Predator-induced changes in population structure and individual quality of Microtus voles: a large-scale field experiment

In small mammal populations with multiannual oscillations in density, observational data have revealed cyclic changes in population structure, reproduction, and individual quality, but mechanisms inducing these changes have remained an open question. We analysed data collected during a 3-year predator reduction experiment to find out the effects of predators on population structure, reproductive parameters, and individual quality of Microtus voles (the field vole M. agrestis and the sibling vole M. rossiaemeridionalis ) in western Finland. Voles were collected by snap trapping in April, June, August, and October during 1997–1999. The yearly reduction of predators from April to October had a clear positive effect on the abundance of sibling voles but did not significantly affect the densities of field voles. Predator reduction apparently also affected the age ratio and mean body size in late summer, as well as pancreatic weights of voles. However, all observed differences between predator reduction and control areas, except those in abundance, were small and may mainly reflect a generally higher survival leading to higher densities of voles in predator reduction areas. Our results also indicated a relative lack of high quality food at population peaks but not because of reduced foraging activity in the presence of predators. We conclude that the indirect effects of vole-eating predators on the population growth of main prey are small compared to the detrimental direct effects on prey survival. In the case of less preferred prey, indirect effects of predation through reduced interspecific competition may play a role at high densities.     

A bias-ed assessment of the use of SNPs in human complex traits

Although many biotechnological advancements have been made in the past decade, there has been very limited success in unraveling the genetic component of complex traits. Heavily invested research has been initiated based on etiological models of unrealistic simplicity and conducted under poor experimental designs, on data sets of insufficient size, leading to an overestimation of the effect sizes of genetic variants and the quantity and quality of linkage disequilibrium (LD). Arguments about whether families or unrelated individuals provide more power for gene mapping have been erroneously debated as issues of whether linkage or LD are more detectable sorts of correlation. Although the latter issue may be subject to debate, there is no doubt that family-based analysis is more powerful for detecting linkage and/or LD. If the recent advances in biotechnology are to be exploited effectively, vastly improved study designs will be imperative, as the reasons for the lack of success to date have much more to do with biology than technology, an issue that has become increasingly clear with the findings of the past years.     

Identification of two AGTR2 mutations in male patients with non-syndromic mental retardation

Mutations in the coding region of the angiotensin II type 2 receptor gene (AGTR2) were recently identified to cause X-linked recessive mental retardation. We report a mutation screening of the AGTR2 gene in 57 Finnish male patients with non-syndromic mental retardation. We identified two mutations, a 62GT transversion, which leads to a substitution of glycine for valine (G21V) and a 157AT transversion, which causes a substitution of isoleucine for phenylalanine (I53F). The patients with AGTR2 sequence variants had severe/profound mental retardation, epileptic seizures, restlessness, hyperactivity, and disturbed development of speech.     

Computational strategies for analyzing data in gene expression microarray experiments

Microarray analysis has become a widely used method for generating gene expression data on a genomic scale. Microarrays have been enthusiastically applied in many fields of biological research, even though several open questions remain about the analysis of such data. A wide range of approaches are available for computational analysis, but no general consensus exists as to standard for microarray data analysis protocol. Consequently, the choice of data analysis technique is a crucial element depending both on the data and on the goals of the experiment. Therefore, basic understanding of bioinformatics is required for optimal experimental design and meaningful interpretation of the results. This review summarizes some of the common themes in DNA microarray data analysis, including data normalization and detection of differential expression. Algorithms are demonstrated by analyzing cDNA microarray data from an experiment monitoring gene expression in T helper cells. Several computational biology strategies, along with their relative merits, are overviewed and potential areas for additional research discussed. The goal of the review is to provide a computational framework for applying and evaluating such bioinformatics strategies. Solid knowledge of microarray informatics contributes to the implementation of more efficient computational protocols for the given data obtained through microarray experiments.     

Urinary Biomarkers Indicative of Apoptosis and Acute Kidney Injury in the Critically Ill

Background

Apoptosis is a key mechanism involved in ischemic acute kidney injury (AKI), but its role in septic AKI is controversial. Biomarkers indicative of apoptosis could potentially detect developing AKI prior to its clinical diagnosis.

Methods

As a part of the multicenter, observational FINNAKI study, we performed a pilot study among critically ill patients who developed AKI (n = 30) matched to critically ill patients without AKI (n = 30). We explored the urine and plasma levels of cytokeratin-18 neoepitope M30 (CK-18 M30), cell-free DNA, and heat shock protein 70 (HSP70) at intensive care unit (ICU) admission and 24h thereafter, before the clinical diagnosis of AKI defined by the Kidney Disease: Improving Global Outcomes -creatinine and urine output criteria. Furthermore, we performed a validation study in 197 consecutive patients in the FINNAKI cohort and analyzed the urine sample at ICU admission for CK-18 M30 levels.

Results

In the pilot study, the urine or plasma levels of measured biomarkers at ICU admission, at 24h, or their maximum value did not differ significantly between AKI and non-AKI patients. Among 20 AKI patients without severe sepsis, the urine CK-18 M30 levels were significantly higher at 24h (median 116.0, IQR [32.3–233.0] U/L) than among those 20 patients who did not develop AKI (46.0 [0.0–54.0] U/L), P = 0.020. Neither urine cell-free DNA nor HSP70 levels significantly differed between AKI and non-AKI patients regardless of the presence of severe sepsis. In the validation study, urine CK-18 M30 level at ICU admission was not significantly higher among patients developing AKI compared to non-AKI patients regardless of the presence of severe sepsis or CKD.

Conclusions

Our findings do not support that apoptosis detected with CK-18 M30 level would be useful in assessing the development of AKI in the critically ill. Urine HSP or cell-free DNA levels did not differ between AKI and non-AKI patients.     

Systematic construction of gene coexpression networks with applications to human T helper cell differentiation process

Motivation: Coexpression networks have recently emerged as anovel holistic approach to microarray data analysis and interpretation.Choosing an appropriate cutoff threshold, above which a gene–geneinteraction is considered as relevant, is a critical task inmost network-centric applications, especially when two or morenetworks are being compared. Results: We demonstrate that the performance of traditionalapproaches, which are based on a pre-defined cutoff or significancelevel, can vary drastically depending on the type of data andapplication. Therefore, we introduce a systematic procedurefor estimating a cutoff threshold of coexpression networks directlyfrom their topological properties. Both synthetic and real datasetsshow clear benefits of our data-driven approach under variouspractical circumstances. In particular, the procedure providesa robust estimate of individual degree distributions, even frommultiple microarray studies performed with different array platformsor experimental designs, which can be used to discriminate thecorresponding phenotypes. Application to human T helper celldifferentiation process provides useful insights into the componentsand interactions controlling this process, many of which wouldhave remained unidentified on the basis of expression changealone. Moreover, several human–mouse orthologs showedconserved topological changes in both systems, suggesting theirpotential importance in the differentiation process. Contact: laliel{at}utu.fi Supplementary information: Supplementary data are availableat Bioinformatics online. Associate Editor: David Rocke     

GOlorize: a Cytoscape plug-in for network visualization with Gene Ontology-based layout and coloring

We have implemented a graph layout algorithm that exposes Gene Ontology (GO) class structure on the network nodes. It can be used in conjunction with BiNGO plug-in to Cytoscape, which finds the GO categories over-represented in a given network. Our plug-in, named GOlorize, first highlights the class members with category-specific color-coding and then constructs an enhanced visualization of the network using a class-directed layout algorithm. Availability: http://www.cytoscape.org/plugins2.php. Supplementary information: Installation instructions and tutorial at http://www.cytoscape.org/plugins/GOlorize/GOlorizeUserGuide.pdf.     

Who is the ocean? Preface to the future seas 2030 special issue

Reviews in Fish Biology and Fisheries -