The influence of the accessory genome on bacterial pathogen evolution

Bacterial pathogens exhibit significant variation in their genomic content of virulence factors. This reflects the abundance of strategies pathogens evolved to infect host organisms by suppressing host immunity. Molecular arms-races have been a strong driving force for the evolution of pathogenicity, with pathogens often encoding overlapping or redundant functions, such as type III protein secretion effectors and hosts encoding ever more sophisticated immune systems. The pathogens’ frequent exposure to other microbes, either in their host or in the environment, provides opportunities for the acquisition or interchange of mobile genetic elements. These DNA elements accessorise the core genome and can play major roles in shaping genome structure and altering the complement of virulence factors. Here, we review the different mobile genetic elements focusing on the more recent discoveries and highlighting their role in shaping bacterial pathogen evolution

Telephone-administered psychotherapy for depression in MS patients: moderating role of social support

Depression is common in individuals with multiple sclerosis (MS). While psychotherapy is an effective treatment for depression, not all individuals benefit. We examined whether baseline social support might differentially affect treatment outcome in 127 participants with MS and depression randomized to either Telephone-administered Cognitive-Behavioral Therapy (T-CBT) or Telephone-administered Emotion-Focused Therapy (T-EFT). We predicted that those with low social support would improve more in T-EFT, since this approach emphasizes the therapeutic relationship, while participants with strong social networks and presumably more emotional resources might fare better in the more structured and demanding T-CBT. We found that both level of received support and satisfaction with that support at baseline did moderate treatment outcome. Individuals with high social support showed a greater reduction in depressive symptoms in the T-CBT as predicted, but participants with low social support showed a similar reduction in both treatments. This suggests that for participants with high social support, CBT may be a more beneficial treatment for depression compared with EFT

The quick motor function test: a new tool to rate clinical severity and motor function in Pompe patients

Pompe disease is a lysosomal storage disorder characterized by progressive muscle weakness. With the emergence of new treatment options, psychometrically robust outcome measures are needed to monitor patients’ clinical status. We constructed a motor function test that is easy and quick to use. The Quick Motor Function Test (QMFT) was constructed on the basis of the clinical expertise of several physicians involved in the care of Pompe patients; the Gross Motor Function Measure and the IPA/Erasmus MC Pompe survey. The test comprises 16 items. Validity and test reliability were determined in a cohort of 91 Pompe patients (5 to 76 years of age). In addition, responsiveness of the scale to changes in clinical condition over time was examined in a subgroup of 18 patients receiving treatment and 23 untreated patients. Interrater and intrarater reliabilities were good (intraclass correlation coefficients: 0.78 to 0.98 and 0.76 to 0.98). The test correlated strongly with proximal muscle strength assessed by hand held dynamometry and manual muscle testing (rs= 0.81, rs=0.89), and showed significant differences between patient groups with different disease severities. A clinical-empirical exploration to assess responsiveness showed promising results, albeit it should be repeated in a larger group of patients. In conclusion, the Quick Motor Function Test can reliably rate clinical severity and motor function in children and adults with Pompe disease

Responsiveness to exercise training in juvenile dermatomyositis: a twin case study

<p>Abstract</p> <p>Background</p> <p>Patients with juvenile dermatomyositis (JDM) often present strong exercise intolerance and muscle weakness. However, the role of exercise training in this disease has not been investigated.</p> <p>Purpose</p> <p>this longitudinal case study reports on the effects of exercise training on a 7-year-old patient with JDM and on her unaffected monozygotic twin sister, who served as a control.</p> <p>Methods</p> <p>Both the patient who was diagnosed with JDM as well as her healthy twin underwent a 16-week exercise training program comprising aerobic and strengthening exercises. We assessed one repetition-maximum (1-RM) leg-press and bench-press strength, balance, mobility and muscle function, blood markers of inflammation and muscle enzymes, aerobic conditioning, and disease activity scores. As a result, the healthy child had an overall greater absolute strength, muscle function and aerobic conditioning compared to her JDM twin pair at baseline and after the trial. However, the twins presented comparable relative improvements in 1-RM bench press, 1-RM leg press, VO_2peak, and time-to-exhaustion. The healthy child had greater relative increments in low-back strength and handgrip, whereas the child with JDM presented a higher relative increase in ventilatory anaerobic threshold parameters and functional tests. Quality of life, inflammation, muscle damage and disease activity scores remained unchanged.</p> <p>Results and Conclusion</p> <p>this was the first report to describe the training response of a patient with non-active JDM following an exercise training regimen. The child with JDM exhibited improved strength, muscle function and aerobic conditioning without presenting an exacerbation of the disease.</p

Anopheles gambiae PGRPLC-Mediated Defense against Bacteria Modulates Infections with Malaria Parasites

Recognition of peptidoglycan (PGN) is paramount for insect antibacterial defenses. In the fruit fly Drosophila melanogaster, the transmembrane PGN Recognition Protein LC (PGRP-LC) is a receptor of the Imd signaling pathway that is activated after infection with bacteria, mainly Gram-negative (Gram−). Here we demonstrate that bacterial infections of the malaria mosquito Anopheles gambiae are sensed by the orthologous PGRPLC protein which then activates a signaling pathway that involves the Rel/NF-κB transcription factor REL2. PGRPLC signaling leads to transcriptional induction of antimicrobial peptides at early stages of hemolymph infections with the Gram-positive (Gram+) bacterium Staphylococcus aureus, but a different signaling pathway might be used in infections with the Gram− bacterium Escherichia coli. The size of mosquito symbiotic bacteria populations and their dramatic proliferation after a bloodmeal, as well as intestinal bacterial infections, are also controlled by PGRPLC signaling. We show that this defense response modulates mosquito infection intensities with malaria parasites, both the rodent model parasite, Plasmodium berghei, and field isolates of the human parasite, Plasmodium falciparum. We propose that the tripartite interaction between mosquito microbial communities, PGRPLC-mediated antibacterial defense and infections with Plasmodium can be exploited in future interventions aiming to control malaria transmission. Molecular analysis and structural modeling provided mechanistic insights for the function of PGRPLC. Alternative splicing of PGRPLC transcripts produces three main isoforms, of which PGRPLC3 appears to have a key role in the resistance to bacteria and modulation of Plasmodium infections. Structural modeling indicates that PGRPLC3 is capable of binding monomeric PGN muropeptides but unable to initiate dimerization with other isoforms. A dual role of this isoform is hypothesized: it sequesters monomeric PGN dampening weak signals and locks other PGRPLC isoforms in binary immunostimulatory complexes further enhancing strong signals

Fully automated high-quality NMR structure determination of small 2H-enriched proteins

Determination of high-quality small protein structures by nuclear magnetic resonance (NMR) methods generally requires acquisition and analysis of an extensive set of structural constraints. The process generally demands extensive backbone and sidechain resonance assignments, and weeks or even months of data collection and interpretation. Here we demonstrate rapid and high-quality protein NMR structure generation using CS-Rosetta with a perdeuterated protein sample made at a significantly reduced cost using new bacterial culture condensation methods. Our strategy provides the basis for a high-throughput approach for routine, rapid, high-quality structure determination of small proteins. As an example, we demonstrate the determination of a high-quality 3D structure of a small 8 kDa protein, E. coli cold shock protein A (CspA), using <4 days of data collection and fully automated data analysis methods together with CS-Rosetta. The resulting CspA structure is highly converged and in excellent agreement with the published crystal structure, with a backbone RMSD value of 0.5 Å, an all atom RMSD value of 1.2 Å to the crystal structure for well-defined regions, and RMSD value of 1.1 Å to crystal structure for core, non-solvent exposed sidechain atoms. Cross validation of the structure with 15N- and 13C-edited NOESY data obtained with a perdeuterated 15N, 13C-enriched 13CH3 methyl protonated CspA sample confirms that essentially all of these independently-interpreted NOE-based constraints are already satisfied in each of the 10 CS-Rosetta structures. By these criteria, the CS-Rosetta structure generated by fully automated analysis of data for a perdeuterated sample provides an accurate structure of CspA. This represents a general approach for rapid, automated structure determination of small proteins by NMR

A quality metric for homology modeling: the H-factor

<p>Abstract</p> <p>Background</p> <p>The analysis of protein structures provides fundamental insight into most biochemical functions and consequently into the cause and possible treatment of diseases. As the structures of most known proteins cannot be solved experimentally for technical or sometimes simply for time constraints, <it>in silico </it>protein structure prediction is expected to step in and generate a more complete picture of the protein structure universe. Molecular modeling of protein structures is a fast growing field and tremendous works have been done since the publication of the very first model. The growth of modeling techniques and more specifically of those that rely on the existing experimental knowledge of protein structures is intimately linked to the developments of high resolution, experimental techniques such as NMR, X-ray crystallography and electron microscopy. This strong connection between experimental and <it>in silico </it>methods is however not devoid of criticisms and concerns among modelers as well as among experimentalists.</p> <p>Results</p> <p>In this paper, we focus on homology-modeling and more specifically, we review how it is perceived by the structural biology community and what can be done to impress on the experimentalists that it can be a valuable resource to them. We review the common practices and provide a set of guidelines for building better models. For that purpose, we introduce the H-factor, a new indicator for assessing the quality of homology models, mimicking the R-factor in X-ray crystallography. The methods for computing the H-factor is fully described and validated on a series of test cases.</p> <p>Conclusions</p> <p>We have developed a web service for computing the H-factor for models of a protein structure. This service is freely accessible at <url>http://koehllab.genomecenter.ucdavis.edu/toolkit/h-factor</url>.</p

Homology modelling and spectroscopy, a never-ending love story

Homology modelling is normally the technique of choice when experimental structure data are not available but three-dimensional coordinates are needed, for example, to aid with detailed interpretation of results of spectroscopic studies. Herein, the state of the art of homology modelling will be described in the light of a series of recent developments, and an overview will be given of the problems and opportunities encountered in this field. The major topic, the accuracy and precision of homology models, will be discussed extensively due to its influence on the reliability of conclusions drawn from the combination of homology models and spectroscopic data. Three real-world examples will illustrate how both homology modelling and spectroscopy can be beneficial for (bio)medical research

Interrogation of the perturbed gut microbiota in gouty arthritis patients through in silico metabolic modeling

Recent studies have shown perturbed gut microbiota associated with gouty arthritis, a metabolic disease characterized by an imbalance between uric acid production and excretion. To mechanistically investigate altered microbiota metabolism associated with gout disease, 16S rRNA gene amplicon sequence data from stool samples of gout patients and healthy controls were computationally analyzed through bacterial community metabolic models. Patient-specific community models constructed with the metagenomics modeling pipeline, mgPipe, were used to perform k-means clustering of samples according to their metabolic capabilities. The clustering analysis generated statistically significant partitioning of samples into a Bacteroides-dominated, high gout cluster and a Faecalibacterium-elevated, low gout cluster. The high gout cluster was predicted to allow elevated synthesis of the amino acids D-alanine and L-alanine and byproducts of branched-chain amino acid catabolism, while the low gout cluster allowed higher production of butyrate, the sulfur-containing amino acids L-cysteine and L-methionine, and the L-cysteine catabolic product H2S. By expanding the capabilities of mgPipe to provide taxa-level resolution of metabolite exchange rates, acetate, D-lactate and succinate exchanged from Bacteroides to Faecalibacterium were predicted to enhance butyrate production in the low gout cluster. Model predictions suggested that sulfur-containing amino acid metabolism generally and H2S more specifically could be novel gout disease markers

Effectiveness of Biodiversity Surrogates for Conservation Planning: Different Measures of Effectiveness Generate a Kaleidoscope of Variation

Conservation planners represent many aspects of biodiversity by using surrogates with spatial distributions readily observed or quantified, but tests of their effectiveness have produced varied and conflicting results. We identified four factors likely to have a strong influence on the apparent effectiveness of surrogates: (1) the choice of surrogate; (2) differences among study regions, which might be large and unquantified (3) the test method, that is, how effectiveness is quantified, and (4) the test features that the surrogates are intended to represent. Analysis of an unusually rich dataset enabled us, for the first time, to disentangle these factors and to compare their individual and interacting influences. Using two data-rich regions, we estimated effectiveness using five alternative methods: two forms of incidental representation, two forms of species accumulation index and irreplaceability correlation, to assess the performance of ‘forest ecosystems’ and ‘environmental units’ as surrogates for six groups of threatened species—the test features—mammals, birds, reptiles, frogs, plants and all of these combined. Four methods tested the effectiveness of the surrogates by selecting areas for conservation of the surrogates then estimating how effective those areas were at representing test features. One method measured the spatial match between conservation priorities for surrogates and test features. For methods that selected conservation areas, we measured effectiveness using two analytical approaches: (1) when representation targets for the surrogates were achieved (incidental representation), or (2) progressively as areas were selected (species accumulation index). We estimated the spatial correlation of conservation priorities using an index known as summed irreplaceability. In general, the effectiveness of surrogates for our taxa (mostly threatened species) was low, although environmental units tended to be more effective than forest ecosystems. The surrogates were most effective for plants and mammals and least effective for frogs and reptiles. The five testing methods differed in their rankings of effectiveness of the two surrogates in relation to different groups of test features. There were differences between study areas in terms of the effectiveness of surrogates for different test feature groups. Overall, the effectiveness of the surrogates was sensitive to all four factors. This indicates the need for caution in generalizing surrogacy tests