Sample extraction techniques for enhanced proteomic analysis of plant tissues

Major improvements in proteomic techniques in recent years have led to an increase in their application in all biological fields, including plant sciences. For all proteomic approaches, protein extraction and sample preparation are of utmost importance for optimal results; however, extraction of proteins from plant tissues represents a great challenge. Plant tissues usually contain relatively low amounts of proteins and high concentrations of proteases and compounds that potentially can limit tissue disintegration and interfere with subsequent protein separation and identification. An effective protein extraction protocol must also be adaptable to the great variation in the sets of secondary metabolites and potentially contaminating compounds that occurs between tissues (e.g., leaves, roots, fruit, seeds and stems) and between species. Here we present two basic protein extraction protocols that have successfully been used with diverse plant tissues, including recalcitrant tissues. The first method is based on phenol extraction coupled with ammonium acetate precipitation, and the second is based on trichloroacetic acid (TCA) precipitation. Both extraction protocols can be completed within 2 d.     

Discovery of cyclicsulfonamide derivatives as 11β-hydroxysteroid dehydrogenase 1 inhibitors

A new series of cyclic sulfonamide derivatives was synthesized and evaluated for their ability to inhibit 11β-HSD1. Cyclic sulfonamides with phenylacetyl substituents at the 2-position showed nanomolar inhibitory activities. Among them, compound 4e exhibited a good in vitro inhibitory activity and selectivity toward human 11β-HSD2.     

Aldo‐keto reductase enzymes detoxify glyphosate and improve herbicide resistance in plants

In recent years, concerns about the use of glyphosate‐resistant crops have increased because of glyphosate residual levels in plants and development of herbicide‐resistant weeds. In spite of identifying glyphosate‐detoxifying genes from microorganisms, the plant mechanism to detoxify glyphosate has not been studied. We characterized an aldo‐keto reductase gene from Pseudomonas (PsAKR1) and rice (OsAKR1) and showed, by docking studies, both PsAKR1 and OsAKR1 can efficiently bind to glyphosate. Silencing AKR1 homologues in rice and Nicotiana benthamiana or mutation of AKR1 in yeast and Arabidopsis showed increased sensitivity to glyphosate. External application of AKR proteins rescued glyphosate‐mediated cucumber seedling growth inhibition. Regeneration of tobacco transgenic lines expressing PsAKR1 or OsAKRI on glyphosate suggests that AKR can be used as selectable marker to develop transgenic crops. PsAKR1‐ or OsAKRI‐expressing tobacco and rice transgenic plants showed improved tolerance to glyphosate with reduced accumulation of shikimic acid without affecting the normal photosynthetic rates. These results suggested that AKR1 when overexpressed detoxifies glyphosate in planta.     

A Partition Function Approximation Using Elementary Symmetric Functions

In statistical mechanics, the canonical partition function can be used to compute equilibrium properties of a physical system. Calculating however, is in general computationally intractable, since the computation scales exponentially with the number of particles in the system. A commonly used method for approximating equilibrium properties, is the Monte Carlo (MC) method. For some problems the MC method converges slowly, requiring a very large number of MC steps. For such problems the computational cost of the Monte Carlo method can be prohibitive. Presented here is a deterministic algorithm – the direct interaction algorithm (DIA) – for approximating the canonical partition function in operations. The DIA approximates the partition function as a combinatorial sum of products known as elementary symmetric functions (ESFs), which can be computed in operations. The DIA was used to compute equilibrium properties for the isotropic 2D Ising model, and the accuracy of the DIA was compared to that of the basic Metropolis Monte Carlo method. Our results show that the DIA may be a practical alternative for some problems where the Monte Carlo method converge slowly, and computational speed is a critical constraint, such as for very large systems or web-based applications.     

Tuning voltage-gated channel activity and cellular excitability with a sphingomyelinase

Voltage-gated ion channels generate action potentials in excitable cells and help set the resting membrane potential in nonexcitable cells like lymphocytes. It has been difficult to investigate what kinds of phospholipids interact with these membrane proteins in their native environments and what functional impacts such interactions create. This problem might be circumvented if we could modify specific lipid types in situ. Using certain voltage-gated K⁺ (K_V) channels heterologously expressed in Xenopus laevis oocytes as a model, our group has shown previously that sphingomyelinase (SMase) D may serve this purpose. SMase D is known to remove the choline group from sphingomyelin, a phospholipid primarily present in the outer leaflet of plasma membranes. This SMase D action lowers the energy required for voltage sensors of a K_V channel to enter the activated state, causing a hyperpolarizing shift of the Q-V and G-V curves and thus activating them at more hyperpolarized potentials. Here, we find that this SMase D effect vanishes after removing most of the voltage-sensor paddle sequence, a finding supporting the notion that SMase D modification of sphingomyelin molecules alters these lipids’ interactions with voltage sensors. Then, using SMase D to probe lipid–channel interactions, we find that SMase D not only similarly stimulates voltage-gated Na⁺ (Na_V) and Ca²⁺ channels but also markedly slows Na_V channel inactivation. However, the latter effect is not observed in tested mammalian cells, an observation highlighting the profound impact of the membrane environment on channel function. Finally, we directly demonstrate that SMase D stimulates both native K_V1.3 in nonexcitable human T lymphocytes at their typical resting membrane potential and native Na_V channels in excitable cells, such that it shifts the action potential threshold in the hyperpolarized direction. These proof-of-concept studies illustrate that the voltage-gated channel activity in both excitable and nonexcitable cells can be tuned by enzymatically modifying lipid head groups.     

Analysis of basic clustering algorithms for numerical estimation of statistical averages in biomolecules.

In statistical mechanics, the equilibrium properties of a physical system of particles can be calculated as the statistical average over accessible microstates of the system. In general, these calculations are computationally intractable since they involve summations over an exponentially large number of microstates. Clustering algorithms are one of the methods used to numerically approximate these sums. The most basic clustering algorithms first sub-divide the system into a set of smaller subsets (clusters). Then, interactions between particles within each cluster are treated exactly, while all interactions between different clusters are ignored. These smaller clusters have far fewer microstates, making the summation over these microstates, tractable. These algorithms have been previously used for biomolecular computations, but remain relatively unexplored in this context. Presented here, is a theoretical analysis of the error and computational complexity for the two most basic clustering algorithms that were previously applied in the context of biomolecular electrostatics. We derive a tight, computationally inexpensive, error bound for the equilibrium state of a particle computed via these clustering algorithms. For some practical applications, it is the root mean square error, which can be significantly lower than the error bound, that may be more important. We how that there is a strong empirical relationship between error bound and root mean square error, suggesting that the error bound could be used as a computationally inexpensive metric for predicting the accuracy of clustering algorithms for practical applications. An example of error analysis for such an application-computation of average charge of ionizable amino-acids in proteins-is given, demonstrating that the clustering algorithm can be accurate enough for practical purposes.     

Molecular modelling and dynamics of CA2 missense mutations causative to carbonic anhydrase 2 deficiency syndrome

Abstract

Carbonic anhydrase 2 (CA2) enzyme deficiency caused by CA2 gene mutations is an inherited disorder characterized by symptoms like osteopetrosis, renal tubular acidosis, and cerebral calcification. This study has collected the CA2 deficiency causal missense mutations and assessed their pathogenicity using diverse computational programs. The 3D protein models for all missense mutations were built, and analyzed for structural divergence, protein stability, and molecular dynamics properties. We found M-CAP as the most sensitive prediction method to measure the deleterious potential of CA2 missense mutations. Free energy dynamics of tertiary structure models of CA2 mutants with DUET, mCSM, and SDM based consensus methods predicted only 50% of the variants as destabilizing. Superimposition of native and mutant CA2 models revealed the minor structural fluctuations at the amino acid residue level but not at the whole protein structure level. Near native molecular dynamic simulation analysis indicated that CA2 causative missense variants result in residue level fluctuation pattern in the protein structure. This study expands the understanding of genotype-protein phenotype correlations underlying CA2 variant pathogenicity and presents a potential avenue for modifying the CA2 deficiency by targeting biophysical structural features of CA2 protein.

Communicated by Ramaswamy H. Sarma     

Exploiting rice–sorghum synteny for targeted development of EST-SSRs to enrich the sorghum genetic linkage map

The sequencing and detailed comparative functional analysis of genomes of a number of select botanical models open new doors into comparative genomics among the angiosperms, with potential benefits for improvement of many orphan crops that feed large populations. In this study, a set of simple sequence repeat (SSR) markers was developed by mining the expressed sequence tag (EST) database of sorghum. Among the SSR-containing sequences, only those sharing considerable homology with rice genomic sequences across the lengths of the 12 rice chromosomes were selected. Thus, 600 SSR-containing sorghum EST sequences (50 homologous sequences on each of the 12 rice chromosomes) were selected, with the intention of providing coverage for corresponding homologous regions of the sorghum genome. Primer pairs were designed and polymorphism detection ability was assessed using parental pairs of two existing sorghum mapping populations. About 28% of these new markers detected polymorphism in this 4-entry panel. A subset of 55 polymorphic EST-derived SSR markers were mapped onto the existing skeleton map of a recombinant inbred population derived from cross N13 × E 36-1, which is segregating for Striga resistance and the stay-green component of terminal drought tolerance. These new EST-derived SSR markers mapped across all 10 sorghum linkage groups, mostly to regions expected based on prior knowledge of rice–sorghum synteny. The ESTs from which these markers were derived were then mapped in silico onto the aligned sorghum genome sequence, and 88% of the best hits corresponded to linkage-based positions. This study demonstrates the utility of comparative genomic information in targeted development of markers to fill gaps in linkage maps of related crop species for which sufficient genomic tools are not available.