Plant Diversity Surpasses Plant Functional Groups and Plant Productivity as Driver of Soil Biota in the Long Term

One of the most significant consequences of contemporary global change is the rapid decline of biodiversity in many ecosystems. Knowledge of the consequences of biodiversity loss in terrestrial ecosystems is largely restricted to single ecosystem functions. Impacts of key plant functional groups on soil biota are considered to be more important than those of plant diversity; however, current knowledge mainly relies on short-term experiments.We studied changes in the impacts of plant diversity and presence of key functional groups on soil biota by investigating the performance of soil microorganisms and soil fauna two, four and six years after the establishment of model grasslands. The results indicate that temporal changes of plant community effects depend on the trophic affiliation of soil animals: plant diversity effects on decomposers only occurred after six years, changed little in herbivores, but occurred in predators after two years. The results suggest that plant diversity, in terms of species and functional group richness, is the most important plant community property affecting soil biota, exceeding the relevance of plant above- and belowground productivity and the presence of key plant functional groups, i.e. grasses and legumes, with the relevance of the latter decreasing in time.Plant diversity effects on biota are not only due to the presence of key plant functional groups or plant productivity highlighting the importance of diverse and high-quality plant derived resources, and supporting the validity of the singular hypothesis for soil biota. Our results demonstrate that in the long term plant diversity essentially drives the performance of soil biota questioning the paradigm that belowground communities are not affected by plant diversity and reinforcing the importance of biodiversity for ecosystem functioning

H3K9me2/3 Binding of the MBT Domain Protein LIN-61 Is Essential for Caenorhabditis elegans Vulva Development

MBT domain proteins are involved in developmental processes and tumorigenesis. In vitro binding and mutagenesis studies have shown that individual MBT domains within clustered MBT repeat regions bind mono- and dimethylated histone lysine residues with little to no sequence specificity but discriminate against the tri- and unmethylated states. However, the exact function of promiscuous histone methyl-lysine binding in the biology of MBT domain proteins has not been elucidated. Here, we show that the Caenorhabditis elegans four MBT domain protein LIN-61, in contrast to other MBT repeat factors, specifically interacts with histone H3 when methylated on lysine 9, displaying a strong preference for di- and trimethylated states (H3K9me2/3). Although the fourth MBT repeat is implicated in this interaction, H3K9me2/3 binding minimally requires MBT repeats two to four. Further, mutagenesis of residues conserved with other methyl-lysine binding MBT regions in the fourth MBT repeat does not abolish interaction, implicating a distinct binding mode. In vivo, H3K9me2/3 interaction of LIN-61 is required for C. elegans vulva development within the synMuvB pathway. Mutant LIN-61 proteins deficient in H3K9me2/3 binding fail to rescue lin-61 synMuvB function. Also, previously identified point mutant synMuvB alleles are deficient in H3K9me2/3 interaction although these target residues that are outside of the fourth MBT repeat. Interestingly, lin-61 genetically interacts with two other synMuvB genes, hpl-2, an HP1 homologous H3K9me2/3 binding factor, and met-2, a SETDB1 homologous H3K9 methyl transferase (H3K9MT), in determining C. elegans vulva development and fertility. Besides identifying the first sequence specific and di-/trimethylation binding MBT domain protein, our studies imply complex multi-domain regulation of ligand interaction of MBT domains. Our results also introduce a mechanistic link between LIN-61 function and biology, and they establish interplay of the H3K9me2/3 binding proteins, LIN-61 and HPL-2, as well as the H3K9MT MET-2 in distinct developmental pathways

Immunological evaluation of lipopeptide group A streptococcus (GAS) vaccine: Structure-activity relationship

Streptococcus pyogenes (group A streptococcus, GAS) is a Gram-positive bacterial pathogen responsible for a wide variety of diseases. To date, GAS vaccine development has focused primarily on the M-protein. The M-protein is highly variable at the amino (N)-terminus (determining serotype) but is conserved at the carboxyl (C)-terminus. Previously a 29 amino acid peptide (named J14) from the conserved region of the M-protein was identified as a potential vaccine candidate. J14 was capable of eliciting protective antibodies that recognized many GAS serotypes when co-administered with immunostimulants. This minimal epitope however showed no immunogenicity when administered alone. In an attempt overcome this immunological non-responsiveness, we developed a self-adjuvanting vaccine candidate composed of three components: the B-cell epitope (J14), a universal helper T-cell epitope (P25) and a lipid moiety consisting of lipoamino acids (Laas) which target Toll-like receptor 2 (TLR2). Immunological evaluation in B10. BR (H-2k) mice demonstrated that the epitope attachment to the point of lipid moiety, and the length of the Laa alkyl chain have a profound effect on vaccine immunogenicity after intranasal administration. It was demonstrated that a vaccine featuring C-terminal lipid moiety containing alkyl chains of 16 carbons, with P25 located at the N-terminus, and J14 attached to the side chain of a central lysine residue was capable of inducing optimal antibody response. These findings have considerable relevance to the development of a broad spectrum J14-based GAS vaccine and in particular provided a rational basis for peptide vaccine design based on this self-adjuvanting lipopeptide technology

Econometric Forecasting

Several principles are useful for econometric forecasters: keep the model simple, use all the data you can get, and use theory (not the data) as a guide to selecting causal variables. But theory gives little guidance on dynamics, that is, on which lagged values of the selected variables to use. Early econometric models failed in comparison with extrapolative methods because they paid too little attention to dynamic structure. In a fairly simple way, the vector autoregression (VAR) approach that first appeared in the 1980s resolved the problem by shifting emphasis towards dynamics and away from collecting many causal variables. The VAR approach also resolves the question of how to make long-term forecasts where the causal variables themselves must be forecast. When the analyst does not need to forecast causal variables or can use other sources, he or she can use a single equation with the same dynamic structure. Ordinary least squares is a perfectly adequate estimation method. Evidence supports estimating the initial equation in levels, whether the variables are stationary or not. We recommend a general-to-specific model-building strategy: start with a large number of lags in the initial estimation, although simplifying by reducing the number of lags pays off. Evidence on the value of further simplification is mixed. If cointegration among variables, then error-correction models (ECMs) will do worse than equations in levels. But ECMs are only sometimes an improvement eve