Expression of Staphylococcus aureus clumping factor A in Lactococcus lactis subsp. cremoris using a new shuttle vector.

Staphylococcus aureus harbors redundant adhesins mediating tissue colonization and infection. To evaluate their intrinsic role outside of the staphylococcal background, a system was designed to express them in Lactococcus lactis subsp. cremoris 1363. This bacterium is devoid of virulence factors and has a known genetic background. A new Escherichia coli-L. lactis shuttle and expression vector was constructed for this purpose. First, the high-copy-number lactococcal plasmid pIL253 was equipped with the oriColE1 origin, generating pOri253 that could replicate in E. coli. Second, the lactococcal promoters P23 or P59 were inserted at one end of the pOri253 multicloning site. Gene expression was assessed by a luciferase reporter system. The plasmid carrying P23 (named pOri23) expressed luciferase constitutively at a level 10,000 times greater than did the P59-containing plasmid. Transcription was absent in E. coli. The staphylococcal clumping factor A (clfA) gene was cloned into pOri23 and used as a model system. Lactococci carrying pOri23-clfA produced an unaltered and functional 130-kDa ClfA protein attached to their cell walls. This was indicated both by the presence of the protein in Western blots of solubilized cell walls and by the ability of ClfA-positive lactococci to clump in the presence of plasma. ClfA-positive lactococci had clumping titers (titer of 4,112) similar to those of S. aureus Newman in soluble fibrinogen and bound equally well to solid-phase fibrinogen. These experiments provide a new way to study individual staphylococcal pathogenic factors and might complement both classical knockout mutagenesis and modern in vivo expression technology and signature tag mutagenesis.

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.

Conference scene: Immune signatures in the tumor and beyond.

Led by key opinion leaders in the field, the Cancer Immunotherapy Consortium of the Cancer Research Institute 2012 Scientific Colloquium included 179 participants who exchanged cutting-edge information on basic, clinical and translational cancer immunology and immunotherapy. The meeting revealed how rapidly this field is advancing. The keynote talk was given by Wolf H Fridman and it described the microenvironment of primary and metastatic human tumors. Participants interacted through oral presentations and panel discussions on topics that included host reactions in tumors, advances in imaging, monitoring therapeutic immune modulation, the benefit and risk of immunotherapy, and immune monitoring activities. In summary, the annual meeting gathered clinicians and scientists from academia, industry and regulatory agencies from around the globe to interact and exchange important scientific advances related to tumor immunobiology and cancer immunotherapy.

Combined clonotyping and gene signatures of individual tumor-reactive CD8 T-cells define their functional relevance following peptide vaccination in melanoma patients

Novel cancer vaccines are capableto efficiently induce and boost humantumor antigen specific T-cells. However,the properties of these CD8T-cells are only partially characterized.For in depth investigation ofT-cells following Melan-A/MART-1peptide vaccination in melanoma patients,we conducted a detailed prospectivestudy at the single cell level.We first sorted individual human naiveand effector CD8 T-cells from peripheralblood by flow cytometry, andtested a modified RT-PCR protocolincluding a global amplification ofexpressed mRNAs to obtain sufficientcDNAfromsingle cells.We successfullydetected the expression ofseveral specific genes of interest evendown to 106-fold dilution (equivalentto 10-5 cell). We then analyzed tumor-specific effector memory (EM)CD8T-cell subpopulations ex vivo, assingle cells from vaccinated melanomapatients. To elucidate the hallmarksof effective immunity the genesignatures were defined by a panel ofgenes related to effector functions(e.g. IFN-, granzyme B, perforin),and individual clonotypes were identifiedaccording to the expression ofdistinct T-cell receptors (TCR). Usingthis novel single cell analysis approach,we observed that T-cell differentiationis clonotype dependent,with a progressive restriction in TCRBV clonotype diversity from EMCD28pos to EMCD28neg subsets. However,the effector function gene imprintingis clonotype-independent,but dependent on differentiation,since it correlates with the subset oforigin (EMCD28pos or EMCD28neg). We also conducted a detailedcomparative analysis after vaccinationwith natural vs. analog Melan-Apeptide. We found that the peptideused for vaccination determines thefunctional outcome of individualT-cell clonotypes, with native peptideinducing more potent effector functions.Yet, selective clonotypic expansionwith differentiation was preservedregardless of the peptide usedfor vaccination. In summary, the exvivo single cell RT-PCR approach ishighly sensitive and efficient, andrepresents a reliable and powerfultool to refine our current view of molecularprocesses taking place duringT-cell differentiation.

Highly inducible synthesis of heterologous proteins in epithelial cells carrying a glucocorticoid-responsive vector.

A glucocorticoid-responsive vector is described which allows for the highly inducible expression of complementary DNAs (cDNAs) in stably transfected mammalian cell lines. This vector, pLK-neo, composed of a variant mouse mammary tumor virus long terminal repeat promoter, containing a hormone regulatory element, a Geneticin resistance-encoding gene in a simian virus 40 transcription unit, and a polylinker insertion site for heterologous cDNAs, was used to express the polymeric immunoglobulin (poly-Ig) receptor and the thymocyte marker, Thy-1, in Madin-Darby canine kidney (MDCK) cells and in murine fibroblast L cells. A high level of poly-Ig receptor or Thy-1 mRNA accumulation was observed in MDCK cells in response to dexamethasone with a parallel ten- to 200-fold increase in protein synthesis depending on the recombinant protein and the transfected cell clone.

Identifying genetic signatures of selection in a non-model species, alpine gentian (Gentiana nivalis L.), using a landscape genetic approach

It is generally accepted that most plant populations are locally adapted. Yet, understanding how environmental forces give rise to adaptive genetic variation is a challenge in conservation genetics and crucial to the preservation of species under rapidly changing climatic conditions. Environmental variation, phylogeographic history, and population demographic processes all contribute to spatially structured genetic variation, however few current models attempt to separate these confounding effects. To illustrate the benefits of using a spatially-explicit model for identifying potentially adaptive loci, we compared outlier locus detection methods with a recently-developed landscape genetic approach. We analyzed 157 loci from samples of the alpine herb Gentiana nivalis collected across the European Alps. Principle coordinates of neighbor matrices (PCNM), eigenvectors that quantify multi-scale spatial variation present in a data set, were incorporated into a landscape genetic approach relating AFLP frequencies with 23 environmental variables. Four major findings emerged. 1) Fifteen loci were significantly correlated with at least one predictor variable (R (adj) (2) > 0.5). 2) Models including PCNM variables identified eight more potentially adaptive loci than models run without spatial variables. 3) When compared to outlier detection methods, the landscape genetic approach detected four of the same loci plus 11 additional loci. 4) Temperature, precipitation, and solar radiation were the three major environmental factors driving potentially adaptive genetic variation in G. nivalis. Techniques presented in this paper offer an efficient method for identifying potentially adaptive genetic variation and associated environmental forces of selection, providing an important step forward for the conservation of non-model species under global change.

Statistical evaluation of the influence of writing postures on on-line signatures. Study of the impact of time.

The aim of this study is to investigate the influence of unusual writing positions on a person's signature, in comparison to a standard writing position. Ten writers were asked to sign their signature six times, in each of four different writing positions, including the standard one. In order to take into consideration the effect of the day-to-day variation, this same process was repeated over 12 sessions, giving a total of 288 signatures per subject. The signatures were collected simultaneously in an off-line and on-line acquisition mode, using an interactive tablet and a ballpoint pen. Unidimensional variables (height to width ratio; time with or without in air displacement) and time-dependent variables (pressure; X and Y coordinates; altitude and azimuth angles) were extracted from each signature. For the unidimensional variables, the position effect was assessed through ANOVA and Dunnett contrast tests. Concerning the time-dependent variables, the signatures were compared by using dynamic time warping, and the position effect was evaluated through classification by linear discriminant analysis. Both of these variables provided similar results: no general tendency regarding the position factor could be highlighted. The influence of the position factor varies according to the subject as well as the variable studied. The impact of the session factor was shown to cover the impact that could be ascribed to the writing position factor. Indeed, the day-to-day variation has a greater effect than the position factor on the studied signature variables. The results of this study suggest guidelines for best practice in the area of signature comparisons and demonstrate the importance of a signature collection procedure covering an adequate number of sampling sessions, with a sufficient number of samples per session.

Potential evidence of parasite avoidance in an avian malarial vector

Epidemiological studies of malaria or other vector-transmitted diseases often consider vectors as passive actors in the complex life cycle of the parasites, assuming that vector populations are homogeneous and vertebrate hosts are equally susceptible to being infected during their lifetime. However, some studies based on both human and rodent malaria systems found that mosquito vectors preferentially selected infected vertebrate hosts. This subject has been scarcely investigated in avian malaria models and even less in wild animals using natural host-parasite associations. We investigated whether the malaria infection status of wild great tits, Parus major, played a role in host selection by the mosquito vector Culex pipiens. Pairs of infected and uninfected birds were tested in a dual-choice olfactometer to assess their attractiveness to the mosquitoes. Plasmodium-infected birds attracted significantly fewer mosquitoes than the uninfected ones, which suggest that avian malaria parasites alter hosts' odours involved in vector orientation. Reaction time of the mosquitoes, that is, the time taken to select a host, and activation of mosquitoes, defined as the proportion of individuals flying towards one of the hosts, were not affected by the bird's infection status. The importance of these behavioural responses for the vector is discussed in light of recent advances in related or similar model systems.

Histology-driven data mining of lipid signatures from multiple imaging mass spectrometry analyses: application to human colorectal cancer liver metastasis biopsies.

Imaging mass spectrometry (IMS) represents an innovative tool in the cancer research pipeline, which is increasingly being used in clinical and pharmaceutical applications. The unique properties of the technique, especially the amount of data generated, make the handling of data from multiple IMS acquisitions challenging. This work presents a histology-driven IMS approach aiming to identify discriminant lipid signatures from the simultaneous mining of IMS data sets from multiple samples. The feasibility of the developed workflow is evaluated on a set of three human colorectal cancer liver metastasis (CRCLM) tissue sections. Lipid IMS on tissue sections was performed using MALDI-TOF/TOF MS in both negative and positive ionization modes after 1,5-diaminonaphthalene matrix deposition by sublimation. The combination of both positive and negative acquisition results was performed during data mining to simplify the process and interrogate a larger lipidome into a single analysis. To reduce the complexity of the IMS data sets, a sub data set was generated by randomly selecting a fixed number of spectra from a histologically defined region of interest, resulting in a 10-fold data reduction. Principal component analysis confirmed that the molecular selectivity of the regions of interest is maintained after data reduction. Partial least-squares and heat map analyses demonstrated a selective signature of the CRCLM, revealing lipids that are significantly up- and down-regulated in the tumor region. This comprehensive approach is thus of interest for defining disease signatures directly from IMS data sets by the use of combinatory data mining, opening novel routes of investigation for addressing the demands of the clinical setting.