The Pheromone of the Cave Cricket, Hadenoecus cumberlandicus, Causes Cricket Aggregation but Does Not Attract the Co-Distributed Predatory Spider, Meta ovalis

Food input by the cave cricket, Hadenoecus cumberlandicus Hubble & Norton (Orthoptera: Rhaphidophoridae), is vital to the cave community, making this cricket a true keystone species. Bioassays conducted on cave walls and in the laboratory show that clustering in H. cumberlandicus is guided by a pheromone, presumably excreta. This aggregation pheromone was demonstrated by using filter paper discs that had previous adult H. cumberlandicus exposure, resulting in > 70% response by either nymphs or adults, prompting attraction (thus, active component is a volatile), followed by reduced mobility (arrestment) on treated surfaces. Adults were similarly responsive to pheromone from nymphs, agreeing with mixed stage composition of clusters in the cave. Effects of [0.001M – 0.1M] uric acid (insect excreta's principle component) on H. cumberlandicus behavior were inconsistent. This pheromone is not a host cue (kairomone) and is not used as a repellent (allomone) as noted through lack of responses to natural H. cumberlandicus pheromone and uric acid concentrations by a co-occurring predatory cave orb weaver spider, Meta ovalis Gertsch (Araneae: Tetragnathidae). This pheromone is not serving as a sex pheromone because nymphs were affected by it and because this population of H. cumberlandicus is parthenogenic. The conclusion of this study is that the biological value of the aggregation pheromone is to concentrate H. cumberlandicus in sheltered sites in the cave conducive for minimizing water stress. Rather than signaling H. cumberlandicus presence and quality, the reduced mobility expressed as a result of contacting this pheromone conceivably may act as a defense tactic (antipredator behavior) against M. ovalis, which shares this favored habitat site

Inhibiting mycobacterial tryptophan synthase by targeting the inter-subunit interface

Drug discovery efforts against the pathogen Mycobacterium tuberculosis (Mtb) have been advanced through phenotypic screens of extensive compound libraries. Such a screen revealed sulfolane 1 and indoline-5-sulfonamides 2 and 3 as potent inhibitors of mycobacterial growth. Optimization in the sulfolane series led to compound 4, which has proven activity in an in vivo murine model of Mtb infection. Here we identify the target and mode of inhibition of these compounds based on whole genome sequencing of spontaneous resistant mutants, which identified mutations locating to the essential α- and β-subunits of tryptophan synthase. Over-expression studies confirmed tryptophan synthase as the biological target. Biochemical techniques probed the mechanism of inhibition, revealing the mutant enzyme complex incurs a fitness cost but does not prevent inhibitor binding. Mapping of the resistance conferring mutations onto a low-resolution crystal structure of Mtb tryptophan synthase showed they locate to the interface between the α- and β-subunits. The discovery of anti-tubercular agents inhibiting tryptophan synthase highlights the therapeutic potential of this enzyme and draws attention to the prospect of other amino acid biosynthetic pathways as future Mtb drug targets

Elucidation of the Dual Role of Mycobacterial MoeZR in Molybdenum Cofactor Biosynthesis and Cysteine Biosynthesis

The pathway of molybdenum cofactor biosynthesis has been studied in detail by using proteins from Mycobacterium species, which contain several homologs associated with the first steps of Moco biosynthesis. While all Mycobacteria species contain a MoeZR, only some strains have acquired an additional homolog, MoeBR, by horizontal gene transfer. The role of MoeBR and MoeZR was studied in detail for the interaction with the two MoaD-homologs involved in Moco biosynthesis, MoaD1 and MoaD2, in addition to the CysO protein involved in cysteine biosynthesis. We show that both proteins have a role in Moco biosynthesis, while only MoeZR, but not MoeBR, has an additional role in cysteine biosynthesis. MoeZR and MoeBR were able to complement an E. coli moeB mutant strain, but only in conjunction with the Mycobacterial MoaD1 or MoaD2 proteins. Both proteins were able to sulfurate MoaD1 and MoaD2 in vivo, while only MoeZR additionally transferred the sulfur to CysO. Our in vivo studies show that Mycobacteria have acquired several homologs to maintain Moco biosynthesis. MoeZR has a dual role in Moco- and cysteine biosynthesis and is involved in the sulfuration of MoaD and CysO, whereas MoeBR only has a role in Moco biosynthesis, which is not an essential function for Mycobacteria

Joint Evolutionary Trees: A Large-Scale Method To Predict Protein Interfaces Based on Sequence Sampling

The Joint Evolutionary Trees (JET) method detects protein interfaces, the core residues involved in the folding process, and residues susceptible to site-directed mutagenesis and relevant to molecular recognition. The approach, based on the Evolutionary Trace (ET) method, introduces a novel way to treat evolutionary information. Families of homologous sequences are analyzed through a Gibbs-like sampling of distance trees to reduce effects of erroneous multiple alignment and impacts of weakly homologous sequences on distance tree construction. The sampling method makes sequence analysis more sensitive to functional and structural importance of individual residues by avoiding effects of the overrepresentation of highly homologous sequences and improves computational efficiency. A carefully designed clustering method is parametrized on the target structure to detect and extend patches on protein surfaces into predicted interaction sites. Clustering takes into account residues' physical-chemical properties as well as conservation. Large-scale application of JET requires the system to be adjustable for different datasets and to guarantee predictions even if the signal is low. Flexibility was achieved by a careful treatment of the number of retrieved sequences, the amino acid distance between sequences, and the selective thresholds for cluster identification. An iterative version of JET (iJET) that guarantees finding the most likely interface residues is proposed as the appropriate tool for large-scale predictions. Tests are carried out on the Huang database of 62 heterodimer, homodimer, and transient complexes and on 265 interfaces belonging to signal transduction proteins, enzymes, inhibitors, antibodies, antigens, and others. A specific set of proteins chosen for their special functional and structural properties illustrate JET behavior on a large variety of interactions covering proteins, ligands, DNA, and RNA. JET is compared at a large scale to ET and to Consurf, Rate4Site, siteFiNDER|3D, and SCORECONS on specific structures. A significant improvement in performance and computational efficiency is shown

Communally breeding bats use physiological and behavioural adjustments to optimise daily energy expenditure

Small endotherms must change roosting and thermoregulatory behaviour in response to changes in ambient conditions if they are to achieve positive energy balance. In social species, for example many bats, energy expenditure is influenced by environmental conditions, such as ambient temperature, and also by social thermoregulation. Direct measurements of daily fluctuations in metabolic rates in response to ambient and behavioural variables in the field have not been technologically feasible until recently. During different reproductive periods, we investigated the relationships between ambient temperature, group size and energy expenditure in wild maternity colonies of Bechstein’s bats (Myotis bechsteinii). Bats used behavioural and physiological adjustments to regulate energy expenditure. Whether bats maintained normothermia or used torpor, the number of bats in the roosts as well changed with reproductive status and ambient temperature. During pregnancy and lactation, bats remained mostly normothermic and daily group sizes were relatively large, presumably to participate in the energetic benefits of social thermoregulation. In contrast, smaller groups were formed on days when bats used torpor, which occurred mostly during the post-lactation period. Thus, we were able to demonstrate on wild animals under natural conditions the significance of behavioural and physiological flexibility for optimal thermoregulatory behaviour in small endotherms

Using Genomic Sequencing for Classical Genetics in E. coli K12

We here develop computational methods to facilitate use of 454 whole genome shotgun sequencing to identify mutations in Escherichia coli K12. We had Roche sequence eight related strains derived as spontaneous mutants in a background without a whole genome sequence. They provided difference tables based on assembling each genome to reference strain E. coli MG1655 (NC_000913). Due to the evolutionary distance to MG1655, these contained a large number of both false negatives and positives. By manual analysis of the dataset, we detected all the known mutations (24 at nine locations) and identified and genetically confirmed new mutations necessary and sufficient for the phenotypes we had selected in four strains. We then had Roche assemble contigs de novo, which we further assembled to full-length pseudomolecules based on synteny with MG1655. This hybrid method facilitated detection of insertion mutations and allowed annotation from MG1655. After removing one genome with less than the optimal 20- to 30-fold sequence coverage, we identified 544 putative polymorphisms that included all of the known and selected mutations apart from insertions. Finally, we detected seven new mutations in a total of only 41 candidates by comparing single genomes to composite data for the remaining six and using a ranking system to penalize homopolymer sequencing and misassembly errors. An additional benefit of the analysis is a table of differences between MG1655 and a physiologically robust E. coli wild-type strain NCM3722. Both projects were greatly facilitated by use of comparative genomics tools in the CoGe software package (http://genomevolution.org/)

Translation Levels Control Multi-Spanning Membrane Protein Expression

Attempts to express eukaryotic multi-spanning membrane proteins at high-levels have been generally unsuccessful. In order to investigate the cause of this limitation and gain insight into the rate limiting processes involved, we have analyzed the effect of translation levels on the expression of several human membrane proteins in Escherichia coli (E. coli). These results demonstrate that excessive translation initiation rates of membrane proteins cause a block in protein synthesis and ultimately prevent the high-level accumulation of these proteins. Moderate translation rates allow coupling of peptide synthesis and membrane targeting, resulting in a significant increase in protein expression and accumulation over time. The current study evaluates four membrane proteins, CD20 (4-transmembrane (TM) helixes), the G-protein coupled receptors (GPCRs, 7-TMs) RA1c and EG-VEGFR1, and Patched 1 (12-TMs), and demonstrates the critical role of translation initiation rates in the targeting, insertion and folding of integral membrane proteins in the E. coli membrane

Modularization of biochemical networks based on classification of Petri net t-invariants

<p>Abstract</p> <p>Background</p> <p>Structural analysis of biochemical networks is a growing field in bioinformatics and systems biology. The availability of an increasing amount of biological data from molecular biological networks promises a deeper understanding but confronts researchers with the problem of combinatorial explosion. The amount of qualitative network data is growing much faster than the amount of quantitative data, such as enzyme kinetics. In many cases it is even impossible to measure quantitative data because of limitations of experimental methods, or for ethical reasons. Thus, a huge amount of qualitative data, such as interaction data, is available, but it was not sufficiently used for modeling purposes, until now. New approaches have been developed, but the complexity of data often limits the application of many of the methods. Biochemical Petri nets make it possible to explore static and dynamic qualitative system properties. One Petri net approach is model validation based on the computation of the system's invariant properties, focusing on t-invariants. T-invariants correspond to subnetworks, which describe the basic system behavior.</p> <p>With increasing system complexity, the basic behavior can only be expressed by a huge number of t-invariants. According to our validation criteria for biochemical Petri nets, the necessary verification of the biological meaning, by interpreting each subnetwork (t-invariant) manually, is not possible anymore. Thus, an automated, biologically meaningful classification would be helpful in analyzing t-invariants, and supporting the understanding of the basic behavior of the considered biological system.</p> <p>Methods</p> <p>Here, we introduce a new approach to automatically classify t-invariants to cope with network complexity. We apply clustering techniques such as UPGMA, Complete Linkage, Single Linkage, and Neighbor Joining in combination with different distance measures to get biologically meaningful clusters (t-clusters), which can be interpreted as modules. To find the optimal number of t-clusters to consider for interpretation, the cluster validity measure, Silhouette Width, is applied.</p> <p>Results</p> <p>We considered two different case studies as examples: a small signal transduction pathway (pheromone response pathway in <it>Saccharomyces cerevisiae</it>) and a medium-sized gene regulatory network (gene regulation of Duchenne muscular dystrophy). We automatically classified the t-invariants into functionally distinct t-clusters, which could be interpreted biologically as functional modules in the network. We found differences in the suitability of the various distance measures as well as the clustering methods. In terms of a biologically meaningful classification of t-invariants, the best results are obtained using the Tanimoto distance measure. Considering clustering methods, the obtained results suggest that UPGMA and Complete Linkage are suitable for clustering t-invariants with respect to the biological interpretability.</p> <p>Conclusion</p> <p>We propose a new approach for the biological classification of Petri net t-invariants based on cluster analysis. Due to the biologically meaningful data reduction and structuring of network processes, large sets of t-invariants can be evaluated, allowing for model validation of qualitative biochemical Petri nets. This approach can also be applied to elementary mode analysis.</p