A process for analysis of microarray comparative genomics hybridisation studies for bacterial genomes

Background: Microarray based comparative genomic hybridisation (CGH) experiments have been used to study numerous biological problems including understanding genome plasticity in pathogenic bacteria. Typically such experiments produce large data sets that are difficult for biologists to handle. Although there are some programmes available for interpretation of bacterial transcriptomics data and CGH microarray data for looking at genetic stability in oncogenes, there are none specifically to understand the mosaic nature of bacterial genomes. Consequently a bottle neck still persists in accurate processing and mathematical analysis of these data. To address this shortfall we have produced a simple and robust CGH microarray data analysis process that may be automated in the future to understand bacterial genomic diversity. Results: The process involves five steps: cleaning, normalisation, estimating gene presence and absence or divergence, validation, and analysis of data from test against three reference strains simultaneously. Each stage of the process is described and we have compared a number of methods available for characterising bacterial genomic diversity, for calculating the cut-off between gene presence and absence or divergence, and shown that a simple dynamic approach using a kernel density estimator performed better than both established, as well as a more sophisticated mixture modelling technique. We have also shown that current methods commonly used for CGH microarray analysis in tumour and cancer cell lines are not appropriate for analysing our data. Conclusion: After carrying out the analysis and validation for three sequenced Escherichia coli strains, CGH microarray data from 19 E. coli O157 pathogenic test strains were used to demonstrate the benefits of applying this simple and robust process to CGH microarray studies using bacterial genomes.

sRNAscanner: A Computational Tool for Intergenic Small RNA Detection in Bacterial Genomes

Background:Bacterial non-coding small RNAs (sRNAs) have attracted considerable attention due to their ubiquitous nature and contribution to numerous cellular processes including survival, adaptation and pathogenesis. Existing computational approaches for identifying bacterial sRNAs demonstrate varying levels of success and there remains considerable room for improvement. Methodology/Principal Findings: Here we have proposed a transcriptional signal-based computational method to identify intergenic sRNA transcriptional units (TUs) in completely sequenced bacterial genomes. Our sRNAscanner tool uses position weight matrices derived from experimentally defined E. coli K-12 MG1655 sRNA promoter and rho-independent terminator signals to identify intergenic sRNA TUs through sliding window based genome scans. Analysis of genomes representative of twelve species suggested that sRNAscanner demonstrated equivalent sensitivity to sRNAPredict2, the best performing bioinformatics tool available presently. However, each algorithm yielded substantial numbers of known and uncharacterized hits that were unique to one or the other tool only. sRNAscanner identified 118 novel putative intergenic sRNA genes in Salmonella enterica Typhimurium LT2, none of which were flagged by sRNAPredict2. Candidate sRNA locations were compared with available deep sequencing libraries derived from Hfq-co-immunoprecipitated RNA purified from a second Typhimurium strain (Sittka et al. (2008) PLoS Genetics 4: e1000163). Sixteen potential novel sRNAs computationally predicted and detected in deep sequencing libraries were selected for experimental validation by Northern analysis using total RNA isolated from bacteria grown under eleven different growth conditions. RNA bands of expected sizes were detected in Northern blots for six of the examined candidates. Furthermore, the 5'-ends of these six Northern-supported sRNA candidates were successfully mapped using 5'-RACE analysis. Conclusions/Significance: We have developed, computationally examined and experimentally validated the sRNAscanner algorithm. Data derived from this study has successfully identified six novel S. Typhimurium sRNA genes. In addition, the computational specificity analysis we have undertaken suggests that similar to 40% of sRNAscanner hits with high cumulative sum of scores represent genuine, undiscovered sRNA genes. Collectively, these data strongly support the utility of sRNAscanner and offer a glimpse of its potential to reveal large numbers of sRNA genes that have to date defied identification. sRNAscanner is available from: http://bicmku.in:8081/sRNAscanner or http://cluster.physics.iisc.ernet.in/sRNAscanner/.

High-quality annotation of promoter regions for 913 bacterial genomes

Motivation: The number of bacterial genomes being sequenced is increasing very rapidly and hence, it is crucial to have procedures for rapid and reliable annotation of their functional elements such as promoter regions, which control the expression of each gene or each transcription unit of the genome. The present work addresses this requirement and presents a generic method applicable across organisms. Results: Relative stability of the DNA double helical sequences has been used to discriminate promoter regions from non-promoter regions. Based on the difference in stability between neighboring regions, an algorithm has been implemented to predict promoter regions on a large scale over 913 microbial genome sequences. The average free energy values for the promoter regions as well as their downstream regions are found to differ, depending on their GC content. Threshold values to identify promoter regions have been derived using sequences flanking a subset of translation start sites from all microbial genomes and then used to predict promoters over the complete genome sequences. An average recall value of 72% (which indicates the percentage of protein and RNA coding genes with predicted promoter regions assigned to them) and precision of 56% is achieved over the 913 microbial genome dataset.

A minimal gene set for cellular life derived by comparison of complete bacterial genomes.

The recently sequenced genome of the parasitic bacterium Mycoplasma genitalium contains only 468 identified protein-coding genes that have been dubbed a minimal gene complement [Fraser, C.M., Gocayne, J.D., White, O., Adams, M.D., Clayton, R.A., et al. (1995) Science 270, 397-403]. Although the M. genitalium gene complement is indeed the smallest among known cellular life forms, there is no evidence that it is the minimal self-sufficient gene set. To derive such a set, we compared the 468 predicted M. genitalium protein sequences with the 1703 protein sequences encoded by the other completely sequenced small bacterial genome, that of Haemophilus influenzae. M. genitalium and H. influenzae belong to two ancient bacterial lineages, i.e., Gram-positive and Gram-negative bacteria, respectively. Therefore, the genes that are conserved in these two bacteria are almost certainly essential for cellular function. It is this category of genes that is most likely to approximate the minimal gene set. We found that 240 M. genitalium genes have orthologs among the genes of H. influenzae. This collection of genes falls short of comprising the minimal set as some enzymes responsible for intermediate steps in essential pathways are missing. The apparent reason for this is the phenomenon that we call nonorthologous gene displacement when the same function is fulfilled by nonorthologous proteins in two organisms. We identified 22 nonorthologous displacements and supplemented the set of orthologs with the respective M. genitalium genes. After examining the resulting list of 262 genes for possible functional redundancy and for the presence of apparently parasite-specific genes, 6 genes were removed. We suggest that the remaining 256 genes are close to the minimal gene set that is necessary and sufficient to sustain the existence of a modern-type cell. Most of the proteins encoded by the genes from the minimal set have eukaryotic or archaeal homologs but seven key proteins of DNA replication do not. We speculate that the last common ancestor of the three primary kingdoms had an RNA genome. Possibilities are explored to further reduce the minimal set to model a primitive cell that might have existed at a very early stage of life evolution.

Extensible motif and aberrant nucleotide extraction in genomic sequences

The project focusses on the discovery of conserved DNA sequences in bacterial genomes and comparative analysis of bacterial genomes to elicit evolutionary trends. The outcomes have produced novel techniques for modelling motifs in DNA and the characterisation of evolutionary processes in medically significant bacterial pathogens.

Multifractal characterisation and analysis of complex networks

Complex networks have been studied extensively due to their relevance to many real-world systems such as the world-wide web, the internet, biological and social systems. During the past two decades, studies of such networks in different fields have produced many significant results concerning their structures, topological properties, and dynamics. Three well-known properties of complex networks are scale-free degree distribution, small-world effect and self-similarity. The search for additional meaningful properties and the relationships among these properties is an active area of current research. This thesis investigates a newer aspect of complex networks, namely their multifractality, which is an extension of the concept of selfsimilarity. The first part of the thesis aims to confirm that the study of properties of complex networks can be expanded to a wider field including more complex weighted networks. Those real networks that have been shown to possess the self-similarity property in the existing literature are all unweighted networks. We use the proteinprotein interaction (PPI) networks as a key example to show that their weighted networks inherit the self-similarity from the original unweighted networks. Firstly, we confirm that the random sequential box-covering algorithm is an effective tool to compute the fractal dimension of complex networks. This is demonstrated on the Homo sapiens and E. coli PPI networks as well as their skeletons. Our results verify that the fractal dimension of the skeleton is smaller than that of the original network due to the shortest distance between nodes is larger in the skeleton, hence for a fixed box-size more boxes will be needed to cover the skeleton. Then we adopt the iterative scoring method to generate weighted PPI networks of five species, namely Homo sapiens, E. coli, yeast, C. elegans and Arabidopsis Thaliana. By using the random sequential box-covering algorithm, we calculate the fractal dimensions for both the original unweighted PPI networks and the generated weighted networks. The results show that self-similarity is still present in generated weighted PPI networks. This implication will be useful for our treatment of the networks in the third part of the thesis. The second part of the thesis aims to explore the multifractal behavior of different complex networks. Fractals such as the Cantor set, the Koch curve and the Sierspinski gasket are homogeneous since these fractals consist of a geometrical figure which repeats on an ever-reduced scale. Fractal analysis is a useful method for their study. However, real-world fractals are not homogeneous; there is rarely an identical motif repeated on all scales. Their singularity may vary on different subsets; implying that these objects are multifractal. Multifractal analysis is a useful way to systematically characterize the spatial heterogeneity of both theoretical and experimental fractal patterns. However, the tools for multifractal analysis of objects in Euclidean space are not suitable for complex networks. In this thesis, we propose a new box covering algorithm for multifractal analysis of complex networks. This algorithm is demonstrated in the computation of the generalized fractal dimensions of some theoretical networks, namely scale-free networks, small-world networks, random networks, and a kind of real networks, namely PPI networks of different species. Our main finding is the existence of multifractality in scale-free networks and PPI networks, while the multifractal behaviour is not confirmed for small-world networks and random networks. As another application, we generate gene interactions networks for patients and healthy people using the correlation coefficients between microarrays of different genes. Our results confirm the existence of multifractality in gene interactions networks. This multifractal analysis then provides a potentially useful tool for gene clustering and identification. The third part of the thesis aims to investigate the topological properties of networks constructed from time series. Characterizing complicated dynamics from time series is a fundamental problem of continuing interest in a wide variety of fields. Recent works indicate that complex network theory can be a powerful tool to analyse time series. Many existing methods for transforming time series into complex networks share a common feature: they define the connectivity of a complex network by the mutual proximity of different parts (e.g., individual states, state vectors, or cycles) of a single trajectory. In this thesis, we propose a new method to construct networks of time series: we define nodes by vectors of a certain length in the time series, and weight of edges between any two nodes by the Euclidean distance between the corresponding two vectors. We apply this method to build networks for fractional Brownian motions, whose long-range dependence is characterised by their Hurst exponent. We verify the validity of this method by showing that time series with stronger correlation, hence larger Hurst exponent, tend to have smaller fractal dimension, hence smoother sample paths. We then construct networks via the technique of horizontal visibility graph (HVG), which has been widely used recently. We confirm a known linear relationship between the Hurst exponent of fractional Brownian motion and the fractal dimension of the corresponding HVG network. In the first application, we apply our newly developed box-covering algorithm to calculate the generalized fractal dimensions of the HVG networks of fractional Brownian motions as well as those for binomial cascades and five bacterial genomes. The results confirm the monoscaling of fractional Brownian motion and the multifractality of the rest. As an additional application, we discuss the resilience of networks constructed from time series via two different approaches: visibility graph and horizontal visibility graph. Our finding is that the degree distribution of VG networks of fractional Brownian motions is scale-free (i.e., having a power law) meaning that one needs to destroy a large percentage of nodes before the network collapses into isolated parts; while for HVG networks of fractional Brownian motions, the degree distribution has exponential tails, implying that HVG networks would not survive the same kind of attack.

Exploring plant genomes by RNA-induced gene silencing

The nucleotide sequences of several animal, plant and bacterial genomes are now known, but the functions of many of the proteins that they are predicted to encode remain unclear. RNA interference is a gene-silencing technology that is being used successfully to investigate gene function in several organisms - for example, Caenorhabditis elegans. We discuss here that RNA-induced gene silencing approaches are also likely to be effective for investigating plant gene function in a high-throughput, genome-wide manner.

Bacterial genomic G + C composition-eliciting environmental adaptation

Bacterial genomes reflect their adaptation strategies through nucleotide usage trends found in their chromosome composition. Bacteria, unlike eukaryotes contain a wide range of genomic G + C. This wide variability may be viewed as a response to environmental adaptation. Two overarching trends are observed across bacterial genomes, the first, correlates genomic G + C to environmental niches and lifestyle, while the other utilizees intra-genomic G + C incongruence to delineate horizontally transferred material. In this review, we focus on the influence of several properties including biochemical, genetic flows, selection biases, and the biochemical-energetic properties shaping genome composition. Outcomes indicate a trend toward high G + C and larger genomes in free-living organisms, as a result of more complex and varied environments (higher chance for horizontal gene transfer). Conversely, nutrient limiting and nutrient poor environments dictate smaller genomes of low GC in attempts to conserve replication expense. Varied processes including translesion repair mechanisms, phage insertion and cytosine degradation has been shown to introduce higher AT in genomic sequences. We conclude the review with an analysis of current bioinformatics tools seeking to elicit compositional variances and highlight the practical implications when using such techniques.

Characteristic enrichment of DNA repeats in different genomes

Using computer programs developed for this purpose, we searched for various repeated sequences including inverted, direct tandem, and homopurine–homopyrimidine mirror repeats in various prokaryotes, eukaryotes, and an archaebacterium. Comparison of observed frequencies with expectations revealed that in bacterial genomes and organelles the frequency of different repeats is either random or enriched for inverted and/or direct tandem repeats. By contrast, in all eukaryotic genomes studied, we observed an overrepresentation of all repeats, especially homopurine–homopyrimidine mirror repeats. Analysis of the genomic distribution of all abundant repeats showed that they are virtually excluded from coding sequences. Unexpectedly, the frequencies of abundant repeats normalized for their expectations were almost perfect exponential functions of their size, and for a given repeat this function was indistinguishable between different genomes.

Highly specific protein sequence motifs for genome analysis

We present a method for discovering conserved sequence motifs from families of aligned protein sequences. The method has been implemented as a computer program called emotif (http://motif.stanford.edu/emotif). Given an aligned set of protein sequences, emotif generates a set of motifs with a wide range of specificities and sensitivities. emotif also can generate motifs that describe possible subfamilies of a protein superfamily. A disjunction of such motifs often can represent the entire superfamily with high specificity and sensitivity. We have used emotif to generate sets of motifs from all 7,000 protein alignments in the blocks and prints databases. The resulting database, called identify (http://motif.stanford.edu/identify), contains more than 50,000 motifs. For each alignment, the database contains several motifs having a probability of matching a false positive that range from 10−10 to 10−5. Highly specific motifs are well suited for searching entire proteomes, while generating very few false predictions. identify assigns biological functions to 25–30% of all proteins encoded by the Saccharomyces cerevisiae genome and by several bacterial genomes. In particular, identify assigned functions to 172 of proteins of unknown function in the yeast genome.

Analysis of the genetic differences between Neisseria meningitidis and Neisseria gonorrhoeae: two closely related bacteria expressing two different pathogenicities.

We have investigated genetic differences between the closely related pathogenic Neisseria species, Neisseria meningitidis and Neisseria gonorrhoeae, as a novel approach to the elucidation of the genetic basis for their different pathogenicities. N. meningitidis is a major cause of cerebrospinal meningitis, whereas N. gonorrhoeae is the agent of gonorrhoea. The technique of representational difference analysis was adapted to the search for genes present in the meningococcus but absent from the gonococcus. The libraries achieved are comprehensive and specific in that they contain sequences corresponding to the presently identified meningococcus-specific genes (capsule, frp, rotamase, and opc) but lack genes more or less homologous between the two species, e.g., ppk and pilC1. Of 35 randomly chosen clones specific to N. meningitidis, DNA sequence analysis has confirmed that the large majority have no homology with published neisserial sequences. Mapping of the cloned DNA fragments onto the chromosome of N. meningitidis strain Z2491 has revealed a nonrandom distribution of meningococcus-specific sequences. Most of the genetic differences between the meningococcus and gonococcus appear to be clustered in three distinct regions, one of which (region 1) contains the capsule-related genes. Region 3 was found only in strains of serogroup A, whereas region 2 is present in a variety of meningococci belonging to different serogroups. At a time when bacterial genomes are being sequenced, we believe that this technique is a powerful tool for a rapid and directed analysis of the genetic basis of inter- or intraspecific phenotypic variations.

Effect of temperature and free ammonia on nitrification and nitrite accumulation in landfill leachate and analysis of its nitrifying bacterial community by FISH

The cause of seasonal failure of a nitrifying municipal landfill leachate treatment plant utilizing a fixed biofilm was investigated by wastewater analyses and batch respirometric tests at every treatment stage. Nitrification of the leachate treatment plant was severely affected by the seasonal temperature variation. High free ammonia (NH3-N) inhibited not only nitrite oxidizing bacteria (NOB) but also ammonia oxidizing bacteria (AOB). In addition, high pH also increased free ammonia concentration to inhibit nitrifying activity especially when the NH4-N level was high. The effects of temperature and free ammonia of landfill leachate on nitrification and nitrite accumulation were investigated with a semi-pilot scale biofilm airlift reactor. Nitrification rate of landfill leachate increased with temperature when free ammonia in the reactor was below the inhibition level for nitrifiers. Leachate was completely nitrified up to a load of 1.5 kg NH4-N m(-3) d(-1) at 28 degrees C. The activity of NOB was inhibited by NH3-N resulting in accumulation of nitrite. NOB activity decreased more than 50% at 0.7 mg NH3-N L-1. Fluorescence in situ hybridization (FISH) was carried out to analyze the population of AOB and NOB in the nitrite accumulating nitrifying biofilm. NOB were located close to AOB by forming small clusters. A significant fraction of AOB identified by probe Nso1225 specifically also hybridized with the Nitrosonlonas specific probe Nsm156. The main NOB were Nitrobacter and Nitrospira which were present in almost equal amounts in the biofilm as identified by simultaneous hybridization with Nitrobacter specific probe Nit3 and Nitrospira specific probe Ntspa662. (c) 2005 Elsevier Ltd. All rights reserved.

Classification of bacterial strains based on DNA profiles

The analysis of bacterial genomes for epidemiological purposes often results in the production of a banding profile of DNA fragments characteristic of the genome under investigation. These may be produced using various methods, many of which involve the cutting or amplification of DNA into defined and reproducible characteristic fragments. It is frequently of interest to enquire whether the bacterial isolates are naturally classifiable into distinct groups based on their DNA profiles. A major problem with this approach is whether classification or clustering of the data is even appropriate. It is always possible to classify such data but it does not follow that the strains they represent are ‘actually’ classifiable into well-defined separate parts. Hence, the act of classification does not in itself answer the question: do the strains consist of a number of different distinct groups or species or do they merge imperceptibly into one another because DNA profiles vary continuously? Nevertheless, we may still wish to classify the data for ‘convenience’ even though strains may vary continuously, and such a classification has been called a ‘dissection’. This Statnote discusses the use of classificatory methods in analyzing the DNA profiles from a sample of bacterial isolates.

Laterally transferred genomic islands in Xanthomonadales related to pathogenicity and primary metabolism

Lateral gene transfer (LGT) is considered as one of the drivers in bacterial genome evolution, usually associated with increased fitness and/or changes in behavior, especially if one considers pathogenic vs. non-pathogenic bacterial groups. The genomes of two phytopathogens, Xanthomonas campestris pv. campestris and Xanthomonas axonopodis pv. citri, were previously inspected for genome islands originating from LGT events, and, in this work, potentially early and late LGT events were identified according to their altered nucleotide composition. The biological role of the islands was also assessed, and pathogenicity, virulence and secondary metabolism pathways were functions highly represented, especially in islands that were found to be recently transferred. However, old islands are composed of a high proportion of genes related to cell primary metabolic functions. These old islands, normally undetected by traditional atypical composition analysis, but confirmed as product of LGT by atypical phylogenetic reconstruction, reveal the role of LGT events by replacing core metabolic genes normally inherited by vertical processes.