Metatranscriptomics of the Hu sheep rumen microbiome reveals novel cellulases

Background Cellulosic biomass has great potential as a renewable biofuel resource. Robust, high-performance enzymes are needed to effectively utilize this valuable resource. In this study, metatranscriptomics was used to explore the carbohydrate-active enzymes (CAZymes), especially glycoside hydrolases (GHs), present in the rumen microbiome of Hu sheep. Select CAZymes were experimentally verified and characterized after cloning and expression in E. coli. Results The metatranscriptomes of six Hu sheep rumen microbiomes yielded 42.3 Gbp of quality-checked sequence data that represented in total 2,380,783 unigenes after de novo assembling using Trinity and clustered with CD-HIT-EST. Annotation using the CAZy database revealed that 2.65% of the unigenes encoded GHs, which were assigned to 111 different CAZymes families. Firmicutes (18.7%) and Bacteroidetes (13.8%) were the major phyla to which the unigenes were taxonomically assigned. In total, 14,489 unigenes were annotated to 15 cellulase-containing GH families, with GH3, GH5 and GH9 being the predominant. From these putative cellulase-encoding unigenes, 4225 open reading frames (ORFs) were predicted to contain 2151 potential cellulase catalytic modules. Additionally, 147 ORFs were found to encode proteins that contain carbohydrate-binding modules (CBMs). Heterogeneous expression of 30 candidate cDNAs from the GH5 family in E. coli BL21 showed that 17 of the tested proteins had endoglucanase activity, while 7 exhibited exoglucanase activity. Interestingly, two of the GH5 proteins (Cel5A-h28 and Cel5A-h11) showed high specific activity against carboxymethylcellulose (CMC) and p-nitrophenyl-β-d-cellobioside (pNPC) (222.2 and 142.8 U/mg), respectively. The optimal pH value for activity of Cel5A-h11 and Cel5A-h28 was 6.0 for both enzymes, and optimal temperatures were 40 and 50 °C, respectively. Both enzymes retained over 70 and 60%, respectively, of their original activities after incubation at 40 °C for 60 min. However, their activities were rapidly diminished upon exposure to higher temperatures. Cel5A-h11 and Cel5A-h28 retained more than 80 and 60% of their maximal enzymatic activities after incubation for 16 h in buffered solutions in the pH range from 4.0 to 9.0. Conclusion The metatranscriptomic results revealed that the rumen microbiome of Hu sheep encoded a repertoire of new enzymes capable of cellulose degradation and metatranscriptomics was an effective method to discover novel cellulases for biotechnological applications. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	He, Bo [verfasserIn] Jin, Shuwen Cao, Jiawen Mi, Lan Wang, Jiakun

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Rumen microbiome Metatranscriptomics Cellulase

Anmerkung:	© The Author(s) 2019

Übergeordnetes Werk:	Enthalten in: Biotechnology for biofuels - London : BioMed Central, 2008, 12(2019), 1 vom: 20. Juni
Übergeordnetes Werk:	volume:12 ; year:2019 ; number:1 ; day:20 ; month:06

Links:	Volltext

DOI / URN:	10.1186/s13068-019-1498-4

Katalog-ID:	SPR030160472

Internformat


LEADER	01000caa a22002652 4500
001	SPR030160472
003	DE-627
005	20230519174820.0
007	cr uuu---uuuuu
008	201007s2019 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1186/s13068-019-1498-4 \|2 doi
035			\|a (DE-627)SPR030160472
035			\|a (SPR)s13068-019-1498-4-e
040			\|a DE-627 \|b ger \|c DE-627 \|e rakwb
041			\|a eng
100	1		\|a He, Bo \|e verfasserin \|4 aut
245	1	0	\|a Metatranscriptomics of the Hu sheep rumen microbiome reveals novel cellulases
264		1	\|c 2019
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
500			\|a © The Author(s) 2019
520			\|a Background Cellulosic biomass has great potential as a renewable biofuel resource. Robust, high-performance enzymes are needed to effectively utilize this valuable resource. In this study, metatranscriptomics was used to explore the carbohydrate-active enzymes (CAZymes), especially glycoside hydrolases (GHs), present in the rumen microbiome of Hu sheep. Select CAZymes were experimentally verified and characterized after cloning and expression in E. coli. Results The metatranscriptomes of six Hu sheep rumen microbiomes yielded 42.3 Gbp of quality-checked sequence data that represented in total 2,380,783 unigenes after de novo assembling using Trinity and clustered with CD-HIT-EST. Annotation using the CAZy database revealed that 2.65% of the unigenes encoded GHs, which were assigned to 111 different CAZymes families. Firmicutes (18.7%) and Bacteroidetes (13.8%) were the major phyla to which the unigenes were taxonomically assigned. In total, 14,489 unigenes were annotated to 15 cellulase-containing GH families, with GH3, GH5 and GH9 being the predominant. From these putative cellulase-encoding unigenes, 4225 open reading frames (ORFs) were predicted to contain 2151 potential cellulase catalytic modules. Additionally, 147 ORFs were found to encode proteins that contain carbohydrate-binding modules (CBMs). Heterogeneous expression of 30 candidate cDNAs from the GH5 family in E. coli BL21 showed that 17 of the tested proteins had endoglucanase activity, while 7 exhibited exoglucanase activity. Interestingly, two of the GH5 proteins (Cel5A-h28 and Cel5A-h11) showed high specific activity against carboxymethylcellulose (CMC) and p-nitrophenyl-β-d-cellobioside (pNPC) (222.2 and 142.8 U/mg), respectively. The optimal pH value for activity of Cel5A-h11 and Cel5A-h28 was 6.0 for both enzymes, and optimal temperatures were 40 and 50 °C, respectively. Both enzymes retained over 70 and 60%, respectively, of their original activities after incubation at 40 °C for 60 min. However, their activities were rapidly diminished upon exposure to higher temperatures. Cel5A-h11 and Cel5A-h28 retained more than 80 and 60% of their maximal enzymatic activities after incubation for 16 h in buffered solutions in the pH range from 4.0 to 9.0. Conclusion The metatranscriptomic results revealed that the rumen microbiome of Hu sheep encoded a repertoire of new enzymes capable of cellulose degradation and metatranscriptomics was an effective method to discover novel cellulases for biotechnological applications.
650		4	\|a Rumen microbiome \|7 (dpeaa)DE-He213
650		4	\|a Metatranscriptomics \|7 (dpeaa)DE-He213
650		4	\|a Cellulase \|7 (dpeaa)DE-He213
700	1		\|a Jin, Shuwen \|4 aut
700	1		\|a Cao, Jiawen \|4 aut
700	1		\|a Mi, Lan \|4 aut
700	1		\|a Wang, Jiakun \|0 (orcid)0000-0002-7213-3721 \|4 aut
773	0	8	\|i Enthalten in \|t Biotechnology for biofuels \|d London : BioMed Central, 2008 \|g 12(2019), 1 vom: 20. Juni \|w (DE-627)563167882 \|w (DE-600)2421351-2 \|x 1754-6834 \|7 nnns
773	1	8	\|g volume:12 \|g year:2019 \|g number:1 \|g day:20 \|g month:06
856	4	0	\|u https://dx.doi.org/10.1186/s13068-019-1498-4 \|z kostenfrei \|3 Volltext
912			\|a GBV_USEFLAG_A
912			\|a SYSFLAG_A
912			\|a GBV_SPRINGER
912			\|a SSG-OLC-PHA
912			\|a GBV_ILN_11
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_151
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_206
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2108
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2119
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
951			\|a AR
952			\|d 12 \|j 2019 \|e 1 \|b 20 \|c 06

Indexfelder

author_variant	b h bh s j sj j c jc l m lm j w jw
matchkey_str	article:17546834:2019----::earncitmcoteuhermnirboeee
hierarchy_sort_str	2019
publishDate	2019
allfields	10.1186/s13068-019-1498-4 doi (DE-627)SPR030160472 (SPR)s13068-019-1498-4-e DE-627 ger DE-627 rakwb eng He, Bo verfasserin aut Metatranscriptomics of the Hu sheep rumen microbiome reveals novel cellulases 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 Background Cellulosic biomass has great potential as a renewable biofuel resource. Robust, high-performance enzymes are needed to effectively utilize this valuable resource. In this study, metatranscriptomics was used to explore the carbohydrate-active enzymes (CAZymes), especially glycoside hydrolases (GHs), present in the rumen microbiome of Hu sheep. Select CAZymes were experimentally verified and characterized after cloning and expression in E. coli. Results The metatranscriptomes of six Hu sheep rumen microbiomes yielded 42.3 Gbp of quality-checked sequence data that represented in total 2,380,783 unigenes after de novo assembling using Trinity and clustered with CD-HIT-EST. Annotation using the CAZy database revealed that 2.65% of the unigenes encoded GHs, which were assigned to 111 different CAZymes families. Firmicutes (18.7%) and Bacteroidetes (13.8%) were the major phyla to which the unigenes were taxonomically assigned. In total, 14,489 unigenes were annotated to 15 cellulase-containing GH families, with GH3, GH5 and GH9 being the predominant. From these putative cellulase-encoding unigenes, 4225 open reading frames (ORFs) were predicted to contain 2151 potential cellulase catalytic modules. Additionally, 147 ORFs were found to encode proteins that contain carbohydrate-binding modules (CBMs). Heterogeneous expression of 30 candidate cDNAs from the GH5 family in E. coli BL21 showed that 17 of the tested proteins had endoglucanase activity, while 7 exhibited exoglucanase activity. Interestingly, two of the GH5 proteins (Cel5A-h28 and Cel5A-h11) showed high specific activity against carboxymethylcellulose (CMC) and p-nitrophenyl-β-d-cellobioside (pNPC) (222.2 and 142.8 U/mg), respectively. The optimal pH value for activity of Cel5A-h11 and Cel5A-h28 was 6.0 for both enzymes, and optimal temperatures were 40 and 50 °C, respectively. Both enzymes retained over 70 and 60%, respectively, of their original activities after incubation at 40 °C for 60 min. However, their activities were rapidly diminished upon exposure to higher temperatures. Cel5A-h11 and Cel5A-h28 retained more than 80 and 60% of their maximal enzymatic activities after incubation for 16 h in buffered solutions in the pH range from 4.0 to 9.0. Conclusion The metatranscriptomic results revealed that the rumen microbiome of Hu sheep encoded a repertoire of new enzymes capable of cellulose degradation and metatranscriptomics was an effective method to discover novel cellulases for biotechnological applications. Rumen microbiome (dpeaa)DE-He213 Metatranscriptomics (dpeaa)DE-He213 Cellulase (dpeaa)DE-He213 Jin, Shuwen aut Cao, Jiawen aut Mi, Lan aut Wang, Jiakun (orcid)0000-0002-7213-3721 aut Enthalten in Biotechnology for biofuels London : BioMed Central, 2008 12(2019), 1 vom: 20. Juni (DE-627)563167882 (DE-600)2421351-2 1754-6834 nnns volume:12 year:2019 number:1 day:20 month:06 https://dx.doi.org/10.1186/s13068-019-1498-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2019 1 20 06
spelling	10.1186/s13068-019-1498-4 doi (DE-627)SPR030160472 (SPR)s13068-019-1498-4-e DE-627 ger DE-627 rakwb eng He, Bo verfasserin aut Metatranscriptomics of the Hu sheep rumen microbiome reveals novel cellulases 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 Background Cellulosic biomass has great potential as a renewable biofuel resource. Robust, high-performance enzymes are needed to effectively utilize this valuable resource. In this study, metatranscriptomics was used to explore the carbohydrate-active enzymes (CAZymes), especially glycoside hydrolases (GHs), present in the rumen microbiome of Hu sheep. Select CAZymes were experimentally verified and characterized after cloning and expression in E. coli. Results The metatranscriptomes of six Hu sheep rumen microbiomes yielded 42.3 Gbp of quality-checked sequence data that represented in total 2,380,783 unigenes after de novo assembling using Trinity and clustered with CD-HIT-EST. Annotation using the CAZy database revealed that 2.65% of the unigenes encoded GHs, which were assigned to 111 different CAZymes families. Firmicutes (18.7%) and Bacteroidetes (13.8%) were the major phyla to which the unigenes were taxonomically assigned. In total, 14,489 unigenes were annotated to 15 cellulase-containing GH families, with GH3, GH5 and GH9 being the predominant. From these putative cellulase-encoding unigenes, 4225 open reading frames (ORFs) were predicted to contain 2151 potential cellulase catalytic modules. Additionally, 147 ORFs were found to encode proteins that contain carbohydrate-binding modules (CBMs). Heterogeneous expression of 30 candidate cDNAs from the GH5 family in E. coli BL21 showed that 17 of the tested proteins had endoglucanase activity, while 7 exhibited exoglucanase activity. Interestingly, two of the GH5 proteins (Cel5A-h28 and Cel5A-h11) showed high specific activity against carboxymethylcellulose (CMC) and p-nitrophenyl-β-d-cellobioside (pNPC) (222.2 and 142.8 U/mg), respectively. The optimal pH value for activity of Cel5A-h11 and Cel5A-h28 was 6.0 for both enzymes, and optimal temperatures were 40 and 50 °C, respectively. Both enzymes retained over 70 and 60%, respectively, of their original activities after incubation at 40 °C for 60 min. However, their activities were rapidly diminished upon exposure to higher temperatures. Cel5A-h11 and Cel5A-h28 retained more than 80 and 60% of their maximal enzymatic activities after incubation for 16 h in buffered solutions in the pH range from 4.0 to 9.0. Conclusion The metatranscriptomic results revealed that the rumen microbiome of Hu sheep encoded a repertoire of new enzymes capable of cellulose degradation and metatranscriptomics was an effective method to discover novel cellulases for biotechnological applications. Rumen microbiome (dpeaa)DE-He213 Metatranscriptomics (dpeaa)DE-He213 Cellulase (dpeaa)DE-He213 Jin, Shuwen aut Cao, Jiawen aut Mi, Lan aut Wang, Jiakun (orcid)0000-0002-7213-3721 aut Enthalten in Biotechnology for biofuels London : BioMed Central, 2008 12(2019), 1 vom: 20. Juni (DE-627)563167882 (DE-600)2421351-2 1754-6834 nnns volume:12 year:2019 number:1 day:20 month:06 https://dx.doi.org/10.1186/s13068-019-1498-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2019 1 20 06
allfields_unstemmed	10.1186/s13068-019-1498-4 doi (DE-627)SPR030160472 (SPR)s13068-019-1498-4-e DE-627 ger DE-627 rakwb eng He, Bo verfasserin aut Metatranscriptomics of the Hu sheep rumen microbiome reveals novel cellulases 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 Background Cellulosic biomass has great potential as a renewable biofuel resource. Robust, high-performance enzymes are needed to effectively utilize this valuable resource. In this study, metatranscriptomics was used to explore the carbohydrate-active enzymes (CAZymes), especially glycoside hydrolases (GHs), present in the rumen microbiome of Hu sheep. Select CAZymes were experimentally verified and characterized after cloning and expression in E. coli. Results The metatranscriptomes of six Hu sheep rumen microbiomes yielded 42.3 Gbp of quality-checked sequence data that represented in total 2,380,783 unigenes after de novo assembling using Trinity and clustered with CD-HIT-EST. Annotation using the CAZy database revealed that 2.65% of the unigenes encoded GHs, which were assigned to 111 different CAZymes families. Firmicutes (18.7%) and Bacteroidetes (13.8%) were the major phyla to which the unigenes were taxonomically assigned. In total, 14,489 unigenes were annotated to 15 cellulase-containing GH families, with GH3, GH5 and GH9 being the predominant. From these putative cellulase-encoding unigenes, 4225 open reading frames (ORFs) were predicted to contain 2151 potential cellulase catalytic modules. Additionally, 147 ORFs were found to encode proteins that contain carbohydrate-binding modules (CBMs). Heterogeneous expression of 30 candidate cDNAs from the GH5 family in E. coli BL21 showed that 17 of the tested proteins had endoglucanase activity, while 7 exhibited exoglucanase activity. Interestingly, two of the GH5 proteins (Cel5A-h28 and Cel5A-h11) showed high specific activity against carboxymethylcellulose (CMC) and p-nitrophenyl-β-d-cellobioside (pNPC) (222.2 and 142.8 U/mg), respectively. The optimal pH value for activity of Cel5A-h11 and Cel5A-h28 was 6.0 for both enzymes, and optimal temperatures were 40 and 50 °C, respectively. Both enzymes retained over 70 and 60%, respectively, of their original activities after incubation at 40 °C for 60 min. However, their activities were rapidly diminished upon exposure to higher temperatures. Cel5A-h11 and Cel5A-h28 retained more than 80 and 60% of their maximal enzymatic activities after incubation for 16 h in buffered solutions in the pH range from 4.0 to 9.0. Conclusion The metatranscriptomic results revealed that the rumen microbiome of Hu sheep encoded a repertoire of new enzymes capable of cellulose degradation and metatranscriptomics was an effective method to discover novel cellulases for biotechnological applications. Rumen microbiome (dpeaa)DE-He213 Metatranscriptomics (dpeaa)DE-He213 Cellulase (dpeaa)DE-He213 Jin, Shuwen aut Cao, Jiawen aut Mi, Lan aut Wang, Jiakun (orcid)0000-0002-7213-3721 aut Enthalten in Biotechnology for biofuels London : BioMed Central, 2008 12(2019), 1 vom: 20. Juni (DE-627)563167882 (DE-600)2421351-2 1754-6834 nnns volume:12 year:2019 number:1 day:20 month:06 https://dx.doi.org/10.1186/s13068-019-1498-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2019 1 20 06
allfieldsGer	10.1186/s13068-019-1498-4 doi (DE-627)SPR030160472 (SPR)s13068-019-1498-4-e DE-627 ger DE-627 rakwb eng He, Bo verfasserin aut Metatranscriptomics of the Hu sheep rumen microbiome reveals novel cellulases 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 Background Cellulosic biomass has great potential as a renewable biofuel resource. Robust, high-performance enzymes are needed to effectively utilize this valuable resource. In this study, metatranscriptomics was used to explore the carbohydrate-active enzymes (CAZymes), especially glycoside hydrolases (GHs), present in the rumen microbiome of Hu sheep. Select CAZymes were experimentally verified and characterized after cloning and expression in E. coli. Results The metatranscriptomes of six Hu sheep rumen microbiomes yielded 42.3 Gbp of quality-checked sequence data that represented in total 2,380,783 unigenes after de novo assembling using Trinity and clustered with CD-HIT-EST. Annotation using the CAZy database revealed that 2.65% of the unigenes encoded GHs, which were assigned to 111 different CAZymes families. Firmicutes (18.7%) and Bacteroidetes (13.8%) were the major phyla to which the unigenes were taxonomically assigned. In total, 14,489 unigenes were annotated to 15 cellulase-containing GH families, with GH3, GH5 and GH9 being the predominant. From these putative cellulase-encoding unigenes, 4225 open reading frames (ORFs) were predicted to contain 2151 potential cellulase catalytic modules. Additionally, 147 ORFs were found to encode proteins that contain carbohydrate-binding modules (CBMs). Heterogeneous expression of 30 candidate cDNAs from the GH5 family in E. coli BL21 showed that 17 of the tested proteins had endoglucanase activity, while 7 exhibited exoglucanase activity. Interestingly, two of the GH5 proteins (Cel5A-h28 and Cel5A-h11) showed high specific activity against carboxymethylcellulose (CMC) and p-nitrophenyl-β-d-cellobioside (pNPC) (222.2 and 142.8 U/mg), respectively. The optimal pH value for activity of Cel5A-h11 and Cel5A-h28 was 6.0 for both enzymes, and optimal temperatures were 40 and 50 °C, respectively. Both enzymes retained over 70 and 60%, respectively, of their original activities after incubation at 40 °C for 60 min. However, their activities were rapidly diminished upon exposure to higher temperatures. Cel5A-h11 and Cel5A-h28 retained more than 80 and 60% of their maximal enzymatic activities after incubation for 16 h in buffered solutions in the pH range from 4.0 to 9.0. Conclusion The metatranscriptomic results revealed that the rumen microbiome of Hu sheep encoded a repertoire of new enzymes capable of cellulose degradation and metatranscriptomics was an effective method to discover novel cellulases for biotechnological applications. Rumen microbiome (dpeaa)DE-He213 Metatranscriptomics (dpeaa)DE-He213 Cellulase (dpeaa)DE-He213 Jin, Shuwen aut Cao, Jiawen aut Mi, Lan aut Wang, Jiakun (orcid)0000-0002-7213-3721 aut Enthalten in Biotechnology for biofuels London : BioMed Central, 2008 12(2019), 1 vom: 20. Juni (DE-627)563167882 (DE-600)2421351-2 1754-6834 nnns volume:12 year:2019 number:1 day:20 month:06 https://dx.doi.org/10.1186/s13068-019-1498-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2019 1 20 06
allfieldsSound	10.1186/s13068-019-1498-4 doi (DE-627)SPR030160472 (SPR)s13068-019-1498-4-e DE-627 ger DE-627 rakwb eng He, Bo verfasserin aut Metatranscriptomics of the Hu sheep rumen microbiome reveals novel cellulases 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2019 Background Cellulosic biomass has great potential as a renewable biofuel resource. Robust, high-performance enzymes are needed to effectively utilize this valuable resource. In this study, metatranscriptomics was used to explore the carbohydrate-active enzymes (CAZymes), especially glycoside hydrolases (GHs), present in the rumen microbiome of Hu sheep. Select CAZymes were experimentally verified and characterized after cloning and expression in E. coli. Results The metatranscriptomes of six Hu sheep rumen microbiomes yielded 42.3 Gbp of quality-checked sequence data that represented in total 2,380,783 unigenes after de novo assembling using Trinity and clustered with CD-HIT-EST. Annotation using the CAZy database revealed that 2.65% of the unigenes encoded GHs, which were assigned to 111 different CAZymes families. Firmicutes (18.7%) and Bacteroidetes (13.8%) were the major phyla to which the unigenes were taxonomically assigned. In total, 14,489 unigenes were annotated to 15 cellulase-containing GH families, with GH3, GH5 and GH9 being the predominant. From these putative cellulase-encoding unigenes, 4225 open reading frames (ORFs) were predicted to contain 2151 potential cellulase catalytic modules. Additionally, 147 ORFs were found to encode proteins that contain carbohydrate-binding modules (CBMs). Heterogeneous expression of 30 candidate cDNAs from the GH5 family in E. coli BL21 showed that 17 of the tested proteins had endoglucanase activity, while 7 exhibited exoglucanase activity. Interestingly, two of the GH5 proteins (Cel5A-h28 and Cel5A-h11) showed high specific activity against carboxymethylcellulose (CMC) and p-nitrophenyl-β-d-cellobioside (pNPC) (222.2 and 142.8 U/mg), respectively. The optimal pH value for activity of Cel5A-h11 and Cel5A-h28 was 6.0 for both enzymes, and optimal temperatures were 40 and 50 °C, respectively. Both enzymes retained over 70 and 60%, respectively, of their original activities after incubation at 40 °C for 60 min. However, their activities were rapidly diminished upon exposure to higher temperatures. Cel5A-h11 and Cel5A-h28 retained more than 80 and 60% of their maximal enzymatic activities after incubation for 16 h in buffered solutions in the pH range from 4.0 to 9.0. Conclusion The metatranscriptomic results revealed that the rumen microbiome of Hu sheep encoded a repertoire of new enzymes capable of cellulose degradation and metatranscriptomics was an effective method to discover novel cellulases for biotechnological applications. Rumen microbiome (dpeaa)DE-He213 Metatranscriptomics (dpeaa)DE-He213 Cellulase (dpeaa)DE-He213 Jin, Shuwen aut Cao, Jiawen aut Mi, Lan aut Wang, Jiakun (orcid)0000-0002-7213-3721 aut Enthalten in Biotechnology for biofuels London : BioMed Central, 2008 12(2019), 1 vom: 20. Juni (DE-627)563167882 (DE-600)2421351-2 1754-6834 nnns volume:12 year:2019 number:1 day:20 month:06 https://dx.doi.org/10.1186/s13068-019-1498-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2019 1 20 06
language	English
source	Enthalten in Biotechnology for biofuels 12(2019), 1 vom: 20. Juni volume:12 year:2019 number:1 day:20 month:06
sourceStr	Enthalten in Biotechnology for biofuels 12(2019), 1 vom: 20. Juni volume:12 year:2019 number:1 day:20 month:06
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Rumen microbiome Metatranscriptomics Cellulase
isfreeaccess_bool	true
container_title	Biotechnology for biofuels
authorswithroles_txt_mv	He, Bo @@aut@@ Jin, Shuwen @@aut@@ Cao, Jiawen @@aut@@ Mi, Lan @@aut@@ Wang, Jiakun @@aut@@
publishDateDaySort_date	2019-06-20T00:00:00Z
hierarchy_top_id	563167882
id	SPR030160472
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR030160472</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519174820.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2019 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s13068-019-1498-4</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR030160472</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s13068-019-1498-4-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">He, Bo</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Metatranscriptomics of the Hu sheep rumen microbiome reveals novel cellulases</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2019</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Cellulosic biomass has great potential as a renewable biofuel resource. Robust, high-performance enzymes are needed to effectively utilize this valuable resource. In this study, metatranscriptomics was used to explore the carbohydrate-active enzymes (CAZymes), especially glycoside hydrolases (GHs), present in the rumen microbiome of Hu sheep. Select CAZymes were experimentally verified and characterized after cloning and expression in E. coli. Results The metatranscriptomes of six Hu sheep rumen microbiomes yielded 42.3 Gbp of quality-checked sequence data that represented in total 2,380,783 unigenes after de novo assembling using Trinity and clustered with CD-HIT-EST. Annotation using the CAZy database revealed that 2.65% of the unigenes encoded GHs, which were assigned to 111 different CAZymes families. Firmicutes (18.7%) and Bacteroidetes (13.8%) were the major phyla to which the unigenes were taxonomically assigned. In total, 14,489 unigenes were annotated to 15 cellulase-containing GH families, with GH3, GH5 and GH9 being the predominant. From these putative cellulase-encoding unigenes, 4225 open reading frames (ORFs) were predicted to contain 2151 potential cellulase catalytic modules. Additionally, 147 ORFs were found to encode proteins that contain carbohydrate-binding modules (CBMs). Heterogeneous expression of 30 candidate cDNAs from the GH5 family in E. coli BL21 showed that 17 of the tested proteins had endoglucanase activity, while 7 exhibited exoglucanase activity. Interestingly, two of the GH5 proteins (Cel5A-h28 and Cel5A-h11) showed high specific activity against carboxymethylcellulose (CMC) and p-nitrophenyl-β-d-cellobioside (pNPC) (222.2 and 142.8 U/mg), respectively. The optimal pH value for activity of Cel5A-h11 and Cel5A-h28 was 6.0 for both enzymes, and optimal temperatures were 40 and 50 °C, respectively. Both enzymes retained over 70 and 60%, respectively, of their original activities after incubation at 40 °C for 60 min. However, their activities were rapidly diminished upon exposure to higher temperatures. Cel5A-h11 and Cel5A-h28 retained more than 80 and 60% of their maximal enzymatic activities after incubation for 16 h in buffered solutions in the pH range from 4.0 to 9.0. Conclusion The metatranscriptomic results revealed that the rumen microbiome of Hu sheep encoded a repertoire of new enzymes capable of cellulose degradation and metatranscriptomics was an effective method to discover novel cellulases for biotechnological applications.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Rumen microbiome</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Metatranscriptomics</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cellulase</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jin, Shuwen</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cao, Jiawen</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mi, Lan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Jiakun</subfield><subfield code="0">(orcid)0000-0002-7213-3721</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Biotechnology for biofuels</subfield><subfield code="d">London : BioMed Central, 2008</subfield><subfield code="g">12(2019), 1 vom: 20. Juni</subfield><subfield code="w">(DE-627)563167882</subfield><subfield code="w">(DE-600)2421351-2</subfield><subfield code="x">1754-6834</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:12</subfield><subfield code="g">year:2019</subfield><subfield code="g">number:1</subfield><subfield code="g">day:20</subfield><subfield code="g">month:06</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1186/s13068-019-1498-4</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2119</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">12</subfield><subfield code="j">2019</subfield><subfield code="e">1</subfield><subfield code="b">20</subfield><subfield code="c">06</subfield></datafield></record></collection>
author	He, Bo
spellingShingle	He, Bo misc Rumen microbiome misc Metatranscriptomics misc Cellulase Metatranscriptomics of the Hu sheep rumen microbiome reveals novel cellulases
authorStr	He, Bo
ppnlink_with_tag_str_mv	@@773@@(DE-627)563167882
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut aut
collection	springer
remote_str	true
illustrated	Not Illustrated
issn	1754-6834
topic_title	Metatranscriptomics of the Hu sheep rumen microbiome reveals novel cellulases Rumen microbiome (dpeaa)DE-He213 Metatranscriptomics (dpeaa)DE-He213 Cellulase (dpeaa)DE-He213
topic	misc Rumen microbiome misc Metatranscriptomics misc Cellulase
topic_unstemmed	misc Rumen microbiome misc Metatranscriptomics misc Cellulase
topic_browse	misc Rumen microbiome misc Metatranscriptomics misc Cellulase
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Biotechnology for biofuels
hierarchy_parent_id	563167882
hierarchy_top_title	Biotechnology for biofuels
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)563167882 (DE-600)2421351-2
title	Metatranscriptomics of the Hu sheep rumen microbiome reveals novel cellulases
ctrlnum	(DE-627)SPR030160472 (SPR)s13068-019-1498-4-e
title_full	Metatranscriptomics of the Hu sheep rumen microbiome reveals novel cellulases
author_sort	He, Bo
journal	Biotechnology for biofuels
journalStr	Biotechnology for biofuels
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2019
contenttype_str_mv	txt
author_browse	He, Bo Jin, Shuwen Cao, Jiawen Mi, Lan Wang, Jiakun
container_volume	12
format_se	Elektronische Aufsätze
author-letter	He, Bo
doi_str_mv	10.1186/s13068-019-1498-4
normlink	(ORCID)0000-0002-7213-3721
normlink_prefix_str_mv	(orcid)0000-0002-7213-3721
title_sort	metatranscriptomics of the hu sheep rumen microbiome reveals novel cellulases
title_auth	Metatranscriptomics of the Hu sheep rumen microbiome reveals novel cellulases
abstract	Background Cellulosic biomass has great potential as a renewable biofuel resource. Robust, high-performance enzymes are needed to effectively utilize this valuable resource. In this study, metatranscriptomics was used to explore the carbohydrate-active enzymes (CAZymes), especially glycoside hydrolases (GHs), present in the rumen microbiome of Hu sheep. Select CAZymes were experimentally verified and characterized after cloning and expression in E. coli. Results The metatranscriptomes of six Hu sheep rumen microbiomes yielded 42.3 Gbp of quality-checked sequence data that represented in total 2,380,783 unigenes after de novo assembling using Trinity and clustered with CD-HIT-EST. Annotation using the CAZy database revealed that 2.65% of the unigenes encoded GHs, which were assigned to 111 different CAZymes families. Firmicutes (18.7%) and Bacteroidetes (13.8%) were the major phyla to which the unigenes were taxonomically assigned. In total, 14,489 unigenes were annotated to 15 cellulase-containing GH families, with GH3, GH5 and GH9 being the predominant. From these putative cellulase-encoding unigenes, 4225 open reading frames (ORFs) were predicted to contain 2151 potential cellulase catalytic modules. Additionally, 147 ORFs were found to encode proteins that contain carbohydrate-binding modules (CBMs). Heterogeneous expression of 30 candidate cDNAs from the GH5 family in E. coli BL21 showed that 17 of the tested proteins had endoglucanase activity, while 7 exhibited exoglucanase activity. Interestingly, two of the GH5 proteins (Cel5A-h28 and Cel5A-h11) showed high specific activity against carboxymethylcellulose (CMC) and p-nitrophenyl-β-d-cellobioside (pNPC) (222.2 and 142.8 U/mg), respectively. The optimal pH value for activity of Cel5A-h11 and Cel5A-h28 was 6.0 for both enzymes, and optimal temperatures were 40 and 50 °C, respectively. Both enzymes retained over 70 and 60%, respectively, of their original activities after incubation at 40 °C for 60 min. However, their activities were rapidly diminished upon exposure to higher temperatures. Cel5A-h11 and Cel5A-h28 retained more than 80 and 60% of their maximal enzymatic activities after incubation for 16 h in buffered solutions in the pH range from 4.0 to 9.0. Conclusion The metatranscriptomic results revealed that the rumen microbiome of Hu sheep encoded a repertoire of new enzymes capable of cellulose degradation and metatranscriptomics was an effective method to discover novel cellulases for biotechnological applications. © The Author(s) 2019
abstractGer	Background Cellulosic biomass has great potential as a renewable biofuel resource. Robust, high-performance enzymes are needed to effectively utilize this valuable resource. In this study, metatranscriptomics was used to explore the carbohydrate-active enzymes (CAZymes), especially glycoside hydrolases (GHs), present in the rumen microbiome of Hu sheep. Select CAZymes were experimentally verified and characterized after cloning and expression in E. coli. Results The metatranscriptomes of six Hu sheep rumen microbiomes yielded 42.3 Gbp of quality-checked sequence data that represented in total 2,380,783 unigenes after de novo assembling using Trinity and clustered with CD-HIT-EST. Annotation using the CAZy database revealed that 2.65% of the unigenes encoded GHs, which were assigned to 111 different CAZymes families. Firmicutes (18.7%) and Bacteroidetes (13.8%) were the major phyla to which the unigenes were taxonomically assigned. In total, 14,489 unigenes were annotated to 15 cellulase-containing GH families, with GH3, GH5 and GH9 being the predominant. From these putative cellulase-encoding unigenes, 4225 open reading frames (ORFs) were predicted to contain 2151 potential cellulase catalytic modules. Additionally, 147 ORFs were found to encode proteins that contain carbohydrate-binding modules (CBMs). Heterogeneous expression of 30 candidate cDNAs from the GH5 family in E. coli BL21 showed that 17 of the tested proteins had endoglucanase activity, while 7 exhibited exoglucanase activity. Interestingly, two of the GH5 proteins (Cel5A-h28 and Cel5A-h11) showed high specific activity against carboxymethylcellulose (CMC) and p-nitrophenyl-β-d-cellobioside (pNPC) (222.2 and 142.8 U/mg), respectively. The optimal pH value for activity of Cel5A-h11 and Cel5A-h28 was 6.0 for both enzymes, and optimal temperatures were 40 and 50 °C, respectively. Both enzymes retained over 70 and 60%, respectively, of their original activities after incubation at 40 °C for 60 min. However, their activities were rapidly diminished upon exposure to higher temperatures. Cel5A-h11 and Cel5A-h28 retained more than 80 and 60% of their maximal enzymatic activities after incubation for 16 h in buffered solutions in the pH range from 4.0 to 9.0. Conclusion The metatranscriptomic results revealed that the rumen microbiome of Hu sheep encoded a repertoire of new enzymes capable of cellulose degradation and metatranscriptomics was an effective method to discover novel cellulases for biotechnological applications. © The Author(s) 2019
abstract_unstemmed	Background Cellulosic biomass has great potential as a renewable biofuel resource. Robust, high-performance enzymes are needed to effectively utilize this valuable resource. In this study, metatranscriptomics was used to explore the carbohydrate-active enzymes (CAZymes), especially glycoside hydrolases (GHs), present in the rumen microbiome of Hu sheep. Select CAZymes were experimentally verified and characterized after cloning and expression in E. coli. Results The metatranscriptomes of six Hu sheep rumen microbiomes yielded 42.3 Gbp of quality-checked sequence data that represented in total 2,380,783 unigenes after de novo assembling using Trinity and clustered with CD-HIT-EST. Annotation using the CAZy database revealed that 2.65% of the unigenes encoded GHs, which were assigned to 111 different CAZymes families. Firmicutes (18.7%) and Bacteroidetes (13.8%) were the major phyla to which the unigenes were taxonomically assigned. In total, 14,489 unigenes were annotated to 15 cellulase-containing GH families, with GH3, GH5 and GH9 being the predominant. From these putative cellulase-encoding unigenes, 4225 open reading frames (ORFs) were predicted to contain 2151 potential cellulase catalytic modules. Additionally, 147 ORFs were found to encode proteins that contain carbohydrate-binding modules (CBMs). Heterogeneous expression of 30 candidate cDNAs from the GH5 family in E. coli BL21 showed that 17 of the tested proteins had endoglucanase activity, while 7 exhibited exoglucanase activity. Interestingly, two of the GH5 proteins (Cel5A-h28 and Cel5A-h11) showed high specific activity against carboxymethylcellulose (CMC) and p-nitrophenyl-β-d-cellobioside (pNPC) (222.2 and 142.8 U/mg), respectively. The optimal pH value for activity of Cel5A-h11 and Cel5A-h28 was 6.0 for both enzymes, and optimal temperatures were 40 and 50 °C, respectively. Both enzymes retained over 70 and 60%, respectively, of their original activities after incubation at 40 °C for 60 min. However, their activities were rapidly diminished upon exposure to higher temperatures. Cel5A-h11 and Cel5A-h28 retained more than 80 and 60% of their maximal enzymatic activities after incubation for 16 h in buffered solutions in the pH range from 4.0 to 9.0. Conclusion The metatranscriptomic results revealed that the rumen microbiome of Hu sheep encoded a repertoire of new enzymes capable of cellulose degradation and metatranscriptomics was an effective method to discover novel cellulases for biotechnological applications. © The Author(s) 2019
collection_details	GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700
container_issue	1
title_short	Metatranscriptomics of the Hu sheep rumen microbiome reveals novel cellulases
url	https://dx.doi.org/10.1186/s13068-019-1498-4
remote_bool	true
author2	Jin, Shuwen Cao, Jiawen Mi, Lan Wang, Jiakun
author2Str	Jin, Shuwen Cao, Jiawen Mi, Lan Wang, Jiakun
ppnlink	563167882
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1186/s13068-019-1498-4
up_date	2024-07-03T14:21:38.813Z
_version_	1803568020264583168
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR030160472</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519174820.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201007s2019 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s13068-019-1498-4</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR030160472</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s13068-019-1498-4-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">He, Bo</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Metatranscriptomics of the Hu sheep rumen microbiome reveals novel cellulases</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2019</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Cellulosic biomass has great potential as a renewable biofuel resource. Robust, high-performance enzymes are needed to effectively utilize this valuable resource. In this study, metatranscriptomics was used to explore the carbohydrate-active enzymes (CAZymes), especially glycoside hydrolases (GHs), present in the rumen microbiome of Hu sheep. Select CAZymes were experimentally verified and characterized after cloning and expression in E. coli. Results The metatranscriptomes of six Hu sheep rumen microbiomes yielded 42.3 Gbp of quality-checked sequence data that represented in total 2,380,783 unigenes after de novo assembling using Trinity and clustered with CD-HIT-EST. Annotation using the CAZy database revealed that 2.65% of the unigenes encoded GHs, which were assigned to 111 different CAZymes families. Firmicutes (18.7%) and Bacteroidetes (13.8%) were the major phyla to which the unigenes were taxonomically assigned. In total, 14,489 unigenes were annotated to 15 cellulase-containing GH families, with GH3, GH5 and GH9 being the predominant. From these putative cellulase-encoding unigenes, 4225 open reading frames (ORFs) were predicted to contain 2151 potential cellulase catalytic modules. Additionally, 147 ORFs were found to encode proteins that contain carbohydrate-binding modules (CBMs). Heterogeneous expression of 30 candidate cDNAs from the GH5 family in E. coli BL21 showed that 17 of the tested proteins had endoglucanase activity, while 7 exhibited exoglucanase activity. Interestingly, two of the GH5 proteins (Cel5A-h28 and Cel5A-h11) showed high specific activity against carboxymethylcellulose (CMC) and p-nitrophenyl-β-d-cellobioside (pNPC) (222.2 and 142.8 U/mg), respectively. The optimal pH value for activity of Cel5A-h11 and Cel5A-h28 was 6.0 for both enzymes, and optimal temperatures were 40 and 50 °C, respectively. Both enzymes retained over 70 and 60%, respectively, of their original activities after incubation at 40 °C for 60 min. However, their activities were rapidly diminished upon exposure to higher temperatures. Cel5A-h11 and Cel5A-h28 retained more than 80 and 60% of their maximal enzymatic activities after incubation for 16 h in buffered solutions in the pH range from 4.0 to 9.0. Conclusion The metatranscriptomic results revealed that the rumen microbiome of Hu sheep encoded a repertoire of new enzymes capable of cellulose degradation and metatranscriptomics was an effective method to discover novel cellulases for biotechnological applications.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Rumen microbiome</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Metatranscriptomics</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cellulase</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jin, Shuwen</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cao, Jiawen</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mi, Lan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Jiakun</subfield><subfield code="0">(orcid)0000-0002-7213-3721</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Biotechnology for biofuels</subfield><subfield code="d">London : BioMed Central, 2008</subfield><subfield code="g">12(2019), 1 vom: 20. Juni</subfield><subfield code="w">(DE-627)563167882</subfield><subfield code="w">(DE-600)2421351-2</subfield><subfield code="x">1754-6834</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:12</subfield><subfield code="g">year:2019</subfield><subfield code="g">number:1</subfield><subfield code="g">day:20</subfield><subfield code="g">month:06</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1186/s13068-019-1498-4</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHA</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_11</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2108</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2119</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">12</subfield><subfield code="j">2019</subfield><subfield code="e">1</subfield><subfield code="b">20</subfield><subfield code="c">06</subfield></datafield></record></collection>
score	7.4009047

Nicht das Richtige dabei?

Schreiben Sie uns!

Metatranscriptomics of the Hu sheep rumen microbiome reveals novel cellulases

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?