Is oxygen supply a limiting factor for survival during rewarming from profound hypothermia?

It has been postulated that unsuccessful resuscitation of victims of accidental hypothermia is caused by insufficient tissue oxygenation. The aim of this study was to test whether inadequate O2 supply and/or malfunctioning O2 extraction occur during rewarming from deep/profound hypothermia of different duration. Three groups of rats (n = 7 each) were used: group 1 served as normothermic control for 5 h; groups 2 and 3 were core cooled to 15 degrees C, kept at 15 degrees C for 1 and 5 h, respectively, and then rewarmed. In both hypothermic groups, cardiac output (CO) decreased spontaneously by > 50% in response to cooling. O2 consumption fell to less than one-third during cooling but recovered completely in both groups during rewarming. During hypothermia, circulating blood volume in both groups was reduced to approximately one-third of baseline, indicating that some vascular beds were critically perfused during hypothermia. CO recovered completely in animals rewarmed after 1 h (group 2) but recovered to only 60% in those rewarmed after 5 h (group 3), whereas blood volume increased to approximately three-fourths of baseline in both groups. Metabolic acidosis was observed only after 5 h of hypothermia (15 degrees C). A significant increase in myocardial tissue heat shock protein 70 after rewarming in group 3, but not in group 2, indicates an association with the duration of hypothermia. Thus mechanisms facilitating O2 extraction function well during deep/profound hypothermia, and, despite low CO, O2 supply was not a limiting factor for survival in the present experiments.     

BOX elements modulate gene expression in Streptococcus pneumoniae: impact on the fine-tuning of competence development

More than 100 BOX elements are randomly distributed in intergenic regions of the pneumococcal genome. Here we demonstrate that these elements can affect expression of neighboring genes and present evidence that they are mobile. Together, our findings show that BOX elements enhance genetic diversity and genomic plasticity in Streptococcus pneumoniae.     

Genetic noise control via protein oligomerization

Background

Biochemical equilibria are usually modeled iteratively: given one or a few fitted models, if there is a lack of fit or over fitting, a new model with additional or fewer parameters is then fitted, and the process is repeated. The problem with this approach is that different analysts can propose and select different models and thus extract different binding parameter estimates from the same data. An alternative is to first generate a comprehensive standardized list of plausible models, and to then fit them exhaustively, or semi-exhaustively.

Results

A framework is presented in which equilibriums are modeled as pairs (g, h) where g = 0 maps total reactant concentrations (system inputs) into free reactant concentrations (system states) which h then maps into expected values of measurements (system outputs). By letting dissociation constants K _d be either freely estimated, infinity, zero, or equal to other K _d , and by letting undamaged protein fractions be either freely estimated or 1, many g models are formed. A standard space of g models for ligand-induced protein dimerization equilibria is given. Coupled to an h model, the resulting (g, h) were fitted to dTTP induced R1 dimerization data (R1 is the large subunit of ribonucleotide reductase). Models with the fewest parameters were fitted first. Thereafter, upon fitting a batch, the next batch of models (with one more parameter) was fitted only if the current batch yielded a model that was better (based on the Akaike Information Criterion) than the best model in the previous batch (with one less parameter). Within batches models were fitted in parallel. This semi-exhaustive approach yielded the same best models as an exhaustive model space fit, but in approximately one-fifth the time.

Conclusion

Comprehensive model space based biochemical equilibrium model selection methods are realizable. Their significance to systems biology as mappings of data into mathematical models warrants their development.     

Analysis of gemini pollen 3 mutant suggests a broad function of AUGMIN in microtubule organization during sexual reproduction in Arabidopsis

In flowering plants, male gametes arise via meiosis of diploid pollen mother cells followed by two rounds of mitotic division. Haploid microspores undergo polar nuclear migration and asymmetric division at pollen mitosis I to segregate the male germline, followed by division of the germ cell to generate a pair of sperm cells. We previously reported two gemini pollen (gem) mutants that produced twin‐celled pollen arising from polarity and cytokinesis defects at pollen mitosis I in Arabidopsis. Here, we report an independent mutant, gem3, with a similar division phenotype and severe genetic transmission defects through pollen. Cytological analyses revealed that gem3 disrupts cell division during male meiosis, at pollen mitosis I and during female gametophyte development. We show that gem3 is a hypomorphic allele (aug6‐1) of AUGMIN subunit 6, encoding a conserved component in the augmin complex, which mediates microtubule (MT)‐dependent MT nucleation in acentrosomal cells. We show that MT arrays are disturbed in gem3/aug6‐1 during male meiosis and pollen mitosis I using fluorescent MT‐markers. Our results demonstrate a broad role for the augmin complex in MT organization during sexual reproduction, and highlight gem3/aug6‐1 mutants as a valuable tool for the investigation of augmin‐dependent MT nucleation and dynamics in plant cells.     

A statistical model of ChIA-PET data for accurate detection of chromatin 3D interactions

Identification of three-dimensional (3D) interactions between regulatory elements across the genome is crucial to unravel the complex regulatory machinery that orchestrates proliferation and differentiation of cells. ChIA-PET is a novel method to identify such interactions, where physical contacts between regions bound by a specific protein are quantified using next-generation sequencing. However, determining the significance of the observed interaction frequencies in such datasets is challenging, and few methods have been proposed. Despite the fact that regions that are close in linear genomic distance have a much higher tendency to interact by chance, no methods to date are capable of taking such dependency into account. Here, we propose a statistical model taking into account the genomic distance relationship, as well as the general propensity of anchors to be involved in contacts overall. Using both real and simulated data, we show that the previously proposed statistical test, based on Fisher''s exact test, leads to invalid results when data are dependent on genomic distance. We also evaluate our method on previously validated cell-line specific and constitutive 3D interactions, and show that relevant interactions are significant, while avoiding over-estimating the significance of short nearby interactions.     

Performance comparison of four exome capture systems for deep sequencing

Background

Recent developments in deep (next-generation) sequencing technologies are significantly impacting medical research. The global analysis of protein coding regions in genomes of interest by whole exome sequencing is a widely used application. Many technologies for exome capture are commercially available; here we compare the performance of four of them: NimbleGen’s SeqCap EZ v3.0, Agilent’s SureSelect v4.0, Illumina’s TruSeq Exome, and Illumina’s Nextera Exome, all applied to the same human tumor DNA sample.

Results

Each capture technology was evaluated for its coverage of different exome databases, target coverage efficiency, GC bias, sensitivity in single nucleotide variant detection, sensitivity in small indel detection, and technical reproducibility. In general, all technologies performed well; however, our data demonstrated small, but consistent differences between the four capture technologies. Illumina technologies cover more bases in coding and untranslated regions. Furthermore, whereas most of the technologies provide reduced coverage in regions with low or high GC content, the Nextera technology tends to bias towards target regions with high GC content.

Conclusions

We show key differences in performance between the four technologies. Our data should help researchers who are planning exome sequencing to select appropriate exome capture technology for their particular application.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-449) contains supplementary material, which is available to authorized users.