Mining genomic databases to identify novel hydrogen producers

The realization that fossil fuel reserves are limited and their adverse effect on the environment has forced us to look into alternative sources of energy. Hydrogen is a strong contender as a future fuel. Biological hydrogen production ranges from 0.37 to 3.3 moles H(2) per mole of glucose and, considering the high theoretical values of production (4.0 moles H(2) per mole of glucose), it is worth exploring approaches to increase hydrogen yields. Screening the untapped microbial population is a promising possibility. Sequence analysis and pathway alignment of hydrogen metabolism in complete and incomplete genomes has led to the identification of potential hydrogen producers.     

A genomic approach to identify hybrid incompatibility genes

Uncovering the genetic and molecular basis of barriers to gene flow between populations is key to understanding how new species are born. Intrinsic postzygotic reproductive barriers such as hybrid sterility and hybrid inviability are caused by deleterious genetic interactions known as hybrid incompatibilities. The difficulty in identifying these hybrid incompatibility genes remains a rate-limiting step in our understanding of the molecular basis of speciation. We recently described how whole genome sequencing can be applied to identify hybrid incompatibility genes, even from genetically terminal hybrids. Using this approach, we discovered a new hybrid incompatibility gene, gfzf, between Drosophila melanogaster and Drosophila simulans, and found that it plays an essential role in cell cycle regulation. Here, we discuss the history of the hunt for incompatibility genes between these species, discuss the molecular roles of gfzf in cell cycle regulation, and explore how intragenomic conflict drives the evolution of fundamental cellular mechanisms that lead to the developmental arrest of hybrids.     

A functional genomic yeast screen to identify pathogenic bacterial proteins

Many bacterial pathogens promote infection and cause disease by directly injecting into host cells proteins that manipulate eukaryotic cellular processes. Identification of these translocated proteins is essential to understanding pathogenesis. Yet, their identification remains limited. This, in part, is due to their general sequence uniqueness, which confounds homology-based identification by comparative genomic methods. In addition, their absence often does not result in phenotypes in virulence assays limiting functional genetic screens. Translocated proteins have been observed to confer toxic phenotypes when expressed in the yeast Saccharomyces cerevisiae. This observation suggests that yeast growth inhibition can be used as an indicator of protein translocation in functional genomic screens. However, limited information is available regarding the behavior of non-translocated proteins in yeast. We developed a semi-automated quantitative assay to monitor the growth of hundreds of yeast strains in parallel. We observed that expression of half of the 19 Shigella translocated proteins tested but almost none of the 20 non-translocated Shigella proteins nor approximately 1,000 Francisella tularensis proteins significantly inhibited yeast growth. Not only does this study establish that yeast growth inhibition is a sensitive and specific indicator of translocated proteins, but we also identified a new substrate of the Shigella type III secretion system (TTSS), IpaJ, previously missed by other experimental approaches. In those cases where the mechanisms of action of the translocated proteins are known, significant yeast growth inhibition correlated with the targeting of conserved cellular processes. By providing positive rather than negative indication of activity our assay complements existing approaches for identification of translocated proteins. In addition, because this assay only requires genomic DNA it is particularly valuable for studying pathogens that are difficult to genetically manipulate or dangerous to culture.     

A network-based approach to identify substrate classes of bacterial glycosyltransferases

Background

Bacterial interactions with the environment- and/or host largely depend on the bacterial glycome. The specificities of a bacterial glycome are largely determined by glycosyltransferases (GTs), the enzymes involved in transferring sugar moieties from an activated donor to a specific substrate. Of these GTs their coding regions, but mainly also their substrate specificity are still largely unannotated as most sequence-based annotation flows suffer from the lack of characterized sequence motifs that can aid in the prediction of the substrate specificity.

Results

In this work, we developed an analysis flow that uses sequence-based strategies to predict novel GTs, but also exploits a network-based approach to infer the putative substrate classes of these predicted GTs. Our analysis flow was benchmarked with the well-documented GT-repertoire of Campylobacter jejuni NCTC 11168 and applied to the probiotic model Lactobacillus rhamnosus GG to expand our insights in the glycosylation potential of this bacterium. In L. rhamnosus GG we could predict 48 GTs of which eight were not previously reported. For at least 20 of these GTs a substrate relation was inferred.

Conclusions

We confirmed through experimental validation our prediction of WelI acting upstream of WelE in the biosynthesis of exopolysaccharides. We further hypothesize to have identified in L. rhamnosus GG the yet undiscovered genes involved in the biosynthesis of glucose-rich glycans and novel GTs involved in the glycosylation of proteins. Interestingly, we also predict GTs with well-known functions in peptidoglycan synthesis to also play a role in protein glycosylation.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-349) contains supplementary material, which is available to authorized users.     

The fast halo assay: an improved method to quantify genomic DNA strand breakage at the single-cell level

The present study describes the improvement of a technique, the alkaline-halo assay (AHA), for the assessment of DNA single-strand breakage at the single-cell level. AHA involves a series of sequential steps in which cells are embedded in melted agarose and spread onto microscope slides, incubated in a high-salt alkaline lysis solution, then in a hypotonic alkaline solution and, finally, stained with ethidium bromide (EB). Under these conditions, single-stranded DNA fragments diffuse radially from the nuclear cage and generate a fluorescent image that resembles a halo concentric to the nuclear remnants: the area of the halo is a direct function of the extent of DNA strand scission. These phenomena can be conveniently monitored with a fluorescence microscope and quantified by image-processing analysis. The behaviour of single-stranded DNA fragments under the conditions of the modified assay, called fast halo assay (FHA), is essentially the same as in AHA. The modifications consist in the simplification of the lysis, denaturation and staining procedures, and allow, as compared with AHA, the preparation of samples within 15 min, with a two-third reduction in total processing time, using only two reagents to promote DNA extraction and staining: NaOH and EB. A variation of the FHA operating at non-denaturing conditions to discriminate apoptotic cells from non-apoptotic cells bearing DNA single-strand breaks is also illustrated. To benchmark FHA sensitivity and reliability, the DNA single-strand breaks (SSBs) resulting either from exposure of cultured mammalian cells to different DNA-damaging agents or from secondary apoptotic DNA cleavage, have been quantified and results compared with the outcomes of reference techniques run in parallel, namely AHA, comet assay and Hoechst 33342 staining. The results indicate that FHA has the same reliability and sensitivity of the reference assays, but presents the additional advantages of being inexpensive, more rapid and strikingly simple.     

A computational approach to identify genes for functional RNAs in genomic sequences

Currently there is no successful computational approach for identification of genes encoding novel functional RNAs (fRNAs) in genomic sequences. We have developed a machine learning approach using neural networks and support vector machines to extract common features among known RNAs for prediction of new RNA genes in the unannotated regions of prokaryotic and archaeal genomes. The Escherichia coli genome was used for development, but we have applied this method to several other bacterial and archaeal genomes. Networks based on nucleotide composition were 80–90% accurate in jackknife testing experiments for bacteria and 90–99% for hyperthermophilic archaea. We also achieved a significant improvement in accuracy by combining these predictions with those obtained using a second set of parameters consisting of known RNA sequence motifs and the calculated free energy of folding. Several known fRNAs not included in the training datasets were identified as well as several hundred predicted novel RNAs. These studies indicate that there are many unidentified RNAs in simple genomes that can be predicted computationally as a precursor to experimental study. Public access to our RNA gene predictions and an interface for user predictions is available via the web.     

A statistical framework for multiparameter analysis at the single-cell level

Phenotypic characterization of individual cells provides crucial insights into intercellular heterogeneity and enables access to information that is unavailable from ensemble averaged, bulk cell analyses. Single-cell studies have attracted significant interest in recent years and spurred the development of a variety of commercially available and research-grade technologies. To quantify cell-to-cell variability of cell populations, we have developed an experimental platform for real-time measurements of oxygen consumption (OC) kinetics at the single-cell level. Unique challenges inherent to these single-cell measurements arise, and no existing data analysis methodology is available to address them. Here we present a data processing and analysis method that addresses challenges encountered with this unique type of data in order to extract biologically relevant information. We applied the method to analyze OC profiles obtained with single cells of two different cell lines derived from metaplastic and dysplastic human Barrett's esophageal epithelium. In terms of method development, three main challenges were considered for this heterogeneous dynamic system: (i) high levels of noise, (ii) the lack of a priori knowledge of single-cell dynamics, and (iii) the role of intercellular variability within and across cell types. Several strategies and solutions to address each of these three challenges are presented. The features such as slopes, intercepts, breakpoint or change-point were extracted for every OC profile and compared across individual cells and cell types. The results demonstrated that the extracted features facilitated exposition of subtle differences between individual cells and their responses to cell-cell interactions. With minor modifications, this method can be used to process and analyze data from other acquisition and experimental modalities at the single-cell level, providing a valuable statistical framework for single-cell analysis.     

Detecting cytokine production at the single-cell level

Cytokines are versatile mediators of intercellular communication. Their functional diversity has aroused considerable interest and prompted the rapid development of a number of techniques for their detection and measurement. However, conventional cytokine assays measure only their bulk release by large numbers of cells and give no indication of the identity or frequency of producer cells. Here, the advantages and disadvantages of a relatively new approach to detect cytokine production by single cells are reviewed.     

A high-throughput screen to identify novel calcineurin inhibitors

Calcineurin is a eukaryotic protein phosphatase important for many signalling and developmental processes in cells. Inhibitors of this enzyme are used clinically and there is interest in identifying novel inhibitors for therapeutic applications. This report describes a high-throughput assay that can be used to screen natural or chemical libraries of compounds to identify new calcineurin inhibitors. The microtitre plate assay is based on a yeast reporter strain and was validated with known inhibitors and tested in a pilot screen of bacterial extracts.     

Simulation of neutron interactions at the single-cell level

We recently reported that the exposure of cancer cells to 14 MeV neutrons at a very low dose rate (0.8 mGy min(-1)) produced a marked increase in cell killing at 5 cGy, followed by a plateau in survival and chromosomal damage. Simulation of the energy deposition events in irradiated cells may help to explain these unusual cell responses. We describe here a Monte Carlo simulation code, Energy Deposition in Cells Irradiated by Neutrons (EDCIN). The procedure considered the experimental setup and a hemispheric cell model. The simulation data fitted the dosimetric measurements performed using tissue-equivalent ionization chambers, Geiger-Müller counters, fission chambers, and silicon diodes. The simulation showed that 80% of the energy deposited in a single cell came from the interactions of neutrons outside the cell and only 20% came from neutron interactions inside the cell. Thus the "external" interactions that result in the production of recoil protons and secondary electrons may induce most of the biological damage, which may be repaired efficiently at low dose rate. The repair process may be triggered from a threshold level of damage, which would explain an initial increase cell death due to unrepaired sublethal damage, and then may compensate for induced damage, resulting in the plateaus.     

A genomic approach to identify novel progesterone receptor regulated pathways in the uterus during implantation

The cellular actions of steroid hormone progesterone (P) are mediated via its nuclear receptors, which regulate the expression of specific target genes. The identity of gene networks that are regulated by the P receptors (PRs) in the uterus at various stages of the reproductive cycle and pregnancy, however, remain largely unknown. In this study, we have used oligonucleotide microarrays to identify mRNAs whose expression in the pregnant mouse uterus is modulated by RU486, a well-characterized PR antagonist, which is also an effective inhibitor of implantation. We found that, in response to RU486, expression of mRNAs corresponding to 78 known genes was down-regulated at least 2-fold in the preimplantation mouse uterus. The PR regulation of several of these genes was ascertained by administering P to ovariectomized wild-type and PR knockout (PRKO) mice. Detailed spatio-temporal analysis of these genes in the pregnant uterus indicated that their expression in the epithelium and stroma could be correlated with the expression of PR in those cell types. Furthermore, time-course studies suggested that many of these genes are likely primary targets of PR regulation. We also identified 70 known genes that were up-regulated at least 2-fold in the pregnant uterus in response to RU486. Interestingly, initial examination of a number of RU486-inducible genes reveals that their uterine expression is also regulated by estrogen. The identification of several novel PR-regulated gene pathways in the reproductive tract is an important step toward understanding how P regulates the physiological events leading to implantation.     

A cell-based high-throughput screen to identify synergistic TRAIL sensitizers

We have developed a high-throughput screen (HTS) to search for novel molecules that can synergize with TRAIL, thus promoting apoptosis of ACHN renal tumor cells in a combinatorial fashion. The HTS detects synthetic compounds and pure natural products that can pre-sensitize the cancer cells to TRAIL-mediated apoptosis, yet have limited toxicity on their own. We have taken into account the individual effects of the single agents, versus the combination, and have identified hits that are synergistic, synergistic-toxic, or additive when combined with TRAIL in promoting tumor cell death. Preliminary mechanistic studies indicate that a subset of the synergistic TRAIL sensitizers act very rapidly to promote cleavage and activation of caspase-8 following TRAIL binding. Caspase-8 is an apical enzyme that initiates programmed cell death via the extrinsic apoptotic pathway. Thus, these TRAIL sensitizers may potentially reduce resistance of tumor cells to TRAIL-mediated apoptosis. Two representative sensitizers were found to increase levels of p53 but did not inhibit the proteasome, suggesting that early DNA damage-sensing pathways may be involved in their mechanisms of action.     

Single-molecule approach to bacterial genomic comparisons via optical mapping

Modern comparative genomics has been established, in part, by the sequencing and annotation of a broad range of microbial species. To gain further insights, new sequencing efforts are now dealing with the variety of strains or isolates that gives a species definition and range; however, this number vastly outstrips our ability to sequence them. Given the availability of a large number of microbial species, new whole genome approaches must be developed to fully leverage this information at the level of strain diversity that maximize discovery. Here, we describe how optical mapping, a single-molecule system, was used to identify and annotate chromosomal alterations between bacterial strains represented by several species. Since whole-genome optical maps are ordered restriction maps, sequenced strains of Shigella flexneri serotype 2a (2457T and 301), Yersinia pestis (CO 92 and KIM), and Escherichia coli were aligned as maps to identify regions of homology and to further characterize them as possible insertions, deletions, inversions, or translocations. Importantly, an unsequenced Shigella flexneri strain (serotype Y strain AMC[328Y]) was optically mapped and aligned with two sequenced ones to reveal one novel locus implicated in serotype conversion and several other loci containing insertion sequence elements or phage-related gene insertions. Our results suggest that genomic rearrangements and chromosomal breakpoints are readily identified and annotated against a prototypic sequenced strain by using the tools of optical mapping.     

A streamlined approach to high-throughput proteomics

Proteomics has rapidly become an important tool for life science research, allowing the integrated analysis of global protein expression from a single experiment. To accommodate the complexity and dynamic nature of any proteome, researchers must use a combination of disparate protein biochemistry techniques, often a highly involved and time-consuming process. Whilst highly sophisticated, individual technologies for each step in studying a proteome are available, true high-throughput proteomics that provides a high degree of reproducibility and sensitivity has been difficult to achieve. The development of high-throughput proteomic platforms, encompassing all aspects of proteome analysis and integrated with genomics and bioinformatics technology, therefore represents a crucial step for the advancement of proteomics research. ProteomIQ? (Proteome Systems) is the first fully integrated, start-to-finish proteomics platform to enter the market. Sample preparation and tracking, centralized data acquisition and instrument control, and direct interfacing with genomics and bioinformatics databases are combined into a single suite of integrated hardware and software tools, facilitating high reproducibility and rapid turnaround times. This review will highlight some features of ProteomIQ, with particular emphasis on the analysis of proteins separated by 2D polyacrylamide gel electrophoresis.     

A streamlined approach to high-throughput proteomics

Proteomics has rapidly become an important tool for life science research, allowing the integrated analysis of global protein expression from a single experiment. To accommodate the complexity and dynamic nature of any proteome, researchers must use a combination of disparate protein biochemistry techniques, often a highly involved and time-consuming process. Whilst highly sophisticated, individual technologies for each step in studying a proteome are available, true high-throughput proteomics that provides a high degree of reproducibility and sensitivity has been difficult to achieve. The development of high-throughput proteomic platforms, encompassing all aspects of proteome analysis and integrated with genomics and bioinformatics technology, therefore represents a crucial step for the advancement of proteomics research. ProteomIQ (Proteome Systems) is the first fully integrated, start-to-finish proteomics platform to enter the market. Sample preparation and tracking, centralized data acquisition and instrument control, and direct interfacing with genomics and bioinformatics databases are combined into a single suite of integrated hardware and software tools, facilitating high reproducibility and rapid turnaround times. This review will highlight some features of ProteomIQ, with particular emphasis on the analysis of proteins separated by 2D polyacrylamide gel electrophoresis.     

Microscopy-based multicolor tissue cytometry at the single-cell level.

Cytomics is a novel perspective from which to look at life. As with genomics and proteomics before, this discipline requires novel and innovative techniques and technologies to focus on its substrate of research--the cytome. With cytomics being the discipline that analyzes cellular systems and their interdependencies, advanced microscopy represents a key technology in cytomics research. Yet, conventional microscopy-based investigations, i.e., "look and conclude" analyses, do not meet the major cytomics criteria of 1) relating multiple parameters to each other, 2) within large populations of cells, 3) on a single-cell basis, and 4) in a quantitative and observer-independent manner. However, emerging improvements in the fields of fluorophore technology, sensitive fluorescence detection devices, and sophisticated image analysis procedures, are important and necessary steps into the cytomics era. Tissue represents an important class of cytomes, hence tissue cytometry--on the single cell level--can be expected to become an important cytomics technology. In this report, the techniques and technologies of microscopy-based multicolor tissue cytometry (MMTC) are outlined and applications are discussed, including the phenotypic characterization of tissue infiltrating leukocytes, in situ quantification of proliferation markers and tumor suppressors, and in situ quantification of apoptosis.