pubmed_abstracts_RNA-seq-2.txt
63 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1. Methods Mol Biol. 2018;1737:77-88. doi: 10.1007/978-1-4939-7634-8_5.
Identification of New Bacterial Small RNA Targets Using MS2 Affinity Purification
Coupled to RNA Sequencing.
Carrier MC(1), Laliberté G(1), Massé E(2).
Author information:
(1)Department of Biochemistry, RNA Group, Université de Sherbrooke, Sherbrooke,
QC, Canada.
(2)Department of Biochemistry, RNA Group, Université de Sherbrooke, Sherbrooke,
QC, Canada. eric.masse@usherbrooke.ca.
Small regulatory RNAs (sRNAs) are ubiquitous regulatory molecules expressed in
living cells. In prokaryotes, sRNAs usually bind to target mRNAs to either
promote their degradation or interfere with translation initiation. Because a
single sRNA can regulate a considerable number of target mRNAs, we seek to
identify those targets rapidly and reliably. Here, we present a robust method
based on the co-purification of target mRNAs bound to MS2-tagged sRNAs expressed
in vivo. After purification of the tagged-sRNA, we use RNAseq to determine the
identity of all RNA interacting partners and their enrichment level. We describe
how to analyze the RNAseq data through the Galaxy Project Platform bioinformatics
tools to identify new mRNA targets. This technique is applicable to most sRNAs of
E. coli and Salmonella.
DOI: 10.1007/978-1-4939-7634-8_5
PMID: 29484588
2. BMC Evol Biol. 2018 Feb 12;18(1):21. doi: 10.1186/s12862-018-1134-0.
A novel short L-arginine responsive protein-coding gene (laoB) antiparallel
overlapping to a CadC-like transcriptional regulator in Escherichia coli O157:H7
Sakai originated by overprinting.
Hücker SM(1)(2), Vanderhaeghen S(1), Abellan-Schneyder I(1)(3), Wecko R(1), Simon
S(4), Scherer S(1)(5), Neuhaus K(6)(7).
Author information:
(1)Chair for Microbial Ecology, Wissenschaftszentrum Weihenstephan, Technische
Universität München, Weihenstephaner Berg 3, 85354, Freising, Germany.
(2)Fraunhofer ITEM-R, Am Biopark 9, 93053, Regensburg, Germany.
(3)Core Facility Microbiome/NGS, ZIEL - Institute for Food & Health, Technische
Universität München, Weihenstephaner Berg 3, 85354, Freising, Germany.
(4)Department of Computer and Information Science, University of Konstanz, Box
78, 78457, Konstanz, Germany.
(5)ZIEL - Institute for Food & Health, Technische Universität München,
Weihenstephaner Berg 3, 85354, Freising, Germany.
(6)Chair for Microbial Ecology, Wissenschaftszentrum Weihenstephan, Technische
Universität München, Weihenstephaner Berg 3, 85354, Freising, Germany.
neuhaus@tum.de.
(7)Core Facility Microbiome/NGS, ZIEL - Institute for Food & Health, Technische
Universität München, Weihenstephaner Berg 3, 85354, Freising, Germany.
neuhaus@tum.de.
BACKGROUND: Due to the DNA triplet code, it is possible that the sequences of two
or more protein-coding genes overlap to a large degree. However, such non-trivial
overlaps are usually excluded by genome annotation pipelines and, thus, only a
few overlapping gene pairs have been described in bacteria. In contrast,
transcriptome and translatome sequencing reveals many signals originated from the
antisense strand of annotated genes, of which we analyzed an example gene pair in
more detail.
RESULTS: A small open reading frame of Escherichia coli O157:H7 strain Sakai
(EHEC), designated laoB (L-arginine responsive overlapping gene), is embedded in
reading frame -2 in the antisense strand of ECs5115, encoding a CadC-like
transcriptional regulator. This overlapping gene shows evidence of transcription
and translation in Luria-Bertani (LB) and brain-heart infusion (BHI) medium based
on RNA sequencing (RNAseq) and ribosomal-footprint sequencing (RIBOseq). The
transcriptional start site is 289 base pairs (bp) upstream of the start codon and
transcription termination is 155 bp downstream of the stop codon. Overexpression
of LaoB fused to an enhanced green fluorescent protein (EGFP) reporter was
possible. The sequence upstream of the transcriptional start site displayed
strong promoter activity under different conditions, whereas promoter activity
was significantly decreased in the presence of L-arginine. A strand-specific
translationally arrested mutant of laoB provided a significant growth advantage
in competitive growth experiments in the presence of L-arginine compared to the
wild type, which returned to wild type level after complementation of laoB in
trans. A phylostratigraphic analysis indicated that the novel gene is restricted
to the Escherichia/Shigella clade and might have originated recently by
overprinting leading to the expression of part of the antisense strand of
ECs5115.
CONCLUSIONS: Here, we present evidence of a novel small protein-coding gene laoB
encoded in the antisense frame -2 of the annotated gene ECs5115. Clearly, laoB is
evolutionarily young and it originated in the Escherichia/Shigella clade by
overprinting, a process which may cause the de novo evolution of bacterial genes
like laoB.
DOI: 10.1186/s12862-018-1134-0
PMCID: PMC5810103
PMID: 29433444
3. PLoS One. 2017 Sep 13;12(9):e0184119. doi: 10.1371/journal.pone.0184119.
eCollection 2017.
Discovery of numerous novel small genes in the intergenic regions of the
Escherichia coli O157:H7 Sakai genome.
Hücker SM(1)(2), Ardern Z(1)(2), Goldberg T(3), Schafferhans A(3), Bernhofer
M(3), Vestergaard G(4), Nelson CW(5), Schloter M(4), Rost B(3), Scherer S(1)(2),
Neuhaus K(1)(6).
Author information:
(1)Chair for Microbial Ecology, Technische Universität München, Freising,
Germany.
(2)ZIEL - Institute for Food & Health, Technische Universität München, Freising,
Germany.
(3)Department of Informatics-Bioinformatics & TUM-IAS, Technische Universität
München, Garching, Germany.
(4)Research Unit Environmental Genomics, Helmholtz Zentrum München, Neuherberg,
Germany.
(5)Sackler Institute for Comparative Genomics, American Museum of Natural History
New York, New York, United States of America.
(6)Core Facility Microbiome/NGS, ZIEL - Institute for Food & Health, Technische
Universität München, Freising, Germany.
In the past, short protein-coding genes were often disregarded by genome
annotation pipelines. Transcriptome sequencing (RNAseq) signals outside of
annotated genes have usually been interpreted to indicate either ncRNA or
pervasive transcription. Therefore, in addition to the transcriptome, the
translatome (RIBOseq) of the enteric pathogen Escherichia coli O157:H7 strain
Sakai was determined at two optimal growth conditions and a severe stress
condition combining low temperature and high osmotic pressure. All intergenic
open reading frames potentially encoding a protein of ≥ 30 amino acids were
investigated with regard to coverage by transcription and translation signals and
their translatability expressed by the ribosomal coverage value. This led to
discovery of 465 unique, putative novel genes not yet annotated in this E. coli
strain, which are evenly distributed over both DNA strands of the genome. For 255
of the novel genes, annotated homologs in other bacteria were found, and a
machine-learning algorithm, trained on small protein-coding E. coli genes,
predicted that 89% of these translated open reading frames represent bona fide
genes. The remaining 210 putative novel genes without annotated homologs were
compared to the 255 novel genes with homologs and to 250 short annotated genes of
this E. coli strain. All three groups turned out to be similar with respect to
their translatability distribution, fractions of differentially regulated genes,
secondary structure composition, and the distribution of evolutionary constraint,
suggesting that both novel groups represent legitimate genes. However, the
machine-learning algorithm only recognized a small fraction of the 210 genes
without annotated homologs. It is possible that these genes represent a novel
group of genes, which have unusual features dissimilar to the genes of the
machine-learning algorithm training set.
DOI: 10.1371/journal.pone.0184119
PMCID: PMC5597208
PMID: 28902868 [Indexed for MEDLINE]
4. Int J Genomics. 2017;2017:6489383. doi: 10.1155/2017/6489383. Epub 2017 Jul 16.
Differential MicroRNA Analyses of Burkholderia pseudomallei- and Francisella
tularensis-Exposed hPBMCs Reveal Potential Biomarkers.
Cer RZ(1)(2), Herrera-Galeano JE(1)(2)(3), Frey KG(1)(2), Schully KL(1)(2), Luu
TV(1)(2), Pesce J(1)(2)(4), Mokashi VP(1)(5), Keane-Myers AM(1)(2)(6),
Bishop-Lilly KA(1)(2).
Author information:
(1)Genomics and Bioinformatics Department, Biological Defense Research
Directorate, Naval Medical Research Center, Frederick, MD, USA.
(2)Henry M. Jackson Foundation for the Advancement of Military Medicine,
Bethesda, MD, USA.
(3)KCE Services and Consulting LLC, Columbia, MD, USA.
(4)Division of Microbiology and Infectious Diseases, National Institute of
Allergy and Infectious Diseases, Bethesda, MD, USA.
(5)Navy Drug Screening Laboratory, Jacksonville, FL, USA.
(6)Immunology, National Institute of Health, Bethesda, MD, USA.
Increasing evidence that microRNAs (miRNAs) play important roles in the immune
response against infectious agents suggests that miRNA might be exploitable as
signatures of exposure to specific infectious agents. In order to identify
potential early miRNA biomarkers of bacterial infections, human peripheral blood
mononuclear cells (hPBMCs) were exposed to two select agents, Burkholderia
pseudomallei K96243 and Francisella tularensis SHU S4, as well as to the
nonpathogenic control Escherichia coli DH5α. RNA samples were harvested at three
early time points, 30, 60, and 120 minutes postexposure, then sequenced. RNAseq
analyses identified 87 miRNAs to be differentially expressed (DE) in a linear
fashion. Of these, 31 miRNAs were tested using the miScript miRNA qPCR assay.
Through RNAseq identification and qPCR validation, we identified differentially
expressed miRNA species that may be involved in the early response to bacterial
infections. Based upon its upregulation at early time points postexposure in two
different individuals, hsa-mir-30c-5p is a miRNA species that could be studied
further as a potential biomarker for exposure to these gram-negative
intracellular pathogens. Gene ontology functional analyses demonstrated that
programmed cell death is the first ranking biological process associated with
miRNAs that are upregulated in F. tularensis-exposed hPBMCs.
DOI: 10.1155/2017/6489383
PMCID: PMC5534298
PMID: 28791299
5. PLoS One. 2017 Jun 14;12(6):e0178966. doi: 10.1371/journal.pone.0178966.
eCollection 2017.
Intestinal organoids model human responses to infection by commensal and Shiga
toxin producing Escherichia coli.
Karve SS(1), Pradhan S(1), Ward DV(2), Weiss AA(1).
Author information:
(1)Department of Molecular Genetics, Biochemistry, and Microbiology, University
of Cincinnati, Cincinnati, Ohio, United States of America.
(2)Center for Microbiome Research and Department of Microbiology and
Physiological Systems, University of Massachusetts Medical School, Worcester,
Massachusetts, United States of America.
Infection with Shiga toxin (Stx) producing Escherichia coli O157:H7 can cause the
potentially fatal complication hemolytic uremic syndrome, and currently only
supportive therapy is available. Lack of suitable animal models has hindered
study of this disease. Induced human intestinal organoids (iHIOs), generated by
in vitro differentiation of pluripotent stem cells, represent differentiated
human intestinal tissue. We show that iHIOs with addition of human neutrophils
can model E. coli intestinal infection and innate cellular responses. Commensal
and O157:H7 introduced into the iHIO lumen replicated rapidly achieving high
numbers. Commensal E. coli did not cause damage, and were completely contained
within the lumen, suggesting defenses, such as mucus production, can constrain
non-pathogenic strains. Some O157:H7 initially co-localized with cellular actin.
Loss of actin and epithelial integrity was observed after 4 hours. O157:H7 grew
as filaments, consistent with activation of the bacterial SOS stress response.
SOS is induced by reactive oxygen species (ROS), and O157:H7 infection increased
ROS production. Transcriptional profiling (RNAseq) demonstrated that both
commensal and O157:H7 upregulated genes associated with gastrointestinal
maturation, while infection with O157:H7 upregulated inflammatory responses,
including interleukin 8 (IL-8). IL-8 is associated with neutrophil recruitment,
and infection with O157:H7 resulted in recruitment of human neutrophils into the
iHIO tissue.
DOI: 10.1371/journal.pone.0178966
PMCID: PMC5470682
PMID: 28614372 [Indexed for MEDLINE]
6. BMC Genet. 2017 Mar 7;18(1):21. doi: 10.1186/s12863-017-0488-4.
Deciphering alternative splicing and nonsense-mediated decay modulate expression
in primary lymphoid tissues of birds infected with avian pathogenic E. coli
(APEC).
Sun H(1).
Author information:
(1)College of Animal Science and Technology, Yangzhou University, Yangzhou,
Jiangsu, 225009, China. hongyans2392@163.com.
BACKGROUND: Avian pathogenic E. coli (APEC) can lead to a loss in millions of
dollars in poultry annually because of mortality and produce contamination.
Studies have verified that many immune-related genes undergo changes in
alternative splicing (AS), along with nonsense mediated decay (NMD), to regulate
the immune system under different conditions. Therefore, the splicing profiles of
primary lymphoid tissues with systemic APEC infection need to be comprehensively
examined.
RESULTS: Gene expression in RNAseq data were obtained for three different immune
tissues (bone marrow, thymus, and bursa) from three phenotype birds
(non-challenged, resistant, and susceptible birds) at two time points.
Alternative 5' splice sites and exon skipping/inclusion were identified as the
major alternative splicing events in avian primary immune organs under systemic
APEC infection. In this study, we detected hundreds of
differentially-expressed-transcript-containing genes (DETs) between different
phenotype birds at 5 days post-infection (dpi). DETs, PSAP and STT3A, with NMD
have important functions under systemic APEC infection. DETs, CDC45, CDK1, RAG2,
POLR1B, PSAP, and DNASE1L3, from the same transcription start sites (TSS)
indicate that cell death, cell cycle, cellular function, and maintenance were
predominant in host under systemic APEC.
CONCLUSIONS: With the use of RNAseq technology and bioinformatics tools, this
study provides a portrait of the AS event and NMD in primary lymphoid tissues,
which play critical roles in host homeostasis under systemic APEC infection.
According to this study, AS plays a pivotal regulatory role in the immune
response in chicken under systemic APEC infection via either NMD or alternative
TSSs. This study elucidates the regulatory role of AS for the immune complex
under systemic APEC infection.
DOI: 10.1186/s12863-017-0488-4
PMCID: PMC5341183
PMID: 28270101 [Indexed for MEDLINE]
7. BMC Genomics. 2017 Feb 28;18(1):216. doi: 10.1186/s12864-017-3586-9.
Differentiation of ncRNAs from small mRNAs in Escherichia coli O157:H7 EDL933
(EHEC) by combined RNAseq and RIBOseq - ryhB encodes the regulatory RNA RyhB and
a peptide, RyhP.
Neuhaus K(1)(2), Landstorfer R(3), Simon S(4), Schober S(5), Wright PR(6), Smith
C(6), Backofen R(6), Wecko R(3), Keim DA(4), Scherer S(3).
Author information:
(1)Lehrstuhl für Mikrobielle Ökologie, Wissenschaftszentrum Weihenstephan,
Technische Universität München, Weihenstephaner Berg 3, D-85354, Freising,
Germany. neuhaus@tum.de.
(2)Core Facility Microbiome/NGS, ZIEL Institute for Food & Health,
Weihenstephaner Berg 3, D-85354, Freising, Germany. neuhaus@tum.de.
(3)Lehrstuhl für Mikrobielle Ökologie, Wissenschaftszentrum Weihenstephan,
Technische Universität München, Weihenstephaner Berg 3, D-85354, Freising,
Germany.
(4)Informatik und Informationswissenschaft, Universität Konstanz, D-78457,
Konstanz, Germany.
(5)Institut für Nachrichtentechnik, Universität Ulm, Albert-Einstein-Allee 43,
D-89081, Ulm, Germany.
(6)Bioinformatics Group, Department of Computer Science and BIOSS Centre for
Biological Signaling Studies, Cluster of Excellence, University of Freiburg,
D-79110, Freiburg, Germany.
BACKGROUND: While NGS allows rapid global detection of transcripts, it remains
difficult to distinguish ncRNAs from short mRNAs. To detect potentially
translated RNAs, we developed an improved protocol for bacterial ribosomal
footprinting (RIBOseq). This allowed distinguishing ncRNA from mRNA in EHEC. A
high ratio of ribosomal footprints per transcript (ribosomal coverage value, RCV)
is expected to indicate a translated RNA, while a low RCV should point to a
non-translated RNA.
RESULTS: Based on their low RCV, 150 novel non-translated EHEC transcripts were
identified as putative ncRNAs, representing both antisense and intergenic
transcripts, 74 of which had expressed homologs in E. coli MG1655. Bioinformatics
analysis predicted statistically significant target regulons for 15 of the
intergenic transcripts; experimental analysis revealed 4-fold or higher
differential expression of 46 novel ncRNA in different growth media. Out of 329
annotated EHEC ncRNAs, 52 showed an RCV similar to protein-coding genes, of
those, 16 had RIBOseq patterns matching annotated genes in other
enterobacteriaceae, and 11 seem to possess a Shine-Dalgarno sequence, suggesting
that such ncRNAs may encode small proteins instead of being solely non-coding. To
support that the RIBOseq signals are reflecting translation, we tested the
ribosomal-footprint covered ORF of ryhB and found a phenotype for the encoded
peptide in iron-limiting condition.
CONCLUSION: Determination of the RCV is a useful approach for a rapid first-step
differentiation between bacterial ncRNAs and small mRNAs. Further, many known
ncRNAs may encode proteins as well.
DOI: 10.1186/s12864-017-3586-9
PMCID: PMC5331693
PMID: 28245801 [Indexed for MEDLINE]
8. ACS Appl Mater Interfaces. 2017 Mar 22;9(11):10047-10060. doi:
10.1021/acsami.7b02380. Epub 2017 Mar 10.
Transcriptome Analysis Reveals Silver Nanoparticle-Decorated Quercetin
Antibacterial Molecular Mechanism.
Sun D(1), Zhang W(1), Mou Z(1), Chen Y(1), Guo F(1), Yang E(1), Wang W(1).
Author information:
(1)School of Life Sciences, Anhui Agricultural University , Hefei 230036, China.
Facile and simple method is developed to synthesize silver-nanoparticle-decorated
quercetin nanoparticles (QA NPs). Modification suggests that synergistic
quercetin (Qe) improves the antibacterial effect of silver nanoparticles (Ag
NPs). Characterization experiment indicates that QA NPs have a diameter of
approximately 10 nm. QA NPs show highly effective antibacterial activities
against drug-resistant Escherichia coli (E. coli) and Staphylococcus aureus (S.
aureus). We explore antibacterial mechanisms using S. aureus and E. coli treated
with QA NPs. Through morphological changes in E. coli and S. aureus, mechanisms
are examined for bacterial damage caused by particulate matter from local
dissociation of silver ion and Qe from QA NPs trapped inside membranes. Moreover,
we note that gene expression profiling methods, such as RNA sequencing, can be
used to predict discover mechanisms of toxicity of QA NPs. Gene ontology (GO)
assay analyses demonstrate the molecular mechanism of the antibacterial effect of
QA NPs. Regarding cellular component ontology, "cell wall organization or
biogenesis" (GO: 0071554) and "cell wall macromolecule metabolic process" (GO:
0044036) are the most represented categories. The present study reports that
transcriptome analysis of the mechanism offers novel insights into the molecular
mechanism of antibacterial assays.
DOI: 10.1021/acsami.7b02380
PMID: 28240544
9. Biotechnol Biofuels. 2017 Feb 3;10:32. doi: 10.1186/s13068-017-0720-5.
eCollection 2017.
Development of a genetically programed vanillin-sensing bacterium for
high-throughput screening of lignin-degrading enzyme libraries.
Sana B(1), Chia KHB(2), Raghavan SS(1), Ramalingam B(3), Nagarajan N(2), Seayad
J(3), Ghadessy FJ(1).
Author information:
(1)p53 Laboratory, Agency for Science Technology And Research (ASTAR), 8A
Biomedical Grove, #06-04/05 Neuros/Immunos, Singapore, 138648 Singapore.
(2)Genome Institute of Singapore, 60 Biopolis Street, Genome, #02-01, Singapore,
138672 Singapore.
(3)Institute of Chemical and Engineering Sciences, 8 Biomedical Grove, Neuros,
#07-01, Singapore, 138665 Singapore.
BACKGROUND: Lignin is a potential biorefinery feedstock for the production of
value-added chemicals including vanillin. A huge amount of lignin is produced as
a by-product of the paper industry, while cellulosic components of plant biomass
are utilized for the production of paper pulp. In spite of vast potential, lignin
remains the least exploited component of plant biomass due to its extremely
complex and heterogenous structure. Several enzymes have been reported to have
lignin-degrading properties and could be potentially used in lignin biorefining
if their catalytic properties could be improved by enzyme engineering. The much
needed improvement of lignin-degrading enzymes by high-throughput selection
techniques such as directed evolution is currently limited, as robust methods for
detecting the conversion of lignin to desired small molecules are not available.
RESULTS: We identified a vanillin-inducible promoter by RNAseq analysis of
Escherichia coli cells treated with a sublethal dose of vanillin and developed a
genetically programmed vanillin-sensing cell by placing the 'very green
fluorescent protein' gene under the control of this promoter. Fluorescence of the
biosensing cell is enhanced significantly when grown in the presence of vanillin
and is readily visualized by fluorescence microscopy. The use of
fluorescence-activated cell sorting analysis further enhances the sensitivity,
enabling dose-dependent detection of as low as 200 µM vanillin. The biosensor is
highly specific to vanillin and no major response is elicited by the presence of
lignin, lignin model compound, DMSO, vanillin analogues or non-specific toxic
chemicals.
CONCLUSIONS: We developed an engineered E. coli cell that can detect vanillin at
a concentration as low as 200 µM. The vanillin-sensing cell did not show
cross-reactivity towards lignin or major lignin degradation products including
vanillin analogues. This engineered E. coli cell could potentially be used as a
host cell for screening lignin-degrading enzymes that can convert lignin to
vanillin.
DOI: 10.1186/s13068-017-0720-5
PMCID: PMC5291986
PMID: 28174601
10. PLoS Negl Trop Dis. 2017 Jan 6;11(1):e0005273. doi: 10.1371/journal.pntd.0005273.
eCollection 2017 Jan.
Transcriptome Sequencing Reveals Large-Scale Changes in Axenic Aedes aegypti
Larvae.
Vogel KJ(1), Valzania L(1), Coon KL(1), Brown MR(1), Strand MR(1).
Author information:
(1)Department of Entomology, The University of Georgia, Athens, Georgia, United
States of America.
Mosquitoes host communities of microbes in their digestive tract that consist
primarily of bacteria. We previously reported that Aedes aegypti larvae colonized
by a native community of bacteria and gnotobiotic larvae colonized by only
Escherichia coli develop very similarly into adults, whereas axenic larvae never
molt and die as first instars. In this study, we extended these findings by first
comparing the growth and abundance of bacteria in conventional, gnotobiotic, and
axenic larvae during the first instar. Results showed that conventional and
gnotobiotic larvae exhibited no differences in growth, timing of molting, or
number of bacteria in their digestive tract. Axenic larvae in contrast grew
minimally and never achieved the critical size associated with molting by
conventional and gnotobiotic larvae. In the second part of the study we compared
patterns of gene expression in conventional, gnotobiotic and axenic larvae by
conducting an RNAseq analysis of gut and nongut tissues (carcass) at 22 h
post-hatching. Approximately 12% of Ae. aegypti transcripts were differentially
expressed in axenic versus conventional or gnotobiotic larvae. However, this
profile consisted primarily of transcripts in seven categories that included the
down-regulation of select peptidases in the gut and up-regulation of several
genes in the gut and carcass with roles in amino acid transport, hormonal
signaling, and metabolism. Overall, our results indicate that axenic larvae
exhibit alterations in gene expression consistent with defects in acquisition and
assimilation of nutrients required for growth.
DOI: 10.1371/journal.pntd.0005273
PMCID: PMC5245907
PMID: 28060822 [Indexed for MEDLINE]
Conflict of interest statement: The authors have declared that no competing
interests exist.
11. Methods. 2017 Mar 15;117:28-34. doi: 10.1016/j.ymeth.2016.11.011. Epub 2016 Nov
19.
Identification of unknown RNA partners using MAPS.
Lalaouna D(1), Prévost K(1), Eyraud A(1), Massé E(2).
Author information:
(1)Department of Biochemistry, RNA Group, Université de Sherbrooke, Sherbrooke,
Québec J1E 4K8, Canada.
(2)Department of Biochemistry, RNA Group, Université de Sherbrooke, Sherbrooke,
Québec J1E 4K8, Canada. Electronic address: eric.masse@usherbrooke.ca.
Recent advances in high-throughput sequencing have led to an explosion in the
rate of small regulatory RNAs (sRNAs) discovery among bacteria. However, only a
handful of them are functionally characterized. Most of the time, little to no
targets are known. In Lalaouna et al. (2015), we proposed a new technology to
uncover sRNAs targetome, which is based on the MS2-affinity purification (MAPS).
We were able to prove its efficiency by applying it on well-characterized sRNAs
of Escherichia coli. Thereafter, we adapted the procedure to other kind of RNA
(mRNAs and tRNA-derived RNA fragments) and bacteria (pathogenic or Gram-positive
strains). Here, we clearly report all improvements and adjustments made to MAPS
technology since it was originally reported.
Copyright © 2016 Elsevier Inc. All rights reserved.
DOI: 10.1016/j.ymeth.2016.11.011
PMID: 27876680 [Indexed for MEDLINE]
12. FEMS Microbiol Lett. 2017 Jan;364(2). pii: fnw262. doi: 10.1093/femsle/fnw262.
Epub 2016 Nov 16.
Transcriptional and translational regulation by RNA thermometers, riboswitches
and the sRNA DsrA in Escherichia coli O157:H7 Sakai under combined cold and
osmotic stress adaptation.
Hücker SM(1), Simon S(2), Scherer S(1), Neuhaus K(3).
Author information:
(1)Chair for Microbial Ecology, Technische Universität München, Weihenstephaner
Berg 3, 85354 Freising, Germany.
(2)Chair for Data Analysis and Visualization, Department of Computer and
Information Science, University of Konstanz, Box 78, 78457 Konstanz, Germany.
(3)Chair for Microbial Ecology, Technische Universität München, Weihenstephaner
Berg 3, 85354 Freising, Germany neuhaus@wzw.tum.de.
The enteric pathogen Escherichia coli O157:H7 Sakai (EHEC) is able to grow at
lower temperatures compared to commensal E. coli Growth at environmental
conditions displays complex challenges different to those in a host. EHEC was
grown at 37°C and at 14°C with 4% NaCl, a combination of cold and osmotic stress
as present in the food chain. Comparison of RNAseq and RIBOseq data provided a
snap shot of ongoing transcription and translation, differentiating
transcriptional and post-transcriptional gene regulation, respectively. Indeed,
cold and osmotic stress related genes are simultaneously regulated at both
levels, but translational regulation clearly dominates. Special emphasis was
given to genes regulated by RNA secondary structures in their 5'UTRs, such as RNA
thermometers and riboswitches, or genes controlled by small RNAs encoded in trans
The results reveal large differences in gene expression between short-time shock
compared to adaptation in combined cold and osmotic stress. Whereas the majority
of cold shock proteins, such as CspA, are translationally downregulated after
adaptation, many osmotic stress genes are still significantly upregulated mainly
translationally, but several also transcriptionally.
© FEMS 2016. All rights reserved. For permissions, please e-mail:
journals.permissions@oup.com.
DOI: 10.1093/femsle/fnw262
PMID: 27856567 [Indexed for MEDLINE]
13. Poult Sci. 2016 Dec 1;95(12):2803-2814. Epub 2016 Jul 27.
Thymus transcriptome reveals novel pathways in response to avian pathogenic
Escherichia coli infection.
Sun H(1)(2), Liu P(3), Nolan LK(4), Lamont SJ(5).
Author information:
(1)College of Animal Science and Technology, Yangzhou University, Yangzhou,
Jiangsu, China, 225009.
(2)Department of Animal Science, Iowa State University, Ames 50011.
(3)Department of Statistics, Iowa State University, Ames 50011.
(4)Department of Veterinary Microbiology and Preventive Medicine, Iowa State
University, Ames 50011.
(5)Department of Animal Science, Iowa State University, Ames 50011
sjlamont@iastate.edu.
Avian pathogenic Escherichia coli (APEC) can cause significant morbidity in
chickens. The thymus provides the essential environment for T cell development;
however, the thymus transcriptome has not been examined for gene expression in
response to APEC infection. An improved understanding of the host genomic
response to APEC infection could inform future breeding programs for disease
resistance and APEC control. We therefore analyzed the transcriptome of the
thymus of birds challenged with APEC, contrasting susceptible and resistant
phenotypes. Thousands of genes were differentially expressed in birds of the
5-day post infection (dpi) challenged-susceptible group vs. 5 dpi non-challenged,
in 5 dpi challenged-susceptible vs. 5 dpi challenged-resistant birds, as well as
in 5 dpi vs. one dpi challenged-susceptible birds. The Toll-like receptor
signaling pathway was the major innate immune response for birds to respond to
APEC infection. Moreover, lysosome and cell adhesion molecules pathways were
common mechanisms for chicken response to APEC infection. The T-cell receptor
signaling pathway, cell cycle, and p53 signaling pathways were significantly
activated in resistant birds to resist APEC infection. These results provide a
comprehensive assessment of global gene networks and biological functionalities
of differentially expressed genes in the thymus under APEC infection. These
findings provide novel insights into key molecular genetic mechanisms that
differentiate host resistance from susceptibility in this primary lymphoid
tissue, the thymus.
© The Author 2016. Published by Oxford University Press on behalf of Poultry
Science Association.
DOI: 10.3382/ps/pew202
PMCID: PMC5144662
PMID: 27466434 [Indexed for MEDLINE]
14. Sci Rep. 2016 Jul 18;6:30025. doi: 10.1038/srep30025.
Systematic analysis of an evolved Thermobifida fusca muC producing malic acid on
organic and inorganic nitrogen sources.
Deng Y(1)(2), Lin J(3), Mao Y(1)(2), Zhang X(4).
Author information:
(1)National Engineering Laboratory for Cereal Fermentation Technology (NELCF),
Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China.
(2)The Key Laboratory of Industrial Biotechnology, Ministry of Education,
Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, China.
(3)College of Life Science, North China University of Science and Technology,
Tangshan 063000, China.
(4)School of pharmaceutical science, Jiangnan University, 1800 Lihu Road, Wuxi,
Jiangsu 214122, China.
Thermobifida fusca is a thermophilic actinobacterium. T. fusca muC obtained by
adaptive evolution preferred yeast extract to ammonium sulfate for accumulating
malic acid and ammonium sulfate for cell growth. We did transcriptome analysis of
T. fusca muC on Avicel and cellobiose with addition of ammonium sulfate or yeast
extract, respectively by RNAseq. The transcriptional results indicate that
ammonium sulfate induced the transcriptions of the genes related to carbohydrate
metabolisms significantly more than yeast extract. Importantly, Tfu_2487,
encoding histidine-containing protein (HPr), didn't transcribe on yeast extract
at all, while it transcribed highly on ammonium sulfate. In order to understand
the impact of HPr on malate production and cell growth of the muC strain, we
deleted Tfu_2487 to get a mutant strain: muCΔ2487, which had
1.33 mole/mole-glucose equivalent malate yield, much higher than that on yeast
extract. We then developed an E. coli-T. fusca shuttle plasmid for
over-expressing HPr in muCΔ2487, a strain without HPr background, forming the
muCΔ2487S strain. The muCΔ2487S strain had a much lower malate yield but faster
cell growth than the muC strain. The results of both mutant strains confirmed
that HPr was the key regulatory protein for T. fusca's metabolisms on nitrogen
sources.
DOI: 10.1038/srep30025
PMCID: PMC4948018
PMID: 27424527
15. PLoS One. 2016 Jun 23;11(6):e0157480. doi: 10.1371/journal.pone.0157480.
eCollection 2016.
The Impact of Intramammary Escherichia coli Challenge on Liver and Mammary
Transcriptome and Cross-Talk in Dairy Cows during Early Lactation Using RNAseq.
Moyes KM(1), Sørensen P(2), Bionaz M(3).
Author information:
(1)Department of Animal and Avian Sciences, University of Maryland, College Park,
Maryland, United States of America.
(2)Center for Quantitative Genetics and Genomics, Department of Molecular Biology
and Genetics, Aarhus University, 8830 Tjele, Denmark.
(3)Department of Animal and Rangeland Sciences, Oregon State University,
Corvallis, Oregon, United States of America.
Our objective was to identify the biological response and the cross-talk between
liver and mammary tissue after intramammary infection (IMI) with Escherichia coli
(E. coli) using RNAseq technology. Sixteen cows were inoculated with live E. coli
into one mammary quarter at ~4-6 weeks in lactation. For all cows, biopsies were
performed at -144, 12 and 24 h relative to IMI in liver and at 24 h post-IMI in
infected and non-infected (control) mammary quarters. For a subset of cows (n =
6), RNA was extracted from both liver and mammary tissue and sequenced using a
100 bp paired-end approach. Ingenuity Pathway Analysis and the Dynamic Impact
Approach analysis of differentially expressed genes (overall effect False
Discovery Rate≤0.05) indicated that IMI induced an overall activation of
inflammation at 12 h post-IMI and a strong inhibition of metabolism, especially
related to lipid, glucose, and xenobiotics at 24 h post-IMI in liver. The data
indicated in mammary tissue an overall induction of inflammatory response with
little effect on metabolism at 24 h post-IMI. We identified a large number of
up-stream regulators potentially involved in the response to IMI in both tissues
but a relatively small core network of transcription factors controlling the
response to IMI for liver whereas a large network in mammary tissue.
Transcriptomic results in liver and mammary tissue were supported by changes in
inflammatory and metabolic mediators in blood and milk. The analysis of potential
cross-talk between the two tissues during IMI uncovered a large communication
from the mammary tissue to the liver to coordinate the inflammatory response but
a relatively small communication from the liver to the mammary tissue. Our
results indicate a strong induction of the inflammatory response in mammary
tissue and impairment of liver metabolism 24h post-IMI partly driven by the
signaling from infected mammary tissue.
DOI: 10.1371/journal.pone.0157480
PMCID: PMC4919052
PMID: 27336699 [Indexed for MEDLINE]
16. BMC Genomics. 2016 Feb 24;17:133. doi: 10.1186/s12864-016-2456-1.
Translatomics combined with transcriptomics and proteomics reveals novel
functional, recently evolved orphan genes in Escherichia coli O157:H7 (EHEC).
Neuhaus K(1), Landstorfer R(2), Fellner L(3), Simon S(4), Schafferhans A(5),
Goldberg T(6), Marx H(7), Ozoline ON(8), Rost B(9), Kuster B(10)(11), Keim
DA(12), Scherer S(13).
Author information:
(1)Lehrstuhl für Mikrobielle Ökologie, Zentralinstitut für Ernährungs- und
Lebensmittelforschung, Wissenschaftszentrum Weihenstephan, Technische Universität
München, Weihenstephaner Berg 3, 85354, Freising, Germany. neuhaus@wzw.tum.de.
(2)Lehrstuhl für Mikrobielle Ökologie, Zentralinstitut für Ernährungs- und
Lebensmittelforschung, Wissenschaftszentrum Weihenstephan, Technische Universität
München, Weihenstephaner Berg 3, 85354, Freising, Germany. r.landstorfer@gmx.de.
(3)Lehrstuhl für Mikrobielle Ökologie, Zentralinstitut für Ernährungs- und
Lebensmittelforschung, Wissenschaftszentrum Weihenstephan, Technische Universität
München, Weihenstephaner Berg 3, 85354, Freising, Germany.
fellnerlea@hotmail.com.
(4)Lehrstuhl für Datenanalyse und Visualisierung, Fachbereich Informatik und
Informationswissenschaft, Universität Konstanz, Box 78, 78457, Konstanz, Germany.
simon@dbvis.inf.uni-konstanz.de.
(5)Department of Informatics - Bioinformatics & TUM-IAS, Technische Universität
München, Boltzmannstraße 3, 85748, Garching, Germany.
andrea.schafferhans@rostlab.org.
(6)Department of Informatics - Bioinformatics & TUM-IAS, Technische Universität
München, Boltzmannstraße 3, 85748, Garching, Germany. goldberg@rostlab.org.
(7)Chair of Proteomics and Bioanalytics, Wissenschaftszentrum Weihenstephan,
Technische Universität München, Emil-Erlenmeyer-Forum 5, 85354, Freising,
Germany. h4r4ld.marx@googlemail.com.
(8)Institute of Cell Biophysics, Russian Academy of Sciences, Moscow Region,
142290, Pushchino, Russia. ozoline@rambler.ru.
(9)Department of Informatics - Bioinformatics & TUM-IAS, Technische Universität
München, Boltzmannstraße 3, 85748, Garching, Germany. rost@rostlab.org.
(10)Chair of Proteomics and Bioanalytics, Wissenschaftszentrum Weihenstephan,
Technische Universität München, Emil-Erlenmeyer-Forum 5, 85354, Freising,
Germany. kuster@wzw.tum.de.
(11)Bavarian Center for Biomolecular Mass Spectrometry (BayBioMS), Technische
Universität München, Gregor-Mendel-Str. 4, 85354, Freising, Germany.
kuster@wzw.tum.de.
(12)Lehrstuhl für Datenanalyse und Visualisierung, Fachbereich Informatik und
Informationswissenschaft, Universität Konstanz, Box 78, 78457, Konstanz, Germany.
keim@informatik.uni-konstanz.de.
(13)Lehrstuhl für Mikrobielle Ökologie, Zentralinstitut für Ernährungs- und
Lebensmittelforschung, Wissenschaftszentrum Weihenstephan, Technische Universität
München, Weihenstephaner Berg 3, 85354, Freising, Germany.
siegfried.scherer@wzw.tum.de.
BACKGROUND: Genomes of E. coli, including that of the human pathogen Escherichia
coli O157:H7 (EHEC) EDL933, still harbor undetected protein-coding genes which,
apparently, have escaped annotation due to their small size and non-essential
function. To find such genes, global gene expression of EHEC EDL933 was examined,
using strand-specific RNAseq (transcriptome), ribosomal footprinting
(translatome) and mass spectrometry (proteome).
RESULTS: Using the above methods, 72 short, non-annotated protein-coding genes
were detected. All of these showed signals in the ribosomal footprinting assay
indicating mRNA translation. Seven were verified by mass spectrometry.
Fifty-seven genes are annotated in other enterobacteriaceae, mainly as
hypothetical genes; the remaining 15 genes constitute novel discoveries. In
addition, protein structure and function were predicted computationally and
compared between EHEC-encoded proteins and 100-times randomly shuffled proteins.
Based on this comparison, 61 of the 72 novel proteins exhibit predicted
structural and functional features similar to those of annotated proteins. Many
of the novel genes show differential transcription when grown under eleven
diverse growth conditions suggesting environmental regulation. Three genes were
found to confer a phenotype in previous studies, e.g., decreased cattle
colonization.
CONCLUSIONS: These findings demonstrate that ribosomal footprinting can be used
to detect novel protein coding genes, contributing to the growing body of
evidence that hypothetical genes are not annotation artifacts and opening an
additional way to study their functionality. All 72 genes are taxonomically
restricted and, therefore, appear to have evolved relatively recently de novo.
DOI: 10.1186/s12864-016-2456-1
PMCID: PMC4765031
PMID: 26911138 [Indexed for MEDLINE]
17. Sci Rep. 2016 Jan 28;6:19899. doi: 10.1038/srep19899.
Global transcriptomic responses of Escherichia coli K-12 to volatile organic
compounds.
Yung PY(1), Grasso LL(1), Mohidin AF(1), Acerbi E(1), Hinks J(1), Seviour T(1),
Marsili E(1)(2)(3), Lauro FM(1)(4).
Author information:
(1)Singapore Centre for Environmental Life Sciences Engineering (SCELSE). 60
Nanyang Drive, SBS-01N-27, Singapore 637551.
(2)School of Chemical and Biomedical Engineering, Nanyang Technological
University, 62 Nanyang Drive, Singapore 637459.
(3)School of Biotechnology, Dublin City University, Collins Avenue, Dublin 9,
Ireland.
(4)Asian School of the Environment, Nanyang Technological University, 50 Nanyang
Avenue, N2-01C-45, Singapore 639798.
Volatile organic compounds (VOCs) are commonly used as solvents in various
industrial settings. Many of them present a challenge to receiving environments,
due to their toxicity and low bioavailability for degradation. Microorganisms are
capable of sensing and responding to their surroundings and this makes them ideal
detectors for toxic compounds. This study investigates the global transcriptomic
responses of Escherichia coli K-12 to selected VOCs at sub-toxic levels. Cells
grown in the presence of VOCs were harvested during exponential growth, followed
by whole transcriptome shotgun sequencing (RNAseq). The analysis of the data
revealed both shared and unique genetic responses compared to cells without
exposure to VOCs. Results suggest that various functional gene categories, for
example, those relating to Fe/S cluster biogenesis, oxidative stress responses
and transport proteins, are responsive to selected VOCs in E. coli. The
differential expression (DE) of genes was validated using GFP-promoter fusion
assays. A variety of genes were differentially expressed even at non-inhibitory
concentrations and when the cells are at their balanced-growth. Some of these
genes belong to generic stress response and others could be specific to VOCs.
Such candidate genes and their regulatory elements could be used as the basis for
designing biosensors for selected VOCs.
DOI: 10.1038/srep19899
PMCID: PMC4730218
PMID: 26818886 [Indexed for MEDLINE]
18. MBio. 2015 Aug 25;6(4). pii: e00998-15. doi: 10.1128/mBio.00998-15.
A New Noncoding RNA Arranges Bacterial Chromosome Organization.
Qian Z(1), Macvanin M(1), Dimitriadis EK(2), He X(3), Zhurkin V(4), Adhya S(5).
Author information:
(1)Laboratory of Molecular Biology, National Cancer Institute, National
Institutes of Health, Bethesda, Maryland, USA.
(2)Biomedical Engineering and Physical Science, National Institute of Biomedical
Imaging and Bioengineering, National Institutes of Health, Bethesda, Maryland,
USA.
(3)Laboratory of Metabolism, National Cancer Institute, National Institutes of
Health, Bethesda, Maryland, USA.
(4)Laboratory of Cell Biology, National Cancer Institute, National Institutes of
Health, Bethesda, Maryland, USA.
(5)Laboratory of Molecular Biology, National Cancer Institute, National
Institutes of Health, Bethesda, Maryland, USA sadhya@helix.nih.gov.
Repeated extragenic palindromes (REPs) in the enterobacterial genomes are usually
composed of individual palindromic units separated by linker sequences. A total
of 355 annotated REPs are distributed along the Escherichia coli genome. RNA
sequence (RNAseq) analysis showed that almost 80% of the REPs in E. coli are
transcribed. The DNA sequence of REP325 showed that it is a cluster of six
repeats, each with two palindromic units capable of forming cruciform structures
in supercoiled DNA. Here, we report that components of the REP325 element and at
least one of its RNA products play a role in bacterial nucleoid DNA condensation.
These RNA not only are present in the purified nucleoid but bind to the bacterial
nucleoid-associated HU protein as revealed by RNA IP followed by microarray
analysis (RIP-Chip) assays. Deletion of REP325 resulted in a dramatic increase of
the nucleoid size as observed using transmission electron microscopy (TEM), and
expression of one of the REP325 RNAs, nucleoid-associated noncoding RNA 4
(naRNA4), from a plasmid restored the wild-type condensed structure.
Independently, chromosome conformation capture (3C) analysis demonstrated
physical connections among various REP elements around the chromosome. These
connections are dependent in some way upon the presence of HU and the REP325
element; deletion of HU genes and/or the REP325 element removed the connections.
Finally, naRNA4 together with HU condensed DNA in vitro by connecting REP325 or
other DNA sequences that contain cruciform structures in a pairwise manner as
observed by atomic force microscopy (AFM). On the basis of our results, we
propose molecular models to explain connections of remote cruciform structures
mediated by HU and naRNA4.IMPORTANCE: Nucleoid organization in bacteria is being
studied extensively, and several models have been proposed. However, the
molecular nature of the structural organization is not well understood. Here we
characterized the role of a novel nucleoid-associated noncoding RNA, naRNA4, in
nucleoid structures both in vivo and in vitro. We propose models to explain how
naRNA4 together with nucleoid-associated protein HU connects remote DNA elements
for nucleoid condensation. We present the first evidence of a noncoding RNA
together with a nucleoid-associated protein directly condensing nucleoid DNA.
Copyright © 2015 Qian et al.
DOI: 10.1128/mBio.00998-15
PMCID: PMC4550694
PMID: 26307168 [Indexed for MEDLINE]
19. PLoS One. 2015 Jun 30;10(6):e0130902. doi: 10.1371/journal.pone.0130902.
eCollection 2015.
Identification of Candidate Adherent-Invasive E. coli Signature Transcripts by
Genomic/Transcriptomic Analysis.
Zhang Y(1), Rowehl L(2), Krumsiek JM(3), Orner EP(2), Shaikh N(4), Tarr PI(5),
Sodergren E(6), Weinstock GM(6), Boedeker EC(7), Xiong X(8), Parkinson J(9),
Frank DN(10), Li E(2), Gathungu G(3).
Author information:
(1)Department of Applied Mathematics and Statistics, Stony Brook University,
Stony Brook, New York, United States of America.
(2)Department of Medicine, Stony Brook University, Stony Brook, New York, United
States of America.
(3)Department of Pediatrics, Stony Brook University, Stony Brook, New York,
United States of America.
(4)Department of Pediatrics, Washington University St. Louis, St. Louis,
Missouri, United States of America.
(5)Department of Pediatrics, Washington University St. Louis, St. Louis,
Missouri, United States of America; Department of Molecular Microbiology,
Washington University St. Louis, St. Louis, Missouri, United States of America.
(6)The Genome Institute, Washington University St. Louis, St. Louis, Missouri,
United States of America.
(7)Department of Medicine, University of New Mexico, Albuquerque, New Mexico,
United States of America.
(8)Program in Molecular Structure and Function, The Hospital for Sick Children,
Toronto, Canada.
(9)Department of Biochemistry & Molecular and Medical Genetics, University of
Toronto, Toronto, Canada.
(10)Department of Medicine, University of Colorado, Denver, Colorado, United
States of America.
Erratum in
PLoS One. 2015;10(7):e0134759.
Adherent-invasive Escherichia coli (AIEC) strains are detected more frequently
within mucosal lesions of patients with Crohn's disease (CD). The AIEC phenotype
consists of adherence and invasion of intestinal epithelial cells and survival
within macrophages of these bacteria in vitro. Our aim was to identify candidate
transcripts that distinguish AIEC from non-invasive E. coli (NIEC) strains and
might be useful for rapid and accurate identification of AIEC by
culture-independent technology. We performed comparative RNA-Sequence (RNASeq)
analysis using AIEC strain LF82 and NIEC strain HS during exponential and
stationary growth. Differential expression analysis of coding sequences (CDS)
homologous to both strains demonstrated 224 and 241 genes with increased and
decreased expression, respectively, in LF82 relative to HS. Transition metal
transport and siderophore metabolism related pathway genes were up-regulated,
while glycogen metabolic and oxidation-reduction related pathway genes were
down-regulated, in LF82. Chemotaxis related transcripts were up-regulated in LF82
during the exponential phase, but flagellum-dependent motility pathway genes were
down-regulated in LF82 during the stationary phase. CDS that mapped only to the
LF82 genome accounted for 747 genes. We applied an in silico subtractive genomics
approach to identify CDS specific to AIEC by incorporating the genomes of 10
other previously phenotyped NIEC. From this analysis, 166 CDS mapped to the LF82
genome and lacked homology to any of the 11 human NIEC strains. We compared these
CDS across 13 AIEC, but none were homologous in each. Four LF82 gene loci
belonging to clustered regularly interspaced short palindromic repeats region
(CRISPR)--CRISPR-associated (Cas) genes were identified in 4 to 6 AIEC and absent
from all non-pathogenic bacteria. As previously reported, AIEC strains were
enriched for pdu operon genes. One CDS, encoding an excisionase, was shared by 9
AIEC strains. Reverse transcription quantitative polymerase chain reaction assays
for 6 genes were conducted on fecal and ileal RNA samples from 22 inflammatory
bowel disease (IBD), and 32 patients without IBD (non-IBD). The expression of Cas
loci was detected in a higher proportion of CD than non-IBD fecal and ileal RNA
samples (p <0.05). These results support a comparative genomic/transcriptomic
approach towards identifying candidate AIEC signature transcripts.
DOI: 10.1371/journal.pone.0130902
PMCID: PMC4509574
PMID: 26125937 [Indexed for MEDLINE]
20. Front Microbiol. 2014 Aug 13;5:402. doi: 10.3389/fmicb.2014.00402. eCollection
2014.
Aromatic inhibitors derived from ammonia-pretreated lignocellulose hinder
bacterial ethanologenesis by activating regulatory circuits controlling inhibitor
efflux and detoxification.
Keating DH(1), Zhang Y(1), Ong IM(1), McIlwain S(1), Morales EH(2), Grass JA(3),
Tremaine M(1), Bothfeld W(1), Higbee A(1), Ulbrich A(4), Balloon AJ(4), Westphall
MS(5), Aldrich J(6), Lipton MS(6), Kim J(7), Moskvin OV(1), Bukhman YV(1), Coon
JJ(8), Kiley PJ(2), Bates DM(1), Landick R(9).
Author information:
(1)Great Lakes Bioenergy Research Center, University of Wisconsin-Madison
Madison, WI, USA.
(2)Great Lakes Bioenergy Research Center, University of Wisconsin-Madison
Madison, WI, USA ; Department of Biomolecular Chemistry, University of
Wisconsin-Madison Madison, WI, USA.
(3)Great Lakes Bioenergy Research Center, University of Wisconsin-Madison
Madison, WI, USA ; Department of Biochemistry, University of Wisconsin-Madison
Madison, WI, USA.
(4)Department of Chemistry, University of Wisconsin-Madison Madison, WI, USA.
(5)Department of Biomolecular Chemistry, University of Wisconsin-Madison Madison,
WI, USA ; Department of Chemistry, University of Wisconsin-Madison Madison, WI,
USA.
(6)Pacific Northwest National Laboratory Richland, WA, USA.
(7)Great Lakes Bioenergy Research Center, University of Wisconsin-Madison
Madison, WI, USA ; Department of Chemical and Biological Engineering, University
of Wisconsin-Madison Madison, WI, USA.
(8)Great Lakes Bioenergy Research Center, University of Wisconsin-Madison
Madison, WI, USA ; Department of Biomolecular Chemistry, University of
Wisconsin-Madison Madison, WI, USA ; Department of Chemistry, University of
Wisconsin-Madison Madison, WI, USA.
(9)Great Lakes Bioenergy Research Center, University of Wisconsin-Madison
Madison, WI, USA ; Department of Biochemistry, University of Wisconsin-Madison
Madison, WI, USA ; Department of Bacteriology, University of Wisconsin-Madison
Madison, WI, USA.
Efficient microbial conversion of lignocellulosic hydrolysates to biofuels is a
key barrier to the economically viable deployment of lignocellulosic biofuels. A
chief contributor to this barrier is the impact on microbial processes and energy
metabolism of lignocellulose-derived inhibitors, including phenolic carboxylates,
phenolic amides (for ammonia-pretreated biomass), phenolic aldehydes, and
furfurals. To understand the bacterial pathways induced by inhibitors present in
ammonia-pretreated biomass hydrolysates, which are less well studied than
acid-pretreated biomass hydrolysates, we developed and exploited synthetic mimics
of ammonia-pretreated corn stover hydrolysate (ACSH). To determine regulatory
responses to the inhibitors normally present in ACSH, we measured transcript and
protein levels in an Escherichia coli ethanologen using RNA-seq and quantitative
proteomics during fermentation to ethanol of synthetic hydrolysates containing or
lacking the inhibitors. Our study identified four major regulators mediating
these responses, the MarA/SoxS/Rob network, AaeR, FrmR, and YqhC. Induction of
these regulons was correlated with a reduced rate of ethanol production, buildup
of pyruvate, depletion of ATP and NAD(P)H, and an inhibition of xylose
conversion. The aromatic aldehyde inhibitor 5-hydroxymethylfurfural appeared to
be reduced to its alcohol form by the ethanologen during fermentation, whereas
phenolic acid and amide inhibitors were not metabolized. Together, our findings
establish that the major regulatory responses to lignocellulose-derived
inhibitors are mediated by transcriptional rather than translational regulators,
suggest that energy consumed for inhibitor efflux and detoxification may limit
biofuel production, and identify a network of regulators for future synthetic
biology efforts.
DOI: 10.3389/fmicb.2014.00402
PMCID: PMC4132294
PMID: 25177315
21. BMC Microbiol. 2014 Aug 1;14:206. doi: 10.1186/s12866-014-0206-6.
Characterization and analysis of the Burkholderia pseudomallei BsaN virulence
regulon.
Chen Y, Schröder I, French CT, Jaroszewicz A, Yee XJ, Teh BE, Toesca IJ, Miller
JF, Gan YH(1).
Author information:
(1)Department of Biochemistry, Yong Loo Lin School of Medicine, National
University of Singapore, Singapore 117597, Singapore. yunn_hwen_gan@nuhs.edu.sg.
BACKGROUND: Burkholderia pseudomallei is a facultative intracellular pathogen and
the causative agent of melioidosis. A conserved type III secretion system (T3SS3)
and type VI secretion system (T6SS1) are critical for intracellular survival and
growth. The T3SS3 and T6SS1 genes are coordinately and hierarchically regulated
by a TetR-type regulator, BspR. A central transcriptional regulator of the BspR
regulatory cascade, BsaN, activates a subset of T3SS3 and T6SS1 loci.
RESULTS: To elucidate the scope of the BsaN regulon, we used RNAseq analysis to
compare the transcriptomes of wild-type B. pseudomallei KHW and a bsaN deletion
mutant. The 60 genes positively-regulated by BsaN include those that we had
previously identified in addition to a polyketide biosynthesis locus and genes
involved in amino acid biosynthesis. BsaN was also found to repress the
transcription of 51 genes including flagellar motility loci and those encoding
components of the T3SS3 apparatus. Using a promoter-lacZ fusion assay in E. coli,
we show that BsaN together with the chaperone BicA directly control the
expression of the T3SS3 translocon, effector and associated regulatory genes that
are organized into at least five operons (BPSS1516-BPSS1552). Using a mutagenesis
approach, a consensus regulatory motif in the promoter regions of BsaN-regulated
genes was shown to be essential for transcriptional activation.
CONCLUSIONS: BsaN/BicA functions as a central regulator of key virulence clusters
in B. pseudomallei within a more extensive network of genetic regulation. We
propose that BsaN/BicA controls a gene expression program that facilitates the
adaption and intracellular survival of the pathogen within eukaryotic hosts.
DOI: 10.1186/s12866-014-0206-6
PMCID: PMC4236580
PMID: 25085508 [Indexed for MEDLINE]
22. Proc Natl Acad Sci U S A. 2014 Jun 24;111(25):E2576-85. doi:
10.1073/pnas.1401853111. Epub 2014 Jun 9.
Correcting direct effects of ethanol on translation and transcription machinery
confers ethanol tolerance in bacteria.
Haft RJ(1), Keating DH(1), Schwaegler T(1), Schwalbach MS(1), Vinokur J(1),
Tremaine M(1), Peters JM(2), Kotlajich MV(3), Pohlmann EL(1), Ong IM(1), Grass
JA(1), Kiley PJ(4), Landick R(5).
Author information:
(1)Great Lakes Bioenergy Research Center and.
(2)Departments of Biochemistry,Genetics.
(3)Departments of Biochemistry.
(4)Great Lakes Bioenergy Research Center andBiomolecular Chemistry, and.
(5)Great Lakes Bioenergy Research Center andDepartments of
Biochemistry,Bacteriology, University of Wisconsin-Madison, Madison, WI 53706
landick@biochem.wisc.edu.
The molecular mechanisms of ethanol toxicity and tolerance in bacteria, although
important for biotechnology and bioenergy applications, remain incompletely
understood. Genetic studies have identified potential cellular targets for
ethanol and have revealed multiple mechanisms of tolerance, but it remains
difficult to separate the direct and indirect effects of ethanol. We used
adaptive evolution to generate spontaneous ethanol-tolerant strains of
Escherichia coli, and then characterized mechanisms of toxicity and resistance
using genome-scale DNAseq, RNAseq, and ribosome profiling coupled with specific
assays of ribosome and RNA polymerase function. Evolved alleles of metJ, rho, and
rpsQ recapitulated most of the observed ethanol tolerance, implicating
translation and transcription as key processes affected by ethanol. Ethanol
induced miscoding errors during protein synthesis, from which the evolved rpsQ
allele protected cells by increasing ribosome accuracy. Ribosome profiling and
RNAseq analyses established that ethanol negatively affects transcriptional and
translational processivity. Ethanol-stressed cells exhibited ribosomal stalling
at internal AUG codons, which may be ameliorated by the adaptive inactivation of
the MetJ repressor of methionine biosynthesis genes. Ethanol also caused aberrant
intragenic transcription termination for mRNAs with low ribosome density, which
was reduced in a strain with the adaptive rho mutation. Furthermore, ethanol
inhibited transcript elongation by RNA polymerase in vitro. We propose that
ethanol-induced inhibition and uncoupling of mRNA and protein synthesis through
direct effects on ribosomes and RNA polymerase conformations are major
contributors to ethanol toxicity in E. coli, and that adaptive mutations in metJ,
rho, and rpsQ help protect these central dogma processes in the presence of
ethanol.
DOI: 10.1073/pnas.1401853111
PMCID: PMC4078849
PMID: 24927582 [Indexed for MEDLINE]
23. Nucleic Acids Res. 2013 Jan;41(Database issue):D1-7. doi: 10.1093/nar/gks1297.
Epub 2012 Nov 30.
The 2013 Nucleic Acids Research Database Issue and the online molecular biology
database collection.
Fernández-Suárez XM(1), Galperin MY.
Author information:
(1)nardatabase@gmail.com
The 20th annual Database Issue of Nucleic Acids Research includes 176 articles,
half of which describe new online molecular biology databases and the other half
provide updates on the databases previously featured in NAR and other journals.
This year's highlights include two databases of DNA repeat elements; several
databases of transcriptional factors and transcriptional factor-binding sites;
databases on various aspects of protein structure and protein-protein
interactions; databases for metagenomic and rRNA sequence analysis; and four
databases specifically dedicated to Escherichia coli. The increased emphasis on
using the genome data to improve human health is reflected in the development of
the databases of genomic structural variation (NCBI's dbVar and EBI's DGVa), the
NIH Genetic Testing Registry and several other databases centered on the genetic
basis of human disease, potential drugs, their targets and the mechanisms of
protein-ligand binding. Two new databases present genomic and RNAseq data for
monkeys, providing wealth of data on our closest relatives for comparative
genomics purposes. The NAR online Molecular Biology Database Collection,
available at http://www.oxfordjournals.org/nar/database/a/, has been updated and
currently lists 1512 online databases. The full content of the Database Issue is
freely available online on the Nucleic Acids Research website
(http://nar.oxfordjournals.org/).
DOI: 10.1093/nar/gks1297
PMCID: PMC3531151
PMID: 23203983 [Indexed for MEDLINE]
24. Nucleic Acids Res. 2012 Nov 1;40(20):e156. doi: 10.1093/nar/gks680. Epub 2012 Jul
19.
RNAsnap™: a rapid, quantitative and inexpensive, method for isolating total RNA
from bacteria.
Stead MB(1), Agrawal A, Bowden KE, Nasir R, Mohanty BK, Meagher RB, Kushner SR.
Author information:
(1)Department of Genetics, University of Georgia, Athens, GA 30602, USA.
RNAsnap™ is a simple and novel method that recovers all intracellular RNA
quantitatively (>99%), faster (<15 min) and less expensively (∼3 cents/sample)
than any of the currently available RNA isolation methods. In fact, none of the
bacterial RNA isolation methods, including the commercial kits, are effective in
recovering all species of intracellular RNAs (76-5700 nt) with equal efficiency,
which can lead to biased results in genome-wide studies involving microarray or
RNAseq analysis. The RNAsnap™ procedure yields ∼60 µg of RNA from 10(8)
Escherichia coli cells that can be used directly for northern analysis without
any further purification. Based on a comparative analysis of specific transcripts
ranging in size from 76 to 5700 nt, the RNAsnap™ method provided the most
accurate measure of the relative amounts of the various intracellular RNAs.
Furthermore, the RNAsnap™ RNA was successfully used in enzymatic reactions such
as RNA ligation, reverse transcription, primer extension and reverse
transcriptase-polymerase chain reaction, following sodium acetate/ethanol
precipitation. The RNAsnap™ method can be used to isolate RNA from a wide range
of Gram-negative and Gram-positive bacteria as well as yeast.
DOI: 10.1093/nar/gks680
PMCID: PMC3488207
PMID: 22821568 [Indexed for MEDLINE]
25. Nucleic Acids Res. 2012 Sep;40(16):7870-84. doi: 10.1093/nar/gks503. Epub 2012
Jun 11.
Altered tRNA characteristics and 3' maturation in bacterial symbionts with
reduced genomes.
Hansen AK(1), Moran NA.
Author information:
(1)Department of Ecology and Evolutionary Biology, West Campus, Yale University,
PO Box 27388 West Haven, CT 06516-7388, USA. allison.hansen@yale.edu
Translational efficiency is controlled by tRNAs and other genome-encoded
mechanisms. In organelles, translational processes are dramatically altered
because of genome shrinkage and horizontal acquisition of gene products. The
influence of genome reduction on translation in endosymbionts is largely unknown.
Here, we investigate whether divergent lineages of Buchnera aphidicola, the
reduced-genome bacterial endosymbiont of aphids, possess altered translational
features compared with their free-living relative, Escherichia coli. Our RNAseq
data support the hypothesis that translation is less optimal in Buchnera than in
E. coli. We observed a specific, convergent, pattern of tRNA loss in Buchnera and
other endosymbionts that have undergone genome shrinkage. Furthermore, many
modified nucleoside pathways that are important for E. coli translation are lost
in Buchnera. Additionally, Buchnera's A + T compositional bias has resulted in
reduced tRNA thermostability, and may have altered aminoacyl-tRNA synthetase
recognition sites. Buchnera tRNA genes are shorter than those of E. coli, as the
majority no longer has a genome-encoded 3' CCA; however, all the expressed,
shortened tRNAs undergo 3' CCA maturation. Moreover, expression of tRNA
isoacceptors was not correlated with the usage of corresponding codons. Overall,
our data suggest that endosymbiont genome evolution alters tRNA characteristics
that are known to influence translational efficiency in their free-living
relative.
DOI: 10.1093/nar/gks503
PMCID: PMC3439896
PMID: 22689638 [Indexed for MEDLINE]