Differential physiological responses to environmental change promote woody shrub expansion

Direct and indirect effects of warming are increasingly modifying the carbon-rich vegetation and soils of the Arctic tundra, with important implications for the terrestrial carbon cycle. Understanding the biological and environmental influences on the processes that regulate foliar carbon cycling in tundra species is essential for predicting the future terrestrial carbon balance in this region. To determine the effect of climate change impacts on gas exchange in tundra, we quantified foliar photosynthesis (A_net), respiration in the dark and light (R_D and R_L, determined using the Kok method), photorespiration (PR), carbon gain efficiency (CGE, the ratio of photosynthetic CO₂ uptake to total CO₂ exchange of photosynthesis, PR, and respiration), and leaf traits of three dominant species – Betula nana, a woody shrub; Eriophorum vaginatum, a graminoid; and Rubus chamaemorus, a forb – grown under long-term warming and fertilization treatments since 1989 at Toolik Lake, Alaska. Under warming, B. nana exhibited the highest rates of A_net and strongest light inhibition of respiration, increasing CGE nearly 50% compared with leaves grown in ambient conditions, which corresponded to a 52% increase in relative abundance. Gas exchange did not shift under fertilization in B. nana despite increases in leaf N and P and near-complete dominance at the community scale, suggesting a morphological rather than physiological response. Rubus chamaemorus, exhibited minimal shifts in foliar gas exchange, and responded similarly to B. nana under treatment conditions. By contrast, E. vaginatum, did not significantly alter its gas exchange physiology under treatments and exhibited dramatic decreases in relative cover (warming: −19.7%; fertilization: −79.7%; warming with fertilization: −91.1%). Our findings suggest a foliar physiological advantage in the woody shrub B. nana that is further mediated by warming and increased soil nutrient availability, which may facilitate shrub expansion and in turn alter the terrestrial carbon cycle in future tundra environments.     

Characterization of a novel type of human microsomal 3alpha -hydroxysteroid dehydrogenase: unique tissue distribution and catalytic properties

We report characterization of a novel member of the short chain dehydrogenase/reductase superfamily. The 1513-base pair cDNA encodes a 319-amino acid protein. The corresponding gene spans over 26 kilobase pairs on chromosome 2 and contains five exons. The recombinant protein produced using the baculovirus system is localized in the microsomal fraction of Sf9 cells and is an integral membrane protein with cytosolic orientation of its catalytic domain. The enzyme exhibits an oxidoreductase activity toward hydroxysteroids with NAD(+) and NADH as the preferred cofactors. The enzyme is most efficient as a 3alpha-hydroxysteroid dehydrogenase, converting 3alpha-tetrahydroprogesterone (allopregnanolone) to dihydroprogesterone and 3alpha-androstanediol to dihydrotestosterone with similar catalytic efficiency (V(max) values of 13-14 nmol/min/mg microsomal protein and K(m) values of 5-7 microm). Despite approximately 44-47% sequence identity with retinol/3alpha-hydroxysterol dehydrogenases, the enzyme is not active toward retinols. The corresponding message is abundant in human trachea and is present at lower levels in the spinal cord, bone marrow, brain, heart, colon, testis, placenta, lung, and lymph node. Thus, the new short chain dehydrogenase represents a novel type of microsomal NAD(+)-dependent 3alpha-hydroxysteroid dehydrogenase with unique catalytic properties and tissue distribution.     

Differential stability of the triple helix of (Pro-Pro-Gly)10 in H2O and D2O: thermodynamic and structural explanations

(Pro-Pro-Gly)10 [(PPG10)], a collagen-like polypeptide, forms a triple-helical, polyproline-II structure in aqueous solution at temperatures somewhat lower than physiological, with a melting temperature of 24.5 degrees C. In this article, we present circular dichroism spectra that demonstrate an increase of the melting temperature with the addition of increasing amounts of D2O to an H2O solution of (PPG)10, with the melting temperature reaching 40 degrees C in pure D2O. A thermodynamic analysis of the data demonstrates that this result is due to an increasing enthalpy of unfolding in D2O vs. H2O. To provide a theoretical explanation for this result, we have used a model for hydration of (PPG)10 that we developed previously, in which inter-chain water bridges are formed between sterically crowded waters and peptide bond carbonyls. Energy minimizations were performed upon this model using hydrogen bond parameters for water, and altered hydrogen bond parameters that reproduced the differences in carbonyl oxygen-water oxygen distances found in small-molecule crystal structures containing oxygen-oxygen hydrogen bonds between organic molecules and H2O or D2O. It was found that using hydrogen bond parameters that reproduced the distance typical of hydrogen bonds to D2O resulted in a significant lowering of the potential energy of hydrated (PPG)10. This lowering of the energy involved energetic terms that were only indirectly related to the altered hydrogen bond parameters, and were therefore not artifactual; the intra-(PPG10) energy, plus the water-(PPG10) van der Waals energy (not including hydrogen bond interactions), were lowered enough to qualitatively account for the lower enthalpy of the triple-helical conformation, relative to the unfolded state, in D2O vs. H2O. This result indicates that the geometry of the carbonyl-D2O hydrogen bonds allows formation of good hydrogen bonds without making as much of an energetic sacrifice from other factors as in the case of hydration by H2O.     

A comparison of profile hidden Markov model procedures for remote homology detection

Profile hidden Markov models (HMMs) are amongst the most successful procedures for detecting remote homology between proteins. There are two popular profile HMM programs, HMMER and SAM. Little is known about their performance relative to each other and to the recently improved version of PSI-BLAST. Here we compare the two programs to each other and to non-HMM methods, to determine their relative performance and the features that are important for their success. The quality of the multiple sequence alignments used to build models was the most important factor affecting the overall performance of profile HMMs. The SAM T99 procedure is needed to produce high quality alignments automatically, and the lack of an equivalent component in HMMER makes it less complete as a package. Using the default options and parameters as would be expected of an inexpert user, it was found that from identical alignments SAM consistently produces better models than HMMER and that the relative performance of the model-scoring components varies. On average, HMMER was found to be between one and three times faster than SAM when searching databases larger than 2000 sequences, SAM being faster on smaller ones. Both methods were shown to have effective low complexity and repeat sequence masking using their null models, and the accuracy of their E-values was comparable. It was found that the SAM T99 iterative database search procedure performs better than the most recent version of PSI-BLAST, but that scoring of PSI-BLAST profiles is more than 30 times faster than scoring of SAM models.     

Whole-proteome interaction mining

MOTIVATION: A major post-genomic scientific and technological pursuit is to describe the functions performed by the proteins encoded by the genome. One strategy is to first identify the protein-protein interactions in a proteome, then determine pathways and overall structure relating these interactions, and finally to statistically infer functional roles of individual proteins. Although huge amounts of genomic data are at hand, current experimental protein interaction assays must overcome technical problems to scale-up for high-throughput analysis. In the meantime, bioinformatics approaches may help bridge the information gap required for inference of protein function. In this paper, a previously described data mining approach to prediction of protein-protein interactions (Bock and Gough, 2001, Bioinformatics, 17, 455-460) is extended to interaction mining on a proteome-wide scale. An algorithm (the phylogenetic bootstrap) is introduced, which suggests traversal of a phenogram, interleaving rounds of computation and experiment, to develop a knowledge base of protein interactions in genetically-similar organisms. RESULTS: The interaction mining approach was demonstrated by building a learning system based on 1,039 experimentally validated protein-protein interactions in the human gastric bacterium Helicobacter pylori. An estimate of the generalization performance of the classifier was derived from 10-fold cross-validation, which indicated expected upper bounds on precision of 80% and sensitivity of 69% when applied to related organisms. One such organism is the enteric pathogen Campylobacter jejuni, in which comprehensive machine learning prediction of all possible pairwise protein-protein interactions was performed. The resulting network of interactions shares an average protein connectivity characteristic in common with previous investigations reported in the literature, offering strong evidence supporting the biological feasibility of the hypothesized map. For inferences about complete proteomes in which the number of pairwise non-interactions is expected to be much larger than the number of actual interactions, we anticipate that the sensitivity will remain the same but precision may decrease. We present specific biological examples of two subnetworks of protein-protein interactions in C. jejuni resulting from the application of this approach, including elements of a two-component signal transduction systems for thermoregulation, and a ferritin uptake network.     

The NFP locus of Medicago truncatula controls an early step of Nod factor signal transduction upstream of a rapid calcium flux and root hair deformation

Establishment of the Rhizobium-legume symbiosis depends on a molecular dialogue, in which rhizobial nodulation (Nod) factors act as symbiotic signals, playing a key role in the control of specificity of infection and nodule formation. Using nodulation-defective (Nod-) mutants of Medicago truncatula to study the mechanisms controlling Nod factor perception and signalling, we have previously identified five genes that control components of a Nod factor-activated signal transduction pathway. Characterisation of a new M. truncatula Nod- mutant led to the identification of the Nod Factor Perception (NFP) locus. The nfp mutant has a novel phenotype among Nod- mutants of M. truncatula, as it does not respond to Nod factors by any of the responses tested. The nfp mutant thus shows no rapid calcium flux, the earliest detectable Nod factor response of wild-type plants, and no root hair deformation. The nfp mutant is also deficient in Nod factor-induced calcium spiking and early nodulin gene expression. While certain genes controlling Nod factor signal transduction also control the establishment of an arbuscular mycorrhizal symbiosis, the nfp mutant shows a wild-type mycorrhizal phenotype. These data indicate that the NFP locus controls an early step of Nod factor signal transduction, upstream of previously identified genes and specific to nodulation.