Background

Early taxonomists classified fungi under the plant kingdom based on their lifestyle. However, with the development of advanced molecular and phylogenetic techniques fungi were given a kingdom status. Evolutionary studies have reported that fungi have evolved in the early Neoproterozoic era followed by evolution over the period of time due to the earthly impacts including oxygen, carbondioxide, solar luminosity (Baldauf and Palmer 1993; Bruns 2006) (Fig. 1a). Although early evolutionary studies have reported that fungi have evolved from a common ancestor with that of unicellular flagellated aquatic organisms. However, there is no accepted phylogenetic theory which explains about the evolution of the early fungi (James et al. 2000, 2006a; Karol et al. 2001; Tanabe et al. 2004). Previous studies have reported that chytridiomycota (flagellated cells) are group of true fungi which are phylogenetically connected to the sister groups mucoromycota, zoopagomycota, glomeromycota, ascomycota and basidiomycota which have experienced an evolutionary loss of the flagellum (James et al. 2000, 2006a; Karol et al. 2001; Tanabe et al. 2004). However, these non-flagellated fungi have evolutionarily adapted to the terrestrial habitat by developing a filamentous growth and aerially dispersed spores. Studies have reported that early fungal phylogeny and fungal tree of life remained unresolved; however, in the recent years, phylogenetic studies have questioned the phylogenetic lineage of the chytridiomycota, mucoromycota and zoopagomycota which resolved the phylogenetic aspects of these basal groups with their relationships with ascomycota and basidiomycota (James et al. 2000, 2006a; Karol et al. 2001; Tanabe et al. 2004) (Fig. 1b). Studies have classified kingdom fungi into six true phyla: chytridiomycota, zygomycota, ascomycota, basidiomycota, glomeromycota and deuteromycota (McLaughlin et al. 2001; Silar 2016). However, the recent higher level phylogenetic studies classified fungi into: Dikarya (ascomycota, basidiomycota), glomeromycota, chytridiomycota, neocallimastigomycota, blastocladiomycota, kickxellomycota, microsporidia and cryptomycota, respectively (Hibbett et al. 2007) (Fig. 1a).

Fig. 1
figure 1

a Pictorial illustration of fungal evolution since the early Neoproterozoic era and the influence of solar luminosity, carbon dioxide, oxygen, earth impacts over the geological timescale of fungal evolution (Hedges and Kumar 2009; Hedges et al. 2015), b hierarchical illustration of fungal phylogenetic classification

Full size image

Advancement of molecular techniques 18srRNA and whole-genome sequencing techniques are currently being used to understand and reveal the phylogenetic relationships among the fungi. Development of advanced genome sequencing techniques and online genomic repositories have enhanced the current days knowledge of fungal lifestyle and evolution. The fungal genomic repositories such as joint genome institute MycoCosm (Grigoriev et al. 2011; Nordberg et al. 2013), 1000 fungal genome project and Hungate collection residing in the rumen microbial genomics network are continuously enhancing the genomic details of various fungi. Studies being conducted to understand the genomic potentials of different fungi belonging to different phyla were compared for their plant cell wall-degrading potentials to explore their applications in biofuel and bioremediation industries (Kameshwar and Qin 2018; King et al. 2011; Rytioja et al. 2014; Sista Kameshwar and Qin 2017; Zhao et al. 2013). Till date, there are 1054 whole-genome sequences of fungi belonging to different phyla in JGI-MycoCosm repository, out of which 444 whole-genome sequences of fungi have been published and publicly available and the remaining 610 whole-genome sequences of fungi were under study and unpublished (Grigoriev et al. 2011; Nordberg et al. 2013).

Fungi belonging to the ascomycota and basidiomycota phyla were highly studied compared to other phyla. Till date, approximately 349 basidiomycetous and 588 ascomycetous fungi whole-genome sequencing studies were reported in public repositories. During the process of evolution, fungi have developed as bio-decomposers/decayers of the organic material. In nature, fungi play various roles ecologically as saprobes, parasites, plant pathogens, symbionts and endophytes; they play a key role in wood-decay, litter decomposition and thus in maintaining the global carbon cycle (Diyarova 2016; Krishna and Mohan 2013; Wal et al. 2013). In the last two decades, various fungal strains were extensively studied for its plant cell wall (lignocellulose)-degrading abilities (Kameshwar and Qin 2016a). In our previous study, we have performed a large-scale comparative analysis of 42 wood rotting fungi representing white rot, brown rot and soft rot fungi (Sista Kameshwar and Qin 2017). This study has majorly reported that white rot fungi tentatively exhibit highest lignocellulolytic and soft rot fungi tentatively exhibit highest cellulolytic and hemicellulolytic potentials (Sista Kameshwar and Qin 2017).

The arbuscular mycorrhizal (AM) fungi belonging to the glomeromycota phyla play a crucial role both ecologically and environmentally (Morton and Benny 1990). AM fungi are asexual organisms and obligate symbiotes of vascular plants (as AM fungi penetrate the plant substrate using its mycelium). Studies have reported that plants depend on the symbiotic mycorrhizae rather than the roots for the uptake of phosphate ion from the soil (Morton and Benny 1990; Schüßler et al. 2001b; Smith and Read 1997). Thus, plants obtain inorganic micronutrients with the aid of AM fungi and in return fungi obtains carbohydrates from plant, this exchange happens through the intracellular symbiotic interfaces. The molecular evolutionary techniques such as small subunit rRNA sequences report that they share a common ancestor route with Dikarya and the present glomeromycota consists four orders: (a) diversisporales, (b) glomerales, (c) archaeosporales and (d) paraglomerales (Redecker et al. 2013; Schüßler et al. 2001a, b). Till date, studies have reported approximately 300 glomeromycota species based on their spore morphology (Chen et al. 2018).

Zygomycota are considered as true fungi and contain chitin in their cell walls. These fungi were found to be emerged from the other fungi approximately 600 to 1400 million years ago (Berbee and Taylor 2001a, b, Heckman et al. 2001). These terrestrial fungi live in decaying plants or animals and soil material, and some fungal species are parasites of plants, insects, while some species are in symbiotic relationships with the plants (Raven et al. 2005). Zygomycetes fungi are filamentous, non-flagellated, and importantly they form zygospores with in the zygosporangium formed as a result of sexual cycle. Major transition from the earliest diverging zoosporic fungi led to the phyla cryptomycota, chytridiomycota, neocallimastigomycota, blastocladiomycota and resulted towards the rise of non-flagellated filamentous multicellular Dikaryan fungi (Spatafora et al. 2016). Studies have reported that some zygomycetes fungi significantly benefit humans by producing commercially important compounds such as lycopene, fatty acids and biodiesel (Papanikolaou et al. 2007; Wang et al. 2011). Hibbett et al. (2007) have classified four divisions: entomophthoromycotina, kickxellomycotina, mucoromycotina and zoopagomycotina under the subphyla incertae sedis and a separate phylum glomeromycota. Molecular phylogenetic methods including rDNA and multigene studies have classified zygomycetes taxa into two major groups which informally include zygomycetes-I (mucoromycotina and mortierellomycotina), and few studies have also included glomeromycota phylum (Chang et al. 2015; James et al. 2006a; White et al. 2006). Phylogenetic studies conducted by Spatafora et al. (2016) have separated zygomycetes fungi into two different phylum’s mucoromycota and zoopagomycota (Spatafora et al. 2016). Mucoromycota fungi are further classified into four orders: (a) glomeromycota, (b) mucoromycotina and (c) mortierellomycotina, and zoopagomycota is further classified into (a) zoopagomycotina, (b) entomophthoromycotina and (c) kickxellomycotina. Mortierellomycotina subphylum includes common soil-inhabiting fungi, root endophytes and saprobes (Summerbell 2005). Zhao et al. (2013) have performed large-scale comparative analysis of 103 fungal proteomes representing Ascomycota, Basidiomycota, Chytridiomycota and Zygomycota. Zhao et al. (2013) have reported that fungi exhibit tremendous diversity in the number and variety of CAZymes, plant pathogenic fungi exhibit highest number of CAZymes followed by necrotrophic, and hemibiotrophic fungi and biotrophic fungi tend to exhibit fewer CAZymes (Zhao et al. 2013). This study has also reported that fungal pathogens infecting dicots exhibit more pectinases than monocot-infecting fungi, saprophytic fungi (highly active plant pathogens) also exhibited fewer CAZymes compared to plant pathogenic fungi (Zhao et al. 2013).

Earlier studies have classified blastocladiomycota, neocallimastigomycota under the phylum chytridiomycota, and with the advancement in molecular phylogenetic techniques, these orders were given the status of phylum. Chytridiomycota is considered as true fungi as it reproduces through the zoospores (motile spores) by having a posterior flagellum (Barr 1992; Bartnicki-Garcia 1970; James et al. 2006b). Together chytridiomycota, blastocladiomycota and neocallimastigomycota fungi can be grouped as true zoosporic fungi (Alexopoulous et al. 1996). Chytrids are characterized by their specific biochemical properties including alpha-aminoadipic acid, lysine synthetic pathway, chitin cell walls and ability to store glycogen (Alexopoulous et al. 1996; Barr 1992; Bartnicki-Garcia 1970; James et al. 2006b; Kendrick 2000). Blastocladiomycota is one of the recently added phyla in the kingdom fungi which was previously included as blastocladiales in the phylum chytridiomycota (Hibbett et al. 2007; James et al. 2006b). Neocallimastigomycota includes anaerobic fungi which are majorly present as symbionts in the digestive tracts of herbivores. These organisms were first discovered in the gut of ruminating animals. Recent high-throughput rumen microbiome sequencing studies have revealed the complete genome sequences of five anaerobic fungi. With the availability of genomic data, further studies were being conducted to understand the growth, development and functional roles of these anaerobic fungi in the rumen (Kameshwar and Qin 2018; Orpin 1975). In our previous study, we have extensively studied Neocallimastigomycota fungi to understand and reveal its lignin-, cellulose-, hemicellulose-, pectin-degrading potentials by extensively analyzing the genome-wide proteomic annotations, especially CAZy, InterPro, KOG, KEGG, SM clusters and MEROPS (Kameshwar and Qin 2018).

Microsporidia are typical spore-forming unicellular parasites which were primarily considered as protists or protozoans. However, recent genomic studies have classified microsporidia as a separate phylum under fungi (Hibbett et al. 2007). Microsporidian fungi were highly studied for their disease-causing properties in animals and humans. In humans, microsporidian fungi cause microsporidiosis, and apart from humans, microsporidian fungi infect various hosts including parasites which infect higher animals such as flatworms (Hoffman 1999). The term “Cryptomycota” was coined to suggest its signature characteristics and cryptic nature, once these fungi were interpreted as intermediates between ancestral protists and fungi (Lara et al. 2010). However, the present knowledge on Cryptomycota phylum is very limited compared to other phyla, and advanced genomic and molecular phylogeny studies must be conducted to understand and reveal about the growth and development of these fungi.

In this systematic review study, we have performed systematic analysis of genome-wide annotations of published fungi belonging to glomeromycota, mucoromycota, zoopagomycota, blastocladiomycota, chytridiomycota, neocallimastigomycota, microsporidia and cryptomycota to understand and reveal the evolutionary loss of genes encoding for plant biomass-degrading enzymes, complete eukaryotic orthologous groups, secondary metabolite clusters, metabolic and regulatory pathways of the selected fungi.

Review and analysis of lower-fungal genomes

In our present study, we have selected and retrieved the annotated proteomic data (including CAZy—carbohydrate active enzymes, KOG—eukaryotic orthologous groups, SM—secondary metabolite clusters, KEGG—Kyoto Encyclopedia of Genes and Genomes) of 56 fungi from https://genome.jgi.doe.gov/programs/fungi/index.jsf JGI (Joint Genome Institute) MycoCosm database.

Glomeromycota: Rhizophagus irregularis DAOM 181602 v1.0 (Tisserant et al. 2013), Rhizophagus irregularis A1 v1.0 (Chen et al. 2018), Rhizophagus irregularis A4 v1.0 (Chen et al. 2018), Rhizophagus irregularis A5 v1.0 (Chen et al. 2018), Rhizophagus irregularis B3 v1.0 (Chen et al. 2018), Rhizophagus irregularis C2 v1.0 (Chen et al. 2018) and Rhizophagus irregularis DAOM 197198 v2.0 (Chen et al. 2018), Gigaspora rosea v1.0 (Morin et al. 2019), Rhizophagus cerebriforme DAOM 227022 v1.0 (Morin et al. 2019), Rhizophagus diaphanus v1.0 (Morin et al. 2019). Mortierellomycotina (Lobosporangium transversale NRRL 3116 v1.0 (Mondo et al. 2017), Mortierella elongata AG-77 v2.0 (Uehling et al. 2017). Mucoromycotina (Absidia repens NRRL 1336 v1.0 (Mondo et al. 2017), Hesseltinella vesiculosa NRRL3301 v2.0 (Mondo et al. 2017), Lichtheimia corymbifera JMRC:FSU:9682 (Schwartze et al. 2014), Mucor circinelloides CBS277.49 v2.0 (Corrochano et al. 2016), Phycomyces blakesleeanus NRRL1555 v2.0 (Corrochano et al. 2016), Rhizopus delemar 99-880 from Broad (Ma et al. 2009), Rhizopus microsporus ATCC11559 v1.0 (Lastovetsky et al. 2016), Rhizopus microsporus var. chinensis CCTCC M201021 (Wang et al. 2013), Rhizopus microsporus var. microsporus ATCC52814 (Lastovetsky et al. 2016), Syncephalastrum racemosum NRRL 2496 v1.0 (Mondo et al. 2017), Endogone sp. FLAS 59071(Chang et al. 2019), Jimgerdemannia flammicorona AD002 (Chang et al. 2019), Jimgerdemannia flammicorona GMNB39 (Chang et al. 2019). Jimgerdemannia lactiflua OSC166217 (Chang et al. 2019). Zoopagomycotina Piptocephalis cylindrospora RSA 2659 single-cell v3.0 (Ahrendt et al. 2018), Syncephalis pseudoplumigaleata Benny S71-1 single-cell v1.0 (Ahrendt et al. 2018), Thamnocephalis sphaerospora RSA 1356 single-cell v1.0 (Ahrendt et al. 2018). Entomophthoromycotina (Conidiobolus coronatus NRRL28638 v1.0 (Chang et al. 2015). Kickxellomycotina (Coemansia reversa NRRL 1564 v1.0 (Chang et al. 2015), Linderina pennispora ATCC 12442 v1.0 (Mondo et al. 2017). Blastocladiomycota (Catenaria anguillulae PL171 v2.0 (Mondo et al. 2017). Chytridiomycota (Gonapodya prolifera v1.0 (Chang et al. 2015), Rhizoclosmatium globosum JEL800 v1.0 (Mondo et al. 2017), Spizellomyces punctatus DAOM BR117 (Russ et al. 2016). Neocallimastigomycota (Anaeromyces robustus v1.0 (Haitjema et al. 2017), Neocallimastix californiae G1 v1.0 (Haitjema et al. 2017), Orpinomyces sp. (Youssef et al. 2013), Piromyces finnis v3.0 (Haitjema et al. 2017), Piromyces sp. E2 v1.0 (Haitjema et al. 2017). Microsporidia (Antonospora locustae HM-2013 (Slamovits et al. 2004), Encephalitozoon cuniculi GB-M1 (Peyretaillade et al. 2009), Encephalitozoon hellem ATCC 50504 (Pombert et al. 2012), Encephalitozoon intestinalis ATCC 50506 (Corradi et al. 2010), Encephalitozoon romaleae SJ-2008 (Pombert et al. 2012), Enterocytozoon bieneusi H348 (Akiyoshi et al. 2009), Nematocida parisii ERTm1 (Cuomo et al. 2012), Nosema ceranae BRL01 (Cornman et al. 2009). Cryptomycota (Rozella allomycis CSF55 (James et al. 2013), Rozella allomycis CSF55 single-cell v1.0 (Ahrendt et al. 2018). We have also reported a tentative average of cellulolytic, hemicellulolytic, ligninolytic and pectinolytic potentials exhibited by the fungi belonging to the selected phyla, by considering the total number of genes encoding for the lignocellulolytic enzymes.

The KOG, SM clusters and KEGG annotations

The eukaryotic cluster of orthologous group classifies protein sequences from a completed genome sequence of eukaryotes based on their orthology (Tatusov et al. 2000). The KOG database of JGI-MycoCosm database classifies the protein sequences of fungal whole-genome sequences among four major groups as (a) cellular signaling and processing (CSP), (b) information storage and processing (ISP), (c) metabolism and (d) poorly characterized. We have retrieved and compared the genome-wide KOG annotations of all the selected fungi and tentatively calculated the average of all the KOG annotations and compared among the divisions (Fig. 2a). Results obtained from our systematic review potentially propose that fungi belonging to microsporidia and cryptomycota divisions might have experienced serious evolutionary loss of several genes classified among KOG groups. Previous studies have reported that compared to other fungal divisions microsporidia fungi have smallest genomes and these fungi have experienced a significant loss of mitochondrial, ribosomal RNAs and Golgi complex genes (Corradi and Selman 2013), whereas the fungi belonging to chytridiomycota, neocallimastigomycota and glomeromycota exhibited higher number of genes encoding for KOG groups (Fig. 2). Understanding the molecular mechanisms involved in degradation of plant cell wall components will significantly benefit in developing recombinant strains with extrinsic degrading potentials. Thus, we have specifically compared the KOG groups corresponding to the fungal defense mechanisms (V), carbohydrate transport and metabolism (G), secondary metabolite biosynthesis, transport and catabolism (Q) and energy production and conversion (C). Results obtained from the comparative analysis have shown that the selected glomeromycota fungi exhibit highest number of genes encoding for secondary metabolite biosynthesis, transport and catabolism (Q) KOG group. The selected neocallimastigomycota fungi have shown highest number of genes encoding for carbohydrate transport and metabolism (G). Similarly, genes encoding for defense mechanisms (V) was found to be highest among the selected neocallimastigomycota and chytridiomycota fungi. Interestingly, the genes encoding for energy production and conversion (C) group was highly observed among most of the selected fungi. Contrastingly, all the selected fungi belonging to microsporidia and cryptomycota exhibited lower number of genes encoding for all the selected KOG groups.

Fig. 2
figure 2

Pictorial illustration of the selected non-Dikarya fungi in descending orders based on the distribution of eukaryotic orthologous groups (KOG) and secondary metabolite clusters. NRPS non-ribosomal peptide synthases, PKS polyketide synthases, NRPS–PKS hybrid, DMATSs prenyl-transferases, TCs terpene cyclases

Full size image

Fungal growth is seriously challenged by various biotic and abiotic stressors ranging from nutrient limitations, environmental conditions such as pH and temperature and other microorganisms competing for nutrients (Macheleidt et al. 2016). As an immediate physiological response, fungi produce a wide range of secondary metabolites (Macheleidt et al. 2016). Fungi are rich sources of various commercially important secondary metabolites including pharmaceutical compounds, antibiotics, etc. Interestingly, genes responsible for the transcriptional regulation, biosynthesis and export of these secondary metabolites were found in adjoining gene clusters. We have retrieved and compared the secondary metabolite clusters (SM clusters) of all the selected fungi from the JGI-MycoCosm database. The secondary metabolite gene clusters are currently divided into non-ribosomal peptide synthases (NRPS), polyketide synthases (PKS), hybrid NRPS–PKS enzymes, prenyl-transferases (DMATSs) and terpene cyclases (TCs) (Khaldi et al. 2010). Interestingly, the systematic comparison of secondary metabolite gene cluster annotations has shown that the selected non-Dikarya except neocallimastigomycota fungi contain single copies of NRPS and PKS gene clusters and completely lacks prenyl-transferases (DMATSs) and hybrid gene clusters, whereas neocallimastigomycota fungi exhibited highest number of NRPS, NRPS-like, PKS and PKS-like gene clusters (Fig. 2).

The JGI-MycoCosm predicted genes are majorly divided into KEGG metabolic and KEGG regulatory pathways which are further divided into reference pathways. The KEGG metabolic and regulatory pathways are divided into 12 reference pathways: (a) amino acid metabolism (AAM), (b) biosynthesis of polyketides and non-ribosomal peptides (BpNp), (c) biosynthesis of secondary metabolites (BSM), (d) carbohydrate metabolism (CM), (e) energy metabolism (EM), (f) glycan biosynthesis and metabolism (GBM), (g) lipid metabolism (LM), (h) metabolism of cofactors and vitamins (MCV), (i) metabolism of other amino acids (MAA), (j) nucleotide metabolism (NM), (k) overview (O) and (l) xenobiotics biodegradation and metabolism (XBM). The results obtained from our systematic comparison have shown that the selected microsporidia, cryptomycota, zoopagomycotina and neocallimastigomycota fungal genomes contained lowest number of genes distributed among KEGG metabolic and regulatory pathways. Interestingly, the selected glomeromycota, mucoromycota, kickxellomycotina, fungal genomes exhibited higher number of genes encoding for KEGG metabolic and regulatory pathways (Fig. 3). We have also compared the important KEGG pathways involved in degradation of plant cell wall components, fungal metabolism and regulatory pathways. These KEGG pathways are: BpNp—biosynthesis of polyketides and non-ribosomal peptides, BSM—biosynthesis of secondary metabolites, CM—carbohydrate metabolism, EM—energy metabolism, and XBM—xenobiotic biodegradation metabolism pathways. Results obtained from this comparative analysis have shown that the selected glomeromycota fungi have exhibited higher number of genes encoding xenobiotic biodegradation metabolism (XBM), biosynthesis of secondary metabolites (BSM) and biosynthesis of polyketides and non-ribosomal peptides (BpNp). In particular, Neocallimastix californiae (Neocallimastigomycota), Gonapodya prolifera (Chytridiomycota), Rhizopus microsporus var. chinensis (Mucoromycotina) and Mortierella elongata (Mortierellomycotina) encode higher number of genes distributed among the pathways except XBM and BpNp pathways. The selected microsporidia and cryptomycota fungi exhibited lower number of genes for all the selected pathways (Table 1).

Fig. 3
figure 3

Pictorial illustration of the selected non-Dikarya fungi in descending order based on the distribution of KEGG pathway classes encoding genes. AAM amino acid metabolism, BpNp biosynthesis of polyketides and non-ribosomal peptides, BSM biosynthesis of secondary metabolites, CM carbohydrate metabolism, EM energy metabolism, GBM glycan biosynthesis and metabolism, LM lipid metabolism, MCV metabolism of cofactors and vitamins, MAA metabolism of other amino acids, NM nucleotide metabolism, O overview, XBM xenobiotics biodegradation and metabolism

Full size image
Table 1 List of all the publicly available non-Dikarya fungi considered for our systematic review
Full size table

Carbohydrate active enzymes (CAZymes)

Fungi secrete a variety of carbohydrate active enzymes for the infection and degradation of the plant cell wall components. The carbohydrates released during the process of degradation are further used for fungal growth and development (Zhao et al. 2013). Several basidiomycetous fungi have been extensively studied for its lignocellulose-degrading abilities. White and brown rot fungi especially Phanerochaete chrysosporium and Postia placenta were highly studied for their extrinsic lignocellulose-degrading ability (Zhao et al. 2013). For most of the fungal pathogens, it is highly important to access plant cytoplasm and reach across the plant tissues. Previous studies have reported that several plant cell wall-degrading fungal enzymes such as xylanases and pectinases play a crucial role in imparting virulence and pathogenicity toward its substrate (Douaiher et al. 2007; Ferrari et al. 2008; Kikot et al. 2009). Till date, 445 fungal genomes have been completely sequenced and extensively studied for their lignocellulose-degrading abilities. Complete genome sequencing studies of Saccharomycetes and Schizosaccharomycetes revealed the functional role of various cell wall-degrading enzymes in plant infection and mainly fungal growth and development (Zhao et al. 2013). Recent genome sequencing studies have also reported about the evolutionary loss of CAZyme-encoding genes in few fungal divisions (Skamnioti et al. 2008). Comparative metadata analysis of fungal genome-wide annotations especially CAZymes has been studied and reported (Kameshwar and Qin 2018; Sista Kameshwar and Qin 2017; Zhao et al. 2013). However, previous studies have majorly focused on higher fungi belonging to Ascomycota and Basidiomycota divisions. The genome-wide annotations of Glomeromycota, Chytridiomycota, Blastocladiomycota, Neocallimastigomycota and Microsporidia phyla were not highly studied compared to its counter parts. The systematic review of 40 fungal genomes belonging to the Glomeromycota, Chytridiomycota, Blastocladiomycota, Neocallimastigomycota and Microsporidia phyla has shown that genes encoding for carbohydrate active enzymes was found to decline during its evolution from Glomeromycota to Microsporidia. The selected fungi belonging to microsporidia and cryptomycota phyla fungi have experienced a serious evolutionary loss of CAZyme-encoding genes. Contrastingly, the analyzed neocallimastigomycota phyla fungi were found to contain higher number of CAZyme-encoding genes compared to other selected fungi (Kameshwar and Qin 2018). The neocallimastigomycota fungi encode highest number of CAZymes followed by mucoromycotina, chytridiomycota, glomeromycota, monoblepharidomycetes, mortierellomycotina, entomophthoromycotina, kickxellomycotina, blastocladiomycota, zoopagomycotina, cryptomycota and microsporidia (Table 7).

Ligninolytic potentials

Most of the microorganisms fail to breakdown lignin compounds; however, few microorganisms especially white rot fungi have developed efficient enzyme system for the degradation of lignin (Kameshwar and Qin 2017). The lignin-degrading enzymes are mostly distributed in auxiliary activity group. However, exact molecular mechanisms and the enzymes employed by the microorganisms for the degradation of lignin are not known till date. However, major ligninolytic enzymes include laccase, peroxidases (lignin peroxidase, versatile peroxidase, manganese peroxidase); these enzymes are also called as lignin-oxidizing enzymes. The high-oxidizing potential and non-specificity are considered as the major attributes of the lignin-oxidizing enzymes (Kameshwar and Qin 2016b). These lignin-degrading enzymes depend on supporting enzymes such as aryl-alcohol oxidase, alcohol oxidase, pyranose oxidase, vanillyl alcohol oxidase, alcohol oxidase, glyoxal oxidase, galactose oxidase, 1,4-benzoquinone reductase for the supply of hydrogen peroxide, which triggers the ligninolytic enzymes (Kameshwar and Qin 2016b). Cellobiose dehydrogenase (CDH) degrades various carbohydrates especially cellobiose, mannose to lactones, CDH transfers the electrons retrieved from the substrates to electron acceptors such as quinones, phenoxy radicals and dioxygen (Cameron and Aust 2001; Henriksson et al. 1995, 2000).The prosthetic and FAD groups of CDH make it suitable for the reduction of metals and radicals, though it exhibits high tendency toward amorphous cellulose and it helps in degradation of other plant cell wall components like lignin and xylan (Cameron and Aust 2001; Henriksson et al. 1995, 2000). Studies have also reported that both CDH and LPMO have exhibited and high-oxidative cleavage of lignin compounds (Cameron and Aust 2001; Henriksson et al. 1995, 2000). Similar to hemicellulose and pectin, lignin is also partially and completely esterified by O-acetyl and methyl groups. The acetylated lignin components of plant cell wall inhibit the activity of ligninolytic enzymes. Thus, feruloyl and glucuronoyl esterases play a most significant role in deacetylating the esterified lignin–carbohydrate complexes. Thus, in our report we have considered CDH, LPMO enzymes, lignin-oxidizing, lignin-degrading auxiliary activity enzymes, feruloyl and glucuronoyl esterases in lignin-degrading CAZymes (Table 2).

Table 2 Distribution of lignin-degrading CAZymes among different classes of glycoside hydrolases, auxiliary activity and carbohydrate-binding modules
Full size table

The glomeromycota, chytridiomycota, blastocladiomycota fungal genomes analyzed in this study lack several copies of ligninolytic auxiliary activity enzymes. The selected glomeromycota fungal genomes specifically lack genes encoding for AA2 (lignin, manganese and versatile peroxidases), AA4 (vanillyl alcohol oxidase), AA8 (iron reductase), AA9, AA10, AA13, AA14 (LPMO), AA12 (pyrroloquinoline quinone-dependent oxidoreductase) and AA15 (lytic cellulose monooxygenase), whereas the selected mortierellomycotina and zoopagomycotina fungal genomes lack genes encoding for AA2, AA4, AA9 (except Coere1 and Linpe1), AA10, AA13, AA14 and AA15 families. Similarly, the analyzed fungal genomes belonging to blastocladiomycota and chytridiomycota have also experienced serious loss of various genes encoding for auxiliary activity class enzymes such as AA2, AA4, AA8, AA10, AA13, AA14 and AA15. All the selected neocallimastigomycota and microsporidia fungal genomes completely lack genes encoding for the auxiliary activity class enzymes. The selected fungi belonging to the phylum’s glomeromycota, kickxellomycotina, entomophthoromycotina, mortierellomycotina, mucoromycotina have exhibited higher ligninolytic potentials followed by the fungi belonging to chytridiomycota, zoopagomycota, monoblepharidomycetes, blastocladiomycota, cryptomycota, neocallimastigomycota, microsporidia (Table 7).

Cellulolytic potentials

Breakdown and conversion of cellulose to glucose is performed by three classes of enzymes: (a) (EnG) endo-β-1-4-glucanase (EC 3.2.1.4), (b) exo-β-1-4-glucanase (EC 3.2.1.94) and (c) β-glucosidase (EC 3.2.1.21) (Silveira et al. 2014). The glycosidic linkages of cellulose are primarily cleaved by EnG on microfibrils surface resulting in long chains with reducing and non-reducing ends making them accessible for ExG and BG enzymes. The ExG acts on the obtained degraded products and further breaks it down to cellobiose and oligosaccharide chains which are further degraded to glucose by BG (Silveira et al. 2014). Apart from these three major cellulolytic hydrolases, microorganisms also secrete other cellulolytic hydrolase called cellodextrinase, which are found to act on the soluble cello-oligosaccharides resulting in cellobiose and shorter oligosaccharide chains (Ferreira et al. 1991; Huang and Forsberg 1987). Studies have reported that cellodextrinases are highly active on soluble cello-oligosaccharides but were totally inactive against insoluble cellulose (Ferreira et al. 1991; Huang and Forsberg 1987). Generally, cellulases are composed of distinct catalytic domain (CD), a linker and a cellulose-binding module (CBM) (Linder and Teeri 1997). The CBM recognizes and binds to the surface of cellulose and acts upon it by cleaving single cellodextrin chain and feeding it in the active site of the enzyme, where the catalytic domains hydrolyze it to cellobiose (Zhao et al. 2008). Fungal CBMs belonging to the carbohydrate-binding domain family 1 exhibit a small wedge-shaped fold composed of three aromatic amino acid residues which features cellulose-binding surface (Kraulis et al. 1989; Mattinen et al. 1997). Previous studies have reported that the aromatic residues present in the active site of these hydrolases play a crucial role in binding of the CBM to the surface of cellulose (Lehtiö et al. 2003). Simulation studies conducted by Nimlos et al. (2012) have reported that the CBM sites are active in binding on the hydrophilic region of the cellulose than on the hydrophobic surface of substrate (Nimlos et al. 2012). These studies have also reported that the CBM can also diffuse from hydrophilic to the hydrophobic regions of the substrates surface, but the opposite is not possible from these simulation experiments (Nimlos et al. 2012). As CBMs play a crucial role in cellulose hydrolysis, it will be incomplete if we do not consider CBM encoding genes in determining the genomic cellulolytic ability. The cellulose-binding modules are distributed among 20 CBM classes.

Degradation of cellulose involves not only glycoside hydrolases but also strong oxidases such as lytic polysaccharide monooxygenases (LPMO). Discovery of LPMO and its involvement in cellulose degradation was considered as a breakthrough in the field of biofuel production, as LPMO cleaves the glycosidic bonds of cellulose and makes it highly susceptible for other cellulolytic hydrolases to act upon it (Harris et al. 2014; Hemsworth et al. 2015; Johansen 2016). The cellulolytic activity of LPMO is triggered by a reducing agent which activates the oxygen present on copper active site positioned on the surface of the enzyme. Electron donors such as ascorbate, gallic acid/pyrogallol, sulfur-containing compounds and other small molecule reductants triggers the activity of various systems including LPMOs, cellobiose dehydrogenase and GMC (glucose–methanol–choline) oxidoreductases. However, recent studies have also reported the involvement of complex enzymes such as LPMO, cellobiose dehydrogenase (CDH), other glycoside hydrolases and enzymes involved in Fenton’s mechanism was found to play a role in conversion and degradation of cellulose (Beeson et al. 2015; Phillips et al. 2011). Studies have also reported that fungal or plant-derived phenols and plant pigments like chlorophyll can also trigger LPMO activity (Garajova et al. 2016; Kracher et al. 2016; Westereng et al. 2015). The cellulolytic LPMOs are classified among the classes AA9, AA10 and AA15, with cellobiose dehydrogenases and GMC-oxidoreductases classified in AA3 class. The oxidative cleavage of cellulose by LPMO results in aldonic acids (glucose units oxidized on C-1 positions) and gemdiols (4-ketoaldoses, if oxidized on C-4 position) (Villares et al. 2017) (Table 3).

Table 3 Distribution of cellulose-degrading CAZymes among different classes of glycoside hydrolases, auxiliary activity and carbohydrate-binding modules
Full size table

Results obtained from the systematic review have shown that the selected glomeromycota fungal genomes lack genes encoding for cellulolytic glycoside hydrolase families GH1, GH2, GH3, GH6, GH7, GH8, GH12, GH38, GH45, GH48, GH74 and GH124. The selected mortierellomycotina and zoopagomycotina fungal genomes have also experienced the loss of glycoside hydrolase families GH1, GH2, GH6, GH7, GH12, GH48, GH74 and GH124. Similarly, the selected chytridiomycota and blastocladiomycota fungal genomes lack genes encoding for GH6, GH7, GH8, GH12, GH48, GH74 and GH124 families. Contrastingly, the analyzed microsporidia and cryptomycota fungal genomes have experienced complete loss of cellulolytic glycoside hydrolase families. Interestingly, the selected neocallimastigomycota phyla fungal genomes encode highest number of genes coding for cellulolytic glycoside hydrolase class enzymes compared to other selected fungi. Neocallimastigomycota phyla consist of different anaerobic fungi; these fungi were reported to develop efficient cellulosomes and hydrogenosomes aiding them in degradation of plant cell wall carbohydrate components (Kameshwar and Qin 2018). Thus, neocallimastigomycota phyla fungi stand out when compared, as they encode higher number of genes coding for carbohydrate-binding modules (CBM), dockerin proteins and glycoside hydrolases (Kameshwar and Qin 2018). The descending order of fungi based on their cellulolytic enzymes is: neocallimastigomycota > mucoromycotina > chytridiomycota > glomeromycota > monoblepharidomycetes, > mortierellomycotina > entomophthoromycotina > kickxellomycotina > blastocladiomycota > zoopagomycotina > cryptomycota > microsporidia (Table 7).

Hemicellulolytic potentials

Hemicellulose is a complex hetero-polysaccharide composed of glucomannan, xylan, glucuronoxylan, arabinoxylan and xyloglucan, In plant cell walls, hemicellulose is found in close associations with lignin, cellulose and pectin units (Scheller and Ulvskov 2010; Sista Kameshwar and Qin 2018). Thus, structurally complex hemicellulose depends on a wide range of enzymes for its degradation and conversion to simple monomers. Microorganisms secrete a wide range of hemicellulose-degrading enzymes including glycoside hydrolases, LPMO (auxiliary activity) and carbohydrate-binding modules. Studies have reported that xylan constitutes a major energy source during microbial fermentation, especially rumen microbiota (Thomson 1993). Hemicellulose is majorly composed of xylan compared to the other sugar constituents. Degradation and conversion of xylan is performed by three classes of enzymes such as endo-β-1-4-xylanase (EnX), exo-β-1-4-xylanase (ExX) and β-1-4-d-xylosidases (BX) (Saha and Bothast 1999; Subramaniyan and Prema 2002). The EnX randomly cleaves xylan backbone from inside resulting in long chains of xylan oligomers, and later BX cleaves the above obtained xylo-oligomers to xylose monomers (Saha and Bothast 1999; Subramaniyan and Prema 2002). Contrastingly, ExX attacks directly on the reducing ends of xylan backbone resulting in short chains of xylan oligomers with degree of polymerization > 2 to 3 further releasing xyloses from the oligomer (Ganju et al. 1989; Honda and Kitaoka 2004; Juturu and Wu 2014; Kubata et al. 1994). α-l-arabinofuranosidase are second most important class of hemicellulolytic enzymes which are involved in breakdown of arabinoxylans, arabinogalactans. Studies have also reported that xylanases, acetyl xylan esterases and α-l-arabinofuranosidase exhibit a strong synergism during the degradation of xylan chains. Similarly, enzymes such as β-glucosidases, β-mannosidases, glucan β-1,3-glucosidase, mannan endo-β-1,4-mannosidase, xyloglucan-specific endo-β-1,4-glucanase, glucuronoarabinoxylan-specific endo-β-1,4-xylanase, arabinoxylan-specific endo-β-1,4-xylanase are involved in degradation of polymeric chains of glucuronoxylan, arabinoxylans, glucomannan and xyloglucans. Hemicellulose is differentially esterified by O-acetyl and methyl groups, which immediately ceases the enzymes activity toward hemicellulose (Sista Kameshwar and Qin 2018). However, fungi secrete a wide range of carbohydrate esterases for N- and O-deacetylation of hemicellulosic chains. The hemicellulose deacetylating acetyl xylan esterases are classified under the carbohydrate esterase families CE1, CE2, CE3, CE4, CE5, CE6 and CE7 (Sista Kameshwar and Qin 2018) (Table 4).

Table 4 Distribution of hemicellulose-degrading CAZymes among different classes of glycoside hydrolases, auxiliary activity and carbohydrate-binding modules
Full size table

Results obtained from this systematic analysis have shown that selected glomeromycota fungi lack genes encoding for GH10, GH11, GH30, GH38, GH39, GH43, GH45, GH53 and GH115 and contain genes coding for CE4 and CE16 class enzymes. Similarly, the analyzed mortierellomycotina and zoopagomycotina fungal genomes completely lack genes encoding for GH10, GH11, GH30, GH39, GH53, GH115, CE1 and CE3 families. The analyzed fungal genomes belonging to blastocladiomycota and chytridiomycota lack genes encoding for GH11, GH39, GH43, GH45, GH115, CE1, CE2, CE3 and CE6 families. Compared to other selected fungal genomes, the analyzed microsporidia fungal genomes completely lack genes coding for hemicellulolytic glycoside hydrolases, whereas the selected cryptomycota fungi only code for GH31, GH38, GH47 and CE4 families. The selected neocallimastigomycota fungal genomes outnumbers in total number of hemicellulolytic CAZymes. However, the lowest number of hemicellulolytic glycoside hydrolase encoding genes were observed in microsporidia and cryptomycota with 1 and 13 (Table 7).

Pectinolytic potentials

Pectin is also heterogeneous in nature and are richly composed of D-galacturonic acid. In plant cell walls, it occurs as galacturonans, rhamnogalacturonans-I and II and it contains anhydrogalacturonic acid backbone which is partially esterified (by methyl groups) and acetylated (on C-2 and C-3 hydroxyl groups). Fungal degradation of pectin is performed by protopectinases, endo and exo-polygalacturonases and pectin methyl esterases. The pectinolytic enzymes are mostly distributed among the GH28, GH78, GH95, GH105, GH115 glycoside hydrolase families, CE8, CE12, CE16 carbohydrate esterases families and PL1, PL3, PL4, PL9, PL11 polysaccharide lyases families (Table 5).

Table 5 Distribution of pectin-degrading CAZymes among different classes of glycoside hydrolases, auxiliary activity and carbohydrate-binding modules
Full size table

Our systematic review revealed that the selected fungi belonging to glomeromycota (except for CE16 class), microsporidia and cryptomycota phyla have experienced serious loss of pectinolytic enzymes. The analyzed mortierellomycotina and zoopagomycotina fungal genomes completely lack genes encoding for PL1 (except Linpe1), PL3 (except Morel2 and Conco1), PL4, PL9, PL11, GH78, GH95 (except Lobtra1 and Morel2), GH105 (except Liccor1and Synrac1), GH115 and CE12 class enzymes. Similarly, the selected chytridiomycota and blastocladiomycota phyla fungal genomes also completely lack genes encoding for pectinolytic enzymes PL1(except Ganpr1), PL3 (except Catan2 and Ganpr1), PL4, PL9, PL11, GH28 (except Ganpr1 and Rhihy1), GH78(except Ganpr1), GH95 and GH105 (Ganpr1), GH115 and CE8 (except Ganpr1), CE12 (except Ganpr1). Compared to other selected phyla fungi, neocallimastigomycota phyla fungal genomes contain several genes encoding for all the pectinolytic enzymes distributed among glycoside hydrolases, carbohydrate esterases and polysaccharide lyases except PL9 (except Anasp1) and PL11 (except Neosp1). Importantly, the selected fungi belonging to microsporidia and cryptomycota phylum exhibited a complete loss of pectinolytic genes (Table 7).

Starch- and inulin-degrading potentials

Starch biosynthesis and depolymerizing CAZymes are distributed among glycoside hydrolases, glycosyl transferases, lytic polysaccharide monooxygenases, carbohydrate-binding modules. The amylases including α-amylases, β-amylase, iso-amylases, glucoamylases are distributed among GH13, GH14, GH57, GH119 and GH126 classes, The inulin-depolymerizing CAZymes are distributed among glycoside hydrolases (GH32 and GH91) and CBM38 class (Kelly 2008; Mensink et al. 2015; Ronkart et al. 2007) (Table 6). Results obtained from our systematic review have shown that all the selected fungal genomes lack several CAZyme families including GH4, GH14, GH57, GH119, GH122 GH126, CBM34, CBM45, CBM53, CBM69, CBM74, CBM82 and AA13 (Table 6). We have observed that among the selected fungal genomes neocallimastigomycota fungi exhibited highest number of starch- and inulin-degrading CAZymes. Contrastingly, selected microsporidia, cryptomycota, zoopagomycota fungal genomes exhibited lowest number of starch- and inulin-degrading CAZymes (Table 7).

Table 6 Distribution of starch- and inulin-degrading CAZymes among different classes of glycoside hydrolases, auxiliary activity and carbohydrate-binding modules
Full size table
Table 7 List of the lignocellulolytic potentials of the selected non-Dikarya fungi arranged in descending order
Full size table

Conclusion

Several studies were continuously being conducted in the last two decades to understand and reveal the plant biomass-degrading abilities of fungi. The development of next-generation sequencing studies has also significantly helped in understanding the genomic complexities and molecular mechanisms underlying several biological processes. However, most of these sequencing studies were mainly focused on fungi belonging to Basidiomycota and Ascomycota phyla. In our present study, we have retrieved and compared the genome-wide annotations of fungi belonging to glomeromycota, zygomycota, chytridiomycota, blastocladiomycota, neocallimastigomycota, microsporidia and cryptomycota phyla. We have specifically analyzed and compared the genes encoding for plant cell wall-degrading enzymes and the molecular mechanisms involved in plant biomass degradation. Results obtained in our study show that fungi belonging to microsporidia and cryptomycota have experienced serious loss of several genes encoding for plant cell wall component-degrading enzymes. Contrastingly, the analyzed fungi belonging to neocallimastigomycota phyla have exhibited extraordinary genomic potentials to degrade plant cell wall carbohydrates. The analyzed fungi belonging to glomeromycota have exhibited higher number of genes distributed under the xenobiotic biodegradation metabolism pathways compared to all the other selected fungi. Results obtained in our study can be used for finding efficient fungal strains from these selected phyla for developing commercially valuable products. However, analyzing just the genome-wide distribution of fungal CAZymes is not enough as it does not completely correspond to the fungal lignocellulolytic abilities. Thus, understanding the genome-wide CAZyme distributions mainly highlights the lignocellulolytic potential of the selected fungi. Developing efficient recombinant microbial strains will have various industrial benefits including (a) biofuel industries (conversion of plant biomass components to commercially valuable products), (b) bioremediation industries (detoxification and biodegradation of toxic environmental pollutants).