The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data

A variety of high-throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes. Common methods for analysis of these large data sets can be problematic. One challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time. We propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays. We demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions. This general strategy can be applied in the analysis of data sets of thousands of genes under different conditions.[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]     

Splitting the BLOSUM Score into Numbers of Biological Significance

Mathematical tools developed in the context of Shannon information theory were used to analyze the meaning of the BLOSUM score, which was split into three components termed as the BLOSUM spectrum (or BLOSpectrum). These relate respectively to the sequence convergence (the stochastic similarity of the two protein sequences), to the background frequency divergence (typicality of the amino acid probability distribution in each sequence), and to the target frequency divergence (compliance of the amino acid variations between the two sequences to the protein model implicit in the BLOCKS database). This treatment sharpens the protein sequence comparison, providing a rationale for the biological significance of the obtained score, and helps to identify weakly related sequences. Moreover, the BLOSpectrum can guide the choice of the most appropriate scoring matrix, tailoring it to the evolutionary divergence associated with the two sequences, or indicate if a compositionally adjusted matrix could perform better.[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]     

MqsR, a Crucial Regulator for Quorum Sensing and Biofilm Formation, Is a GCU-specific mRNA Interferase in Escherichia coli

The mqsR gene has been shown to be positively regulated by the quorum-sensing autoinducer AI-2, which in turn activates a two-component system, the qseB-qseC operon. This operon plays an important role in biofilm formation in Escherichia coli. However, its cellular function has remained unknown. Here, we found that 1 base downstream of mqsR there is a gene, ygiT, that is co-transcribed with mqsR. Induction of mqsR caused cell growth arrest, whereas ygiT co-induction recovered cell growth. We demonstrate that MqsR (98 amino acid residues), which has no homology to the well characterized mRNA interferase MazF, is a potent inhibitor of protein synthesis that functions by degrading cellular mRNAs. In vivo and in vitro primer extension experiments showed that MqsR is an mRNA interferase specifically cleaving mRNAs at GCU. The mRNA interferase activity of purified MqsR was inhibited by purified YgiT (131 residues). MqsR forms a stable 2:1 complex with YgiT, and the complex likely functions as a repressor for the mqsR-ygiT operon by specifically binding to two different palindromic sequences present in the 5′-untranslated region of this operon.It has been reported that quorum sensing is involved in biofilm formation (1–4). mqsR expression was found to be induced by 8-fold in biofilms (5) and also by the quorum-sensing signal autoinducer AI-2, which is a species-nonspecific signaling molecule produced by both Gram-negative and Gram-positive bacteria, including Escherichia coli (6). It has been reported that induction of mqsR activates a two-component system, the qseB-qseC operon, which is known to play an important role in biofilm formation (6). Thus, it has been proposed that MqsR (98 amino acid residues) is a regulator of biofilm formation because it activates qseB, which controls the flhDC expression required for motility and biofilm formation in E. coli (6). However, the cellular function of MqsR has remained unknown.Interestingly, all free-living bacteria examined to date contain a number of suicide or toxin genes in their genomes (7, 8). Many of these toxins are co-transcribed with their cognate antitoxins in an operon (termed toxin-antitoxin (TA)² operon) and form a stable complex in the cell, so their toxicity is subdued under normal growth conditions (9–11). However, the stability of antitoxins is substantially lower than that of their cognate toxins, so any stress causing cellular damage or growth inhibition that induces proteases alters the balance between toxin and antitoxin, leading to toxin release in the cell.To date, 16 (12) TA systems have been reported on the E. coli genome, including relB-relE (13, 14), chpBI-chpBK (15), mazEF (16–18), yefM-yoeB (19, 20), dinJ-yafQ (21, 22), hipBA and hicAB (23, 24), prlF-yhaV (25), and ybaJ-hha (26). Interestingly, all of these TA operons appear to use similar modes of regulation: the formation of complexes between antitoxins and their cognate toxins to neutralize toxin activity and the ability of TA complexes to autoregulate their expression. The cellular targets of some toxins have been identified. CcdB directly interacts with gyrase A and blocks DNA replication (27, 28). RelE, which by itself has no endoribonuclease activity, appears to act as a ribosome-associating factor that promotes mRNA cleavage at the ribosome A-site (13, 29, 30). PemK (31), ChpBK (15), and MazF (32) are unique among toxins because they target cellular mRNAs for degradation by functioning as sequence-specific endoribonucleases to effectively inhibit protein synthesis and thereby cell growth.MazF, ChpBK, and PemK have been characterized as sequence-specific endoribonucleases that cleave mRNA at the ACA, ACY (Y is U, A, or G), and UAH (H is C, A, or U) sequences, respectively. They are completely different from other known endoribonucleases such as RNases E, A, and T1, as these toxins function as protein synthesis inhibitors by interfering with the function of cellular mRNAs. It is well known that small RNAs, such as mRNA-interfering cRNA (33), microRNA (34), and small interfering RNA (35), interfere with the function of specific RNAs. These small RNAs bind to specific mRNAs to inhibit their expression. Ribozymes also act on their target RNAs specifically and interfere with their function (36). Therefore, MazF, ChpBK, and PemK homologs form a novel endoribonuclease family that exhibits a new mRNA-interfering mechanism by cleaving mRNAs at specific sequences. Thus, they have been termed “mRNA interferases” (2).During our search for TA systems on the E. coli genome, we found that the mqsR gene is co-transcribed with a downstream gene, ygiT. These two genes appear to function as a TA system, as their size is small (98 residues for MqsR and 131 residues for YgiT) and their respective open reading frames are separated by 1 bp. In this study, we demonstrate that MqsR-YgiT is a new E. coli TA system consisting of a toxin, MqsR, and an antitoxin, YgiT. Moreover, we identify MqsR as a novel mRNA interferase that does not exhibit homology to MazF. This toxin cleaves RNA at GCU sequences in vivo and in vitro. The implication of this finding as to how this mRNA interferase is involved in cell physiology and biofilm formation will be discussed.