Marine Microbial Metagenomics: From Individual to the Environment

Microbes are the most abundant biological entities on earth, therefore, studying them is important for understanding their roles in global ecology. The science of metagenomics is a relatively young field of research that has enjoyed significant effort since its inception in 1998. Studies using next-generation sequencing techniques on single genomes and collections of genomes have not only led to novel insights into microbial genomics, but also revealed a close association between environmental niches and genome evolution. Herein, we review studies investigating microbial genomics (largely in the marine ecosystem) at the individual and community levels to summarize our current understanding of microbial ecology in the environment.     

Advancing Small‐Molecule‐Based Chemical Biology with Next‐Generation Sequencing Technologies

Next‐generation‐sequencing (NGS) technologies enable us to obtain extensive information by deciphering millions of individual DNA sequencing reactions simultaneously. The new DNA‐sequencing strategies exceed their precursors in output by many orders of magnitude, resulting in a quantitative increase in valuable sequence information that could be harnessed for qualitative analysis. Sequencing on this scale has facilitated significant advances in diverse disciplines, ranging from the discovery, design, and evaluation of many small molecules and relevant biological mechanisms to maturation of personalized therapies. NGS technologies that have recently become affordable allow us to gain in‐depth insight into small‐molecule‐triggered biological phenomena and empower researchers to develop advanced versions of small molecules. In this review we focus on the overlooked implications of NGS technologies in chemical biology, with a special emphasis on small‐molecule development and screening.     

Increased single-nucleotide discrimination of PCR by primer probes bearing hydrophobic 4'C modifications

We report on significantly increased selectivity of real-time PCR through employment of primer probes that bear hydrophobic 4'C modifications at the 3'-terminal nucleotide. The primer probes were designed to bind the target sequences in such a way that the 3'-terminal nucleotide defines whether a matched or a single mismatched basepair is present depending on the respective target sequence. Several commercially available thermostable DNA polymerases belonging to different DNA polymerase families were tested for their efficacy in discriminating between PCR amplification of matched substrates and duplexes that contain a single mismatch. It turned out that, depending on the 4'C modification and the employed DNA polymerase, significantly increased differentiation between single matches and mismatches could be observed with real-time PCR. The degrees of the observed effects varied with the employed 4'C modification and the sequence context studied. The system is robust enough to work faithfully under several buffer conditions. Our approach should be useful for the direct diagnosis of single nucleotide variations within genes, like single nucleotide polymorphisms or mutations, by PCR without the need for further time- and cost-intensive post-PCR analysis.     

High-Performance Algorithm Engineering for Computational Phylogenetics

A phylogeny is the evolutionary history of a group of organisms; systematists (and other biologists) attempt to reconstruct this history from various forms of data about contemporary organisms. Phylogeny reconstruction is a crucial step in the understanding of evolution as well as an important tool in biological, pharmaceutical, and medical research. Phylogeny reconstruction from molecular data is very difficult: almost all optimization models give rise to NP-hard (and thus computationally intractable) problems. Yet approximations must be of very high quality in order to avoid outright biological nonsense. Thus many biologists have been willing to run farms of processors for many months in order to analyze just one dataset. High-performance algorithm engineering offers a battery of tools that can reduce, sometimes spectacularly, the running time of existing phylogenetic algorithms, as well as help designers produce better algorithms. We present an overview of algorithm engineering techniques, illustrating them with an application to the breakpoint analysis method of Sankoff et al., which resulted in the GRAPPA software suite. GRAPPA demonstrated a speedup in running time by over eight orders of magnitude over the original implementation on a variety of real and simulated datasets. We show how these algorithmic engineering techniques are directly applicable to a large variety of challenging combinatorial problems in computational biology.     

Deletion of six open reading frames from the left arm of chromosome IV of Saccharomyces cerevisiae

The construction of six deletion mutants of Saccharomyces cerevisiae and their basic phenotypic characterization are described. Open reading frames YDL148c, YDL109c, YDL021w, YDL019c, YDL018c and YDL015c from the left arm of chromosome IV were deleted using a polymerase chain reaction (PCR)‐based disruption technique, introducing the kanMX4 resistance marker into the respective genes. Gene replacement cassettes (pYORCs) for use in other strain backgrounds were cloned by PCR using DNA templates from haploid or diploid deletion mutants, and inserted into episomal plasmids. Cognate clones of all six ORFs were obtained by gap repair. Deletions were carried out in diploid cells and, after sporulation, yielded four viable spores for clones disrupted in YDL109c, YDL021w, YDL019c and YDL018c. Spores harbouring disruptions in ORFs YDL148c and YDL015c germinated but underwent only a few divisions before ceasing growth, suggesting that the respective genes are essential for vegetative growth on YPD complete media. The other deletion mutants grew like wild‐type at different temperatures and on different carbon sources. A brief computational analysis of the six ORFs studied in this work is presented. Copyright © 1999 John Wiley & Sons, Ltd.     

Quantitative analysis of yeast gene function using competition experiments in continuous culture

One possible route to the evaluation of gene function is a quantitative approach based on the concepts of metabolic control analysis (MCA). An important first step in such an analysis is to determine the effect of deleting individual genes on the growth rate (or fitness) of S. cerevisiae. Since the specific growth-rate effects of most genes are likely to be small, we employed competition experiments in chemostat culture to measure the proportion of deletion mutants relative to that of a standard strain by using a quantitative PCR method. In this paper, we show that both densitometry and GeneScan^™ analysis can be used with similar accuracy and reproducibility to determine the proportions of (at least) two strains simultaneously, in the range 10–90% of the total cell population. Furthermore, we report on a model competition experiment between two diploid nuclear petite mutants, homozygous for deletions in the cox5a or pet191 genes, and the standard strain (ho::kanMX4/ho::kanMX4) in chemostat cultures under six different physiological conditions. The results indicate that competition experiments in continuous culture are a suitable method to distinguish quantitatively between deletion mutants that qualitatively exhibit the same phenotype. © 1998 John Wiley & Sons, Ltd.     

细菌的泛基因组分析

细菌基因组学在不同领域的广泛应用得益于新一代测序(next generation sequencing,NGS)技术所实现的基因组及转录组测序的发展.大量的细菌基因组数据为不同方面深入了解物种内部的多样性提供了数据资源.近年来,泛基因组分析(pan-genome analysis)方法受到研究者广泛的关注.通过泛基因组...     

An analysis of the Candida albicans genome database for soluble secreted proteins using computer-based prediction algorithms

We sought to identify all genes in the Candida albicans genome database whose deduced proteins would likely be soluble secreted proteins (the secretome). While certain C. albicans secretory proteins have been studied in detail, more data on the entire secretome is needed. One approach to rapidly predict the functions of an entire proteome is to utilize genomic database information and prediction algorithms. Thus, we used a set of prediction algorithms to computationally define a potential C. albicans secretome. We first assembled a validation set of 47 C. albicans proteins that are known to be secreted and 47 that are known not to be secreted. The presence or absence of an N-terminal signal peptide was correctly predicted by SignalP version 2.0 in 47 of 47 known secreted proteins and in 47 of 47 known non-secreted proteins. When all 6165 C. albicans ORFs from CandidaDB were analysed with SignalP, 495 ORFs were predicted to encode proteins with N-terminal signal peptides. In the set of 495 deduced proteins with N-terminal signal peptides, 350 were predicted to have no transmembrane domains (or a single transmembrane domain at the extreme N-terminus) and 300 of these were predicted not to be GPI-anchored. TargetP was used to eliminate proteins with mitochondrial targeting signals, and the final computationally-predicted C. albicans secretome was estimated to consist of up to 283 ORFs. The C. albicans secretome database is available at http://info.med.yale.edu/intmed/infdis/candida/