and Gilbert 2022; Yu et al. 2022; Huang et al. 2023). A recent study used a combination of functional genomics and solid-state NMR spectroscopy to identify key genes involved in the cell wall assembly and remodeling of the fungal cell wall architecture of Neurospora crassa, and provided new insights into the spatial arrangement and interactions of cell wall components, improving our understanding of the structural response of fungal pathogens to stresses and revealing potential targets for antifungal therapies (Chakraborty et al. 2021). Advances in genome editing tools, such as CRISPR-Cas9, have opened up new paths for functional genomics research in fungi, enabling precise modifications to fungal genomes and accelerating the advances in fungal biology and pathogenicity (Kowalski et al. 2021; Duan et al. 2021; Wang et al. 2023a, b, c). Kowalski et al. (2021) used CRISPR-Cas9 to discover the critical role of its gene CZF1 in Candida glabrata virulence and biofilm formation, highlighting its potential as a therapeutic target, while Duan et al. (2021) used a similar approach to identify the function of FgHOG1, in regulating stress responses and virulence in Fusarium graminearum. Characterizing these genes is crucial, as they can be utilized in developing green super crops that exhibit superior productivity and resilience to abiotic/biotic stresses using functional genomics and multi-omics technologies, holding great potential for addressing global food security challenges and reducing the environmental impact of agriculture (Varshney et al. 2019; Yu et al. 2022).

Limitations on functional genomic studies

Several constraints limit the progress of functional genomics research and our understanding of fungal biology and interactions. In general, fungal genomes are complex, with varied genome sizes, repeated sequences, and high levels of genetic plasticity (Noble and Andrianopoulos 2013; Möller and Stukenbrock 2017). One major challenge for conducting functional genomic research is that the functions of a significant portion of fungal genes remain unknown or hypothetical, making it challenging to interpret complex regulatory networks and pathways (Bouhired et al. 2007; Schäpe et al. 2019). While advanced genetic manipulation tools are available in many fungal species, these genetic tools and techniques are lacking in certain fungal species, hindering the efficient, targeted manipulation and functional analysis of specific genes (Huang and Cook 2022; Wang et al. 2023a, b, c). Furthermore, functional genomic research has focused primarily on well-studied model fungi (Magee et al. 2003; Lee and Dighton 2013), leaving many fungal species of ecological or industrial value with a scarcity of data. Gene and functional redundancy is often associated with fungal genomes (Noble and Andrianopoulos 2013; Herzog et al. 2020), affecting and restricting the success of functional genomics studies, as knocking out a single gene might not result in a noticeable phenotype due to the presence of redundant or compensatory mechanisms that contribute to similar or overlapping functions (El-Brolosy and Stainier 2017). Additionally, functional genomic studies often rely on phenotypic assays, which can be complex and time-consuming for characters that are difficult to quantify or visualize. These assays may not fully capture interactions in natural fungal habitats (Franco-Duarte et al. 2019), limiting their applicability to fungi. Environmental factors, developmental phases, and interactions with other organisms impact fungal gene functions (Rangel et al. 2015; Momin and Webb 2021; Lin et al. 2023a, b). A further limitation of functional genomics research is that it often focuses on specific circumstances or time points, which may not capture the entire range of fungal gene functions. Addressing and overcoming these limitations requires continuous advancements in genetic manipulation techniques, improvements in genome annotations, and the development of standardized methodologies, leading to a more thorough and complete understanding of fungal biology and interactions.

Future perspectives for functional genomic research in fungi

The future of functional genomic research in fungi holds great promise. Advancements in high-throughput sequencing technologies, including single-cell genomics and long-read sequencing integrated with multi-omics approaches, facilitate the comprehensive characterization of fungal genomes and transcriptomes, thereby providing a more holistic understanding of fungal biology (Lorrain et al. 2019; Li et al. 2021a, b; Tedersoo et al. 2021a, b; Massart et al. 2022). The future of functional research should be expanded to a broader array of fungal taxa, including non-model fungi and those with agricultural, biotechnological, economical, and ecological relevance to overcome one of the previously mentioned limitations (Swift et al. 2019). In addition, future research should delve into the genetic basis of symbiotic relationships, pathogenicity, and host-microbe interactions (Bosch et al. 2019; Fiorilli et al. 2020). Understanding the molecular mechanisms underlying these interactions will affect agriculture, human health, and ecosystem dynamics.

The widespread application of cutting-edge gene editing tools such as CRISPR-Cas9, pathway engineering, and synthetic gene circuits will be increasingly applied to functional fungal research (García-Granados et al. 2019; Xia et al. 2019; Otero-Muras and Carbonell 2021). This research will facilitate the design and engineering of fungi to produce valuable compounds, bioremediation, and other biotechnological applications (Arun et al. 2023; Ghosh et al. 2023). Further research is needed to improve these tools to achieve more efficient delivery methods, increased precision, and the ability to target specific genomic loci, thereby facilitating the identification of key genes and regulatory elements for synthetic biology applications with greater precision and scalability (Porto et al. 2020). Furthermore, rapidly advancing technologies such as single-cell genomics, transcriptomics, and automated, high-throughput phenotyping tools will be applied to fungal functional genomics research for studying cellular processes and gene expression at the single-cell level and enable understanding of the contributions of specific cell populations to fungal physiology and development (Maviane-Macia et al. 2019; Wösten 2019; Jansen et al. 2021; Seto et al. 2023). The automated, high-throughput phenotyping tools are specifically used to simultaneously investigate multiple phenotypic traits across a large fungal population, thereby accelerating research progress (Maviane-Macia et al. 2019; Jansen et al. 2021). Automated imaging, robotic systems, and machine learning algorithms assess the functions of genetic modifications and aid in identifying novel gene functions, characterizing gene networks, and discovering phenotypes associated with specific genetic variants (Shariff et al. 2010; Wainaina and Taherzadeh 2022). Moreover, future functional genomic research should increasingly be integrated into environmental metagenomics studies to investigate the gene expression patterns, metabolic adaptations, and functional traits of fungi in diverse environmental conditions, thereby contributing to our understanding of fungal ecology, ecosystem dynamics, and the impact of environmental changes on fungal communities (Gómez-Silva et al. 2019; Zhao et al. 2023).

In addition to those mentioned above, functional research integrated with multiple omics data sets (genomics, transcriptomics, proteomics, metabolomics, and epigenomics) to provide a system-level understanding of fungal biology and its responses to environment will continue to be a major trend in the future as well (Gómez-Silva et al. 2019; Swift et al. 2019; Li et al. 2021a, b; Wijayawardene et al. 2023a, b; Zhao et al. 2023). Therefore, data integration methods and computational analysis tools require advancements to successfully uncover intricate regulatory networks, metabolic pathways, and molecular interactions within fungal cells and communities (Wijayawardene et al. 2023a, b). Functional annotation tools and methods require further improvements to ensure the accuracy and reliability of gene function predictions in fungal genomes. This can be achieved by establishing improved, reliable annotation pipelines by integrating experimental data, comparative genomics, and functional assays. The future of fungal functional genomics relies on collaborative research efforts and data sharing among research communities. Therefore, establishing a centralized database, repositories, and platforms for sharing functional genomics data will be of utmost importance (Byrd et al. 2020; Schatz et al. 2022) as they can accelerate discoveries and enable the broader scientific community to access and utilize the wealth of information generated through functional genomics studies.

Current trends, limitations, and future research in fungi and the biocircular economy

Current trends

Fungi are versatile organisms. They can produce a wide range of products, from food and feed to chemicals and fuels (Lübeck and Lübeck 2022; Copetti 2019; Hyde et al. 2019; Meyer et al. 2021; Vandelook et al. 2021; Strong et al. 2022). They can also break down organic and inorganic materials and compounds, making them a potential waste management and pollution control tool (Vaksmaa et al. 2023; Dashtban et al. 2010; Harms et al. 2011; Deshmukh et al. 2016). As a result, fungi are increasingly seen as critical players in the biocircular economy, an economic system that aims to minimise waste and maximise the reuse of resources (Kirchherr et al. 2017). With their ability to grow on various organic feedstocks and be functionalised into a range of diverse material types, fungi have the potential to revolutionise the way we think about and approach sustainability.

Fungi decompose, meaning they break down organic matter into smaller molecules that other organisms can reuse (Osono 2007; Holden et al. 2013, Niego et al. 2023a, b), a vital process for the ecosystem's functioning. This ability makes them a potential tool for waste management and pollution control. Fungi can be crucial in utilising farm waste and producing fertiliser or food. Agricultural waste, such as crop straw and livestock manure, can be treated through composting or aerobic fermentation to produce organic fertiliser. This process can help reduce dependence on chemical fertilisers and improve the application rate of organic fertiliser in soil (Mengqi et al. 2021).

Additionally, agro-industrial wastes can be used as raw materials for producing biofuels, enzymes, vitamins, antioxidants, animal feed, antibiotics, and other chemicals through solid-state fermentation using fungi and other microorganisms (Sadh et al. 2018). Varma et al. (2015) showed that the decomposition rate of agricultural waste increased when inoculated with a white-rot fungus, while Aspergillus niger was efficient in producing cellulases and agro-waste materials (Jasani et al. 2016). Inoculating Trametes versicolor and Fomes fomentarius on the compost of an organic fraction of municipal solid waste led to a higher degrading ratio and increase of enzymatic activities (Voběrková et al. 2017).

Fungi can produce high-value food and feed products like mushrooms, Quorn, and tempeh (Amara and El-Baky 2023). These products are often considered more sustainable than traditional animal-based products, requiring less land and water to produce (Boland et al. 2013). Some species or isolates can synthesise chemicals like enzymes and antibiotics (e.g., Jakubczyk and Dussart 2020; Sanchez and Demain 2017; Khan et al. 2014; Conrado et al. 2022; De Silva et al. 2012, 2013). These products can be used in different industries, including the food, pharmaceutical, and energy sectors. Others can produce several materials, such as biopolymers, biocomposites, and mycelium-based foams, that can be used in various applications, including packaging, construction, and medical devices (e.g., Sydor et al. 2022; Yang et al. 2021; Manan et al. 2021; Alemu et al. 2022; Vandelook et al. 2021; Bitting et al. 2022; Biala and Ostermann 2022).

Fungal biotechnology also produces biomaterials that can replace petroleum-based ones in various industries, including food, packaging, textile, leather, and automotive (Cerimi et al. 2019; Raman et al. 2022; Meyer et al. 2020). For example, the production of lipase by strains of Aspergillus niger, used in several industries, including food, pharmaceutical, and cosmetics, has been studied and optimised (Colla et al. 2016; Alabdalall et al. 2020). A new and more efficient bioprocess has been developed to produce bioethanol from agricultural waste (starch of avocado seeds) by using a natural strain of Saccharomyces cerevisiae) (Caballero-Sanchez et al. 2023). A new mycelium-based biodegradable and recyclable foam that could be used in many applications, such as packaging and insulation, has been developed (Gandia et al. 2021; Karana et al. 2018; Yang et al. 2009). This could lead to the construction of healthier buildings made of components that are grown instead of manufactured and can be triggered to biodegrade at the end of their life.

Fungi, especially mycelium, are increasingly recognized for their potential in carbon sequestration and as a source of bio-based materials, keeping climate-warming carbon dioxide out of the atmosphere. Indeed, using biodegradable building materials can contribute significantly to carbon sequestration. Bio-based materials integrate various mitigation techniques, including low embodied energy and carbon, affordability, recyclability, utilisation of locally sourced materials, and the ability to repurpose waste and byproducts (Alemu et al. 2022). Biological materials offer indirect benefits in reducing organic waste. This is because the raw materials used to produce microbial-based materials are often locally available organic wastes. This promotes recycling and waste reduction, contributes to local economies, and reduces the carbon footprint associated with transporting materials. According to Kumarappan et al. (2018), using biological materials in construction could reduce carbon emissions by nearly 800 million tons annually.

Fungi have also been found to break down contaminants such as polyaromatic hydrocarbons, heavy metals, herbicides, pesticides, cyanotoxins, pharmaceuticals, antibiotics, phthalates, dyes, and detergents (Akhtara and Mannana 2020). In this process, fungi produce enzymes that can break down specific types of waste. For example, fungi can produce enzymes that can break down plastics (e.g., Andler and Goddard 2018; Okal et al. 2023; Ekanayaka et al. 2022; Temporiti et al. 2022; Ren et al. 2021), wood, and other materials (e.g., Orth et al. 1993; Hammel et al. 1997; Azelee et al. 2020; Goodell et al. 2020; Beltrán-Flores et al. 2022; Geethanjali et al. 2020). These enzymes can then treat waste materials in various settings, such as industrial wastewater treatment plants and landfills, and grow them directly on the substrate. In this way, fungi can also be used to produce biogas from different organic materials, including food waste, agricultural waste, and sewage sludge. They can break down these materials and produce methane, the main component of biogas (Dollhofer et al. 2015; Kazda et al. 2014; Kovács et al. 2022). In addition to their natural ability to break down waste materials, scientists are also developing new biotechnological solutions to harness the power of fungi for waste disposal and ecosystem restoration (Kulshreshtha et al. 2014; Akhtar and Mannan 2020; Akpasi et al. 2023).

Limitations

Even though fungi have great potential and diverse applications, several downsides must be addressed. One of the critical areas of research in fungi and the biocircular economy is the development of new fungal strains and bioprocesses to produce high-value products (Tiwari and Dufossé 2023). The development of even more sustainable and cost-effective ways to make these products, will significantly impact the biocircular economy. Developing new fungal strains and bioprocesses for producing high-value products is however, complex and challenging. For example, there is a need for further research and development to optimise the use of fungi as sources for novel compounds and as cell factories for the large-scale manufacture of bio-based products (Lübeck and Lübeck 2022; Meyer et al. 2016; Vandelook et al. 2021).

Additionally, controlling fungal pathogenicity is a significant challenge that needs to be addressed to improve the efficiency and safety of using fungi in biotechnology (Meyer et al. 2016). Also, their slow growth rate, the intrinsic characteristics of a species and the ecological conditions it is exposed to (Gostinčar et al. 2022), and sensitivity to environmental conditions (Bakar et al. 2020) are constraints to their utilisation on the industrial scale. Research should address these limitations by developing or selecting faster-growing and more efficient fungal strains, optimising bioprocesses for producing high-value products from fungi and developing new methods for their use.

There is also the potential for fungi to produce harmful metabolites such as toxins (Bennett and Klich 2003). This includes mycotoxins such as aflatoxins, ochratoxins, and trichothecenes that can cause a variety of health problems, including food poisoning, liver damage, and cancer (World Health Organization 2023; Bennett and Klich 2003; Omotayo et al. 2019; El-Sayed et al. 2022), alkaloids such as psilocybin, psilocin, and ergot alkaloids that can have a variety of effects on humans, including hallucinogenic, stimulant, and sedative effects (Plazas and Faraone 2023); Polyketides including penicillin, griseofulvin, and lovastatin that can act antibiotic, antifungal, and anti-inflammatory effects on humans (Prescott et al. 2023; Conrado et al. 2022). However, several steps can be taken to mitigate this risk of exposure to harmful metabolites produced by fungi, such as growing fungi in controlled environments, testing fungi for the presence of harmful metabolites, and processing fungi in ways that can inactivate toxic metabolites.

Furthermore, while mycelium-based materials have excellent properties and ecological advantages, challenges remain to overcome. One of the primary challenges is their variable structural strength depending on the substrate, strain, incubation time, and fabrication process (Alemu et al. 2022), which means they cannot support much weight and restricts their use in specific applications, making it challenging to produce consistent, high-quality materials. While the raw materials for growing mycelium are inexpensive, the initial costs associated with mass production and distribution, typical of industrial fabrication, are high. Furthermore, mycelium, like any living organism, can be unpredictable, which could lead to inconsistencies in the final product.

Another challenge is scaling up the production of mycelium biomaterials to a level that can significantly contribute to global carbon sequestration. Also, the longevity of mycelium materials in different environments is not fully understood, and if these materials degrade quickly, they may release stored carbon back into the atmosphere. It becomes evident that the lack of established standards or regulations for using these materials in many applications, could slow their adoption. Besides, as with any new technology, public perception and acceptance will play a role in the widespread adoption of mycelium materials. These challenges underscore the need for further research and development in this field.

Future research

Fungi play a significant role in the biocircular economy by providing sustainable alternatives to traditional products and materials. Despite many challenges, fungal biotechnology has a growing trend towards producing fungal-based biomaterials that can contribute to a more sustainable and circular economy (Meyer et al. 2016; Kržišnik et al. 2023; Delvendahl et al. 2023; Wikandari et al. 2022). However, some challenges and limitations must be addressed, such as the slow growth rate of some fungi, their sensitivity to environmental conditions, and the potential for some fungi to produce harmful metabolites.

Future research in fungi and the biocircular economy will likely focus on overcoming these limitations and developing new and innovative ways to use fungi to create a more sustainable future. Developing new methods to drive more sustainable ways to deal with waste materials using fungi is a crucial area of research in the biocircular economy. This may include developing more robust fungal strains, optimising bioprocesses for producing high-value products and developing new methods for degrading and recycling waste materials using fungi. Developing new fungi-based materials with improved properties, studying the potential of fungi to produce harmful metabolites, and developing strategies to mitigate this risk are also important. By addressing the challenges and unlocking the full potential of fungi, we can create new opportunities to develop bio-based products.

Several key factors underscore the necessity for research on mycelium-based composites. Firstly, A deeper understanding of the properties of mycelium materials and how to manipulate them is required. Secondly, research can aid in devising more efficient and cost-effective production techniques. Thirdly, the absence of established standards or regulations for using mycelium materials in many applications necessitates research to help formulate these standards and regulations, thereby facilitating the adoption of these materials. Furthermore, mycelium-based materials hold potential in various applications, from construction to carbon sequestration. Research can assist in exploring these applications and optimising the materials for each use. Lastly, more research is imperative to comprehend the environmental impact of mycelium materials, such as their longevity and potential for carbon sequestration. This research needs to underscore the potential of mycelium-based composites and emphasize the importance of continued investigation in this field.

Finally, selecting highly promising strains is also an active area of investigation. Researchers are working on large-scale phenotyping of fungal strains to evaluate their potential for degrading non-natural, industrial compounds (Navarro et al. 2021). Additionally, using innovative approaches, such as genetic engineering for enzymes, fuels, and chemicals from lignocellulose biomass is important (Madhavan et al. 2022). These efforts aim to unlock the full potential of fungi as sources for novel compounds and as cell factories for the large-scale manufacture of bio-based products.

Current trends, limitations, and future research in the quest for species numbers

The number of fungal species is a hotly debated topic among mycologists (Hawksworth et al. 1991; Hawksworth and Lücking 2017; Hyde et al. 2020a, b). The estimated number is currently higher than the known species (Phukhamsakda et al. 2022). A large number of novel taxa have been introduced in recent years (Fig. 4). In this section, we discuss current trends in introducing and estimating species numbers. How do these methods and approaches result in novel species. Do the current papers dealing with the topic, overestimate the species number? We address future direction needs in the quest for the species number?

Fig. 4
figure 4

Number of new species introduced from 2000 to 2023. (Numbers extracted from Index Fungorum.org (https://www.indexfungorum.org/names/Names.asp)

Current trends

Novel fungi, are they an untapped potential?

Introducing novel species is common practice, but what is the point? Besides the need to understand fungal diversity worldwide, taxa also have a huge potential in biotechnology (Sandargo et al. 2019; Hyde et al. 2020a, b; Niego et al. 2023a, b) and therefore it is essential to search for and describe novel taxa. Figure 5 illustrates the number of novel species introduced in the last 22 years. Although there have been yearly fluctuations, since the turn of the century, the number of new species that have been described has increased from 1264 (in 2000) to 2734 (in 2022), peaking at 3216 in 2020. The figures for 2021 and 2022 were slightly lower (2772, 2734), probably due to the restrictions due to Covid-19. Similarly, the number of new genera described has increased from 98 in 2000, tripling to 319 in 2020, with a peak of 340 in 2015. Again, figures for 2021 and 2022 were lower (206, 233).

Fig. 5
figure 5

Number of new genera introduced from 2000 to 2023. (Numbers extracted from Index Fungorum.org (https://www.indexfungorum.org/names/Names.asp)

The increase in the number of novel fungi described yearly might be as a result of expanding collection sites or increased studies in certain countries (e.g., China, Thailand) and advancement of DNA techniques which have become standard practice (Cheek et al. 2020). Based on the ratio of plants to fungi, Hawksworth (1991) estimated there could be around 1.5 million fungal species, of which only about 10% had been described. However, based on high-throughput sequencing (HTS), it is estimated that the number of fungal species could be as high as 11.7 to 13.2 million (Blackwell 2011; Baldrian et al. 2021). However, HTS results in a large number of unidentified taxa and technically compromised sequences (Nilsson et al. 2014), thus estimations based on HTS might be an overestimation.

In 2022, a study series was published to estimate the global numbers of fungi. In this series, Wijayawardene et al. (2022a, b) reported that around 30,000 anamorphic (or asexual) species belonging to 3800 genera have been described, while Senanayake et al. (2022) estimated 1.37 to 2.56 million teleomorphic (sexual) species of which 83,000 have been described. The number of basidiomycetes species is estimated to be between 1.4 and 4.2 million (He et al. 2022), whereas single-celled yeast species are estimated to be 20,000 (Boekhout et al. 2021). New yeast species have been introduced at a very rapid rate, particularly due to an extensive research contribution from Asia, in which China has yielded many taxonomic novelties (Boekhout et al. 2021). Several studies have focused on estimating the number of species based on ecological niches. Mora et al. (2011) predicted that there could be over 50,000 (0.005 million) marine species. On the other hand, Sarma (2019) showed that the number of marine species increased from 530 in 2009 to 1112 species by 2015. Jones et al. (2019) listed 1257 marine species, which included 943 ascomycetes. Thus, the number of species of from marine environments is increasing gradually. However, we are far behind in ocean exploration as compared to that in terrestrial habitats. Research on freshwater fungi has also greatly increased and numerous new species have been described, particularly from China and Thailand (Calabon et al. 2023; Yang et al. 2023). Dong et al. (2020) introduced nine new genera of freshwater Dothideomycetes and 33 new species, whereas Luo et al. (2019) introduced two new families, three new genera, and 47 new species belonging to Sordariomycetes.

Hyde et al. (2020a, b) considered that the newly introduced species curve had not reached asymptote, based on studies in Asia. Mycology was well-established in Australia, Europe, New Zealand, South Africa and the USA, whereas in the past two decades it has been increasing in Asia and South America (Hyde et al. 2020a, b). The earliest studies on fungal taxonomy were by European mycologists (Wijesinghe et al. 2023). However, based on the State of the World’s Plants and Fungi (Royal Botanic Gardens (Kew; Antonelli et al. 2020), the proportion of fungi introduced since 2019 from Europe is lower (23%) as compared to the Asia (41%).

The current introduction of novel species and genera introduced from 2020 to 2022 is likely to continue to increase due to several factors. (1) There are no longer travel restrictions and forays will become commonplace. (2) Annually published collections such as in the Fungal diversity notes (Boonmee et al. 2021; Jayawardena et al. 2022), Fungal planet description sheets (Crous et al. 2022; Tan et al. 2022) and Mycosphere notes (Thambugala et al. 2018; Manawasinghe et al. 2022) which encourage collections of novel taxa and have higher citation scores as compared to the average of other papers in mycology. Some open-access journals are also encouraging papers comprising novel species with designated special issues as this is beneficial for improving journal citation indexes.

What is a fungal species?

In order to estimate fungal numbers, it is crucial to understand what a species is. The definition of a genus and species has received much attention (Liu et al. 2016; Chethana et al. 2021; Lücking et al. 2021). Publications have focused on providing recommendations to introduce new species. Aime et al. (2021) provide a basis to accurately introduce a new species or a name. Chethana et al. (2021) provided overall recommendations for introducing new species. Recommendations for establishing species for specific groups of fungi include namely lower fungi (Voigt et al. 2021), ascomycetes (Maharachchimbura et al. 2021), plant pathogens (Jayawardene et al. 2021; Manawasinghe et al. 2021), and yeasts (Boekhout et al. 2022). In all cases, a polyphasic approach has been recommended, which includes the combination of morphology, phylogeny, ecology, chemistry, and any other useful evidence. It is not clear that these recommendations have been followed in recent publications. Introducing new taxa based on only one strain, only sequence variations, missing required gene regions, invalid species names, and poor taxon sampling (Kularathnage et al. 2023; Tang et al. 2023; Yasanthika et al. 2023; Zhu et al. 2023) are some examples.

With the increased availability of molecular data, the phylogenetic species concept has resulted in a number species becoming cryptic such as in pestalotiod genera (Maharachchikumbura et al. 2014a, b), Diaporthe (Udayanga et al. 2014) and Colletotrichum (Jayawardena et al. 2022). When introducing new species, it is essential to establish a complete sampling of representative taxon in the phylogenetic analysis. For example, the Diaporthe eres species complex is highly diverse and D. eres is polyphyletic (Udayanga et al. 2014). Therefore, it is necessary to add additional strains as well as the type species when introducing a new species in this complex. Diaporthe rosicola (Wanasinghe et al. 2018) and D. mahothocarpi (as mahothocarpus in Gao et al. 2015) were introduced without adding adequate strains. A combined multigene (ITS, tub2, calmodulin (cal), and tef1) analysis, D. rosicola and D. mahothocarpus clustered together with D. eres with an increased number of strains D. eres (Manawasinghe et al. 2019) suggesting that both D. rosicola and D. mahothocarpus are genotypes of D. eres.

Fungi are one of the most diverse groups of organisms, and therefore it is necessary to take into account population biology when introducing novel taxa. Inter-species diversity is based on the host or geographical regions. Bhunjun et al. (2022) predicted that specious genera could contain more species than expected. The limiting factor in defining a species in a specious genus however, depends on the ability to determine the amount of variation that can be accepted in a single species. For example, Colletotrichum has 14 species complexes and 248 accepted species, of which C. gloeosporioides is one of the most diverse and widely distributed species (Jayawardena et al. 2022). Colletotrichum gloeosporioides has 12% and 88% genetic differentiation between and within host populations, respectively, based on the genetic diversity of the glyceraldehyde-3-phosphate dehydrogenase (GAPDH) region (Liu et al. 2023). Therefore, to define a new species which is closely related to C. gloeosporioides, we assume that it should exceed this genetic variation. Therefore, within-species diversity that must be concisely dealt with for most of the asexual and specious genera. In the present era of the phylogenetic species concept, morphology tends to have become less important. Most novel introductions of species provide excellent descriptions. However, environmental factors may affect characters such as spore length and width. Thus, describing a novel species using a single strain may be questionable. Therefore, when introducing a new species, it is recommended to use more than one strain (Manawasinghe et al. 2021). The use of the single strain has the risk of introducing a new genotype with sequencing errors. There are numerous examples of this. Pseudopithomyces kunmingensis was introduced by Karun and Hyde [as 'kunmingnensis'], in Hyde et al. (2017) using a single strain. Abeywickrama et al. (2023) found no significant nucleotide differences in GAPDH and tef1-α sequence data between P. chartarum and P. kunmingensis. Thus, they synonymised Pseudopithomyces kunmingensis under P. chartarum, based on morphological similarities and phylogenetic analysis. Zhang et al. (2021a, b) synonymised recently published Botryosphaeria species; B. auasmontanum, B. minutispermatia, B. quercus, B. sinensis, B. wangensis and B. qinlingensis under B. dothidea based on sequence similarities where all these species share 99% or over similarities with the type sequence of B. dothidea. Zhang et al. (2023a, b) reduced C. pandanicola (Tibpromma et al. 2018) to C. siamense as there are only few variations in four gene regions (ITS four bp, gapdh six bp, chs-1 four bp, act two bp, and tub2 one bp).

There is no universal method to characterise a new species and species concepts and approaches in mycology will always be subjective. However, it is necessary to standarise sequence-based novel species introduction. Otherwise rather than describing distinct novel taxa we may separate a single taxon into several species.

Limitations

There are many under-studied geographic regions (e.g., Africa, South America) and more research effort is needed in these areas (Antonelli et al. 2020). Contributions to the fungi in North America and Europe have, however, decreased in recent years as compared to those in Asia (Hyde et al. 2020a, b). One possible reason for this could be the lack resources allocated for basic mycological research (Rambold et al. 2013). African rainforests have a huge plant biodiversity, which is subjected to climate change, forest expansion, deforestation and human interactions (Mahli et al. 2013) and therefore the study of their fungi is important before they become extinct.

The multifarious regulatory measures adopted by individual nations greatly restrict access and exploration of biological hotspots for those interested in sampling and studying their fungal biodiversity. Ironically, most such biodiversity hotspots are based in tropical and subtropical regions (Kumar et al. 2017; Deharveng and Bedos 2019) and fall within the category of developing countries. As the name suggests, these countries have limited financial and physical resources. Therefore, the economic policies of these developing nations cannot afford biodiversity explorations. This situation gets more complex when these countries are signatory to the Convention on Biological Diversity (CBD), Access and Benefit-sharing (ABS) rules and other bureaucratic barriers preventing joint research or international collaborations. One example is the status of mycology in Sri Lanka. Even though mycology in Asia has a rapid growth, Sri Lanka, with its most prominent biodiversity, lacks sufficient studies due to limited resources and stringent biodiversity policies (Wijayawardene et al. 2023a, b).

Despite being poorly studied ecological niches, studies of fungi in terrestrial habitats have been greater as compared to aquatic and marine habitats (Jones et al. 2019). The fungi associated with freshwater streams, mangroves, karst regions, and rocks are some habitats that have recently been explored (Osorio et al. 2017; Norphanphoun et al. 2019; Calabon et al. 2023; Yang et al. 2023). Mangrove fungi represent the second-largest ecological group of marine fungi (Deshmukh et al. 2018). Several novel taxa were introduced in early studies on marine fungi (Hyde and Jones 1988; Hyde et al. 1992). The majority of these taxa lacked sequence data, and need to be recollected, sequenced and published as new collections. Studies on rock fungi are less common, despite their diversity, probably because these species being slow-growing, poor competitors, widespread in nature, and occurring commonly (Coleine et al. 2021). Entomopathogenic fungi are well-researched due to their potential as biocontrol agents, yet compared to phytopathogenic fungi, the introduction of novel species associated with ants and arthropods is comparatively low. A recent study by Zhang et al. (2023a, b) introduced five novel nematode-trapping fungi associated with soil and freshwater sediment samples. Thus, it reflects expanding studies on fungi associated with arthropods, nematodes and other animals that have a high potential to result in novel taxa.

Fungal lifestyles are diverse and have been discussed in detail. Yet, several lifestyles have been either poorly studied or overlooked. Endophytes are one of the most studied fungal lifestyles (Purahong and Hyde 2011) yet remain far behind in numbers of new taxa because not many species were sequenced. Concurrently, the HTS approach presupposes very high fungal numbers within the same community (Dissanayake et al. 2018; Jayawardena et al. 2018; Abeywickrama et al. 2023). It has been estimated that number of endophytes be approximately one million (Sun and Guo 2012) however, current isolation techniques and the inability to culture them on artificial media have become limiting factors to finding new species.

Future research needs

Fungi have an amazing potential in resolving future global needs such as renewable energy, human nutrition, health, and sustainable agricultural. Fundamental research in mycology is a separate discipline to zoology and botany (Christensen 1989; Rambold et al. 2013) and should be treated as such (Rambold et al. 2013). Though fungi play a crucial role in ecosystem stability, the involvement of mycologists in ecological and diversity studies is less when compared to botanists and zoologists (Schmit and Mueller 2007; Rambold et al. 2013). This is the case even when dealing with topical studies such as climate change (Tibpromma et al. 2021). Fungi need to be appreciated as a key component in ecosystem stability, and their contribution should never be underestimated. Simultaneously mycologists need to diversify and integrate their research into subdisciplines, such as biotechnology, biochemistry, ecology, genetics, natural products and nutritional science, and pathology.

Over the last two decades, DNA sequencing, high-throughput technology and molecular methods have advanced significantly (Nilsson et al. 2019a, b; Mapook et al. 2022). It is important to verify species in genera such as Colletotrichum, Diaporthe, Fusarium and Pestalotiopsis as they an important phytopathogens and may also produce a number of metabolites (Manawasinghe et al. 2019; Matio Kemkuignou et al. 2022).

The use of polyphasic approaches and other techniques to justify the introduction of novel species has been stipulated (Chethana et al. 2021). However, in a situation where the answer to ‘What is a fungal species?’ changes constantly, introducing a new species will continue to be a challenge. At present, most studies rely on the phylogenetic species concept combined with morphology. Poor taxon sampling and biased referencing must be avoided. However, taxonomy is subjective and open to critical evaluations.

The quest for fungal numbers should be expanded to understudied regions and habitats including Africa, Australia, South America, and some Asian countries. Furthermore, a concerted effort must be in place for future collections to adequately cover all the underexplored niches like forest ecosystems, deep sea, marine, and extreme conditions, including caves, hot springs, and rock-associated fungi. The necessity to explore fungal species from extreme environments is important because that is where the potential lies for the development of new secondary metabolites due to the adaptive nature of fungi to extreme environmental conditions (Gostinčar et al. 2022; Yu et al. 2023).

Fungal species number estimations require further research (Hyde et al. 2020a, b). Even though the actual number of species might be lower or higher than the present estimates, it is obvious that there are a numerous understudied and unexplored regions with hidden mycota. Without a doubt, these novel species will contribute to the development of agriculture, industrial biotechnology, medicine, and disease management.

Current trends, limitations, and future research in machine learning in mycology

As a subfield of artificial intelligence, machine learning is normally categorised into two primary types: unsupervised and supervised learning (Mahesh 2020). Unsupervised learning algorithms recognize hidden patterns, structures, or relationships within the given observations where the input data does not have corresponding output labels or target values. K-means clustering and principal component analysis are the most typical unsupervised learning methods to find elusive patterns in high-dimensionality datasets (Ding and He 2004). In some sense, phylogenetic trees can be treated as outcomes of unsupervised algorithms that the algorithm does not have prior knowledge of the tree topology or relationships between the sequences. Instead, it seeks to find the most likely tree that best explains the observed sequence data without relying on predefined classifications. While supervised learning algorithms rely on well-constructed training datasets with input data and the corresponding outputs, develop a predicting model by learning the hidden associated patterns, and use the model to predict the outputs of new input instances. In the era of data explosion, a large number of biological data are being generated by advanced analytic instruments, and machine learning algorithms have been a widely used analytical method to extract information and reveal hidden patterns from overwhelming amounts of data or complex data (Ma et al. 2014; Greener et al. 2022). Some image-recognition-based fungal classification and disease diagnosis models have been developed for fungi to identify fungal species or detect specific morphological features. Zahan et al. (2021) developed a deep learning-based approach to classify the edible, inedible, and poisonous mushroom types. Also, they developed a model to detect mushroom diseases (Zahan et al. 2022). Mikhail et al. (Genaev et al. 2020) developed a model to recognize wheat rust diseases and can differentiate between leaf rust and stem rust. In genomics, some machine learning algorithms were used in well-known programs, such as AUGUSTUS, to de novo annotate gene models in new sequenced fungal and other eukaryotic genomes, and effectorP to predict fungal effector protein candidates from entire secreted protein datasets (Sperschneider and Dodds 2022). Machine learning algorithms are being used to predict the ecological roles and functional traits of fungi based on genomic traits (Fijarczyk et al. 2022; Chen et al. 2023b), which can help in the early identification of devastating fungal species with potential high pathogenicity to humans or plants. There have been some applications of machine learning algorithms in mining secondary metabolites (Aghdam and Brown 2021; Skinnider et al. 2016, 2020).

Limitations

In mycology, the limited availability of comprehensive and reliable datasets, particularly for rare or understudied fungi, can be challenging. Machine learning models by image recognition to identify fungal species have been developed only for economic macrofungi, whereas researchers cannot obtain enough morphological images for training the predictive model for most wild macrofungi. In the study of microfungi, it is not easy to obtain the image data because we cannot directly observe the useful diagnostic characteristics without the assistance of optical microscopes. Highly similar characteristics in the morphology of some plant pathogens make image recognition useless in identifying microfungi. Machine learning algorithms, especially deep learning, often act as "black boxes" with limited interpretability (Azodi et al. 2020). However, mycologists need to understand the specific biological mechanisms and identification processes.

Future

For image recognition in macrofungi, a shared database of image data checked by taxonomists should be developed for obtaining more diverse and reliable morphological images of some rare fungi species. Integration of multi-omics data, such as genomic, transcriptomic, proteomic, and metabolomic data, can provide a more comprehensive understanding of fungal biology. Machine learning models can uncover the hidden association between the genomic traits and the capacity of producing beneficial secondary metabolites, which avoids duplicated screening and expands screening populations in genome mining, helping to quickly target the fungal strains that are candidates for producing candidate drugs (Fungal genomes scoured for drugs 2018). Collaborations between mycologists and data scientists and the availability of high-reliable data and advanced computational techniques will drive further progress in this field.

Current trends, limitations, and future research in detecting plant pathogens

Plant pathology is the field of study of plant diseases and disease-causing agents, across a diverse range of natural and man-made environments. They cause substantial yield losses in several economically important crops, resulting in economic and social adversity (Venbrux et al. 2023). Plant pathogens can be microfungi, macrofungi, and fungus-like organisms (Hyde et al. 2014a, b; Jayawardena et al. 2019). Human practices such as monoculture farming and global trade affect the spread of plant pathogens and the emergence/reemergence of diseases (Gomdola et al. 2022). Accurate estimates of disease incidence, severity, and the negative effects of the disease on quality are very important. Hence, the early detection and identification of pathogens are important to reduce the associated agricultural and social losses. These pathogens have been identified and are based mainly on morphology and molecular analyses (Senanayake et al. 2020; Dissanayake et al. 2020). Several techniques are available to detect plant pathogens including culture-based, PCR-based, sequencing-based, and immunology-based techniques. The evolutionary processes of these pathogenic fungi were often fuelled by genetic variation in pathogen populations (Manawasinghe et al. 2021). Based on multi-loci analyses, a better understanding of pathogenic fungi has been obtained. Many cryptic species, in genera such as Colletotrichum, and Diaporthe were identified (Talhinas and Baroncelli 2021; Norphanphoun et al. 2022).

Non-invasive optical and spectral detection methods such as imaging efficiently detect plant diseases (Venbrux et al. 2023). It has been shown that stressed or diseased plants produce a different spectral signature compared to that of healthy plants (Zubler and Yoon 2020). According to Singh et al. (2021), spectral analysis can be applied at different scales, ranging from taking high-resolution images from a single leaf to an entire field. In large cultivation areas, imaging can help to detect a ‘hot spot’ that is experiencing biotic stress. Fusarium virguliforme, the causative agent of sudden death syndrome in soybean can be detected via satellite imaging (Raza et al. 2020). This technique provides real-time detection and detects biotic stress without any sample collection.

Cultivation-based methods are used to isolate and grow the pathogen on a selective or semi-selective medium (Senanayake et al. 2020). Isolates obtained via this method need to be confirmed by morphological, molecular, and biochemical assays (Ferone et al. 2020). Different types of PCR-based diagnostic methods have been used to identify fungal phytopathogens (Table 1). For a better resolution of the pathogens, analytical profile index, microplates, matrix-assisted laser desorption/ionisation, and fatty acid profiling (Chen et al. 2020; Ferone et al. 2020). Fatty acid profile analysis allows taxonomic identification of the pathogen up to the species level and is cost-effective as well as a rapid method in species identification (Lacey et al. 2021). In taxonomic identification, DNA barcoding also plays an important role (Hyde et al. 2014a, b; Chethana et al. 2021). In recent years various molecular techniques such as Amplified Fragment Length Polymorphism (AFLP), Inter Simple Sequence Repeats (ISSR), Random Amplified Polymorphic DNAs (RAPD), Simple Sequence Repeats (SSR), Sequence-Related Amplified Polymorphism (SRAP), and Inter Primer Binding Site (iPBS) amplification techniques have been successfully used in plant pathogen identification (Cannon et al. 2012; Longya et al. 2020). Genealogical Concordance Phylogenetic Species Recognition is used to differentiate species that lack distinguished morphological characteristics when enough independent samples per lineage are provided (Jayawardena et al. 2021a, b). Bhunjun et al. (2020; 2021) highlighted the importance of using different molecular approaches such as Automatic Barcode Gap Discovery (ABGD), General mixed Yule‑coalescent, and Objective clustering in plant pathogen identification. Fusarium head blight caused by Fusarium graminearum can be identified by a CRISPR-Cas12a-based dual recognition technique developed to detect this pathogen as low as 1 fg/μL of total DNA (). For the detection of Botrytis cinerea and Didymella bryoniae, two DNA targets were used in the development of a reusable microfluidic bioassay. Thermal denaturation of DNA was then performed to regenerate the oligonucleotide sequence and a quick assessment of multiple target detection using the laser-induced fluorescence detection technique was done in less than 10 min (Qu et al. 2017). Loop-mediated isothermal amplification (LAMP) assays have been used recently in plant pathogen detection. A quick, sensitive, and focused LAMP-based test has been used to detect Sclerotinia sclerotiorum (Grabicoski et al. 2020). Wang et al. (2020) mentioned that highly specific LAMP primers for Fusarium proliferatum are appropriate for the TEF-1 region. The use of LAMP assay for quick detection of F. proliferatum causing ear and kernel rot in maize was also described by Wang et al. (2020). Karimi et al. (2019) described the detection of Colletotrichum nymphaeae infection in asymptomatic strawberry plants based on LAMP assays.

Table 1 Different types of PCR used in phytopathogen identification

To speed up the identification of plant pathogens and allow their identification in the field, a number of serological methods have been developed, mainly based on the enzyme-linked immunosorbent assays (ELISA), which can be considered an immunological method (Luchi et al. 2020). Different immunoassay methods have been used to visualise the binding of a specific antibody to its related antigen (Miller and Martin 1988). As ELISA needs a laboratory facility, a simple paper-based dip-stick assay, namely lateral flow devices (LFD) to detect the presence or absence of a target analyte in a liquid sample was developed. This method is widespread as it allows rapid in-field detection of plant pathogens in a few minutes (Boonham et al. 2014; Tomlinson et al. 2010). Quantum dots offer a unique optical property known as fluorescence resonance energy transfer between two reactive molecules (Sanzari et al. 2019). This property is exploited by QT-FRET immunoassays for visual identification of pathogen infections such as Aspergillus amstelodami (Safarpour et al. 2012).

Digital agriculture is an innovative approach to farming that uses modern technologies to enhance agricultural practices. High-resolution remote sensing and data analytics are being utilised to monitor and predict disease outbreaks. This digital agriculture technology allows for timely and targeted interventions, reducing the need for blanket applications of pesticides. Biosensors comprise devices that consist of a biorecognition element combined with a physicochemical transducer that generates a measurable signal upon the binding of the target analyte with the biorecognition element (Bridle and Desmulliez 2021). Biosensors are promising tools for point-of-care applications, as they are generally low-cost, easy to use, and can provide fast results (Bridle and Desmulliez 2021).

Studies of the population genetics of fungi and fungi-like pathogens are essential to identify and clarify the disease epidemiology as well as to devise management strategies (Atallah and Subbarao 2012). Genome sequencing of fungal pathogens has provided information on extensive variation in genome structure and composition between species, especially between individuals of the same species (Eschenbrenner et al. 2020). Hundreds of fungal pathogen genomes are now available and analysing these genomes provide information about the genes that are responsible for causing the diseases (Plissonneau et al. 2017). A study done by Zhang et al. (2023a, b) revealed that the comparative genome of Colletotrichum species from different lineages revealed that expanded gene families encoding CAZymes are thought to be one of the likely explanations for the widespread and polyphagous nature of species in the C. acutatum, C. boninense and C. gloeosporioides species complexes. Liu et al. (2022) used 94 Colletotrichum species based on 1893 single-copy orthologous genes to provide the most comprehensive genome tree. Datasets from population genomics built on NGS can be used to identify variations including single nucleotide polymorphisms (SNPs), insertions and deletions (INDELS), and structural variations (Potgieter et al. 2020). Next-Generation Sequencing (NGS) is another popular current trend in the identification of phytopathogenic fungi. Calonectria pseudonaviculata was detected by using NGS (Malapi-Wight et al. 2016). Puccinia striiformis f. sp. tritici is the causative agent of the wheat yellow stripe. In order to identify the population of this emerging pathogen field pathogenomics was done using RNA-Seq-based NGS of pathogen-infested wheat leaves (Hubbard et al. 2015). It was shown that there is a dramatic shift in the pathogenic population in the UK, probably due to an introduction of a different set of emerging and exotic pathogen lineages. Molecular diagnostics for the cucurbit downy mildew pathogen Pseudoperonospora cubensis, was conducted via RNA and DNA-based NGS approaches. Based on the comparative genomic analyses based on RNA-Seq of closely related species of Pseudoperonospora humuli, seven specific regions were identified in P. cubensis (Withers et al. 2016).

A hybrid and hierarchical de novo association strategy was used to sequence the genome of Monilinia fructicola (Mfrc123), the brown rot pathogen, through a combination of Illumina short-read NGS and Pacific Biosciences (PacBio) long-read third-generation sequencing platforms (Angelini et al. 2019). The genome of the coffee rust fungus Hemileia vastarix was sequenced using PacBio RS II and Illumina HiSeq platforms (Porto et al. 2019).

Limitations

The major limitation in plant pathology is the emergence and resurgence of pathogens. New plant pathogens or more aggressive strains evolve due to climate changes, global trade, and human activities (Olsen et al. 2011). Identifying these novel pathogens and managing their threats is a significant challenge for plant pathologists (Zeilinger et al. 2016). Karnal bunt is a fungal disease of wheat, durum, and rye triticale caused by Tilletia indica. This disease first emerged in Karnal, India during 1931 and was initially restricted to South Asia and Iraq (Singh et al. 1989). It became of global importance following its discovery in Mexico in 1972, and USA in 1996 (Bonde et al. 1997). The disease decreases seed viability and flour quality and wheat consisting of 3% or greater bunted kernels is considered unfit for human consumption (Bansal et al. 1984; Brennan et al. 1992). The causative agent of chestnut blight is Cryphonectria parasitica and it was restricted to East Asia. Japanese chestnut (Castanea crenata) which is resistant to this disease was introduced from Japan to North America during the late nineteenth century. The causative agent has been moved with imported chestnut plants and infected to American chestnut and has killed most mature American chestnut (C. dentate) trees within their natural range (Anagnostakis 2001). Anderson et al. (2004) listed many emerging and resurgent cases of plant pathogens worldwide.

Morphological observations and interpretations in pathogen identification can be rather difficult and are often based on the interpretative skills and experience of the analyst (Rajapaksha et al. 2019). Not all pathogens can be cultivated in an artificial environment (Jayawardena et al. 2018), leading to reliance on sequence data obtained directly from the infected material, which has proven to be difficult. With the use of DNA sequence data, mycologists came up with different genes that can be used to give a better resolution in species identification. However, there is no standard as to which gene(s) should be analyzed or how much sequence divergence is needed, or what statistical support is required at both the individual locus and the combined concatenated sequence levels to determine whether different strains belong to different species (Lücking et al. 2020). This has resulted in dubious species identification.

Another limitation is that some taxonomic studies rely on relatively few samples (one strain) when introducing phytopathogens. However, further analyses of closely related DNA sequences based on ‘phylogenetic or genotypic cluster species’ with a larger sample size, may reveal an abundance of recombination (Liu et al. 2016; Zhang et al. 2023a, b). The best examples of this are Colletotrichum siamense and Diaporthe eres. Separate species designations are mainly due to the inadequate sampling size (Liu et al. 2016). In the use of the GCPSR concept, the resulting trees are only as informative as the specific loci chosen for sequencing and the alignment used as input data (Taylor et al. 1999). If the sequences that are used are wrong, the result will also be wrong leading to misidentification of species. The major limitation in NGS is the time consumption during assembly and analysis of large amounts of sequence data (Espindola et al. 2015) as well as low yield and/or integrity, stability and impurities (Cortés-Maldonado et al. 2020). Also, expertise in bioinformatics and mycology are necessary for NGS analysis and are mandatory to avoid any misinterpretations.

Plant diseases can be caused by multiple fungal genera that affect diverse hosts with different tissue specificities involving a range of symptoms (Bhunjun et al. 2021). In order to confirm whether a species is actually the disease-causing agent, a pathogenicity assay is needed. There are limitations from selecting inoculation methods (wounded, non-wounded, spore suspension, mycelium plug) to analysing the disease symptoms (disease charts) (Bhunjun et al. 2021). Determining whether a fungus is a true pathogen needs more studies. Some species have been introduced or reported as associated with a disease when Kochs’ postulates cannot be implemented. In the case of Colletotrichum, a pathogenic genus, pathogenicity assay data are available for only one-third of its species. Disease epidemiology of fungal genera or species is not well-studied or identified. Hence, this makes it difficult to identify how the diseases are spread and when to apply the control measures.

Due to the changes in climate and weather patterns, global trade, and human activities, many new fungal pathogens are emerging, or more aggressive strains are re-emerging (Gomdola et al. 2022). Identifying and managing these novel threats is a significant challenge for plant pathologists. While imaging techniques can detect biotic stress in the plant before visual symptoms appear, the technique lacks capability to identify specific pathogens. More novel approaches for determining, characterising, and monitoring fungal pathogens are required as traditional methods are time-consuming and have other limitations.

Future

Incorporating a new disease paradigm such as pathobiome can provide more information about the disease epidemiology. The pathobiome concept, has been invoked in cases where the disease is believed to result from interactions between a set of organisms (including eukaryotic, microbial and viral communities) within the plant and its biotic environment, leading to the deterioration of host health status is needed (Collinge et al. 2022). Although the concept of pathobiome is relatively new in plant pathology, several studies have reported on diseases caused by multi-species “disease complexes” (Mazzola and Freilich 2017). Examples of diseases caused by multi-species complexes include tomato pith necrosis, soft rot in broccoli, and young grapevine decline. These diseases are caused by multiple bacterial or fungal species that interact synergistically to impact disease development. It is suggested that the pathobiome concept be applied to postharvest diseases, providing a more comprehensive perspective on disease development involving intricate assemblages of microorganisms (Droby et al. 2022).

The developments in omics approaches in plant disease ecology have been particularly important as fungi can be spread around the world via globalisation changing the composition and ecology of habitats. Invasive pathogens with a broad range of hosts can cause chaotic results in ecosystems. Shifting temperatures and the frequency and duration of weather conditions over time result in phenomena such as the rapid evolution of microbial pathogens or environmental stress which can weaken plant hosts. Omics are primarily aimed to enhance the understanding of plant-pathogen interactions at the molecular level (Crandall et al. 2020). A multi-omics approach allows for a detailed account of plant-microbial interactions and can eventually allow us to build predictive models for how microbes and plants will respond to stress under environmental changes (Santini et al. 2015; Gilbert et al. 2010; Rizzo et al. 2002). These technologies will identify new targets for disease control and improve the development of resistant plant varieties (Filgueiras et al. 2019). Pangenomics can be used to identify virulence processes in a rapidly evolving fungal plant pathogen (Chen et al. 2023a).

Climate-resilient plant pathology is studying the impact of climate change on plant diseases and developing adaptive strategies to combat disease outbreaks under changing climatic conditions (Velásquez et al. 2018). Disease forecasting tools are not used abundantly at present. This can help the farmers to identify when a disease is going to occur as well as the best time to use fungicides (El Jarroudi et al. 2017; Maddalena et al. 2023).

Current trends, limitations, and future research in HTS

The study of fungal diversity is a rapidly growing field, with researchers worldwide working to understand their distribution in diverse ecosystems. The development of high-throughput sequencing (HTS) technology and the application of meta-approaches, such as microbiome sequencing, has revolutionised how researchers analyse and interpret fungal diversity (Gutleben et al. 2018). High-throughput sequencing is based on the sequencing of the internal transcribed spacer (ITS) region (as the primary barcode), followed by the small (SSU) and large subunit ribosomal ribonucleic acid (LSU) and their combinations (Taberlet et al. 2012; Nilsson et al. 2019a, b; Tedersoo et al. 2020a, b; Semenov 2021). The sequencing generates multiple sequences from the same samples, which have output as operational taxonomic units (OTUs) (Nilsson et al. 2019a, b; Tedersoo et al. 2020a, b). Runnel et al. (2022) demonstrated the effectiveness of long-read HTS, specifically PacBio HTS, for the taxonomic identification of fungal specimens. Their study showed the advantages of long-read HTS over the traditional Illumina sequencing (Sanger method), including higher success rates and the ability to detect gene polymorphism, taxonomic delimitation, and ecological and population-level studies.

High-throughput sequencing can provide comprehensive insights into taxonomic and functional diversity of fungal communities. High-throughput sequencing techniques have enabled the identification and characterisation of diverse fungal species in various environments such as soil, plants, and the human body. It can recover DNA sequences in extreme environments (Tedersoo et al. 2020a, b; Rämä et al. 2017; Ogaki et al. 2021a, b) where cultivation is difficult or impracticable (Semenov 2021). These methods have also shed light on fungal microbiome diversity, composition, and functional potential (D’Hondt et al. 2021; Fan et al. 2023). In the most recent studies, HTS has significantly impacted estimating the number of fungi (Wu et al. 2019; Baldrian et al. 2022a, b). Põlme et al. (2020) introduced the user-friendly database FungalTraits, a stand-alone spreadsheet dataset covering 17 lifestyle-related traits of fungal and Stramenopila genera, and the endemicity of soil taxa (Tedersoo et al. 2022). The GlobalFungi (https://globalfungi.com/, Větrovský et al. 2020) and the Global Soil Mycobiome consortium dataset (Tedersoo et al. 2021a, b) have attempted to gather information on soil fungal diversity and construct a curated HTS sequence database, respectively. Other studies have focused on fungi to answer specific questions. For example, Baldrian et al. (2022a, b) estimated the number of fungi; Sun et al. (2019) used different HTS platforms for measuring Fungi and Oomycetes.

Several examples have highlighted the potential of HTS in fungal studies. In clinical settings, it has been used for the early identification of infection in culture-negative and food-born pathogens. In ecology, it has been used to study fungi in several environments, such as aerial (Mbareche et al. 2019), aquatic (Hassett et 2017; Lepère et al. 2019; Souza et al. 2021; Garmendia et al. 2021) and soil (e.g., Young et al. 2016; Tedersoo et al. 2020a, b, 2021a, b, 2022; Yasanthika et al. 2022; Wydro et al. 2022). It has also been used in plant pathogen detection and surveillance (Bérubé et al. 2018; Piombo et al. 2021).

Limitations

While high-throughput sequencing has revolutionised microbiome research, it is not without its limitations. HTS methods have challenges and possible sources of error, which require careful consideration when using and interpreting HTS data and outcomes. One major challenge is the high rate of erroneous base calls produced by HTS technologies, such as Illumina sequencing machines, which have errors at approximately 0.1-1E10−2 per base sequenced (Lou et al. 2013). This error rate can present a profound barrier in contexts where rare genetic variants are sought. Methods are also unable to differentiate living and dead cells or organisms, amplifying inactive DNA (Tuininga et al. 2009). This can lead to inaccurate estimations of microbiome structures and, consequently, functional capacities (Dlott et al. 2015; Carini et al. 2020; Nagler et al. 2021). This effect would be most noticeable if artefact DNA is abundant and if the taxa represented in the relic DNA pool do not accurately reflect the taxa present as living cells (Carini et al. 2020).

Additionally, during the analysis stage, the absence of reference databases and the amplification of selected barcodes, results in challenges in assigning OTUs at lower taxonomic ranks, resulting in erroneous or unclassified taxa (Hongsanan et al. 2018; Wu et al. 2019). Comparisons between datasets such as the Global Fungi dataset (Větrovský et al. 2020) and GSMc (Tedersoo et al. 2021a, b) have shown significant levels of contradictions in the richness of certain fungal groups due to different sampling strategies and analytical biases such as the use of different primers for metabarcoding and lack of properly annotated reference sequences (Tedersoo et al. 2021a, b).

High-throughput sequencing requires storage facilities, computational power, and specialised personnel (Rincon-Florez et al. 2013; Sota et al. 2014; Nilsson et al. 2019a, b; Wu et al. 2019), which are limited to a few research institutions or specialised companies. However, the successful implementation of high-throughput sequencing extends beyond these resources. It requires a close collaboration between bioinformaticians and mycologists. This interdisciplinary collaboration is crucial for meaningful results. Bioinformaticians bring their expertise in managing and interpreting large datasets and help design experiments and analyse data in ways that answer the specific questions which mycologists are interested in, while mycologists provide the necessary biological context and understanding of the studied organisms and guide bioinformaticians in understanding the biological significance of their findings.

HTS analysis lacks standardised methods for sample collection, DNA extraction, library preparation, and data analysis. The variability introduced by these differences can hinder the comparability of findings across studies. Standardisation of protocols and methodologies is necessary to ensure reproducibility and facilitate meta-analyses (Li et al. 2022a, b, c). Accurately identifying and classifying fungal species using HTS data is another limitation in mycobiome research. Fungal taxonomy is complex and constantly evolving, and reference databases for fungal sequences are often incomplete or inadequate. Additional techniques or curated databases may be required to accurately assign taxonomy to fungal sequences (D’Hondt et al. 2021). To overcome these limitations and shape future research, several promising directions have emerged. Integrating multi-omics data, such as metagenomics, metatranscriptomics, and metabolomics, can provide a holistic understanding of microbial community dynamics (Muller et al. 2013; Abram 2015). Establishing standardised protocols for sample collection, storage, and data analysis is crucial to ensure the reliability and comparability of results (Bella et al. 2013; Wang et al. 2023a, b, c). Moreover, exploring microbiome-based therapeutics and the role of microbiomes in environmental sustainability, including soil health and sustainable agriculture, holds promise for addressing global challenges (Tedersoo et al. 2017).

Future research

Notwithstanding these limitations, HTS remains a powerful tool for microbiome research, providing comprehensive insights into the taxonomic and functional diversity of microbial communities. Advancements in DNA and RNA sequencing technologies have enabled the integrative study of fungal communities, including their taxonomic profiles and functional and ecological attributes. This allows for a better interpretation of communities and the ability to address questions related to ecosystem functioning, including intra- and interkingdom interactions. However, standardisation, taxonomy challenges, and multi-omics data integration remain critical focus areas in mycobiome research. Ongoing advancements in HTS technology, such as long-read sequencing and data analysis, help to address some of these limitations and improve the accuracy and reliability of HTS-generated data. As HTS data accumulate, it becomes increasingly important to use those data to explore new research questions, hypotheses, and theories.

Fungal metabarcoding is an interdisciplinary and reproducible research strategy that requires expertise in mycology, ecology, Earth sciences, bioinformatics, statistics, and laboratory and analytical procedures. Indeed, while HTS does present technical challenges, it also opens up a world of opportunities for interdisciplinary collaboration. This collaboration is beneficial and essential to fully harness the potential of HTS in mycology and other biological fields. The synergy between bioinformatics and mycology can lead to what were previously impossible breakthroughs within a single discipline. It allows for a more comprehensive understanding of the data, leading to more accurate and meaningful results. By working together, bioinformaticians and mycologists can unlock new insights and push the boundaries of what is possible in mycology. Mycology must ask significant scientific questions and target large-scale patterns and processes. Producing reproducible results by following applicable standards and protocols, providing ample detail on data processing and analysis, and making all relevant data freely and openly available is crucial. Failure to do so will maintain the view that fungi matter only to mycologists, a belief that has haunted mycology for far too long.

Fungal nanotechnology: fungi-based nanoparticles, current applications, challenges and prospects for future research

Nanotechnology is a rapidly advancing field that involves manipulating and controlling substances at the nanoscale, which is about 10–9 m in size (Bayda et al. 2020). It encompasses a wide range of scientific disciplines, i.e., biology and engineering, to understand and harness the unique properties of materials at the atomic and molecular levels (Paramasivam et al. 2021). This field holds tremendous potential for various industries, including electronics, medicine, energy, and manufacturing, by enabling the development of novel materials and devices with enhanced properties and functionalities (Green et al. 2015; Peer et al. 2007; Meyers et al. 2006). In nanotechnology, nanoparticles have become an integral part of our daily lives, often working behind the scenes to enhance product functionality and user experience. A prevalent example is in the field of cosmetics, where nanoparticles, particularly of titanium dioxide and zinc oxide, are employed in sunscreens to offer broad-spectrum UV protection without leaving a white residue on the skin (Smijs and Pavel 2011). In medicine, nanoparticles have shown promise in targeted drug delivery, allowing for increased drug efficiency and reduced side effects (Yusuf et al. 2023). The electronics industry harnesses the unique electronic, optical, and mechanical properties of nanoparticles to develop improved displays, batteries, and memory storage devices (Malik et al. 2023). Nanoparticles also enhance the protective nature of some textiles, filling them with water-repellent, stain-resistant, or even antimicrobial properties (Saleem and Zaidi 2020). In the environmental sector, nanoparticles aid in water purification processes by binding to and removing contaminants (Kumar 2023). Thus, from personal care products to high-tech gadgets and environmental solutions, nanoparticles play an essential role in driving advancements and refining everyday experiences.

The demand for clean, non-toxic, and environmentally friendly approaches, commonly referred to as "green chemistry", in nanoparticle synthesis and assembly is increasing. This has prompted researchers to explore biological systems for inspiration (Mukherjee et al. 2001). Recently, the utilization of microorganisms and plants for synthesising metal nanoparticles (MtNPs) has gained recognition as an efficient and sustainable method, enabling further exploration of microorganisms as “nanofactories”. Microorganisms, cultivated on a large scale, play a crucial role as nanofactories due to their ability to accumulate and detoxify heavy metals. This capability is attributed to the presence of diverse reductase enzymes that facilitate the reduction of metal salts into MtNPs.

Fungi, as a diverse group of organisms, have gained significant attention in recent years for their potential application in various fields, including nanotechnology. Moreover, fungi present a convenient option for cultivation as nanofactories, allowing for the production of nanoparticles with precise control over their size and morphology (Gade et al. 2008; Ahluwalia et al. 2014; Azmath et al. 2016; Khan et al. 2017, Guilger-Casagrande de Lima 2019). The utilization of fungi in nanoparticle synthesis represents a promising and sustainable approach within the rapidly evolving field of nanotechnology, with applications in areas such as medicine, agriculture, and environmental remediation (Guilger-Casagrande de Lima 2019; Moond et al. 2022; Sharma et al. 2023). Myconanotechnology, as it is commonly known, is thus defined as the interface between nanotechnology and mycology (Hanafy 2018; Sousa et al. 2020; Adebayo et al. 2021). In this section of the article, our aim is to discuss the fungi-based nanoparticles, current applications, limitations, and potential future prospects in this captivating field of research.

Biosynthesis of nanoparticles by fungi

Fungi have become a valuable addition to the microorganisms used in nanoparticle production. They are effective candidates for synthesizing metal nanoparticles, both intracellularly and extracellularly. In intracellular synthesis, fungi serve as nanofactories for the production of metal nanoparticles within their cellular structures. This process entails the uptake and accumulation of metal ions by fungal cells, subsequently reducing these ions to generate nanoparticles. Conversely, extracellular synthesis involves the utilization of fungal biological systems to generate metal nanoparticles outside the confines of the cells. This mechanism is facilitated through the secretion of enzymes or biomolecules by fungi, which act as reducing agents to convert metal ions into nanoparticles within the surrounding environment. Fungal-synthesized nanoparticles have excellent dispersion and stability. Fungi offer advantages such as the presence of specific enzymes, ease of handling in the lab, scalability, and cost-effectiveness (Castro-Longoria et al. 2012; Siddiqi and Husen 2016; Guilger-Casagrande and Lima 2019; Bourzama et al. 2021). Fungi can produce various metal nanoparticles, including silver, gold, platinum, palladium, copper, iron, selenium and tellurium nanoparticles.

Silver nanoparticles

The utilization of fungi for the biological synthesis of silver nanoparticles (AgNPs) has garnered significant attention in scientific research over the past two decades. Notably, more than 120 fungal species from diverse taxa, including Ascomycota, have demonstrated the ability to produce nanosilver (Loshchinina et al. 2023). Basidiomycota, in particular, have attracted considerable interest as bio-objects for nanoparticle fabrication, as highlighted by Loshchinina et al. (2023). Their unique properties and potential applications make the mycosynthesis of AgNPs a compelling area of investigation in the field of nanotechnology. These nanoparticles have been found to possess remarkable antibacterial, antifungal, anticancer, antioxidant, and larvicidal activities, among other beneficial properties (Khan et al. 2018; Ratan et al. 2020). Notably, the same nanoparticles have been shown to exhibit a diverse range of biological activities, underscoring their versatility and potential for various applications (Rafique et al. 2017a, b; Razak et al. 2021). The broad spectrum of therapeutic and functional attributes displayed by mycosynthesized AgNPs reinforces their significance in fields such as medicine, biotechnology, and environmental science.

Gold nanoparticles

Gold nanoparticles (AuNPs) have found widespread application in diverse processes, ranging from chemical and biological sensing to bio-imaging, nonlinear optics, catalysis, targeted drug delivery, and gene delivery (Elahi and Baghersad 2018). Additionally, they exhibit antimicrobial and antioxidant properties, making them valuable in cancer and infectious disease therapy (Ahmed et al. 2016). The biological synthesis of AuNPs by fungi has also been extensively investigated, with various species demonstrating the ability to form these nanoparticles. Notably, these AuNPs show promise as dual-modal (chemo-photothermal) therapeutic agents for anticancer applications. It was observed that synthesized gold nanospheres (10–50 nm) effectively inhibited the growth of clinically significant Gram-positive and Gram-negative bacteria, as well as pathogenic fungi (Loshchinina et al. 2023). These findings highlight the potential of mycosynthesized AuNPs as versatile and effective therapeutic candidates.

Platinum nanoparticles

Platinum nanoparticles (PtNPs) have catalytic, magnetic, and optical properties, as well as antimicrobial, antioxidant, and anticancer properties (Jeyaraj et al. 2019; Fahmy et al. 2020). While the mycosynthesis of PtNPs has been less well-studied compared to silver and gold nanoparticles, some Ascomycota species have been found to have the ability to form PtNPs. In a study by Borse et al. (2015), PtNPs synthesized by Saccharomyces boulardii showed anticancer activity.

Palladium nanoparticles

The synthesized PdNPs exhibit unique physicochemical properties, such as excellent catalytic activity and enhanced stability. These properties make them suitable for various applications, such as the development of novel photothermal, photoacoustic, antimicrobial, and antitumor agents, gene/drug carriers, prodrug activators, and biosensors (Phan et al. 2019). The mycosynthesis of palladium nanoparticles (PdNPs) in fungal cultures has received limited attention, with only a few publications reporting its occurrence in Agaricus bisporus, Inonotus obliquus, and Saccharomyces cerevisiae (Mohana et al. 2020, Gil et al. 2018, Sriramulu et al. 2018, Saitoh et al. 2020).

Copper nanoparticles

Biosynthesized copper nanoparticles (CuNPs) possess a range of beneficial properties, including antibacterial, antifungal, antiviral, and anticancer activities. These nanoparticles have potential applications in targeted drug delivery, cosmetics, catalysis, microelectronics, gas sensors, high-temperature superconductors, solar cells, bactericide agents, wound dressings, biopesticides, bioremediation, biodegradation, and energy storage (Rafique et al. 2017a, b; Al-Hakkani 2020; Chaerun et al. 2022). However, the mycosynthesis of CuNPs remains an area of limited research, with species such as Agaricus, Aspergillus, Fusarium, Hypocrea, Shizophyllum, Stereum, and Trichoderma reported to be capable of producing CuNPs (Loshchinina et al. 2023).

Iron nanoparticles

Iron nanoparticles (FeNPs) and iron-based nanomaterials play a crucial role in addressing environmental pollution through their ability to degrade organic dyes, remove heavy metals, and treat wastewater. In addition, these nanomaterials hold promise in biomedicine as antimicrobial agents (Saif et al. 2016; Pasinszki and Krebsz 2020). The production of FeNPs has been primarily investigated in Ascomycota micromycetes, with species such as Alternaria, Aspergillus, Fusarium, Penicillium, Pleurotus, Rhizopus, and Trichoderma being reported as capable of synthesizing FeNPs (Loshchinina et al. 2023).

Selenium nanoparticles

Selenium nanoparticles (SeNPs) have garnered significant attention due to their reduced toxicity compared to inorganic and organic selenium compounds, as well as their biocompatibility, bioavailability, and biomedical properties. Nano-selenium demonstrates remarkable antimicrobial, anticancer, antidiabetic, antiparasitic, and antioxidant activities (Bisht et al. 2022). SeNPs find applications in targeted drug delivery, bioremediation, nanobiosensors, food supplements, and various other fields (Shoeibi et al. 2017). Selenium nanoparticles (SeNPs) synthesized through mycosynthesis using culture liquid of Aspergillus flavus and Candida albicans have demonstrated potent antifungal activity. These nanoparticles exhibit the ability to inhibit fungal growth at lower concentrations compared to conventional antifungal drugs (Bafghi et al. 2021). The biological synthesis of SeNPs has been observed in a substantial number of fungal species, with Aspergillus, Penicillium, and Trichoderma being among the most extensively studied genera in this regard (Loshchinina et al. 2023).

Tellurium nanoparticles

These nanoparticles possess a wide range of properties, including photoconductivity, thermoconductivity, piezoelectricity, non-linear optical behavior, antioxidant activity, antimicrobial activity, anticancer effects, immunomodulation, and cytotoxicity. Moreover, TeNPs show potential for applications in drug delivery, bioremediation, and biorecovery, making them an area of significant interest (Zambonino et al. 2021). However, the formation of TeNPs in fungi remains relatively understudied. Thus far, tellurium nanospheres have been successfully obtained from species such as Aspergillus welwitschiae, Aureobasidium pullulans, Mortierella humilis, Penicillium chrysogenum, Phanerochaete chrysosporium, Phoma glomerata, and Trichoderma harzianum (Loshchinina et al. 2023).

Current trends in fungal nanobiotechnology

Current trends in myconanotechnology applications highlight the significant potential of this field across various sectors. The unique properties and capabilities of fungi make them valuable tools in medicine, agriculture, environmental remediation, and materials science. Recent developments in fungal nanoparticle synthesis and manipulation have paved the way for novel applications and outcomes. Over the years, significant advancements have been made in the synthesis and manipulation of fungal-derived nanoparticles, leading to innovative applications and results. The combination of fungal biology and nanotechnology holds great promise for addressing societal challenges and revolutionizing industries.

Medicine and biomedical applications

Fungal nanotechnology has shown great promise in the field of medicine. Fungal-derived nanoparticles have been utilized in drug delivery systems, diagnostics, and therapeutics. For example, the antifungal properties of silver nanoparticles synthesised by fungi have been explored for the treatment of fungal infections (Gajbhiye et al. 2009; Liang et al. 2022). These nanoparticles exhibit enhanced efficacy and reduced toxicity compared to traditional antifungal drugs (Bafghi et al. 2021). Furthermore, fungal nanocarriers have been developed to deliver drugs to specific target sites, improving drug bioavailability and reducing side effects (Bafghi et al. 2021). Fungal nanoparticles have also been used in biosensing and imaging applications (Ahmed et al. 2016, Elahi and Baghersad 2018, Kalimuthu et al. 2020). Their unique optical and magnetic properties make them suitable for developing biosensors, imaging agents, and contrast agents for various imaging modalities, including magnetic resonance imaging (MRI) and fluorescence imaging. Fungal nanotechnology holds great potential in early disease detection, personalized medicine, and targeted therapy (Rai et al. 2019; Mota et al. 2023).

Agriculture and crop improvement

Fungal nanotechnology has found applications in agriculture and crop improvement. Fungal nanoparticles can be used as biofertilizers, biopesticides, and growth promoters (Mishra and Kumar 2009; Prasad et al. 2014; Ponmurugan et al. 2016). For example, nanoparticles synthesized from fungal extracts have been shown to enhance plant growth, improve nutrient uptake, and increase crop yield (Tripathi et al. 2017; Khalifa and Hasaneen 2018). These nanoparticles can also act as carriers for delivering nutrients, pesticides, and genetic materials to plants, enabling more efficient and targeted delivery (Kumar et al. 2017; Tripathi et al. 2017; Cao et al. 2018). Additionally, fungal nanotechnology has the potential to address challenges in plant disease management (Wang et al. 2021). Fungal nanoparticles can inhibit the growth of plant pathogens and suppress the development of diseases (Abbacia et al. 2014; Adebayo et al. 2021). They can also be used in the development of nanosensors for the early detection of plant diseases, allowing for timely interventions and reduced crop losses.

Environmental remediation

Fungal nanotechnology offers promising solutions for environmental remediation and pollution control. Fungi have the ability to synthesize nanoparticles with unique properties that can be employed in the removal of pollutants from soil, water, and air. Fungal nanoparticles can act as efficient adsorbents, catalysts, or photocatalysts for the degradation of organic pollutants, heavy metal ions, and even radioactive contaminants (Viswanath et al. 2008, 2014; Bahrulolum et al. 2021). These nanoparticles can be used in water treatment, wastewater purification, and soil remediation processes, contributing to sustainable environmental management.

Industrial science

The industrial application of fungal nanobiotechnology holds significant potential for various sectors. Fungal nanoparticles can be utilized in the development of advanced materials, such as nanocomposites, coatings, and membranes, with improved properties and performance (Muñoz et al. 2006; Tsivileva et al. 2021). These materials find applications in the automotive, aerospace, electronics, and packaging industries. Fungal nanobiotechnology also offers opportunities for the production of high-value chemicals, enzymes, and biofuels through the use of fungal bioreactors (Williamson et al. 1998, Polizeli et al. 2005, Azin et al. 2007, Shraddha et al. 2011, Uday et al. 2016, Elegbede and Lateef 2018). The ability of fungi to synthesize nanoparticles with unique properties, combined with their scalability and sustainability, makes them attractive candidates for industrial processes.

Current challenges in fungal nanobiotechnology

Since fungi-based nanotechnology is developing, it faces several limitations and challenges that need to be overcome for successful implementation. One of the major hurdles is the limited exploration of fungal diversity suitable for nanotechnology applications. The availability of fungal species with the ability to produce nanoparticles and possess desired properties is restricted, hampering the discovery of potential fungal candidates (Adebayo et al. 2021). Another challenge lies in the incomplete understanding of fungal nanoparticle synthesis mechanisms (Guilger-Casagrande de Lima 2019). The comple