The influence of terrestrial ecosystems on climate

Terrestrial ecosystems influence climate by affecting how much solar energy is absorbed by the land surface and by exchanging climatically important gases with the atmosphere. Recent model analyses show widespread qualitative agreement that terrestrial ecological processes will have a net positive feedback effect on 21st-century global warming, and, therefore, cannot be ignored in climate-change projections. However, the quantitative uncertainty in the net feedback is large. The uncertainty in 21st-century carbon dioxide emissions resulting from terrestrial carbon cycle-climate feedbacks is second in magnitude only to the uncertainty in anthropogenic emissions. We estimate that this translates into an uncertainty in global warming owing to the land surface of 1.5 degrees C by 2100. We also emphasise the need to improve our understanding of terrestrial ecological processes that influence land-atmosphere interactions at relatively long timescales (decadal-century) as well as at shorter intervals (e.g. hourly).     

T-Cell activation by t(9;22) acute lymphoblastic leukemia-derived dendritic-like cells is associated with increased tapasin expression

Dendritic-like cells from t(9;22) acute lymphoblastic leukemia (ALL) blasts can activate T cells, while the original unmodified leukemic blasts cannot. To determine whether these functional differences were associated with differences in antigen-processing machinery (APM) component expression, we have measured the level of APM component expression in unmodified blasts and ALL-derived dendritic-like cells. Seven t(9;22) ALL patient samples and one cell line were studied for APM component expression utilizing a unique panel of recently developed monoclonal antibodies and a recently developed intracellular staining technique. In addition, the HLA class I antigen cell surface expression was measured. HLA class I antigens were similarly expressed on the unmodified blasts and on the autologous dendritic-like cells. Intracellular HLA class I antigen and tapasin expression (P=0.03 for both) were upregulated in all t(9;22) ALL-derived dendritic-like cells, in comparison to the unmodified blasts. These results provide a potential mechanism for the ability of t(9;22) ALL-derived dendritic-like cells to induce T-cell activation and, suggest that tapasin upregulation may serve as a marker to standardize and monitor the quality of the dendritic-like cells used in immunotherapy. Supported partially by The Heidi Leukemia Research Fund, Buffalo, NY, and by PHS grants CA 67108 and CA 16056 awarded by the National Cancer Institute, DHHS.     

Inhibitors can activate proteases to catalyze the synthesis and hydrolysis of peptides

Competitive inhibitors can activate proteases (papain, trypsin, and cathepsin S) to catalyze the synthesis of peptide bonds and accelerate the hydrolysis of poor substrates (from 1 to 99%). Reaction mixtures contained intermediate molecules that were formed by the coupling of the inhibitor with the poor substrate. This and other findings suggest the following chain of events. Part of the binding energy of formation of the enzyme-inhibitor complex was used to activate the inhibitor, i.e., to form acyl-enzyme species with a high-energy bond (e.g., a thioester bond in the case of papain) required for coupling the inhibitor with the substrate to form the intermediate molecule. The latter was subjected to successive reactions which led to a stepwise degradation of the substrate, as well as to the regeneration of the inhibitor. One mole of the inhibitor could catalyze rapid hydrolysis of at least 53 mol of substrate. The intermediate molecules were the species undergoing rapid hydrolysis. Therefore, 1 mol of inhibitor was involved in the synthesis of 53 mol of intermediate molecules; i.e., the inhibitor functioned as a cofactor that catalyzed the synthesis of peptides. Thus, the binding energy of formation of the enzyme-inhibitor complex can be utilized to catalyze the synthesis of peptide bonds in the absence of an exogenous energy source (e.g., ATP).     

Reprint of "on the size of the active site in proteases. I. Papain"

α-Synuclein is the major component of Lewy bodies and Lewy neurites, the pathological hallmarks of surviving neuronal cells in Parkinson's disease patients. However, the physiological role played by α-synuclein remains unclear. In this study, spectrin beta non-erythrocyte 1 (SPTBN1) interacted with α-synuclein in phage display assays using a normalized human brain cDNA library. A direct interaction between α-synuclein and SPTBN1 was confirmed by GST pull-down and co-immunoprecipitation assays. SPTBN1 and α-synuclein proteins colocalized in N2a neuronal cells. Transfection of SPTBN1 caused human SH-SY5Y dopaminergic neuron cells to inappropriately induce neurites, which extended from cell bodies. Cotransfection with α-synuclein reversed SPTBN1-induced excessive neurite branching in SH-SY5Y cells, and only a single neurite extended from each neuron. These results suggest that α-synuclein modulates neurite outgrowth by interacting with cytoskeletal proteins such as SPTBN1.     

Annual variation in soil respiration and its component parts in two structurally contrasting woody savannas in Central Brazil

Background and aims

Due to the high spatial and temporal variation in soil CO₂ efflux, terrestrial carbon budgets rely on a detailed understanding of the drivers of soil respiration from a diverse range of ecosystems and climate zones. In this study we aim to evaluate the independent influence of vegetation structure and climate on soil CO₂ efflux within cerrado ecosystems.

Methods

We examine the seasonal and diel variation of soil CO₂ efflux, including its autotrophic and heterotrophic components, within two adjacent and structurally contrasting woody savannas in central Brazil.

Principle results

We found no significant difference in the annual soil CO₂ efflux between the two stands (p?=?0.53) despite a clear disparity in both LAI (p?<?0.01) and leaf litterfall (p?<?0.01). The mean annual loss of carbon from the soil was 17.32(±1.48) Mg C?ha^?1 of which approximately 63% was accounted for by autotrophic respiration. The relative contribution of autotrophic respiration varied seasonally between 55% in the wet season to 79% of the total soil CO₂ efflux in the dry season. Furthermore, seasonal fluctuations of all the soil respiration components were strongly correlated with soil moisture (R ²?=?0.79–0.90, p?<?0.01).

Conclusions

Across these two structurally distinct cerrado stands, seasonal variations in rainfall, was the main driver of soil CO₂ efflux and its components. Consequently, soil respiration within these ecosystems is likely to be highly sensitive to any changes in seasonal precipitation patterns.     

Functional and computational analysis of amino acid patterns predictive of type III secretion system substrates in Pseudomonas syringae

Bacterial type III secretion systems (T3SSs) deliver proteins called effectors into eukaryotic cells. Although N-terminal amino acid sequences are required for translocation, the mechanism of substrate recognition by the T3SS is unknown. Almost all actively deployed T3SS substrates in the plant pathogen Pseudomonas syringae pathovar tomato strain DC3000 possess characteristic patterns, including (i) greater than 10% serine within the first 50 amino acids, (ii) an aliphatic residue or proline at position 3 or 4, and (iii) a lack of acidic amino acids within the first 12 residues. Here, the functional significance of the P. syringae T3SS substrate compositional patterns was tested. A mutant AvrPto effector protein lacking all three patterns was secreted into culture and translocated into plant cells, suggesting that the compositional characteristics are not absolutely required for T3SS targeting and that other recognition mechanisms exist. To further analyze the unique properties of T3SS targeting signals, we developed a computational algorithm called TEREE (Type III Effector Relative Entropy Evaluation) that distinguishes DC3000 T3SS substrates from other proteins with a high sensitivity and specificity. Although TEREE did not efficiently identify T3SS substrates in Salmonella enterica, it was effective in another P. syringae strain and Ralstonia solanacearum. Thus, the TEREE algorithm may be a useful tool for identifying new effector genes in plant pathogens. The nature of T3SS targeting signals was additionally investigated by analyzing the N-terminus of FtsX, a putative membrane protein that was classified as a T3SS substrate by TEREE. Although the first 50 amino acids of FtsX were unable to target a reporter protein to the T3SS, an AvrPto protein substituted with the first 12 amino acids of FtsX was translocated into plant cells. These results show that the T3SS targeting signals are highly mutable and that secretion may be directed by multiple features of substrates.     

KCN1, a Novel Synthetic Sulfonamide Anticancer Agent: In Vitro and In Vivo Anti-Pancreatic Cancer Activities and Preclinical Pharmacology

The purpose of the present study was to determine the in vitro and in vivo anti-cancer activity and pharmacological properties of 3,4-dimethoxy-N-[(2,2-dimethyl-2H-chromen-6-yl)methyl]-N-phenylbenzenesulfonamide, KCN1. In the present study, we investigated the in vitro activity of KCN1 on cell proliferation and cell cycle distribution of pancreatic cancer cells, using the MTT and BrdUrd assays, and flow cytometry. The in vivo anti-cancer effects of KCN1 were evaluated in two distinct xenograft models of pancreatic cancer. We also developed an HPLC method for the quantitation of the compound, and examined its stability in mouse plasma, plasma protein binding, and degradation by mouse S9 microsomal enzymes. Furthermore, we examined the pharmacokinetics of KCN1 following intravenous or intraperitoneal injection in mice. Results showed that, in a dose-dependent manner, KCN1 inhibited cell growth and induced cell cycle arrest in human pancreatic cancer cells in vitro, and showed in vivo anticancer efficacy in mice bearing Panc-1 or Mia Paca-2 tumor xenografts. The HPLC method provided linear detection of KCN1 in all of the matrices in the range from 0.1 to 100 µM, and had a lower limit of detection of 0.085 µM in mouse plasma. KCN1 was very stable in mouse plasma, extensively plasma bound, and metabolized by S9 microsomal enzymes. The pharmacokinetic studies indicated that KCN1 could be detected in all of the tissues examined, most for at least 24 h. In conclusion, our preclinical data indicate that KCN1 is a potential therapeutic agent for pancreatic cancer, providing a basis for its future development.     

Platelet inhibition by nitrite is dependent on erythrocytes and deoxygenation

BACKGROUND: Nitrite is a nitric oxide (NO) metabolite in tissues and blood, which can be converted to NO under hypoxia to facilitate tissue perfusion. Although nitrite is known to cause vasodilation following its reduction to NO, the effect of nitrite on platelet activity remains unclear. In this study, the effect of nitrite and nitrite+erythrocytes, with and without deoxygenation, on platelet activity was investigated. METHODOLOGY/FINDING: Platelet aggregation was studied in platelet-rich plasma (PRP) and PRP+erythrocytes by turbidimetric and impedance aggregometry, respectively. In PRP, DEANONOate inhibited platelet aggregation induced by ADP while nitrite had no effect on platelets. In PRP+erythrocytes, the inhibitory effect of DEANONOate on platelets decreased whereas nitrite at physiologic concentration (0.1 μM) inhibited platelet aggregation and ATP release. The effect of nitrite+erythrocytes on platelets was abrogated by C-PTIO (a membrane-impermeable NO scavenger), suggesting an NO-mediated action. Furthermore, deoxygenation enhanced the effect of nitrite as observed from a decrease of P-selectin expression and increase of the cGMP levels in platelets. The ADP-induced platelet aggregation in whole blood showed inverse correlations with the nitrite levels in whole blood and erythrocytes. CONCLUSION: Nitrite alone at physiological levels has no effect on platelets in plasma. Nitrite in the presence of erythrocytes inhibits platelets through its reduction to NO, which is promoted by deoxygenation. Nitrite may have role in modulating platelet activity in the circulation, especially during hypoxia.     

Inside the Mind of a Medicinal Chemist: The Role of Human Bias in Compound Prioritization during Drug Discovery

Medicinal chemists’ “intuition” is critical for success in modern drug discovery. Early in the discovery process, chemists select a subset of compounds for further research, often from many viable candidates. These decisions determine the success of a discovery campaign, and ultimately what kind of drugs are developed and marketed to the public. Surprisingly little is known about the cognitive aspects of chemists’ decision-making when they prioritize compounds. We investigate 1) how and to what extent chemists simplify the problem of identifying promising compounds, 2) whether chemists agree with each other about the criteria used for such decisions, and 3) how accurately chemists report the criteria they use for these decisions. Chemists were surveyed and asked to select chemical fragments that they would be willing to develop into a lead compound from a set of ∼4,000 available fragments. Based on each chemist’s selections, computational classifiers were built to model each chemist’s selection strategy. Results suggest that chemists greatly simplified the problem, typically using only 1–2 of many possible parameters when making their selections. Although chemists tended to use the same parameters to select compounds, differing value preferences for these parameters led to an overall lack of consensus in compound selections. Moreover, what little agreement there was among the chemists was largely in what fragments were undesirable. Furthermore, chemists were often unaware of the parameters (such as compound size) which were statistically significant in their selections, and overestimated the number of parameters they employed. A critical evaluation of the problem space faced by medicinal chemists and cognitive models of categorization were especially useful in understanding the low consensus between chemists.