Microbe-aliphatic hydrocarbon interactions in soil: implications for biodegradation and bioremediation

Aliphatic hydrocarbons make up a substantial portion of organic contamination in the terrestrial environment. However, most studies have focussed on the fate and behaviour of aromatic contaminants in soil. Despite structural differences between aromatic and aliphatic hydrocarbons, both classes of contaminants are subject to physicochemical processes, which can affect the degree of loss, sequestration and interaction with soil microflora. Given the nature of hydrocarbon contamination of soils and the importance of bioremediation strategies, understanding the fate and behaviour of aliphatic hydrocarbons is imperative, particularly microbe-contaminant interactions. Biodegradation by microbes is the key removal process of hydrocarbons in soils, which is controlled by hydrocarbon physicochemistry, environmental conditions, bioavailability and the presence of catabolically active microbes. Therefore, the aims of this review are (i) to consider the physicochemical properties of aliphatic hydrocarbons and highlight mechanisms controlling their fate and behaviour in soil; (ii) to discuss the bioavailability and bioaccessibility of aliphatic hydrocarbons in soil, with particular attention being paid to biodegradation, and (iii) to briefly consider bioremediation techniques that may be applied to remove aliphatic hydrocarbons from soil.     

Streptococcal toxins: role in pathogenesis and disease

Group A Streptococcus (Streptococcus pyogenes), group B Streptococcus (Streptococcus agalactiae) and Streptococcus pneumoniae (pneumococcus) are host‐adapted bacterial pathogens among the leading infectious causes of human morbidity and mortality. These microbes and related members of the genus Streptococcus produce an array of toxins that act against human cells or tissues, resulting in impaired immune responses and subversion of host physiological processes to benefit the invading microorganism. This toxin repertoire includes haemolysins, proteases, superantigens and other agents that ultimately enhance colonization and survival within the host and promote dissemination of the pathogen.     

Distinguishing low frequency mutations from RT-PCR and sequence errors in viral deep sequencing data

Background

RNA viruses have high mutation rates and exist within their hosts as large, complex and heterogeneous populations, comprising a spectrum of related but non-identical genome sequences. Next generation sequencing is revolutionising the study of viral populations by enabling the ultra deep sequencing of their genomes, and the subsequent identification of the full spectrum of variants within the population. Identification of low frequency variants is important for our understanding of mutational dynamics, disease progression, immune pressure, and for the detection of drug resistant or pathogenic mutations. However, the current challenge is to accurately model the errors in the sequence data and distinguish real viral variants, particularly those that exist at low frequency, from errors introduced during sequencing and sample processing, which can both be substantial.

Results

We have created a novel set of laboratory control samples that are derived from a plasmid containing a full-length viral genome with extremely limited diversity in the starting population. One sample was sequenced without PCR amplification whilst the other samples were subjected to increasing amounts of RT and PCR amplification prior to ultra-deep sequencing. This enabled the level of error introduced by the RT and PCR processes to be assessed and minimum frequency thresholds to be set for true viral variant identification. We developed a genome-scale computational model of the sample processing and NGS calling process to gain a detailed understanding of the errors at each step, which predicted that RT and PCR errors are more likely to occur at some genomic sites than others. The model can also be used to investigate whether the number of observed mutations at a given site of interest is greater than would be expected from processing errors alone in any NGS data set. After providing basic sample processing information and the site’s coverage and quality scores, the model utilises the fitted RT-PCR error distributions to simulate the number of mutations that would be observed from processing errors alone.

Conclusions

These data sets and models provide an effective means of separating true viral mutations from those erroneously introduced during sample processing and sequencing.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1456-x) contains supplementary material, which is available to authorized users.     

Design and synthesis of pyridone inhibitors of non-nucleoside reverse transcriptase

Next generation NNRTIs are sought which possess both broad spectrum antiviral activity against key mutant strains and a high genetic barrier to the selection of new mutant viral strains. Pyridones were evaluated as an acyclic conformational constraint to replace the aryl ether core of MK-4965 (1) and the more rigid indazole constraint of MK-6186 (2). The resulting pyridone compounds are potent inhibitors of HIV RT and have antiviral activity in cell culture that is superior to other next generation NNRTI’s.     

Normal variation in thermal radiated temperature in cattle: implications for foot-and-mouth disease detection

Background

Malignant canine mammary tumors represent 50% of all neoplasms in female dogs. Matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs) are thought to be involved in tumor progression, and they are also associated with the reactive stroma, which provides structural and vascular support for tumor growth.

Results

MMP-2, MMP-9 and MT1-MMP were expressed at both the mRNA and protein levels in tumor samples. MMP-2 and MMP-9 immunohistochemical reactions were evident both in the epithelial tumor cells and in the stromal compartment to varying degrees; in particular, the intensity of the MMP-2 staining was stronger in the stromal fibroblasts close to epithelial tumor cells in simple carcinomas than in adenomas. These data were supported by gelatin-zymography; bands for the active form of MMP-2 were found in 94% of carcinoma samples, compared with 17% of benign tumor samples. The gene expression and immunohistochemical results for MT1-MMP were comparable to those for MMP-2. The immunoreactivity for MMP-13 and TIMP-2 was lower in carcinomas than in adenomas, confirming the mRNA data for MMP-13 and the other MMP inhibitors that were evaluated. The active form of MMP-9, but not the active form of MMP-2, was identified in the plasma of all of the tested dogs.

Conclusions

Our findings suggest that MMP-9, MMP-2 and MT1-MMP, which are synthesized by epithelial cancer cells and cancer-associated fibroblasts, play an important role in malignant canine mammary tumors. The reduction of MMP-13 and TIMP-2 could also be a significant step in malignant transformation. MMP-2 and MT1-MMP could be further evaluated as future biomarkers for predicting the progression and prognosis of canine mammary tumors.     

On the use of multi-objective evolutionary algorithms for the induction of fuzzy classification rule systems

Extracting comprehensible and general classifiers from data in the form of rule systems is an important task in many problem domains. This study investigates the utility of a multi-objective evolutionary algorithm (MOEA) for this task. Multi-objective evolutionary algorithms are capable of finding several trade-off solutions between different objectives in a single run. In the context of the present study, the objectives to be optimised are the complexity of the rule systems, and their fit to the data. Complex rule systems are required to fit the data well. However, overly complex rule systems often generalise poorly on new data. In addition they tend to be incomprehensible. It is, therefore, important to obtain trade-off solutions that achieve the best possible fit to the data with the lowest possible complexity. The rule systems produced by the proposed multi-objective evolutionary algorithm are compared with those produced by several other existing approaches for a number of benchmark datasets. It is shown that the algorithm produces less complex classifiers that perform well on unseen data.     

Beetles, boxes and brain cells: neural mechanisms underlying valuation and learning

Sensory cues in the environment can predict the availability of reward. Through experience, humans and animals learn these predictions and use them to guide their actions. For example, we can learn to discriminate chanterelles from ordinary champignons through experience. Assuming the development of a taste for the complex and lingering flavors of chanterelles, we therefore learn to value the same action--picking mushrooms--differentially depending upon the appearance of a mushroom. One major goal of cognitive neuroscience is to understand the neural mechanisms that underlie this sort of learning. Because the acquisition of rewards motivates much behavior, recent efforts have focused on describing the neural signals related to learning the value of stimuli and actions. Neurons in the basal ganglia, in midbrain dopamine areas, in frontal and parietal cortices and in other brain areas, all modulate their activity in relation to aspects of learning. By training monkeys on various behavioral tasks, recent studies have begun to characterize how neural signals represent distinct processes, such as the timing of events, motivation, absolute (objective) and relative (subjective) valuation, and the formation of associative links between stimuli and potential actions. In addition, a number of studies have either further characterized dopamine signals or sought to determine how such signaling might interact with target structures, such as the striatum and rhinal cortex, to underlie learning.     

Additive inhibition of complement deposition by pneumolysin and PspA facilitates Streptococcus pneumoniae septicemia

Streptococcus pneumoniae is a common cause of septicemia in the immunocompetent host. To establish infection, S. pneumoniae has to overcome host innate immune responses, one component of which is the complement system. Using isogenic bacterial mutant strains and complement-deficient immune naive mice, we show that the S. pneumoniae virulence factor pneumolysin prevents complement deposition on S. pneumoniae, mainly through effects on the classical pathway. In addition, using a double pspA-/ply- mutant strain we demonstrate that pneumolysin and the S. pneumoniae surface protein PspA act in concert to affect both classical and alternative complement pathway activity. As a result, the virulence of the pspA-/ply- strain in models of both systemic and pulmonary infection is greatly attenuated in wild-type mice but not complement deficient mice. The sensitivity of the pspA-/ply- strain to complement was exploited to demonstrate that although early innate immunity to S. pneumoniae during pulmonary infection is partially complement-dependent, the main effect of complement is to prevent spread of S. pneumoniae from the lungs to the blood. These data suggest that inhibition of complement deposition on S. pneumoniae by pneumolysin and PspA is essential for S. pneumoniae to successfully cause septicemia. Targeting mechanisms of complement inhibition could be an effective therapeutic strategy for patients with septicemia due to S. pneumoniae or other bacterial pathogens.