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. 2018 Aug 23;10(2):61–74. doi: 10.1080/21501203.2018.1500400

A preliminary DNA barcode selection for the genus Russula (Russulales, Basidiomycota)

Guo-Jie Li a,b, Rui-Lin Zhao b,c,✉, Chu-Long Zhang a, Fu-cheng Lin a,✉
PMCID: PMC6493256  PMID: 31069120

ABSTRACT

Russula is a worldwid genus which has a high species diversity . Aiming accurate and rapid species identification, candidate genes nLSU (28S), ITS, tef-1α, mtSSU, rpb1, and rpb2, were analysed as potential DNA barcodes. This analysis included 433 sequences from 38 well-circumscribed Russula species of eight subgenera. Two vital standards were analysed for success species identification using DNA barcodes, specifically inter- and intra-specific variations together with the success rates of PCR amplification and sequencing. Although the gap between inter- and intra-specific variations was narrow, ITS met the qualification standards for a target DNA barcode. Overlapping inter- and intra-specific pairwise distances were observed in nLSU, tef-1α, mtSSU, and rpb2. The success rates of PCR amplification and sequencing in mtSSU and rpb1 were lower than those of others. Gene combinations were also investigated for resolution of species recognition. ITS-rpb2 was suggested as the likely target DNA barcode for Russula, owing to the two viatal standards above. Since nLSU has the lowest minimum of inter-specific variation, and tef-1α has the highest overlap between intra- and inter-species variations among the candidate genes, they are disqualified from the selection for DNA barcode of Russula.

KEYWORDS: Barcode gap, fungal identification, intra-and inter-specific variation, Russulaceae, species recognition

Introduction

The genus Russula Pers. is a group of gilled mushrooms with brightly coloured pileus and non-lactic fragile basidiocarps. It belongs to the family Russulaceae (Russulales, Agaricomycetes) (Romagnesi 1985; Sarnari 1998, 2005; Li 2014). This genus comprises over 780 species which is the second largest genus among Agaricomycetes. Russula species are frequently growing in almost all kinds of forests and is the dominant ectomycorrhizal (ECM) mushrooms, with a geographic range from the arctic tundra to tropical forests (Singer 1986; Buyck et al. 1996; Kirk et al. 2008; Geml et al. 2009, Wang et al. 2009; Li 2014). Although the majority of Russula species are edible, a few members are poisonous and some are even lethal (Li et al. 2010a; Chen et al. 2016).

Morphological characters have been regarded as the main criterions for specific identification in Russula for a long time in history. The large number of species, high intra-specific variability, and inaccurate descriptions in the literature caused considerable taxonomic inconvenience and confusions (Romagnesi 1985; Sarnari 1998, 2005; Li 2014). For example, R. virescens (Schaeff.) Fr. was originally described from Europe, while the illustrations of “Russula virescens” in some previous North American field guide books (Metzler and Metzler 1992, Roody 2003, Miller OK and Miller HH 2006, Kuo 2007) have been proved to be R. parvovirescens Buyck, D. Mitch. & Parrent; the “R. virescens-R. crustosa” group in North America is suggested to be much more complex than suspected, which contains at least a dozen of Russula taxa in the eastern US (Buyck et al. 2006; Kuo 2007). Another similar example is “R. vinosa Lindblad” in several Chinese fungal monographs (Teng 1963; Tai 1979; Ying et al. 1982, 1987; Wang et al. 2004) should be another species and named as R. griseocarnosa X.H. Wang et al. after morphological and ITS-nLSU phylogenetic analyses (Wang et al. 2009). More recently, the molecular analysis indicated that this “species” has three divergent lineages: one of them represents to R. griseocarnosa and the other two possibly correspond to unknown taxa (Li et al. 2010b). The genus Russula is easily separated from other genera in morphology; however, morphological distinction at species level within this genus is complicated and time-consuming. A mechanism for the accurate and rapid identification of Russula species is, thus, vital and critical for both theoretical and applied research.

DNA barcoding makes use of a short gene sequence as a universal and standard genetic marker for species identification (Hebert et al. 2003; Stockinger et al. 2010). Compared with molecular phylogenetic analyses, DNA barcoding aims to identify unknown samples and cryptic species based on current classifications, rather than elucidating patterns of phylogenetic relationships (Kress et al. 2005). The ideal barcode sequence must be easily amplified and sequenced, conserved within a species, and variable between species (Taberlet et al. 2007). The first attempt at DNA barcoding was to target the mitochondrial gene, cytochrome oxidase I (COI or COX1), for the identification of specific animals and protists (Hebert et al. 2003). However, this gene proved to be too highly conserved and was not suitable for DNA barcoding in the plant kingdom (Ning et al. 2008). Two genes, rbcL and matK, within the chloroplast coding region and trnH-psbA, within the chloroplast noncoding region, together with the ITS and ITS2 regions of ribosomal RNA, were, thus, selected as appropriate DNA barcodes for plants (Hollingsworth et al. 2009; Chen et al. 2010; Li et al. 2011).

DNA barcoding of fungi has only recently been performed. Despite a successful attempt in the genus Penicillum (Seifert et al. 2007) and class Oomycetes (Martin 2000; Martin and Tooley 2003; Robideau et al. 2011, Long et al. 2014), the COI gene failed to qualify as a universal fungal target due to unequal intron numbers, an absence of primer commonality, and difficulties in primer design and sequence alignment (Geiser et al. 2007; Gilmore et al. 2009; Vialle et al. 2009). The β-tubulin gene could be used as a suitable DNA barcode for the genera, Aspergillus (Geiser et al. 2007; Varga et al. 2011), Penicillum (Samson et al. 2004), and Tuber (Zampieri et al. 2009), but was not suitable for Parmeliaceae and Sordariomycetes (Thell et al. 2004; Tang et al. 2007). The gene for transcription elongation factor 1-alpha (tef-1α) was suggested as a DNA barcode for the genus Fusarium (Geiser et al. 2004), which, along with the second largest RNA polymerase II subunit (rpb2), could precisely distinguish the species of genera Hypocera (Jaklitsch et al. 2006) and Neonectria (Zhao et al. 2011a; b, Zeng et al. 2012). Among the ribosomal RNA genes that are commonly used in molecular phylogenetic analyses, the 18S and 28S rDNA subunits show a high primer commonality; while they were chosen as the DNA barcode for Glomeromycota (Schüßler et al. 2001; Schüßler and Walker 2010), they are not appropriate for specific identification because of their low mutation rates (Krüger et al. 2009).

The ITS1-5.8S-ITS2 (ITS) region of ribosomal RNA is the most widely analysed for fungal species identification, e.g. Amanita and Cortinarius of marco-fungi (Zhang et al. 2004, 2010; Frøslev et al. 2007), Chrysomyxa and Melampsora of smut fungi (Vialle et al. 2009), Trichoderma (Druzhinina et al. 2005), Lichenized fungi of Ascomycota (Kelly et al. 2011), and Mucorales of Mucoromycotina (Schwarz et al. 2006). ITS has been suggested to be the universal DNA barcode marker for fungi (Schoch et al. 2012); however, there are multiple paralogous or nonorthologous copies that lead to ITS sequence polymorphism (O’Donnell and Cigelnik 1997; Smith et al. 2007; Kovács et al. 2011; Lindner and Banik 2011). It is, thus, necessary to select DNA barcode substitutions to achieve multi-locus fungal identification (Roe et al. 2010).

Several gene makers have been analysed in molecular studies of Russula, some of which are phylogenetic analyses, e.g. nLSU (28S) analysed by Miller et al. (2001) and Shimono et al. (2004), ITS by Miller and Buyck (2002), Li (2014), Zhang (2014), Guo et al. (2014) and Liu et al. (2017), ITS and nLSU by Eberhardt (2002) and Shimono et al. (2014), ITS, nLSU, and rpb2 by Buyck et al. (2008), ITS, nLSU, rpb1 and rpb2 by Looney et al. (2016), and nLSU, mtSSU, tef-1α, rpb1 and rpb2 by Buyck and Hofstetter (2018). For species delimitation of Russula, more analyses focused in ITS region (Wang and Sun 2004; Yin et al. 2008; Hampe et al. 2013, Adamčík et al. 2016a; 2016b; Looney 2014). There are relatively fewer researches in which multiple genes were analysed, e.g. ITS, mtSSU, nLSU and rpb2 in Li et al. (2010b), ITS, nLSU and rpb2 in Park et al. (2013), ITS and nLSU in Park et al. (2014), ITS, rpb2, atp6, cox3 and chsi in Cao et al. (2013) and ITS, mtSSU and rpb2 in Caboň et al. (2017). In the present study, six genes, namely nLSU (28S), ITS, tef-1α, mtSSU, rpb1, and rpb2, which have been widely analysed in molecular phylogeny, were selected as candidate biomarkers. The efficiency of species identification and the feasibility of these genes to act as DNA barcodes for the genus Russula were evaluated.

Materials and methods

Materials

A total of 398 sequences of ITS, nLSU (28S), tef-1α, mtSSU, rpb1 and rpb2 genes from 59 Russula specimens, which represented 27 species, were newly produced from this study. Another 28 sequences of 15 species were retrieved from GenBank (see Table 1 for accession numbers). The total 38 Russula species were involved. All of the sampling species can be recognised in morphology and six-gene phylogenetic analyses. For those Chinese specimens under European and North American names, stable morphological resemblance and over 99% ITS sequence identities were regarded as criteria when other genes of other continents were not available. Members of each subgenus in Romagnesi (1985) were representatively sampled.

Table 1.

Specimens and sequences in this study.

Taxon name Herbarium LSU ITS tef-1α mtSSU rpb1 rpb 2 Subgenus Location
Russula acrifolia HMAS267774 KX441351 KX441104 MF893436 KX441598 KX441845 KX442092 Compactae China Jilin Changbaishan Erdaobaihe
Russula acrifolia PC 543/BB 08.662 KU237535 NA KU237965 KU237381 KU237684 KU237821 Compactae Europe
Russula amara GENT FH12-213 KT933859 KT933998 NA NA KT957370 NA Incrustatula Europe
Russula amara PC 532/BB 07.782 KU237524 NA KU237954 KU237370 KU237674 NA Incrustatula Europe
Russula amoenipes HMAS263065 KX441319 NA MF893404 KX441566 KX441813 KX442060 Polychromidia China Yunnan Kunming Qiongzhusi
Russula amoenipes HMAS263067 MG493214 NA MG495119 MG518376 MG495099 NA Polychromidia China Jilin Changbaishan Erdaobaihe
Russula amoenolens HMAS252622 KX441282 KX441035 MF893367 KX441529 KX441776 KX442023 Ingratae China Jilin Changbaishan Erdaobaihe
Russula amoenolens HMAS264497 KX441325 KX441078 MF893410 KX441572 KX441819 KX442066 Ingratae China Jilin Longjing Tianfuozhishan
Russula aurea HMAS250932 KX441261 NA MF893346 NA KX441755 KX442002 Coccinula China Jilin Changbaishan Huangsongpu
Russula aurea HMAS262377 MG493215 NA MG495120 MG518377 MG495101 MG495109 Coccinula China Jilin Changbaishan Erdaobaihe
Russula aurea PC 547/BB 07.211 KU237539 NA KU237969 KU237385 KU237688 NA Coccinula Europe
Russula brevipes HMAS252596 KX441277 KX441030 MF893362 KX441524 KX441771 KX442018 Brevipes China Jilin Changbaishan Xizhuxian
Russula brevipes HMAS252611 KX441280 KX441033 MF893365 KX441527 KX441774 KX442021 Brevipes China Jilin Changbaishan Erdaobaihe
Russula carneipes HMAS252682 KX441286 KX441039 MF893371 NA KX441780 KX442027 Russula China Sichuan Dawo Tainingyuke
Russula carneipes HMAS268187 KX441363 KX441116 MF893448 NA KX441857 KX442104 Russula China Sichuan Dawo Tainingyuke
Russula changbaiensis HMAS262355 KX441304 KX441057 MF893389 KX441551 KX441798 KX442045 Genuina China Jilin Changbaishan Erdaobaihe
Russula changbaiensis HMAS267736 MG493216 MG493202 MG495121 MG518378 MG495106 NA Genuina China Neimenggu Yakeshi Nanmu
Russula compacta TENN067133 BPL227 KT933810 KT933952 NA NA NA KT933881 Malodorae North America
Russula compacta TENN067303 BPL242 KT933819 KT933960 NA NA KT957330 KT933890 Malodorae North America
Russula crustosa TENN067418 BPL265 KT933826 KT933966 NA NA KT957338 KT933898 Malodorae North America
Russula crustosa TENN070180 BPL251 KT933822 KT933963 NA NA KT957334 KT933894 Malodorae North America
Russula decolorans GENT FH12-196 KT933853 KT933992 NA NA KT957364 KT933924 Tenellula Europe
Russula decolorans PC 549/BB 07.322 KU237541 NA KU237971 KU237387 KU237735 NA Tenellula Europe
Russula exalbicans HMAS268774 MG493219 MG493205 NA NA NA MG495110 Russula Sichuan Jiuzhaigou Zhangzha
Russula exalbicans HMAS269713 KX441408 KX441161 MF893493 NA NA KX442149 Russula Sichuan Jiuzhaigou Zhangzha
Russula fellea GENT FH12-185 KT933850 KT933989 NA NA KT957361 KT933921 Russula Europe
Russula fellea PC 444/BB 07.281 KU237507 NA KU237936 KU237352 KU237656 KU237793 Russula Europe
Russula firmula HMAS271096 MG493220 NA MG495124 MG518381 NA MG495111 Russula China Sichuan Yajiang Kazilashan
Russula firmula HMAS271140 KX441459 NA MF893544 KX441706 KX441953 KX442200 Russula China Sichuan Yajiang Kazilashan
Russula foetens HMAS271173 KX441470 KX441223 MF893555 KX441717 KX441964 KX442211 Ingratae China Sichuan Litang Cunge
Russula foetens HMAS271230 KX441476 KX441229 MF893561 KX441723 KX441970 KX442217 Ingratae China Sichuan Litang Cunge
Russula fontqueri HMAS260632 MG493217 MG493203 MG495122 MG518379 MG495098 NA Tenellula China Heilongjiang Suifenhe Forest Park
Russula fontqueri HMAS262398 MG493218 MG493204 MG495123 MG518380 MG495097 NA Tenellula China Jilin Changbaishan Erdaobaihe
Russula fontqueri HMAS267744 KX441343 KX441096 NA KX441590 KX441837 KX442084 Tenellula China Jilin Changbaishan Erdaobaihe
Russula fragilis GENT FH12-197 NA KT933993 NA NA KT957365 KT933925 Russula Europe
Russula fragilis PC 443/BB 07.791 NA NA NA KU237351 KU237655 KU237792 Russula Europe
Russula globispora HMAS269239 KX441383 KX441136 MF893468 KX441630 KX441877 KX442124 Insidiosula China Sichuan Aba S209 Road
Russula globispora PC 436/BB 07.243 KU237499 NA KU237929 KU237344 NA KU237785 Insidiosula Europe
Russula gracillima GENT FH12-264 KR364226 KR364094 NA NA KR364472 KR364342 Russula Europe
Russula gracillima HMAS262340 MG493221 MG493206 MG495125 MG518382 NA MG495112 Russula China Jilin Changbaishan Erdaobaihe
Russula gracillima PC 441/BB 07.785 KU237504 NA KU237934 KU237349 KU237653 KU237790 Russula Europe
Russula gracillima PC 584/BB 07.786 KU237568 NA KU237996 KU237416 KU237712 KU237854 Russula Europe
Russula insignis HMAS267732 MG493222 MG493207 MG495126 MG518383 NA NA Ingratae China Neimenggu Zalantun Xiushui
Russula insignis HMAS267740 KX441341 KX441094 MF893426 KX441588 KX441835 KX442082 Ingratae China Neimenggu Yakeshi Nanmu
Russula insignis HMAS267751 KX441346 KX441099 MF893431 KX441593 KX441840 KX442087 Ingratae China Neimenggu Zalantun Xiushui
Russula integra GENT FH12-172 KT933845 KT933984 NA NA KT957356 KT933916 Polychromidia Europe
Russula integra PC 518/BB 07.198 KU237513 NA KU237943 KU237359 KU237663 KU237799 Polychromidia Europe
Russula integriformis HMAS262393 KX441312 KX441065 MF893397 NA KX441806 KX442053 Polychromidia China Jilin Changbaishan Erdaobaihe
Russula integriformis HMAS262403 KX441313 KX441066 MF893398 NA KX441807 KX442054 Polychromidia China Jilin Changbaishan Erdaobaihe
Russula katarinae HMAS269080 KX441380 KX441133 MF893465 NA NA KX442121 Polychromidia China Yunnan Nanhua Zixishan
Russula katarinae HMAS269755 KX441410 KX441163 MF893495 NA KX441904 KX442151 Polychromidia China Yunnan Nanhua Zixishan
Russula luteotacta GENT FH12-187 KT933852 KT933991 NA NA KT957363 KT933923 Russula Europe
Russula luteotacta PC 452/BB 07.188 KU237512 NA KU237942 KU237358 KU237662 KU237798 Russula Europe
Russula medullata HMAS251747 KX441268 KX441021 MF893353 NA KX441762 KX442009 Heterophyllidia China Xizang Mainling Nanyi
Russula medullata HMAS251761 MG493212 MG493200 MG495118 MG518374 NA NA Heterophyllidia China Xizang Mainling Nanyi
Russula medullata HMAS262348 MG493213 MG493201 NA MG518375 MG495100 MG495108 Heterophyllidia Jilin Changbaishan Erdaobaihe
Russula murrillii HMAS271049 KX441438 KX441191 MF893523 KX441685 KX441932 KX442179 Incrustatula China Yunnan Dêqên Baimangxueshan
Russula murrillii HMAS271144 KX441460 KX441213 MF893545 KX441707 KX441954 KX442201 Incrustatula China Yunnan Dêqên Baimangxueshan
Russula nigricans PC 429/BB 07.342 KU237495 NA KU237924 KU237339 KU237643 KU237781 Compactae Europe
Russula nigricans UPS UE20.09.2004–07 DQ422010 DQ422010 NA NA NA DQ421952 Compactae Europe
Russula ochroleuca GENT FH12-211 KT933857 KT933996 NA NA KT957368 KT933928 Russula Europe
Russula ochroleuca PC 527/BB 07.303 KU237519 NA KU237949 KU237365 KU237669 KU237805 Russula Europe
Russula pascua HMAS252594 KX441276 KX441029 MF893361 KX441523 KX441770 NA Polychromidia China Jilin Changbaishan Erdaobaihe
Russula pascua HMAS253222 MG493223 NA MG495127 MG518384 MG495103 MG495113 Polychromidia China Xizang Mainling Nanyi
Russula pascua HMAS262382 NA MG493208 MG495128 MG518385 MG495105 MG495114 Polychromidia China Jilin Changbaishan Erdaobaihe
Russula pseudocyanoxantha HMAS252849 NA KX441048 MF893380 KX441542 KX441789 KX442036 Cyanoxanthinae China Yunnan Jingdong Ailaoshan
Russula pseudocyanoxantha HMAS271691 NA KX441236 MF893568 KX441730 KX441977 KX442224 Cyanoxanthinae China Yunnan Puer Laiyanghe
Russula pseudograta HMAS250432 KX441259 KX441012 MF893344 KX441506 KX441753 KX442000 Ingratae China Xizang Nyingchi Nanyi
Russula pseudograta HMAS251868 KX441273 KX441026 MF893358 KX441520 KX441767 KX442014 Ingratae China Xizang Nyingchi Nanyi
Russula pseudograta HMAS253194 KX441296 KX441049 MF893381 KX441543 KX441790 KX442037 Ingratae China Xizang Nyingchi Nanyi
Russula pseudopectinatoides HMAS251523 KX441263 KX441016 MF893348 KX441510 KX441757 KX442004 Ingratae China Xizang Yadong Xiasima
Russula pseudopectinatoides HMAS251552 MG493224 MG493209 MG495129 MG518386 MG495104 MG495115 Ingratae China Xizang Yadong Xiasima
Russula pseudopectinatoides HMAS264895 MG493225 MG493210 MG495130 MG518387 MG495102 MG495116 Ingratae China Xizang Yadong Xiasima
Russula pseudopectinatoides HMAS265020 KX441336 KX441089 MF893421 KX441583 KX441830 KX442077 Ingratae China Xizang Gongbogyamda Cuogaohu
Russula pseudopersicina HMAS264484 KX441324 KX441077 MF893409 KX441571 KX441818 KX442065 Russula China Jilin Longjing Tianfuozhishan
Russula pseudopersicina HMAS267779 KX441352 KX441105 MF893437 KX441599 KX441846 KX442093 Russula China Neimenggu Yakeshi Nanmu
Russula queleti HMAS271076 MG493226 MG493211 MG495131 NA NA MG495117 Russula China Yunnan Dêqên Baimangxueshan
Russula queleti HMAS271149 KX441462 KX441215 MF893547 KX441709 NA KX442203 Russula China Yunnan Dêqên Baimangxueshan
Russula rosea HMAS253340 KX441299 KX441052 MF893384 KX441546 NA NA Incrustatula China Yunnan Yulong Botany Garden
Russula rosea HMAS276801 LT602946 LT602969 NA NA KX442534 KX442557 Incrustatula China Fujian Sanming Yangshan
Russula sinica HMAS271022 KX441433 KX441186 MF893518 KX441680 KX441927 KX442174 Russula China Yunnan Yulong Botany Garden
Russula sinica HMAS271024 KX441434 KX441187 MF893519 KX441681 KX441928 KX442175 Russula China Yunnan Yulong Botany Garden
Russula turci HMAS271703 KX441484 KX441237 MF893569 KX441731 KX441978 KX442225 Incrustatula China Yunnan Puer Laiyanghe
Russula turci HMAS271765 KX441489 KX441242 MF893574 KX441736 KX441983 KX442230 Incrustatula China Yunnan Puer Laiyanghe
Russula turci HMAS271794 KX441493 KX441246 MF893578 KX441740 KX441987 KX442234 Incrustatula China Yunnan Yiliang Xiaolongmen
Russula zvarae GENT FH12-175 KT933847 KT933986 NA NA KT957358 KT933918 Incrustatula Europe
Russula zvarae PC 538/BB 08.639 KU237530 NA KU237960 KU237376 KU237680 KU237816 Incrustatula Europe
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DNA extraction, PCR amplification, and sequencing

DNA extraction was performed, as per the procedure described by Li et al. (2012). The six candidate genes were amplified and sequenced using the following primer pairs: ITS1/ITS5 (ITS, White et al. 1990), LROR/LR5 (nLSU, Moncalvo et al. 2000, 2002), EF1-983F/EF1-1567R (tef-1α, Morehouse et al. 2003), MS1/MS2 (mtSSU, White et al. 1990), RPB1-Ac/RPB1-Cr (rpb1, Stiller and Hall 1997; Matheny et al. 2002), and bRPB2-6F/fRPB2-7cR (rpb2, Liu et al. 1999; Matheny 2005). PCR was performed in a Techne Prime Thermal Cycler (Cole-Parmer, Staffordshire, UK) using a 50 μL reaction volume composed of 25 μL Biomed 2× Taq Plus PCR MasterMix (Biomed, Beijing, China), 21 μL ddH2O, 1.5 μL of each primer (10 μmol/L), and 1 μL DNA template. PCR reaction conditions followed those of Li et al. (2012) for ITS and nLSU, Stenglein et al. (2010) for tef-1α and mtSSU, and Matheny (2005) for rpb1 and rpb2. PCR products were purified and sequenced by the Biomed Biotech Company (Beijing) using the ABI 3130 DNA sequencer and ABI BigDye 3.1 Terminator Cycle Sequencing Kit (Applied Biosystems, Foster City, CA, USA).

Comparison of intra- and inter-specific divergence

Sequences were aligned using Mafft 7.311 (Katoh and Standley 2013), and the aligned sequences were manually adjusted in Bioedit 7.0 (Hall 1999). Similarity matrices were calculated using the MegAlign program in DNAStar v7.1 (Lasergene, WI, USA) and the resulting output was analysed and visualised in TaxonGap 2.4.1 (Slabbinck et al. 2008). The intra- and inter-specific pairwise distances were analysed in MEGA 7.0.26 with Kimura’s two-parameter (K2P) model (Kumar et al. 2016) and SpeciesIdentifier 1.8 in TaxonDNA (Meier et al. 2006). The DNA barcode gap between the frequency distributions of intra- and inter-specific pairwise distances was calculated using Microsoft Office Excel 2013. The incongruence length difference (ILD) test was carried out to calculate the probability values (p-values) in partition homogeneity tests using PAUP 4.0 Beta 10 (Swofford 2004). The p-value criterion (p ≥ 0.01) proposed in Farris et al. (1995) and Cunningham (1997) was followed to test the feasibility that two genes were congruent so they can be analysed together as a combination. Maximum likelihood (ML) phylogenetic analyses of the six genes were carried out using RAxML 8 (Stamatakis 2014) to estimate the intra- and inter-specific genetic distances.

Success rates of sequence acquisition

The success rates of PCR amplification and sequencing were calculated and evaluated. In electrophoresis running gel, a single and clear band that fit for the length of target gene can be regarded as the criterion of successful PCR amplification. A chromatogram which has high but not mixed peaks was regarded as the standard of successful sequencing. A success rate of PCR amplification and sequencing is the product of two respective rates.

Results

The overall analysis involved a total of 426 sequences from 38 Russula species, targeting six candidate genes, namely nLSU, ITS, tef-1α, mtSSU, rpb1, and rpb2 (Table 1). The sequences were shortened to meet standard DNA barcode requirements. Sequence lengths were as follows: 880 bp for nLSU, 472 bp for ITS, 581 bp for tef-1α, 538 bp for mtSSU, 918 bp for rpb1, and 712 bp for rpb2.

The intra- and inter-specific variations are the important standards in determining the feasibility of candidate genes for DNA barcode selection. The resolution of current species, PCR, and sequencing success rates are also essential factors. A clear distinction between intra- and inter-specific divergences is a must for the identification of an ideal specific DNA barcode. Comparisons among sequences of the six candidate genes for each Russula species used in this study were analysed with TaxonGap 2.4.1 and the result is presented in Figure 1. ITS had the highest minimum of inter-specific variations of 3.2%, followed by rpb2 (2.2%), tef-1α (1.4%), rpb1 (1.2%), mtSSU (1.2%), and nLSU (0.7%). It appeared that rpb2 had a marginally higher resolution than nLSU, mtSSU, tef-1α, and rpb1. For rpb2, all species showed intra-specific variations lower than 2.2%, apart from R. acrifolia, R. delica, and R. queleti. The minimum inter-specific variation of the six candidate genes also indicated that the ability of nLSU to specifically identify Russula species was the least among all the genes tested this low ability is due to nLSU having the lowest minimum of inter-specific variation. As shown in Figs. 1 and 2, an overlap was observed between the inter- and intra-specific variations in the tef-1α (26.3%), rpb2 (7.9%), mtSSU (2.6%), and nLSU (2.6%) genes, suggesting these genes were inadequate as individual DNA barcodes for Russula. Although no overlap was observed in rpb1, the low minimum inter-specific variation (1.2%) hindered its ability to identify Russula species (Figure 1). Of all six candidate genes under analysis, ITS is most suitable for distinguishing between species. However, it remained restricted by the narrow gap between its intra- and inter-specific variations (Figs. 1 and 2).

Figure 1.

Figure 1.

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Comparisons of intra- and inter-specific variations among nLSU, ITS, tef-1α, mtSSU, rpb1 and rpb2 genes of Russula generated by TaxonGap. The inter- and intra-specific variations were presented as the black and grey bars respectively. The minimums of inter-specific variations for each gene were shown as the vertical lines. Taxon names followed the black bars represented the closest species of this analysis.

Figure 2.

Figure 2.

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Comparisons of frequency distribution of intra- and inter-specific variation pairwise distances among nLSU, ITS, tef-1α, mtSSU, rpb1 and rpb2 genes of Russula generated by MEGA and Excel. The interand intra-specific distances are presented as yellow and blue bars respectively.

The applications of nLSU and tef-1α genes in DNA barcode were not available, because nLSU has the low inter-specific variations minimum of (0.7%) and tef-1α has an obvious overlap between its inter- and intra-specific variations (26.3%). Combinations of the other genes, ITS, mtSSU, rpb1, and rpb2, were subsequently analysed. Application of the two-gene combinations provided improved variation compared to that of single genes, with all intra-specific variations being lower than the minimum inter-specific variations (Figs. 3 and 4). The combination of ITS-mtSSU and ITS-rpb2 showed a minimum inter-specific variation of over 4%, which were more appropriate for species identification (Figure 3). The gap between intra- and inter-specific variations of these two combinations was also clear (Figure 4). An alternate combination of mtSSU-rpb2 was found to be best for its minimum inter-specific variation of 3.8% when commonly used ITS sequences were unavailable (Figure 3).

Figure 3.

Figure 3.

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Comparisons of intra- and inter-specific variations among ITS-mtSSU, ITS-rpb1, ITS-rpb2, mtSSUrpb1, mtSSU-rpb2 and rpb1-rpb2 gene combinations of Russula generated by TaxonGap. The inter- and intra-specific variations were presented as the black and grey bars respectively. The minimums of interspecific variations for each gene were shown as the vertical lines. Taxon names followed the black bars represented the closest species of this analysis.

Figure 4.

Figure 4.

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Comparisons of frequency distribution ofintra- and inter-specific variation pairwise distances among ITS-mtSSU, ITS-rpb1, ITS-rpb2, mtSSU-rpb1, mtSSU-rpb2 and rpb1-rpb2 gene combinations of Russula generated by MEGA and Excel. The inter- and intra-specific distances are presented as yellow and blue bars respectively.

The inter- and intra-specific pairwise distances of the candidate genes were evaluated from their ML trees (Figs 5–10). These results generally agree with those of TaxonGap. Although every species of this study can be well-separated from each other as independent clades with high bootstrap values, overlaps between inter- and intra-specific variations can be observed in phylogenetic topologies of nLSU (Figure 5) tef-1α (Figure 7), mtSSU (Figure 8), and rpb2 (Figure 10), in contrast, absent in those of ITS (Figure 6) and rpb1 (Figure 9).

Sequence clustering was calculated based on pairwise distances, with the given threshold, using TaxonDNA/Species Identifier 1.8. The intra- and inter-specific divergence of the candidate genes were also evaluated, with the maximum intra-specific distance set as the clustering threshold. Corresponding levels of coincidence between clusters and species for the candidate biomarkers are presented in Table 2. For tef-1α, a total of 33 clusters were recognised, suggesting this gene was able to separately identify 33 of the 35 species (94.3%); by contrast nLSU was only capable of distinguishing between eight species. The other genes could also successfully distinguish between the Russula species used in this analysis.

Table 2.

Clustering at a given threshold of the candidate genes of Russula DNA barcode derived using TaxonDNA/species identified.

Candidate genes Largest intra-specific distance Number of cluster Corresponding to species taxa
ITS 1.06% 35 35(100%)
nLSU 2.95% 8 36 (22.2%)
tef-1α 2.58% 33 35(94.3%)
mtSSU 1.30% 32 32(100%)
rpb1 1.09% 36 36(100%)
rpb2 2.02% 37 37 (100%)
ITS-mtSSU 0.59% 32 29 (100%)
ITS-rpb1 0.79% 33 33 (100%)
ITS-rpb2 0.76% 36 34 (100%)
mtSSU-rpb1 0.89% 31 31 (100%)
mtSSU-rpb2 1.44% 31 31 (100%)
rpb1-rpb2 1.23% 35 35 (100%)
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PCR and sequencing success rates are another standard requirement of eligible DNA barcode genes. ITS, nLSU, and tef-1α could be easily amplified and sequenced with success rates of over 90%. On the other hand, the mtSSU gene had a relatively low PCR and sequencing success rate (78.3%) (Table 3). The primers commonly used in phylogenetic analysis of Basidiomycota were suitable for most species of the Russula genus.

Table 3.

PCR and sequencing successful rate of the candidate genes.

Candidate genes PCR Sequencing PCR and sequencing
ITS 98.3% 89.6% 88.1%
nLSU 100% 94.9% 94.9%
tef-1α 100% 93.2% 93.2%
mtSSU 94.9% 84.0% 79.7%
rpb1 93.2% 87.1% 81.2%
rpb2 93.2% 94.5% 88.1%
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Congruencies of individual partitions were calculated using the partition homogeneity test. The p-values of the gene combinations were ITS-mtSSU (0.20), ITS-rpb1 (0.08), ITS-rpb2 (0.02), mtSSU-rpb1 (0.05), mtSSU-rpb2 (0.01), and rpb1-rpb2 (0.90). All of these results are equal or greater than 0.01. So it is suggested that the individual partitions of these gene combinations were congruent.

Discussion

The two vital conditions for DNA barcode evaluation are sufficient intra- and inter-specific variation, as well as high PCR and sequencing success rates (Zhao et al. 2011a, 2011b; Zeng 2012; Zhu et al. 2014). Taking both these standards into consideration, the use of ITS was considered to be an adequate primary Russula DNA barcode in situations of single gene analysis. We found that ITS had relatively high PCR and sequencing rates (Table 3), and that all the species used in this analysis could be recognised, when this gene was targeted (Table 2). Targeting ITS as the universal fungal DNA barcode has also been previously suggested (Seifert 2009; Schoch et al. 2012). Although no overlap was observed between the intra- and inter-specific distances in ITS (Figs. 1 and 6), the gap between the two variations was narrow (Figure 2). Gene combinations were, thus, considered necessary to get sufficient resolution at the species level.

Our analyses showed that the ITS-rpb2 combination could act as a suitable DNA barcode for the genus Russula, demonstrating the best performance as a DNA barcode for various Russula species. First, there were suitable intra- and inter-specific variations (Figs. 3 and 4) with the DNA barcode gap being the largest among all candidate genes and gene combinations analysed. In addition, this gene combination recognised all 34 Russula species. This conclusion was also supported by the analysis using TaxonGap (Slabbinck et al. 2008) and SpeciesIdentifier in TaxonDNA (Meier et al. 2006), as shown in Table 2. Second, the PCR amplification and sequencing success rates were relatively higher in ITS and rpb2 (88.1% in Table 3). This combination was, thus, recommended as the primary DNA barcode for the genus Russula in situations where multigene analysis may be performed. Our analyses also suggested that the combination of mtSSU-rpb2 was the best DNA barcode substitute for identifying Russula when PCR or sequencing targeting ITS was unsuccessful because of the gap between intra- and inter-species variation (Figs. 3 and 4).

The nuclear large subunit ribosomal RNA gene (nLSU) has often been analysed to elucidate the phylogenetic relationships of fungal groups at the generic or higher taxonomic ranks (Johnson and Vilgalys 1998). It has also been suggested to be the most appropriate DNA barcode for yeast-like fungi (Kurtzman and Robnett 1998; Fell et al. 2000; Ninet et al. 2003). Of the 36 species involved in this study, only six were recognised as a single cluster when analysed through TaxonDNA (Table 2). Although targeting nLSU had the highest PCR and sequencing success rates (Table 3), our analyses indicated that nLSU was not a suitable DNA marker because of its inability to specifically recognise Russula species (Figs. 1, 2 and 5). nLSU, thus, failed to act as the target DNA barcode for this genus.

Another gene often used in fungal phylogenetic analyses is tef-1α (Jaklitsch et al. 2006; Stenglein et al. 2010; Zhao et al. 2016, Zhao et al. 2017; He et al. 2017), which had the second highest PCR and sequencing success rates (Table 3). This gene has previously been regarded as the target DNA barcode in certain groups (Geiser et al. 2004; Druzhinina et al. 2005; Li et al. 2013); however, our analyses showed that tef-1α the occurrence of overlap between intra- and inter-species variation among the candidate genes (Figs. 1, 2 and 7) was the highest for this gene. For this reason, tef-1α was excluded as the target DNA barcode for Russula.

The genes of the first and second largest RNA polymerase II subunits (rpb1 and rpb2) and the mitochondrial small subunit (mtSSU), which have been commonly analysed in fungal phylogeny (Matheny et al. 2007; Nordin et al. 2010; Stenglein et al. 2010; Sekimoto et al. 2011; Chen et al. 2012), were also employed as candidate biomarkers for this study. Overlap between intra- and inter-species variation was detected in both mtSSU and rpb2 (Figs. 1, 2, 8 and 10). For rpb1, although no overlap was observed (Figs. 1, 2 and 9), the low minimum inter-specific variation (1.2%) made the gap between the two variations too narrow (Figs. 1 and 2). The gene rpb1 also had relatively low PCR and sequencing success rates (81.2%, Table 3), which further hampered its practicality as an eligible DNA barcode.

Our results indicate that ITS-rpb2 combination meets the requirements for a good DNA barcode for Russula. The barcode gap of this combination is visible in Fig. 4. It is much wider than that of ITS in Fig. 2, which is invisible in the same abscissa axis. For single genes, ITS and nLSU possessed high PCR and sequencing rates, but the gap between inter- and intra-specific variations of ITS was narrow, nLSU is inefficient in specific recognition. Overlapping occurred between the two variations in tef-1α, rpb2, mtSSU, and nLSU, which may lead to misidentification. PCR and sequencing success rates are relatively low in mtSSU and rpb1.

Funding Statement

This work was supported by the National Natural Science Foundation of China [grant number 31500013] to GJL, [grant number 31000013, 31360014, 31470152] to RLZ, and Beijing Innovative Consortium of Agriculture Research System [Project ID: BAIC05-2018].

Acknowledgments

The authors express their deep gratitude and thanks to Mao-Qiang He, Sheng-Yu Su, Xu-Ming Bai, Rong-Chun Dai (Southwest Forestry University), Sai-Fei Li, Hua-An Wen, Dong Zhao, Tie-Zheng Wei and Ming-Zhe Zhang in specimen collection, to Liu Yang (Institute of Microbiology, Chinese Academy of Sciences) for the loan of herbarium specimens, and to Yan-Lei Ding and Xin-Yu Zhu (Baotou Normal College) for assistance with DNA extraction and sequencing.

Disclosure statement

No potential conflict of interest was reported by the authors.

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