The complex relationship between mycobacteria and macrophages: it's not all bliss

Mycobacteria are uniquely adapted to grow inside host macrophages. As Clay et al. show in this issue of Cell Host & Microbe and as van der Wel et al. show in a recent issue of Cell, there are both benefits and drawbacks for the pathogen in adopting this strategy, and some of our fundamental assumptions about how the host cell and bacterium interact might need to be reexamined.     

RFLP approach to breeding for quantitative traits in plants—a critique

A number of recent papers suggest that use of RFLPs as markers offers a clear advantage in breeding for improvement in quantitative traits (QTs). The concepts underlying establishment of linkage between RFLP markers and QT loci stem from three papers on tomato published during 1987–1991. Essentially, continuously varying QT phenotypes are assigned to RFLP genotype classes, which can be considered to be determined by a single, diallelic gene with codominant alleles. Linkage is inferred through statistical analysis. Similarly, interaction between markers and QT is also tested by an analysis of variance. Here, the statistical methods employed in these three papers to detect linkage are critically evaluated, especially because subsequent investigations take the concepts developed in these papers as proven. In this paper, we examine the three fundamental papersde novo. We scrutinize the methods employed and the inferences drawn to bring to light what we believe are conceptual drawbacks.     

Engineering biocatalytic systems in organic media with low water content

The use of organic media in biocatalysis stems from the fact that in many cases biocatalytic processes can hardly be conducted (if at all) in aqueous solutions because of extremely low solubilities of substrates and/or unfavorable shift of the reaction equilibrium in water. The growing interest in this biotechnological area that has sprung up over the past few years has resulted in various approaches to enzyme stabilization against organic solvents. Thus, the main goal of the present review is to formulate a comprehensive classification of numerous successful nonaqueous biocatalytic systems based on a few fundamental principles. Typical examples are considered, along with the advantages and drawbacks inherent in each of the approaches discussed.     

An efficient strategy to isolate full-length cDNAs based on an mRNA cap retention procedure (CAPture).

The ability to generate cDNA libraries is one of the most fundamental procedures in contemporary molecular biology. One of the major drawbacks of current methods is that most cDNAs present in any given library are incomplete, rendering the characterization of genes an inefficient and time-consuming task. We have developed an affinity selection procedure using a fusion protein containing the murine cap-binding protein (eukaryotic initiation factor 4E), coupled to a solid support matrix, that allows for the purification of mRNAs via the 5' cap structure. When combined with a single-strand-specific RNase digestion step, specific retention of complete cDNA-RNA duplexes following first-strand synthesis is achieved. This method can be used to generate cDNA libraries in which polyadenylated and nonpolyadenylated mRNAs are equally represented and to enrich for full-length or 5'-end clones, thus facilitating cDNA cloning and promoter mapping.     

CpGIF: an algorithm for the identification of CpG islands

CpG islands (CGIs) play a fundamental role in genome analysis and annotation, and contribute to improving the accuracy of promoter prediction. Besides, CGIs in promoter regions are abnormally methylated in cancer cells and thus can be used as tumor markers. However, current methods for identifying CGIs suffer from various drawbacks. We present a new algorithm for detecting CGIs, called CpG Island Finder (CpGIF), which combines the best features in the most commonly used algorithms and avoids their disadvantages as much as possible. Five public tools for CpG island searching are used to compare with CpGIF for the assessment of accuracy and computational efficiency. The results reveal that CpGIF has higher performance coefficient and correlation coefficient than these previous methods, which indicates that CpGIF is able to provide high sensitivity and specificity at the same time. CpGIF is also faster than those methods with comparable prediction accuracy.     

A Lagrangian particle method for reaction–diffusion systems on deforming surfaces

Reaction–diffusion processes on complex deforming surfaces are fundamental to a number of biological processes ranging from embryonic development to cancer tumor growth and angiogenesis. The simulation of these processes using continuum reaction–diffusion models requires computational methods capable of accurately tracking the geometric deformations and discretizing on them the governing equations. We employ a Lagrangian level-set formulation to capture the deformation of the geometry and use an embedding formulation and an adaptive particle method to discretize both the level-set equations and the corresponding reaction–diffusion. We validate the proposed method and discuss its advantages and drawbacks through simulations of reaction–diffusion equations on complex and deforming geometries.     

A dynamically growing self-organizing tree (DGSOT) for hierarchical clustering gene expression profiles

MOTIVATION: The increasing use of microarray technologies is generating large amounts of data that must be processed in order to extract useful and rational fundamental patterns of gene expression. Hierarchical clustering technology is one method used to analyze gene expression data, but traditional hierarchical clustering algorithms suffer from several drawbacks (e.g. fixed topology structure; mis-clustered data which cannot be reevaluated). In this paper, we introduce a new hierarchical clustering algorithm that overcomes some of these drawbacks. RESULT: We propose a new tree-structure self-organizing neural network, called dynamically growing self-organizing tree (DGSOT) algorithm for hierarchical clustering. The DGSOT constructs a hierarchy from top to bottom by division. At each hierarchical level, the DGSOT optimizes the number of clusters, from which the proper hierarchical structure of the underlying dataset can be found. In addition, we propose a new cluster validation criterion based on the geometric property of the Voronoi partition of the dataset in order to find the proper number of clusters at each hierarchical level. This criterion uses the Minimum Spanning Tree (MST) concept of graph theory and is computationally inexpensive for large datasets. A K-level up distribution (KLD) mechanism, which increases the scope of data distribution in the hierarchy construction, was used to improve the clustering accuracy. The KLD mechanism allows the data misclustered in the early stages to be reevaluated at a later stage and increases the accuracy of the final clustering result. The clustering result of the DGSOT is easily displayed as a dendrogram for visualization. Based on a yeast cell cycle microarray expression dataset, we found that our algorithm extracts gene expression patterns at different levels. Furthermore, the biological functionality enrichment in the clusters is considerably high and the hierarchical structure of the clusters is more reasonable. AVAILABILITY: DGSOT is available upon request from the authors.     

Biological computation: hearts and flytraps

The original computers were people using algorithms to get mathematical results such as rocket trajectories. After the invention of the digital computer, brains have been widely understood through analogies with computers and now artificial neural networks, which have strengths and drawbacks. We define and examine a new kind of computation better adapted to biological systems, called biological computation, a natural adaptation of mechanistic physical computation. Nervous systems are of course biological computers, and we focus on some edge cases of biological computing, hearts and flytraps. The heart has about the computing power of a slug, and much of its computing happens outside of its forty thousand neurons. The flytrap has about the computing power of a lobster ganglion. This account advances fundamental debates in neuroscience by illustrating ways that classical computability theory can miss complexities of biology. By this reframing of computation, we make way for resolving the disconnect between human and machine learning.

     

Species names in phylogenetic nomenclature

Linnaean binomial nomenclature is logically incompatible with the phylogenetic nomenclature of de Queiroz and Gauthier (1992, Annu. Rev. Ecol. Syst. 23:449-480): The former is based on the concept of genus, thus making this rank mandatory, while the latter is based on phylogenetic definitions and requires the abandonment of mandatory ranks. Thus, if species are to receive names under phylogenetic nomenclature, a different method must be devised to name them. Here, 13 methods for naming species in the context of phylogenetic nomenclature are contrasted with each other and with Linnaean binomials. A fundamental dichotomy among the proposed methods distinguishes those that retain the entire binomial of a preexisting species name from those that retain only the specific epithet. Other relevant issues include the stability, uniqueness, and ease of pronunciation of species names; their capacity to convey phylogenetic information; and the distinguishability of species names that are governed by a code of phylogenetic nomenclature both from clade names and from species names governed by the current codes. No method is ideal. Each has advantages and drawbacks, and preference for one option over another will be influenced by one's evaluation of the relative importance of the pros and cons for each. Moreover, sometimes the same feature is viewed as an advantage by some and a drawback by others. Nevertheless, all of the proposed methods for naming species in the context of phylogenetic nomenclature provide names that are more stable than Linnaean binomials.     

Comparison of statistical significance criteria

We study and compare two classes of statistical criteria to assess the significance of exceptional words. Indeed, the Z-score-like criteria, or the normal approximation that is a strict equivalent, suffer from several drawbacks in terms of sensitivity and specificity. Thanks to the combinatorial structure of words, a computation of the exact P-value has been made possible by recent mathematical results. We study here the drawbacks of the Z-score, the choice of the threshold and the tightness to the P-value. A major conclusion is that the normal approximation is always very poor and overestimates statistical significance.     

An evaluation of the use of two-dimensional gel electrophoresis in proteomics

With whole genomes being sequenced almost routinely, the next logical step towards a better understanding of cellular mechanisms lies in studying the functional units of gene expression-proteins. One of the fundamental approaches in proteomics is the use of two-dimensional gel electrophoresis as a mode of separation and visualization of complex protein mixtures. Despite several limitations of the method, its ability to separate large numbers of proteins, including their post-translationally modified forms, ensures that it will continue to be popular in several well-defined areas of proteomics. In this article, we discuss the merits and drawbacks of two-dimensional gels and compare them with alternative systems such as one-dimensional gels and liquid chromatography-based separation methods. In the wake of recent advances in mass spectrometry and related areas, we outline areas where two-dimensional gels can best be utilized as the preferred separation method in proteomic strategies.     

CRISPR/Cas9-mediated genome editing assists protein dynamics studies in live cells

Spatial and temporal regulation of molecular reactions dictates cell fate. Thus, studying molecular dynamics is essential to understand how cells decide what to do and the fundamental perturbations causing disease. Classically, molecular dynamics has been studied by protocols based in the overexpression of fluorescent fusion proteins. However, overexpression is associated to altered stoichiometry, molecular dynamics and subcellular distribution. We here discuss the necessity to study molecular dynamics of fluorescent fusion proteins expressed under physiological mechanisms in the cell, pointing to CRISPR/Cas9-mediated genome editing as the ideal means to do so. Current genome editing protocols enable us to study molecular dynamics while avoiding drawbacks associated to overexpression.     

Production of alien chromosome additions and their utility in plant genetics

Breeding programs aiming at transferring desirable genes from one species to another through interspecific hybridization and backcrossings often produce monosomic and disomic additions as intermediate crossing products. Such aneuploids contain alien chromosomes added to the complements of the recipient parent and can be used for further introgression programs, but lack of homoeologous recombination and inevitable segregation of the alien chromosome at meiosis make them often less ideal for producing stable introgression lines. Monosomic and disomic additions can have specific morphological characteristics, but more often they need additional confirmation of molecular marker analyses and assessment by fluorescence in situ hybridization with genomic and chromosome-specific DNA as probes. Their specific genetic and cytogenetic properties make them powerful tools for fundamental research elucidating regulation of homoeologous recombination, distribution of chromosome-specific markers and repetitive DNA sequences, and regulation of heterologous gene expression. In this overview we present the major characteristics of such interspecific aneuploids highlighting their advantages and drawbacks for breeding and fundamental research.     

Measurement of plasma volume using fluorescent silica-based nanoparticles

Plasma volume (PV) is an important determinant of cardiovascular function and organ perfusion, and it is the target of infusion and diuretic therapies in daily clinical practice. Despite its fundamental importance PV is not commonly measured because available methods of tracer dilution are reliant on dye substances that suffer from numerous drawbacks including binding plasma proteins, spectral changes, and clearance kinetics that complicate analysis and interpretation. To address these issues, we have tested the utility of fluorescent nanoparticles comprised of a dye-rich silica core and polyethylene glycol-coated shell. Photophysical and visual analysis showed discrete size-gradated nanoparticle populations could be synthesized within a distribution tolerance of ±4 nm, which were optically unaffected in the presence of plasma/albumin. In normal mice, the cutoff for renal filtration of nanoparticles from blood into urine was ≤11 nm. A linear relationship between body weight and PV was readily determined in mice administered far red fluorescent nanoparticles sized either 20 or 30 nm. PV measurements using nanoparticles were correlated to values obtained with Evans blue dye. Induced expansion or contraction of PV was demonstrated with albumin or furosemide administration, respectively, in mice. Longitudinal experiments >30 min required matched untreated control mice to correct for nanoparticle loss (≈30%) putatively to the reticuloendothelial/phagocyte system. Collectively, the findings support a nanotechnology-based solution to methodological problems in measure of PV, notably in clinical settings where information on hemodynamic changes may improve treatment of injury and disease.     

Treatment of the rat hepatic stellate cell line,PAV-1, by retinol and palmitic acid leads to a convenient model to study retinoids metabolism

The main site of vitamin A storage in the liver is the hepatic stellate cells (HSC). Involvement of HSC in vitamin A metabolism has mainly been studied using primary culture, which represents the most physiological model but technically suffers several drawbacks (yield, low reproducibility, etc.). To circumvent these problems, we have previously established and characterised an immortalised rat HSC line named PAV-1. This study aimed to investigate in PAV-1 and in primary HSC (i) the incorporation of retinol and its esterification, (ii) the cellular retinol-binding protein (CRBP) content, (iii) the acid retinyl ester hydrolase activity (aREH), (iv) the thermal susceptibility and (v) the lipid composition of the membranes, which may play a crucial role in retinol transport across cellular membrane. In routine conditions of culture, the rate of retinol esterification in PAV-1 was low (5.2%) compared to that obtained with primary HSC (69.9%). Retinol pre-treatment doubled this esterification rate (10.7%) and the CRBP content in PAV-1. The co-incubation with retinol and palmitic acid enabled PAV-1 to esterify retinol with a rate close to that of primary HSC (66.2% vs. 69.9%) and with similar retinyl ester profiles. aREH activity was higher in primary HSC than in PAV-1. Thermal susceptibility and phospholipid composition of membranes in PAV-1 treated cells were similar to those of primary HSC. In conclusion, our study shows that PAV-1 cells treated with retinol and palmitic acid is a sound and convenient model for studying vitamin A mobilisation, a fundamental physiological event occurring in HSC.     

Determination of the hydrophobicity of local anesthetic agents

Hydrophobicity, a term used to describe a fundamental physicochemical property of local anesthetics, was in the past obtained by octanol/buffer partitioning. It has been suggested that the octanol method, despite its obvious advantages, also has some drawbacks. HPLC has become an attractive alternative for the measurement of hydrophobicity and has been applied to local anesthetics recently. However, the methods in current use for measuring the hydrophobicity of local anesthetics suffer from a number of limitations and remain obscure. This study introduces a new HPLC method for measuring the hydrophobicity of eight local anesthetics in current clinical use. Using a C(18) derivatized polystyrene-divinylbenzene stationary phase HPLC column, the log k'(w) values of local anesthetics were determined by measuring the capacity factor k'(i) in the process of chromatographic separation using a hydrophobic stationary phase and a hydrophilic mobile phase. A rapid reversed-phase HPLC method was developed to directly measure log k'(w) of eight local anesthetics. A high correlation between log k'(w) and hydrophobicity (log P(oct)) from the traditional shake-flask method was obtained for the local anesthetics, demonstrating the reliability of the method. The results reveal an improved method for measuring the hydrophobicity of the local anesthetic agents in the unionized form. This simple, sensitive and reproducible approach may serve as a valuable tool for describing the physicochemical properties of novel local anesthetics.     

Cryovial with partial membrane sealing can prevent liquid nitrogen penetration in submerged storage

Cryopreservation is one of the fundamental techniques in life science. To preserve the viability of cells and tissues, many researchers use plastic cryogenic vials and immerse them into liquid nitrogen for long-term storage. However, the non-sterile liquid nitrogen usually infiltrates into the vials and may cause a high rate of microbial contamination, and even some explosive incidents upon retrieval. To prevent these drawbacks while retaining the benefit of constant ultra-low temperature in submerged liquid nitrogen, we used a heat-sealable membrane to cover the upper portion of vials. After heat-sealing, the vials were completely free of liquid nitrogen penetration in the submerging test. Moreover, the sealing process did not affect the cell viability. This modified protocol provides an easy and efficient tool to ensure the integrity of biospecimens in long-term storage without interfering with existing cryobox storage systems.     

Animal models of biliary injury and altered bile acid metabolism

In the last 25 years, a number of animal models, mainly rodents, have been generated with the goal to mimic cholestatic liver injuries and, thus, to provide in vivo tools to investigate the mechanisms of biliary repair and, eventually, to test the efficacy of innovative treatments. Despite fundamental limitations applying to these models, such as the distinct immune system and the different metabolism regulating liver homeostasis in rodents when compared to humans, multiple approaches, such as surgery (bile duct ligation), chemical-induced (3,5-diethoxycarbonyl-1,4-dihydrocollidine, DDC, α-naphthylisothiocyanate, ANIT), viral infections (Rhesus rotavirustype A, RRV-A), and genetic manipulation (Mdr2, Cftr, Pkd1, Pkd2, Prkcsh, Sec63, Pkhd1) have been developed. Overall, they have led to a range of liver phenotypes recapitulating the main features of biliary injury and altered bile acid metabolisms, such as ductular reaction, peribiliary inflammation and fibrosis, obstructive cholestasis and biliary dysgenesis. Although with a limited translability to the human setting, these mouse models have provided us with the ability to probe over time the fundamental mechanisms promoting cholestatic disease progression. Moreover, recent studies from genetically engineered mice have unveiled ‘core’ pathways that make the cholangiocyte a pivotal player in liver repair. In this review, we will highlight the main phenotypic features, the more interesting peculiarities and the different drawbacks of these mouse models. This article is part of a Special Issue entitled: Cholangiocytes in Health and Disease edited by Jesus Banales, Marco Marzioni, Nicholas LaRusso and Peter Jansen.