Monitoring genome-wide expression in plants

When completed this year, the Arabidopsis genome will represent the first plant genome to be fully sequenced. This sequence information, together with the large collection of expressed sequence tags, has established the basics for new approaches to studying gene expression patterns in plants on a global scale. We can now look at biology from the perspective of the whole genome. This revolution in the study of how all genes in an organism respond to certain stimuli has encouraged us to think in new dimensions. Expression profiles can be determined over a range of experimental conditions and organized into patterns that are diagnostic for the biological state of the cell. The field of genome-wide expression in plants has yet to produce its fruit; however, the current application of microarrays in yeast and human research foreshadows the diverse applications this technology could have in plant biology and agriculture.     

Dynamics of reductive genome evolution in mitochondria and obligate intracellular microbes

Reductive evolution in mitochondria and obligate intracellular microbes has led to a significant reduction in their genome size and guanine plus cytosine content (GC). We show that genome shrinkage during reductive evolution in prokaryotes follows an exponential decay pattern and provide a method to predict the extent of this decay on an evolutionary timescale. We validated predictions by comparison with estimated extents of genome reduction known to have occurred in mitochondria and Buchnera aphidicola, through comparative genomics and by drawing on available fossil evidences. The model shows how the mitochondrial ancestor would have quickly shed most of its genome, shortly after its incorporation into the protoeukaryotic cell and prior to codivergence subsequent to the split of eukaryotic lineages. It also predicts that the primary rickettsial parasitic event would have occurred between 180 and 425 million years ago (MYA), an event of relatively recent evolutionary origin considering the fact that Rickettsia and mitochondria evolved from a common alphaproteobacterial ancestor. This suggests that the symbiotic events of Rickettsia and mitochondria originated at different time points. Moreover, our model results predict that the ancestor of Wigglesworthia glossinidia brevipalpis, dated around the time of origin of its symbiotic association with the tsetse fly (50-100 MYA), was likely to have been an endosymbiont itself, thus supporting an earlier proposition that Wigglesworthia, which is currently a maternally inherited primary endosymbiont, evolved from a secondary endosymbiont.     

Structural analysis of metabolic networks based on flux centrality

Metabolic reactions are fundamental to living organisms, and a large number of reactions simultaneously occur at a given time in living cells transforming diverse metabolites into each other. There has been an ongoing debate on how to classify metabolites with respect to their importance for metabolic performance, usually based on the analysis of topological properties of genome scale metabolic networks. However, none of these studies have accounted quantitatively for flux in metabolic networks, thus lacking an important component of a cell’s biochemistry.We therefore analyzed a genome scale metabolic network of Escherichia coli by comparing growth under 19 different growth conditions, using flux balance analysis and weighted network centrality investigation. With this novel concept of flux centrality we generated metabolite rankings for each particular growth condition. In contrast to the results of conventional analysis of genome scale metabolic networks, different metabolites were top-ranking dependent on the growth condition. At the same time, several metabolites were consistently among the high ranking ones. Those are associated with pathways that have been described by biochemists as the most central part of metabolism, such as glycolysis, tricarboxylic acid cycle and pentose phosphate pathway. The values for the average path length of the analyzed metabolite networks were between 10.5 and 12.6, supporting recent findings that the metabolic network of E. coli is not a small-world network.     

Phototroph genomics ten years on

The onset of the genome era means different things to different people, but it is clear that this new age brings with it paradigm shifts that will forever affect biological research. Less clear is just how these shifts are changing the scope and scale of research. Are gigabases of raw data more useful than a single well-understood gene? Do we really need a full genome to understand the physiology of a single organism? The photosynthetic field is poised at the periphery of the bulk of genome sequencing work--understandably skewed toward health-related disciplines--and, as such, is subject to different motivations, limitations, and primary focus for each new genome. To understand some of these differences, we focus here on various indicators of the impact that genomics has had on the photosynthetic community, now a full decade since the publication of the first photosynthetic genome. Many useful indicators are indexed in public databases, providing pre- and post-genome sequence snapshots of changes in factors such as publication rate, number of proteins characterized, and sequenced genome coverage versus known diversity. As more genomes are sequenced and metagenomic projects begin to pour out billions of bases, it becomes crucial to understand how to harness this data in order to accumulate possible benefits and avoid possible pitfalls, especially as resources become increasingly directed toward natural environments governed by photosynthetic activity, ranging from hot springs to tropical forest ecosystems to the open ocean.     

Multi-scale lung modeling

Multi-scale modeling of biological systems has recently become fashionable due to the growing power of digital computers as well as to the growing realization that integrative systems behavior is as important to life as is the genome. While it is true that the behavior of a living organism must ultimately be traceable to all its components and their myriad interactions, attempting to codify this in its entirety in a model misses the insights gained from understanding how collections of system components at one level of scale conspire to produce qualitatively different behavior at higher levels. The essence of multi-scale modeling thus lies not in the inclusion of every conceivable biological detail, but rather in the judicious selection of emergent phenomena appropriate to the level of scale being modeled. These principles are exemplified in recent computational models of the lung. Airways responsiveness, for example, is an organ-level manifestation of events that begin at the molecular level within airway smooth muscle cells, yet it is not necessary to invoke all these molecular events to accurately describe the contraction dynamics of a cell, nor is it necessary to invoke all phenomena observable at the level of the cell to account for the changes in overall lung function that occur following methacholine challenge. Similarly, the regulation of pulmonary vascular tone has complex origins within the individual smooth muscle cells that line the blood vessels but, again, many of the fine details of cell behavior average out at the level of the organ to produce an effect on pulmonary vascular pressure that can be described in much simpler terms. The art of multi-scale lung modeling thus reduces not to being limitlessly inclusive, but rather to knowing what biological details to leave out.     

Augmenting Chinese hamster genome assembly by identifying regions of high confidence

Chinese hamster Ovary (CHO) cell lines are the dominant industrial workhorses for therapeutic recombinant protein production. The availability of genome sequence of Chinese hamster and CHO cells will spur further genome and RNA sequencing of producing cell lines. However, the mammalian genomes assembled using shot‐gun sequencing data still contain regions of uncertain quality due to assembly errors. Identifying high confidence regions in the assembled genome will facilitate its use for cell engineering and genome engineering. We assembled two independent drafts of Chinese hamster genome by de novo assembly from shotgun sequencing reads and by re‐scaffolding and gap‐filling the draft genome from NCBI for improved scaffold lengths and gap fractions. We then used the two independent assemblies to identify high confidence regions using two different approaches. First, the two independent assemblies were compared at the sequence level to identify their consensus regions as ”high confidence regions“ which accounts for at least 78 % of the assembled genome. Further, a genome wide comparison of the Chinese hamster scaffolds with mouse chromosomes revealed scaffolds with large blocks of collinearity, which were also compiled as high‐quality scaffolds. Genome scale collinearity was complemented with EST based synteny which also revealed conserved gene order compared to mouse. As cell line sequencing becomes more commonly practiced, the approaches reported here are useful for assessing the quality of assembly and potentially facilitate the engineering of cell lines.     

Analysis of repair mechanism choice during homologous recombination

Double-strand breaks (DSBs) occur frequently during cell growth. Due to the presence of repeated sequences in the genome, repair of a single DSB can result in gene conversion, translocation, deletion or tandem duplication depending on the mechanism and the sequence chosen as partner for the recombinational repair. Here, we study how yeast cells repair a single, inducible DSB when there are several potential donors to choose from, in the same chromosome and elsewhere in the genome. We systematically investigate the parameters that affect the choice of mechanism, as well as its genetic regulation. Our results indicate that intrachromosomal homologous sequences are always preferred as donors for repair. We demonstrate the occurrence of a novel tri-partite repair product that combines ectopic gene conversion and deletion. In addition, we show that increasing the distance between two repeated sequences enhances the dependence on Rad51 for colony formation after DSB repair. This is due to a role of Rad51 in the recovery from the checkpoint signal induced by the DSB. We suggest a model for the competition between the different homologous recombination pathways. Our model explains how different repair mechanisms are able to compensate for each other during DSB repair.     

Evaluation of paired-end sequencing strategies for detection of genome rearrangements in cancer

Paired-end sequencing is emerging as a key technique for assessing genome rearrangements and structural variation on a genome-wide scale. This technique is particularly useful for detecting copy-neutral rearrangements, such as inversions and translocations, which are common in cancer and can produce novel fusion genes. We address the question of how much sequencing is required to detect rearrangement breakpoints and to localize them precisely using both theoretical models and simulation. We derive a formula for the probability that a fusion gene exists in a cancer genome given a collection of paired-end sequences from this genome. We use this formula to compute fusion gene probabilities in several breast cancer samples, and we find that we are able to accurately predict fusion genes in these samples with a relatively small number of fragments of large size. We further demonstrate how the ability to detect fusion genes depends on the distribution of gene lengths, and we evaluate how different parameters of a sequencing strategy impact breakpoint detection, breakpoint localization, and fusion gene detection, even in the presence of errors that suggest false rearrangements. These results will be useful in calibrating future cancer sequencing efforts, particularly large-scale studies of many cancer genomes that are enabled by next-generation sequencing technologies.     

Observing micro-evolutionary processes of viral populations at multiple scales

Advances in sequencing technology coupled with new integrative approaches to data analysis provide a potentially transformative opportunity to use pathogen genome data to advance our understanding of transmission. However, to maximize the insights such genetic data can provide, we need to understand more about how the microevolution of pathogens is observed at different scales of biological organization. Here, we examine the evolutionary processes in foot-and-mouth disease virus observed at different scales, ranging from the tissue, animal, herd and region. At each scale, we observe analogous processes of population expansion, mutation and selection resulting in the accumulation of mutations over increasing time scales. While the current data are limited, rates of nucleotide substitution appear to be faster over individual-to-individual transmission events compared with those observed at a within-individual scale suggesting that viral population bottlenecks between individuals facilitate the fixation of polymorphisms. Longer-term rates of nucleotide substitution were found to be equivalent in individual-to-individual transmission compared with herd-to-herd transmission indicating that viral diversification at the herd level is not retained at a regional scale.     

Integrating Sequencing Technologies in Personal Genomics: Optimal Low Cost Reconstruction of Structural Variants

The goal of human genome re-sequencing is obtaining an accurate assembly of an individual's genome. Recently, there has been great excitement in the development of many technologies for this (e.g. medium and short read sequencing from companies such as 454 and SOLiD, and high-density oligo-arrays from Affymetrix and NimbelGen), with even more expected to appear. The costs and sensitivities of these technologies differ considerably from each other. As an important goal of personal genomics is to reduce the cost of re-sequencing to an affordable point, it is worthwhile to consider optimally integrating technologies. Here, we build a simulation toolbox that will help us optimally combine different technologies for genome re-sequencing, especially in reconstructing large structural variants (SVs). SV reconstruction is considered the most challenging step in human genome re-sequencing. (It is sometimes even harder than de novo assembly of small genomes because of the duplications and repetitive sequences in the human genome.) To this end, we formulate canonical problems that are representative of issues in reconstruction and are of small enough scale to be computationally tractable and simulatable. Using semi-realistic simulations, we show how we can combine different technologies to optimally solve the assembly at low cost. With mapability maps, our simulations efficiently handle the inhomogeneous repeat-containing structure of the human genome and the computational complexity of practical assembly algorithms. They quantitatively show how combining different read lengths is more cost-effective than using one length, how an optimal mixed sequencing strategy for reconstructing large novel SVs usually also gives accurate detection of SNPs/indels, how paired-end reads can improve reconstruction efficiency, and how adding in arrays is more efficient than just sequencing for disentangling some complex SVs. Our strategy should facilitate the sequencing of human genomes at maximum accuracy and low cost.     

Kinetic versus thermodynamic control of mutational effects on protein homeostasis: A perspective from computational modeling and experiment

The effect of mutations in individual proteins on protein homeostasis, or “proteostasis,” can in principle depend on the mutations' effects on the thermodynamics or kinetics of folding, or both. Here, we explore this issue using a computational model of in vivo protein folding that we call FoldEcoSlim. Our model predicts that kinetic versus thermodynamic control of mutational effects on proteostasis hinges on the relationship between how fast a protein's folding reaction reaches equilibrium and a critical time scale that characterizes the lifetime of a protein in its environment: for rapidly dividing bacteria, this time scale is that of cell division; for proteins that are produced in heterologous expression systems, this time scale is the amount of time before the protein is harvested; for proteins that are synthesized in and then exported from the eukaryotic endoplasmic reticulum, this time scale is that of protein secretion, and so forth. This prediction was validated experimentally by examining the expression yields of the wild type and several destabilized mutants of a model protein, the mouse ortholog of cellular retinoic acid‐binding protein 1.     

The structure of the sleeping genome: Implications of sperm DNA organization for somatic cells

The tertiary structure of the DNA that makes up the eukaryotic genome is remarkably plastic, taking many different forms in response to the different needs of the cell. During the cell cycle of one cell, the DNA is replicated, reorganized into mitotic chromosomes, and decondensed into interphase chromatin. Within one cell at any given point in time, the chromatin is divided into hetero- and euchromatin reflecting active and inactive states of the DNA. This organization varies within one organism since different parts of the genome are active in different cell types. This article focuses on the most dramatic cell-type-specific DNA organization, that found in spermatozoa, in which the entire genome is reorganized into an inactive state that is more highly condensed than mitotic chromosomes. This unique example of eukaryotic DNA organization offers some interesting clues to the still unanswered questions about the role that the three-dimensional packaging of DNA plays in its function. © 1994 Wiley-Liss, Inc.     

The Role of Chromosome–Nuclear Envelope Attachments in 3D Genome Organization

Chromosomes are intricately folded and packaged in the cell nucleus and interact with the nuclear envelope. This complex nuclear architecture has a profound effect on how the genome works and how the cells function. The main goal of review is to highlight recent studies on the effect of chromosome–nuclear envelope interactions on chromatin folding and function in the nucleus. The data obtained suggest that chromosome–nuclear envelope attachments are important for the organization of nuclear architecture in various organisms. A combination of experimental cell biology methods with computational modeling offers a unique opportunity to explore the fundamental relationships between different aspects of 3D genome organization in greater details. This powerful interdisciplinary approach could reveal how the organization and function of the genome in the nuclear space is affected by the chromosome–nuclear envelope attachments and will enable the development of novel approaches to regulate gene expression.