Inevitability or contingency: how many chromosomes do we really need?

<正>In an essay written by the evolutionary biologist Theodosius Dobzhansky in 1973, he pointed out that "Nothing in Biology Makes Sense Except in the Light of Evolution." An interesting phenomenon in biology is the presence of variable numbers of chromosomes in different organisms. Besides several species, which possess multiple circular chromosomes or simply linear chromosomes (Baril et al.1989; Suwanto and Kaplan, 1989; Jumas-Bilak et al., 1998),most prokaryotes only possess one circular chromosome. In contrast, the genomes of eukaryotic species are usually packaged into linear chromosomes with numbers varying     

From co-expression to co-regulation: how many microarray experiments do we need?

Background  

Cluster analysis is often used to infer regulatory modules or biological function by associating unknown genes with other genes that have similar expression patterns and known regulatory elements or functions. However, clustering results may not have any biological relevance.     

Replication origins: why do we need so many?

During the G1 phase of the cell cycle, replication origins are prepared to fire, a process that is referred to as origin licensing. It was often pondered what a cell's fate would be if not all of its replication origins were licensed and subsequently activated during S phase. One obvious prediction was that S phase would simply be prolonged. As it turns out, however, the consequences are much more complex. A short G1 phase enforced by premature entry into S phase, or other events that negatively affect origin licensing, will ultimately compromise the cell's ability to complete DNA replication before entering mitosis. As a result, the cell becomes genomically unstable when it attempts to repair unreplicated DNA during anaphase. Thus, the density of active replication origins in the chromosomes of eukaryotic cells determines S phase dynamics and chromosome stability during mitosis.     

Examining the developing bone: What do we measure and how do we do it?

The clinical tools available to evaluate bone development in children are often ambiguous, and difficult to interpret. Unfortunately bone densitometry methods (i.e., dual energy X-ray absorptiometry, DXA) which have a relatively straightforward application in adult osteoporosis, are far more difficult to evaluate in the growing skeleton. Even with adequate "adjustment" for bone size or maturity, bone "density" (areal or volumetric) alone often gives an inaccurate assessment of bone strength--especially in children. Ideally, we would like to measure both material and geometric properties of bone to accurately estimate "strength". Mechanically meaningful measures of bone geometry (bone cross-sectional area, cortical thickness) and estimates of bending strength (section modulus, or SSI) are available with non-invasive techniques such as (p)QCT and some DXA software. With new technology it might be possible to also measure bone material properties, which will be especially important in some pediatric disorders. In children, we also need to know something about the loads imposed on a child's bone and consider not only absolute bone strength, but also the strength of bone relative to the physiologic loads. Interpreting bone strength in light of the loads imposed (particularly muscle force) is critical for an accurate diagnosis of the developing bone.     

Marine phytoplankton: how many species in the world ocean?

Towards the end of the 1980s, living plankton flora of the worldocean amounted to 474-504 genera and 3444–4375 speciesif one neglects the increase rate of taxa during the latestyears In the above figures, the lower estimate is that of the‘reliable’ taxa (of practical value for identificationtasks), whereas the higher estimate includes the insufficientlyknown or doubtful organisms, and synonyms are excluded. Thefrequency distribution of the numbers of species per genus confirmsthe general hyperbolic law, which implies that a relativelylarge number of genera are uni- or paucispecific.     

Where in the world do bacteria experience oxidative stress?

Reactive oxygen species – superoxide, hydrogen peroxide and hydroxyl radicals – have long been suspected of constraining bacterial growth in important microbial habitats and indeed of shaping microbial communities. Over recent decades, studies of paradigmatic organisms such as Escherichia coli, Salmonella typhimurium, Bacillus subtilis and Saccharomyces cerevisiae have pinpointed the biomolecules that oxidants can damage and the strategies by which microbes minimize their injuries. What is lacking is a good sense of the circumstances under which oxidative stress actually occurs. In this MiniReview several potential natural sources of oxidative stress are considered: endogenous ROS formation, chemical oxidation of reduced species at oxic–anoxic interfaces, H₂O₂ production by lactic acid bacteria, the oxidative burst of phagocytes and the redox-cycling of secreted small molecules. While all of these phenomena can be reproduced and verified in the lab, the actual quantification of stress in natural habitats remains lacking – and, therefore, we have a fundamental hole in our understanding of the role that oxidative stress actually plays in the biosphere.     

Why and how do we model circadian rhythms?

In our attempts to understand the circadian system, we unavoidably rely on abstractions. Instead of describing the behavior of the circadian system in all its complexity, we try to derive basic features from which we form a global concept on how the system works. Such a basic concept is a model of reality. The author discusses why it is advantageous or even necessary to transform conceptual models into mathematical formulations. As examples to demonstrate those advantages, the author reviews 4 types of mathematical models: negative feedback models thought to operate within pacemaker cells, models on coupling between pacemaker cells to generate pacemaker output, oscillator models describing the behavior of the composite circadian pacemaker, and models describing how the circadian pacemaker influences behavior.     

Actin cytoskeleton and small heat shock proteins: how do they interact?

Actin and small heat shock proteins (sHsps) are ubiquitous and multifaceted proteins that exist in 2 reversible forms, monomers and multimers, ie, the microfilament of the cytoskeleton and oligomers of the sHsps, generally, supposed to be in a spherical and hollow form. Two situations are described in the literature, where the properties of actin are modulated by sHsps; the actin polymerization is inhibited in vitro by some sHsps acting as capping proteins, and the actin cytoskeleton is protected by some sHsps against the disruption induced by various stressful conditions. We propose that a direct actin-sHsp interaction occurs to inhibit actin polymerization and to participate in the in vivo regulation of actin filament dynamics. Protection of the actin cytoskeleton would result from an F-actin-sHsp interaction in which microfilaments would be coated by small oligomers of phosphorylated sHsps. Both proteins share common structural motives suggesting direct binding sites, but they remain to be demonstrated. Some sHsps would behave with the actin cytoskeleton as actin-binding proteins capable of either capping a microfilament when present as a nonphosphorylated monomer or stabilizing and protecting the microfilament when organized in small, phosphorylated oligomers.     

Obtaining data from randomised controlled trials: how much do we need for reliable and informative meta-analyses?

Many randomised controlled trials compare treatments that will produce only moderate differences in outcome, but these differences can be clinically important. However, they are difficult to assess reliably and require a large amount of randomised evidence. This can be achieved through large prospective randomised trials which will accrue future patients, the meta-analysis of results from randomised trials involving patients from the past, or--ideally--both. The techniques require that all possible biases are minimised, and in meta-analyses this can best be achieved by ensuring that all of the randomised evidence--both trials and participants in those trials--is included. The meta-analysis of individual patient data has been described as the gold standard for this approach. It will remove many of the problems associated with relying solely on published data and some of the problems arising from a reliance on aggregate data, and will also add to the analyses that can be performed. Such projects, however, require considerable time and effort.     

Why do we need to regulate autophagy (and how can we do it)? A cartoon depiction

In a recent issue of this journal I attempted to explain the purpose of macroautophagy/autophagy to a non-specialist audience through the use of cartoons. In the present article, I am continuing this approach by considering the topic of autophagy regulation—why does the cell need to modulate the autophagic response, and what are the basic morphological mechanisms that can be used to attain different levels of autophagy activity?