Crystallization of dimers of the manganese-stabilizing protein of Photosystem II

The manganese-stabilizing protein (MSP) of Photosystem II was purified from spinach photosynthetic membranes. The MSP was crystallized in the presence of calcium. Despite the apparent purity of the isolated protein, the crystals grew to only about 0.05 mm in their largest dimension. The MSP was analyzed to identify possible sources of protein heterogeneity that could hinder crystal growth. Tandem reverse-phase HPLC/ electronspray ionization mass spectrometry analysis of the MSP showed a major peak and four smaller peaks. All five peaks had molecular masses of 26 535, as expected for mature MSP, indicating the absence of heterogeneities due to covalent modifications. MALDI mass spectroscopy was utilized to identify heterogeneities in the MSP oligomeric state. These measurements showed that purified MSP in solution is a mixture of monomers and dimers, while solubilized MSP crystals contained only dimers. Size-exclusion chromatography and dynamic light scattering were used to probe the effect of the crystallization conditions on the MSP. Size-exclusion chromatography of concentrated MSP showed the presence of aggregates and monomers, while dilute MSP contained monomers. Dynamic light scattering experiments in the absence, or in the presence of 10–50 mM or 100 mM calcium, yielded calculated molecular mass values of 34 kDa, 48 kDa and 68 kDa, respectively. These changes in the observed molecular mass of the MSP could have been caused by the formation of dimers and higher oligomers and/or significant conformational changes. Based on the results reported in this study, a model is presented which details the effect of oligomeric heterogeneity on the inhibition of MSP crystal growth. This revised version was published online in June 2006 with corrections to the Cover Date.     

Remotely sensed spectral heterogeneity as a proxy of species diversity: Recent advances and open challenges

Environmental heterogeneity is considered to be one of the main factors associated with biodiversity given that areas with highly heterogeneous environments can host more species due to their higher number of available niches. In this view, spatial variability extracted from remotely sensed images has been used as a proxy of species diversity, as these data provide an inexpensive means of deriving environmental information for large areas in a consistent and regular manner. The aim of this review is to provide an overview of the state of the art in the use of spectral heterogeneity for estimating species diversity. We will examine a number of issues related to this theme, dealing with: i) the main sensors used for biodiversity monitoring, ii) scale matching problems between remotely sensed and field diversity data, iii) spectral heterogeneity measurement techniques, iv) types of species taxonomic diversity measures and how they influence the relationship between spectral and species diversity, v) spectral versus genetic diversity, and vi) modeling procedures for relating spectral and species diversity. Our review suggests that remotely sensed spectral heterogeneity information provides a crucial baseline for rapid estimation or prediction of biodiversity attributes and hotspots in space and time.     

Amino acid starvation sensitizes cancer cells to proteasome inhibition

We explored the crosstalk between protein degradation and synthesis in cancer cells. The tumorigenic cell line, MCF7, showed enhanced proteasome activity compared to the nontumorigenic line, MCF10A. Although there was no difference in the sensitivity of MCF7 and MCF10A cells to proteasome inhibition in complete growth medium, combining proteasome inhibition with amino acid deprivation led to reduced protein synthesis and survival of MCF7 cells, with a lesser effect on MCF10A cells. Additional cancer cell lines (including CAG and A431) could be strongly sensitized to proteasome inhibition by concomitant amino acid deprivation, whereas others were completely resistant to proteasome inhibition. We hypothesize that protein catabolism contributes to the pool of free amino acids available for protein synthesis, leading to a crucial role of the proteasome in cell survival during amino acid depletion, in some tumor cell lines.     

Comparative cross-species alternative splicing in plants

Alternative splicing (AS) can add significantly to genome complexity. Plants are thought to exhibit less AS than animals. An algorithm, based on expressed sequence tag (EST) pairs gapped alignment, was developed that takes advantage of the relatively small intron and exon size in plants and directly compares pairs of ESTs to search for AS. EST pairs gapped alignment was first evaluated in Arabidopsis (Arabidopsis thaliana), rice (Oryza sativa), and tomato (Solanum lycopersicum) for which annotated genome sequence is available and was shown to accurately predict splicing events. The method was then applied to 11 plant species that include 17 cultivars for which enough ESTs are available. The results show a large, 3.7-fold difference in AS rates between plant species with Arabidopsis and rice in the lower range and lettuce (Lactuca sativa) and sorghum (Sorghum bicolor) in the upper range. Hence, compared to higher animals, plants show a much greater degree of variety in their AS rates and in some plant species the rates of animal and plant AS are comparable although the distribution of AS types may differ. In eudicots but not monocots, a correlation between genome size and AS rates was detected, implying that in eudicots the mechanisms that lead to larger genomes are a driving force for the evolution of AS.     

Intracellular cysteine residues in the tail of MHC class I proteins are crucial for extracellular recognition by leukocyte Ig-like receptor 1

The activity of NK cells is regulated by activating receptors that recognize mainly stress-induced ligands and by inhibitory receptors that recognize mostly MHC class I proteins on target cells. Comparing the cytoplasmic tail sequences of various MHC class I proteins revealed the presence of unique cysteine residues in some of the MHC class I molecules which are absent in others. To study the role of these unique cysteines, we performed site specific mutagenesis, generating MHC class I molecules lacking these cysteines, and demonstrated that their expression on the cell surface was impaired. Surprisingly, we demonstrated that these cysteines are crucial for the surface binding of the leukocyte Ig-like receptor 1 inhibitory receptor to the MHC class I proteins, but not for the binding of the KIR2DL1 inhibitory receptor. In addition, we demonstrated that the cysteine residues in the cytoplasmic tail of MHC class I proteins are crucial for their egress from the endoplasmic reticulum and for their palmitoylation, thus probably affecting their expression on the cell surface. Finally, we show that the cysteine residues are important for proper extracellular conformation. Thus, although the interaction between leukocyte Ig-like receptor 1 and MHC class I proteins is formed between two extracellular surfaces, the intracellular components of MHC class I proteins play a crucial role in this recognition.     

Can we predict butterfly diversity along an elevation gradient from space?

An important challenge in ecology is to predict patterns of biodiversity across eco‐geographical gradients. This is particularly relevant in areas that are inaccessible, but are of high research and conservation value, such as mountains. Potentially, remotely‐sensed vegetation indices derived from satellite images can help in predicting species diversity in vast and remote areas via their relationship with two of the major factors that are known to affect biodiversity: productivity and spatial heterogeneity in productivity. Here, we examined whether the Normalized Difference Vegetation Index (NDVI) can be used effectively to predict changes in butterfly richness, range size rarity and beta diversity along an elevation gradient. We examined the relationship between butterfly diversity and both the mean NDVI within elevation belts (a surrogate of productivity) and the variability in NDVI within and among elevation belts (surrogates for spatial heterogeneity in productivity). We calculated NDVI at three spatial extents, using a high spatial resolution QuickBird satellite image. We obtained data on butterfly richness, rarity and beta diversity by field sampling 100 m quadrats and transects between 500 and 2200 m in Mt Hermon, Israel. We found that the variability in NDVI, as measured both within and among adjacent elevation belts, was strongly and significantly correlated with butterfly richness. Butterfly range size rarity was strongly correlated with the mean and the standard deviation of NDVI within belts. In our system it appears that it is spatial heterogeneity in productivity rather than productivity per se that explained butterfly richness. These results suggest that remotely‐sensed data can provide a useful tool for assessing spatial patterns of butterfly richness in inaccessible areas. The results further indicate the importance of considering spatial heterogeneity in productivity along elevation gradients, which has no lesser importance than productivity in shaping richness and rarity, especially at the local scale.     

MicroRNA pharmacogenomics: post-transcriptional regulation of drug response

The field of pharmacogenomics aims to predict which drugs will be most effective and safe for a particular individual based on their genome sequence or expression profile, thereby allowing personalized treatment. The bulk of pharmacogenomic research has focused on the role of single nucleotide polymorphisms, copy number variations or differences in gene expression levels of drug metabolizing or transporting genes and drug targets. In this review paper, we focus instead on microRNAs (miRNAs): small noncoding RNAs, prevalent in metazoans, that negatively regulate gene expression in many cellular processes. We discuss how miRNAs, by regulating the expression of pharmacogenomic-related genes, can play a pivotal role in drug efficacy and toxicity and have potential clinical implications for personalized medicine.