Let’s see what’s new in PLoS Genetics, PLoS Computational Biology, PLoS Pathogens and PLoS ONE this week. As always, you should rate the articles, post notes and comments and send trackbacks when you blog about the papers. Here are my own picks for the week – you go and look for your own favourites:
Predicting the Herd Immunity Threshold during an Outbreak: A Recursive Approach:
The objective was to develop a novel algorithm that can predict, based on field survey data, the minimum vaccination coverage required to reduce the mean number of infections per infectious individual to less than one (the Outbreak Response Immunization Threshold or ORIT) from up to six days in the advance. First, the relationship between the rate of immunization and the ORIT was analyzed to establish a link. This relationship served as the basis for the development of a recursive algorithm that predicts the ORIT using survey data from two consecutive days. The algorithm was tested using data from two actual measles outbreaks. The prediction day difference (PDD) was defined as the number of days between the second day of data input and the day of the prediction. The effects of different PDDs on the prediction error were analyzed, and it was found that a PDD of 5 minimized the error in the prediction. In addition, I developed a model demonstrating the relationship between changes in the vaccination coverage and changes in the individual reproduction number. The predictive algorithm for the ORIT generates a viable prediction of the minimum number of vaccines required to stop an outbreak in real time. With this knowledge, the outbreak control agency may plan to expend the lowest amount of funds required stop an outbreak, allowing the diversion of the funds saved to other areas of medical need.
The fact that functionally more important genes or DNA sequences evolve more slowly than less important ones is commonly believed and frequently used by molecular biologists. However, previous genome-wide studies of a diverse array of organisms found only weak, negative correlations between the importance of a gene and its evolutionary rate. We show, here, that the weakness of the correlation is not because gene importance measured in lab conditions deviates from that in an organism’s natural environments. Neither is it due to a potentially biased among-gene distribution of functional density. We suggest that the weakness of the correlation is factual, rather than artifactual. These findings notwithstanding, we show that the principle of slower evolution of more important genes does have some predictive power when genes with vastly different evolutionary rates are compared, explaining why the principle can be practically useful for tasks such as identifying functional non-coding sequences despite the weakness of the correlation.
Peptide hormones, or neuropeptides, are made up of a string of amino acids ranging from approximately 3 to 50 residues. These peptides are processed from a larger protein called a prohormone and activate a class of proteins called G-protein-coupled receptors (GPCRs). Neuropeptides signal neurons and other cells leading to changes in cellular biochemistry and potentially gene expression. There are a number of “orphan” GPCRs, i.e., receptors that have been discovered either by genomic sequence or by cloning, in which its respective peptide hormone is unknown. We have devised a computational method that models patterns in protein sequence simultaneously with evolutionary differences across species in order to identify previously unknown peptide hormones. We have used this computational methodology to identify a previously unknown putative prohormone that contains up to four potential neuropeptides, and we have characterized this prohormone with respect to location in rat brain and various human tissues. This computational technique will be useful for the identification of additional neuropeptides and help to characterize orphan GPCRs. Because roughly half of all pharmaceuticals act through activation or inhibition of GPCRs, this technique should lead to the identification of additional pharmaceutical targets and ultimately clinically used drugs.
Fear is one of the most potent emotional experiences and is an adaptive component of response to potentially threatening stimuli. On the other hand, too much or inappropriate fear accounts for many common psychiatric problems. Cumulative evidence suggests that the amygdala plays a central role in the acquisition, storage and expression of fear memory. Here, we developed an inducible striatal neuron ablation system in transgenic mice. The ablation of striatal neurons in the adult brain hardly affected the auditory fear learning under the standard condition in agreement with previous studies. When conditioned with a low-intensity unconditioned stimulus, however, the formation of long-term fear memory but not short-tem memory was impaired in striatal neuron-ablated mice. Consistently, the ablation of striatal neurons 24 h after conditioning with the low-intensity unconditioned stimulus, when the long-term fear memory was formed, diminished the retention of the long-term memory. Our results reveal a novel form of the auditory fear memory depending on striatal neurons at the low-intensity unconditioned stimulus.
The spatial-temporal expression pattern of a gene, which is crucial to its function, is controlled by cis-regulatory DNA sequences. Forming the basic units of regulatory sequences are transcription factor binding sites, often organized into larger modules that determine gene expression in response to combinatorial environmental signals. Understanding the conservation and change of regulatory sequences is critical to our knowledge of the unity as well as diversity of animal development and phenotypes. In this paper, we study the evolution of sequences involved in the regulation of body patterning in the Drosophila embryo. We find that mutations of nucleotides within a binding site are constrained by evolutionary forces to preserve the site’s binding affinity to the cognate transcription factor. Functional binding sites are frequently destroyed during evolution and the rate of loss across evolutionary spans is roughly constant. We also find that the evolutionary fate of a site strongly depends on its context; a pair of interacting sites are more likely to survive mutational forces than isolated sites. Together, these findings provide new insights and pose new challenges to our understanding of cis-regulatory sequences and their evolution.