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RNA Thermometers

Structured RNA regions influence every step in the gene expression program. Many bacterial heat shock and virulence genes are regulated by RNA thermometers (RNATs), which are molecular zippers that control translation initiation. They adopt a structure that blocks the ribosome binding site at low temperatures. A temperature upshift to 37°C (virulence genes) or higher (heat shock genes) destabilizes the structure and permits translation.

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Regulated proteolysis

The FtsH protease of E. coli controls important cellular processes like the heat shock response and lipopolysaccharide (LPS) biosynthesis by controlled proteolysis of the responsible key factors. Induction of the heat shock response requires the alternative sigma factor RpoH (Sigma32). Degradation of RpoH under non-stress conditions requires FtsH and the DnaKJ chaperone system. Little is known about proteolysis of LpxC, the key enzyme in LPS biosynthesis. We study how the FtsH protease recognizes its substrate and attempt to identify the turnover elements in RpoH and LpxC by genetic and biochemical approaches.

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Bacterial Membrane Biosynthesis

Every living cell is enveloped by at least one membrane, which provides the first line of defense against toxic compounds and harmful conditions. We are interested in the biosynthesis of phosphatidylcholine (PC), an unusual phospholipid in bacteria that plays an important role in stress resistance and host-microbe interactions. We biochemically characterize the enzymes of three different PC biosynthesis pathways that operate either at the phospholipid head group or at the fatty acid site chains.

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Bacterial predation behavior

Bacteria have evolved different strategies for surviving in competitive environments. One such strategy is bacterial predation, where predators kill bacteria of another species to use their biomass as an energy source. We study predation behavior of the social soil bacterium Myxococcus xanthus to reveal molecular mechanisms that mediate the specific recognition and lysis of bacterial prey.

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RNA Structures of Upstream Regions in Bacteria

Welcome to the RNA-SURIBA homepage!

The database RNA-SURIBA contains RNA-secondary structure predictions and a set of additional
parameters for all upstream regions of all genes of diverse bacterial genomes.

It can be downloaded free of charge.

Details of the database are published in the article ‘Genome-wide bioinformatic prediction and
experimental evaluation of potential RNA thermometers’
by Waldminghaus, T., Gaubig, L. C., and Narberhaus, F.; Molecular Genetics and Genomics, 2007.

For other articles related to the database see publications.

Download options are specified in the manual.

Any comments and questions should be directed to E-Mail.


Please contact us by mail and we will provide you with the datafiles.


Narberhaus, F., Waldminghaus, T., and Chowdhury, S. (2006) RNA thermometers. FEMS Microbiol Rev 30: 3-16.

Waldminghaus, T., Heidrich, N., Brantl, S. and Narberhaus, F. (2007) FourU – A novel type of RNA thermometer in Salmonella. Mol Microbiol 65(2): 413-24.

Waldminghaus, T., Fippinger, A., Alfsmann, J., and Narberhaus, F. (2005) RNA thermometers are common in alpha - and gamma -proteobacteria. Biol Chem 386: 1279-1286.


RNA Structures od untranslated Regions in Bacteria

Database description

The database RNA-SURIBA contains RNA-secondary structure predictions and a set of additional parameters for all upstream regions of all genes from 62 bacterial genomes.

Construction and content of the database RNA-SURIBA are described in detail in the article: Genome-wide bioinformatic prediction and experimental evaluation of potential RNA thermometers by Waldminghaus, T., Gaubig, L. C., and Narberhaus, F.; Molecular Genetics and Genomics, 2007.

RNA-SURIBA contains 62 tables, one for each genome. Tables are named according to the bacterial species (see table 1). Each table consists of 36 colums with the structure predictions, sequences, gene annotation data and calculated parameters for each structure (see table 2 for details).


We provide the database RNA-SURIBA in two different formats. First, a SQL-DUMP that allows the simple use of RNA-SURIBA with any SQL-server independently of the platform. Second, ASCII flat files of each table which provide the possibility to download only the dataset of interest and not the whole database. The order of rows in the flat files is consistent with the order in table 2.

Example for installing RNA-SURIBA from the DUMP-file on a postgresql system:

  1. Start the SQL-server /usr/bin/pg_ctl -D /home/charly/database/postgresql/data -l /home/charly/tmp/logfile start
  2. Create a database with the name RNA-SURIBA shell> /usr/local/pgsql/bin/createdb RNA-SURIBA
  3. Read in the DUMP shell> psql RNA-SURIBA RNA-SURIBA.dump
  4. Connect to the database RNA-SURIBA shell> psql RNA-SURIBA
    Additional Material

In addition to the database, drawings of the structures in GIF-format of each structure prediction are provided. The drawings are stored in one zip-file for every genome named after the corresponding genome (see table 1). The files are stored in folders named after the corresponding genome (see table 1). Each file is designated according to the start of the gene on the chromosome (column 2 in each RNA-SURIBA table) in combination with the folding number (column 6 in each RNA-SURIBA table).


For gene thrL from E. coli the gene start is 190 and structure prediction with the lowest energy of -11.0 represents folding 1. The corresponding file is stored as EC/Foldings/190_1.gif.

Table 1: Genomes included in the database RNA-SURIBA

Bacterial species Abbreviation in RNA-SURIBA
Acinetobacter sp. ADP1 AC
Agrobacterium tumefaciens C58 AT
Aquifex aeolicus VF5 AA
Bacillus cereus ATCC 14579 BC
Bacillus halodurans C-125 BAH
Bacillus subtilis 168 BS
Bartonella henselae str. Houston-1 BH
Bifidobacterium longum NCC2705 BL
Bordetella pertussis Tohama I BP
Borrelia burgdorferi B31 BB
Bradyrhizobium japonicum USDA 110 BJ
Brucella melitensis 16M BM
Buchnera aphidicola str. APS BA
Campylobacter jejuni subsp. jejuni NCTC 11168 CJ
Candidatus Blochmannia floridanus CBF
Caulobacter crescentus CB15 CC
Chlamydia muridarum CM
Chlamydophila pneumoniae TW-183 CP
Clostridium acetobutylicum ATCC 824 CA
Corynebacterium glutamicum ATCC 13032 CG
Coxiella burnetii RSA 493 CB
Deinococcus radiodurans R1 DR
Desulfovibrio vulgaris subsp vulg. str. Hildenborough DV
Enterococcus faecalis V583 EF
Erwinia carotovora subsp. atroseptica SCRI1043 ER
Escherichia coli K12 EC
Gloeobacter violoceus PCC7421 GV
Haemophilus influenzae Rd KW 20 HI
Helicobacter pylori 26695 HP
Lactobacillus plantarum WCFS1 LP
Lactococcus lactis subsp. lactis Ill403 LL
Listeria monocytogenes LM
Mesorhizobium loti ML
Mycobacterium tuberculosis H37 Rv MT
Mycoplasma genitalium G-37 MG
Neisseria meningitidis MC58 NM
Nitrosomonas europaea ATCC19718 NE
Pasteurella multocida subsp. multocida str. Pm70 PM
Photorabdus luminescens subsp. lumondii TT01 PL
Pseudomonas aeroginosa PAO1 PA
Pyrococcus furiosus DSM 3638 PF
Ralstonia solanacearum GMI1000 RS
Rhodopseudomonas palustris CGA009 RHP
Ricketsia prowazekii str. MadridE RP
Rickettsia conorii str Malish 7 RC
Salmonella enterica subsp. enterica serovar Thphi str.CT18 SE
Shewanella oneidensis MR-1 SO
Shigella flexneri 2a str. 301 SF
Sinorhizobium meliloti SM
Staphylococcus aureus subsp. aureus MW2 SA
Staphylococcus epidermidis ATCC 12228 STE
Streptococcus pneumoniae R6 SP
Streptomyces coelicolor A3(2) SC
Synechocystis sp. PCC6803 SS
Thermus thermophilus HB27 TT
Treponema deticola ATCC 35405 TD
Vibrio cholerae 01 biovar eltor str. N16961 VC
Wigglesworthia glossinidia WG
Wolbachia endosymbiont of Drosophila melanogaster WE
Xanthomonas axonopodis pv. citri str. N16961 XA
Xylella fastidiosa 9a5c XF
Yersinia pestis CO92 YP

Table 2: Parameters contained in the database RNA-SURIBA

Column in database Name of variable Data type Description
1 geneName string Gene name according to genome annotation.
2 geneStart int Gene start according to genome annotation.
3 geneEnd int Gene end according to genome annotation.
4 geneProduct string Gene product according to genome annotation.
5 ene rgy double Free energy of RNA secondary structure prediction with mfold.
6 folding int Numbers indicate suboptimal RNA structure predictions (1: prediction with lowest Δ G, 2 and 3: second and third best predictions).
7 ggagg boolean Is the sequence “ggagg” (ideal Shine-Dalgarno) contained in the last 30 nucleotides upstream of the 3’-end?
8 plus boolean True, if sequence corresponds to the plus-strand.
9 aContent float Content of adenin in the sequence (%).
10 cContent float Content of cytosine in the sequence (%).
11 gContent float Content of guanine in the sequence (%).
12 uContent float Content of uracil in the sequence (%).
13 endLoopNumber int Number of end-loops in the structure.
14 endLoop4Number int Number of tetra-loops in the structure.
15 endLoop5Number int Number of penta-loops in the structure.
16 bulgeNumber int Number of bulges in the structure.
17 internalLoopNumber int Number of internal loops in the structure.
18 joinNumber int Number of joins in the structure.
19 componentNumber int Number of substructures in the structure.
20 basePairNumber int Number of base-pairs in the structure.
21 endLoopSize float Average size of end-loops.
22 internalLoopSize float Average size of internal loops.
23 bulgeSize float Average size of bulges.
24 endLoopBases float Number of nucleotides in end-loops (%).
25 internalLoopBases float Number of nucleotides in internal loops (%).
26 bulgeBases float Number of nucleotides in bulges (%).
27 stackBases float Number of nucleotides in stacks (%).
28 joinBases float Number of nucleotides in joins (%).
29 externalBases float Number of external nucleotides at the 3'- and 5'-end plus intern external nucleotides (%).
30 internExternalNumber int Number of unpaired nucleotides on the baseline (not in loops or joins and not at the 3'- and 5'-end).
31 external5Number int Number of external nucleotides at the 5'-end.
32 external3Number int Number of external nucleotides at the 3'-end.
33 loopDegree float Average loop-degree (How many stacks originate from one loop?).
34 seq string 110 nucleotides of genes (AUG +4 nucleotides of the coding region and 103 nucleotides upstream of AUG).
35 struct string Structure prediction in dot-bracked annotation.
36 connectStruct string Structure prediction in graph-annotation, corresponding to ct-file from mfold output.