Population structure and geographical subdivision of the Leishmania major vector Phlebotomus papatasi as revealed by microsatellite variation

Abstract Multi‐locus microsatellite typing (MLMT) has been employed to infer the population structure of Phlebotomus papatasi (Scopoli) (Diptera: Psychodidae) sandflies and assign individuals to populations. Phlebotomus papatasi sandflies were collected from 35 sites in 15 countries. A total of 188 P. papatasi individuals were typed using five microsatellite loci, resulting in 113 different genotypes. Unique microsatellite signatures were observed for some of the populations analysed. Comparable results were obtained when the data were analysed with Bayesian model and distance‐based methods. Bayesian statistic‐based analyses split the dataset into two distinct genetic clusters, A and B, with further substructuring within each. Population A consisted of five subpopulations representing large numbers of alleles that were correlated with the geographical origins of the sandflies. Cluster B comprised individuals collected in the Middle East and the northern Mediterranean area. The subpopulations B1 and B2 did not, however, show any further correlation to geographical origin. The genetic differentiation between subpopulations was supported by F statistics showing statistically significant (Bonferroni‐corrected P < 0.005) values of 0.221 between B2 and B1 and 0.816 between A5 and A4. Identification of the genetic structure of P. papatasi populations is important for understanding the patterns of dispersal of this species and to developing strategies for sandfly control.     

Morphometric and molecular differentiation of Phlebotomus (Phlebotomus) sandflies

The closely related sandfly species of the subgenus Phlebotomus namely, Phlebotomus papatasi (Scopoli, 1786), Phlebotomus duboscqi Neveu‐Lemair, 1906 and Phlebotomus bergeroti Parrot, 1934 (Diptera: Psychodidae), are major vectors of Leishmania major (Kinetoplastida: Trypanosomatidae), the causative agent of cutaneous leishmaniasis in the Old World. Although allopatric in most of their distribution, the three species exist sympatrically in many places in central and eastern Sudan. Males of the three species can be distinguished using morphological characters; however, females are much harder to identify, thus complicating epidemiological studies. We carried out a morphometric and a molecular study to determine reliable morphological features and develop a polymerase chain reaction (PCR) assay for distinguishing females of these species. Males and females from each species were collected from sites in Sudan, East Africa and from one site in Mali, West Africa. Males were analysed morphologically and 20 characters and 10 character ratios were used in a stepwise discriminant analysis. This led to the identification of four characters with high discriminant loading scores sufficient for accurate male species identification. Male DNA was then used for the development of a PCR‐based species diagnostic based on the second internal transcribed spacer (ITS2) of the ribosomal DNA. A set of four primers was developed to generate fragment sizes that are specific to each species and can reliably identify females as well as hybrid DNA. Both the morphometric and the molecular findings of this study have important applications for studies of the epidemiology of cutaneous leishmaniasis.     

Elevated expression of stromal palladin predicts poor clinical outcome in renal cell carcinoma

The role that stromal renal cell carcinoma (RCC) plays in support of tumor progression is unclear. Here we sought to determine the predictive value on patient survival of several markers of stromal activation and the feasibility of a fibroblast-derived extracellular matrix (ECM) based three-dimensional (3D) culture stemming from clinical specimens to recapitulate stromal behavior in vitro. The clinical relevance of selected stromal markers was assessed using a well annotated tumor microarray where stromal-marker levels of expression were evaluated and compared to patient outcomes. Also, an in vitro 3D system derived from fibroblasts harvested from patient matched normal kidney, primary RCC and metastatic tumors was employed to evaluate levels and localizations of known stromal markers such as the actin binding proteins palladin, alpha-smooth muscle actin (α-SMA), fibronectin and its spliced form EDA. Results suggested that RCCs exhibiting high levels of stromal palladin correlate with a poor prognosis, as demonstrated by overall survival time. Conversely, cases of RCCs where stroma presents low levels of palladin expression indicate increased survival times and, hence, better outcomes. Fibroblast-derived 3D cultures, which facilitate the categorization of stromal RCCs into discrete progressive stromal stages, also show increased levels of expression and stress fiber localization of α-SMA and palladin, as well as topographical organization of fibronectin and its splice variant EDA. These observations are concordant with expression levels of these markers in vivo. The study proposes that palladin constitutes a useful marker of poor prognosis in non-metastatic RCCs, while in vitro 3D cultures accurately represent the specific patient's tumor-associated stromal compartment. Our observations support the belief that stromal palladin assessments have clinical relevance thus validating the use of these 3D cultures to study both progressive RCC-associated stroma and stroma-dependent mechanisms affecting tumorigenesis. The clinical value of assessing RCC stromal activation merits further study.     

Further evidence of the African antiquity of hyaenodontid (‘Creodonta’, Mammalia) evolution

We report here the new ‘creodont’ Lahimia selloumi gen. et sp. nov. from the late Palaeocene of the Ouled Abdoun Basin (Morocco) as the oldest known Hyaenodontidae with Tinerhodon from the Ouarzazate Basin (Morocco). By contrast to Tinerhodon, Lahimia is unexpectedly derived. Most of its specializations, such as the shortening of the anterior dentition (e.g. loss of P₁) and the talonid reduction and simplification, are strikingly shared with Boualitomus from the Ypresian of the Ouled Abdoun Basin, and are distinctive from other hyaenodontids, including ‘proviverrines’. They are interpreted as synapomorphies evidencing a precociously specialized early African hyaenodontid lineage. Although Lahimia and Boualitomus remain known only by the lower dentition, their relationships with Koholia are suggested by comparison of their molar occlusal pattern. Lahimia and Boualitomus are referred to the Koholiinae, which is representative of an old African endemic lineage, as initially recognized. This remarkable lineage is characterized by synapomorphies of Lahimia and Boualitomus, and also by a shared original prevallum/postvallid shearing. The discovery of Lahimia provides direct evidence for the antiquity of the African evolution of the Hyaenodontidae. This is in agreement with an African origin of the Hyaenodontidae, and with the probable diphyletism of the ‘Creodonta’. Lahimia and the Koholiinae, as well as the diversity of the first Laurasian hyaenodontid lineages, emphasize our poor knowledge of the striking early African hyaenodontid radiation.     

Genotype × environment interaction in the allometry of body,genitalia and signal traits in Enchenopa treehoppers (Hemiptera: Membracidae)

Developmental plasticity may promote divergence by exposing genetic variation to selection in novel ways in new environments. We tested for this effect in the static allometry (i.e. scaling on body size) of traits in advertisement signals, body and genitalia. We used a member of the Enchenopa binotata species complex of treehoppers – a clade of plant‐feeding insects in which speciation is associated with colonization of novel environments involving marked divergence in signals, subtle divergence in body size and shape, and no apparent divergence in genitalia. We found no change in mean allometric slopes across environments, but substantial genetic variation and genotype × environment interaction (G × E) in allometry. The allometry of signal traits showed the most genetic variation and G × E, and that of genitalia showed the weakest G × E. Our findings suggest that colonizing novel environments may have stronger diversifying consequences for signal allometry than for genitalia allometry. © 2011 The Linnean Society of London, Biological Journal of the Linnean Society, 2012, 105 , 187–196.     

The mesenchymal tumor microenvironment: A drug-resistant niche

Drug and radiation resistance represent a challenge for most anticancer therapies. Diverse experimental approaches have provided evidence that the tumor-associated microenvironment constitutes both a protective shell that impedes drug or radiation access and a permissive or promotive microenvironment that encourages a nurturing cancer (i.e., cancer stem cell) niche where tumor cells overcome treatment- and cancer-induced stresses. Better understanding of the effects of the tumor microenvironment on cancer cells before, during and immediately after chemo- or radiotherapy is imperative to design new therapies aimed at targeting this tumor-protective niche. This review summarizes some of the known mesenchymal stromal effects that account for drug resistance, the main signal transduction pathways associated with this resistance and the therapeutic efforts directed to increase the success of current therapies. Special emphasis is given to environment-mediated drug resistance in general and to cell adhesion-mediated drug resistance in particular.     

Management of Braula orientalis Örösi (Diptera: Braulidae) in honeybee colonies with tobacco smoke under semiarid conditions

In the present study, bee colonies were smoked with tobacco smoke in order to evaluate the monthly changes in the numbers of worker bees, the infestation rates of worker bees and queens with bee lice, and the annual average honey production per colony. In July of each year, 12 colonies were smoked with tobacco smoke; the remaining hives not smoked with tobacco smoke served as the control. The results indicated that the applications of tobacco smoke during July gave rise to an impressive reduction in the Braula infestation rate on workers (below 1.8%) and reduced the amount of bee lice on the queen to zero throughout the 2–3 months following smoke treatment. In the colonies not treated with smoke, the Braula infestation rates on worker bees started to increase in May and continued to increase constantly during the rest of year, reaching maximum infestation rates of 28.2% and 33.8% in December, with an average of 15 and 17 lice per queen in November in the first and second years, respectively. Worker bee populations peaked in April and July of each year in both treatments. The average honey production per colony was significantly higher in the colonies treated with smoke than those that were not for the first and second years. In conclusion, early summer months may be the crucial time to smoke the colonies with tobacco smoke in order to keep bee lice at low levels for the remaining seasons.     

Integrating functional genomics data using maximum likelihood based simultaneous component analysis

Background  

In contemporary biology, complex biological processes are increasingly studied by collecting and analyzing measurements of the same entities that are collected with different analytical platforms. Such data comprise a number of data blocks that are coupled via a common mode. The goal of collecting this type of data is to discover biological mechanisms that underlie the behavior of the variables in the different data blocks. The simultaneous component analysis (SCA) family of data analysis methods is suited for this task. However, a SCA may be hampered by the data blocks being subjected to different amounts of measurement error, or noise. To unveil the true mechanisms underlying the data, it could be fruitful to take noise heterogeneity into consideration in the data analysis. Maximum likelihood based SCA (MxLSCA-P) was developed for this purpose. In a previous simulation study it outperformed normal SCA-P. This previous study, however, did not mimic in many respects typical functional genomics data sets, such as, data blocks coupled via the experimental mode, more variables than experimental units, and medium to high correlations between variables. Here, we present a new simulation study in which the usefulness of MxLSCA-P compared to ordinary SCA-P is evaluated within a typical functional genomics setting. Subsequently, the performance of the two methods is evaluated by analysis of a real life Escherichia coli metabolomics data set.