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Which is the best model organism to implement an evolutionary study for P.falciparum?

Which is the best model organism to implement an evolutionary study for P.falciparum?


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I have to study and implement some evolutionary statistics for P.falciparum. In your opinion and by your experience, which organism should I consider in order to take the orthologs to implement this study?In general, If I wanted to extend this study to P.knowlesi, P.vivax and in general to all the parasites evolutionary related to the asian monkey plasmodia, which organism you suggest me to consider as a template? I thought about P.berghei but, since I have to consider also the Vinckeia parasites (mouse plasmodia) is there a good organism, out of the plasmodium genus, that could come in handy serving as a reference point for all these species?

Obliged


Population genetic structure and fitness of Daphnia pulicaria across a pH gradient in three North American lakes

Understanding the evolutionary response of organisms to environmental gradients is important in light of increasing anthropogenic changes to our environment. In this study, we use ecological genetic tools to determine local adaptation of the model organism, Daphnia pulicaria, across a pH gradient in three North American lakes. We predicted that there would be genetic differentiation and local adaptation among the three populations of D. pulicaria. To assess the degree of genetic differentiation, we genotyped individual D. pulicaria using 15 microsatellite loci across the three populations and performed a STRUCTURE analysis corroborated with PCA based upon Nei’s genetic distance and multiple F st comparisons. To test for signatures of local adaptation, a survivorship experiment across a pH gradient under common-garden conditions was performed. We determined that each of the three populations was genetically differentiated from one another, with Hill and Madison Lake populations of D. pulicaria being more similar to each other than that of the Frenchman Lake population. The results of the survivorship experiment showed a signal of local adaptation, with Frenchman Lake showing higher survivorship at lower pH [

6.5] when compared to Hill and Madison populations, while both Hill and Madison had higher survivorship at higher pH [7.9 and 8.6, respectively] when compared to the Frenchman population.

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Abstract

Background

With low and markedly seasonal malaria transmission, increasingly sensitive tools for better stratifying the risk of infection and targeting control interventions are needed. A cross-sectional survey to characterize the current malaria transmission patterns, identify hotspots, and detect recent changes using parasitological and serological measures was conducted in three sites of the Peruvian Amazon.

Material and Methods

After full census of the study population, 651 participants were interviewed, clinically examined and had a blood sample taken for the detection of malaria parasites (microscopy and PCR) and antibodies against P. vivax (PvMSP119, PvAMA1) and P. falciparum (PfGLURP, PfAMA1) antigens by ELISA. Risk factors for malaria infection (positive PCR) and malaria exposure (seropositivity) were assessed by multivariate survey logistic regression models. Age-specific seroprevalence was analyzed using a reversible catalytic conversion model based on maximum likelihood for generating seroconversion rates (SCR, λ). SaTScan was used to detect spatial clusters of serology-positive individuals within each site.

Results

The overall parasite prevalence by PCR was low, i.e. 3.9% for P. vivax and 6.7% for P. falciparum, while the seroprevalence was substantially higher, 33.6% for P. vivax and 22.0% for P. falciparum, with major differences between study sites. Age and location (site) were significantly associated with P. vivax exposure while location, age and outdoor occupation were associated with P. falciparum exposure. P. falciparum seroprevalence curves showed a stable transmission throughout time, while for P. vivax transmission was better described by a model with two SCRs. The spatial analysis identified well-defined clusters of P. falciparum seropositive individuals in two sites, while it detected only a very small cluster of P. vivax exposure.

Conclusion

The use of a single parasitological and serological malaria survey has proven to be an efficient and accurate method to characterize the species specific heterogeneity in malaria transmission at micro-geographical level as well as to identify recent changes in transmission.

Citation: Rosas-Aguirre A, Speybroeck N, Llanos-Cuentas A, Rosanas-Urgell A, Carrasco-Escobar G, Rodriguez H, et al. (2015) Hotspots of Malaria Transmission in the Peruvian Amazon: Rapid Assessment through a Parasitological and Serological Survey. PLoS ONE 10(9): e0137458. https://doi.org/10.1371/journal.pone.0137458

Editor: Luzia Helena Carvalho, Centro de Pesquisa Rene Rachou/Fundação Oswaldo Cruz (Fiocruz-Minas), BRAZIL

Received: April 21, 2015 Accepted: August 17, 2015 Published: September 10, 2015

Copyright: © 2015 Rosas-Aguirre et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The study was funded by the Directorate General for Development Cooperation (DGCD) of the Belgian Government within the Third Framework Agreement of the Institutional Collaboration between the Institute of Tropical Medicine “Alexander von Humboldt” - Universidad Peruana Cayetano Heredia, Lima and the Institute of Tropical Medicine in Antwerp, Belgium (FA3-II project, 2011-2013)). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.


Introduction

Understanding spatiotemporal patterns of pathogen spread is crucial to implement effective actions to contain epidemics (Ostfeld et al. 2005 Vander Wal et al. 2014). Wildlife pathogens, including some that can be very harmful to humans and livestock, are transmitted when infected hosts come in direct or indirect contact with uninfected individuals. In both directly and indirectly transmitted diseases, the extent and speed of propagation is expected to be linked to the dispersal ability of the hosts (Biek and Real 2010). Thus, information about movement and dispersal of hosts is required to identify potential spread pathways. As an example, rivers and highways appear to slow the spread of chronic wasting disease in white-tailed deer (Odocoileus virginianus), most likely because they act as barriers to dispersal and gene flow for this species (Blanchong et al. 2008). Similarly, large rivers hamper gene flow in raccoons (Procyon lotor) and may reduce the propagation of the raccoon rabies variant (RRV Cullingham et al. 2009). Control operations that aim at containing and eventually eradicating a given disease are thus likely to be more efficient if positioned alongside these barriers to strengthen their effect (Russell et al. 2006). This strategy was adopted and prevented the northward spread of RRV in Ontario (Canada), in 1999 (Rosatte et al. 2001). Distribution of oral vaccine baits along major rivers to control rabies was performed as early as the 1980s for red foxes (Vulpes vulpes), eventually contributing to the elimination of rabies from Switzerland (Wandeler et al. 1988).

Not all environmental barriers to host dispersal and pathogen dissemination, whether natural or of anthropogenic origin, are spatially discrete or easily identifiable, such as rivers and roads, but may instead be continuous or follow a gradient of biotic or abiotic conditions (Storfer et al. 2007). Climate (Geffen et al. 2004), elevation (Shirk et al. 2010) and presence of unsuitable habitats (Goldberg and Waits 2010) are all examples of such limiting conditions. Based on the ecology, behaviour and dispersal ability of host species, these features may restrict pathogen dispersal or promote it through dispersal corridors. Integrating environmental features in models of disease spread can help in predicting the spread and geographic expansion of a disease (Ostfeld et al. 2005).

Different approaches are available to understand the effects of habitat composition on animal movement and dispersal. The first relies on trapping of animals to determine their resource selection and density (Manly et al. 2002), which requires important time and resources to gather large sample sizes. Studies have also been conducted using very high frequency (VHF) transmitters and, more recently, global positioning system (GPS) radio-telemetry to track animal movement and analyse habitat use (Cagnacci et al. 2010). Despite constant technological improvements, collecting large GPS data sets remains very costly and logistically challenging for several species. Spatial simulations can also be used to characterize factors affecting movement and connectivity among individuals in a population (Russell et al. 2006 Rees et al. 2013). While these models can bring insights on the links between habitat and dispersal, the quality of model outputs will depend on an appropriate characterization of ecological processes, which can only be obtained through empirical evaluation. Finally, another approach relies on tools provided by landscape genetics, a discipline integrating aspects of population genetics, landscape ecology and spatial analysis. This field has tremendously progressed in the past 10 years (Manel and Holderegger 2013). Typically, landscape geneticists are interested in describing how gene flow among populations or subpopulations is influenced in often heterogeneous, fragmented landscapes, leading to estimates of functional connectivity (Manel and Holderegger 2013). However, measuring gene flow among groups of individuals imposes limitations on the interpretability of results in terms of functional connectivity because (i) there may be important discrepancies between gene flow and ecological dispersal, that is, movement among habitat patches may not necessarily be associated with opportunities for mating (Garant et al. 2007), and (ii) gene flow measured among populations reflects migration that has occurred for several generations in the past and may not accurately reflect current ecological processes (Epps et al. 2007), including sex-specific differences. Ideally, the operational unit in landscape genetics should be the individual (Manel et al. 2003), in which case estimates of pairwise genetic relatedness can be used as the response variable to model landscape connectivity according to habitat features (Segelbacher et al. 2010 Etherington 2011 Shafer et al. 2012).

Rabies is enzootic to many species of bats and carnivores throughout the world and has a relatively long average incubation period (between 30 and 90 days) in comparison with a short morbidity period (2� days) that almost always leads to death (Leung et al. 2007). In eastern North America, the predominant terrestrial rabies strain is the RRV, which has spread in wild populations of both raccoons and striped skunks (Mephitis mephitis, hereafter skunks, Guerra et al. 2003). This rabies variant was historically restricted to Florida, but infected raccoons were inadvertently moved to Virginia in the late 1970s and the virus has since expanded northward at a rate of 30� km/year (Rupprecht et al. 2002). In Canada, it was first detected in southern Ontario in 1999 (Rosatte et al. 2001), then in New Brunswick in 2000 and finally in Qu󩯬 in 2006 (Rees et al. 2011). Here, we used estimates of genetic relatedness derived from multilocus microsatellite genotypes to determine which landscape features promoted or limited dispersal of the two main hosts of RRV in an intensively studied area of southern Qu󩯬 where this viral disease is still under surveillance, control and research activities (Boyer et al. 2011 Houle et al. 2011 Rees et al. 2011 Côté et al. 2012 Mainguy et al. 2012 Talbot et al. 2012).

Previous work in the study area (south-eastern Qu󩯬) has shown very little genetic structuring in resident raccoons and skunks, with highways and rivers generally generating a rather weak effect on patterns of genetic differentiation (Côté et al. 2012 Talbot et al. 2012). These equivocal results may conceal the effect of unmeasured spatial variables and do not allow modelling mesocarnivore dispersal at the landscape scale. Our main objective here was to build on the population genetic results obtained in the previous work, using an approach that applies landscape genetic analyses and spatially explicit models, to predict the most likely pathways of skunk and raccoon dispersal and, by extension, terrestrial rabies spread in this area. Based on the ecological knowledge of habitat use by these two hosts, we expected dispersal in both species to be reduced in agricultural fields, but did expect movement to be increased in habitats characterized by a high density of edges (e.g. Glueck et al. 1988 Dijak and Thompson 2000 Larivière and Messier 2000). We expected skunks to be more sensitive to the presence of fields and residential areas than raccoons, as raccoons typically show a greater affinity for dispersal and use cornfields and other human-related food sources (Riley et al. 1998 Prange et al. 2004). We also expected females to be more sensitive to landscape structure than males in both species, as dispersal is usually male-biased in mammals in general (Greenwood 1980), including raccoons and skunks (Cullingham et al. 2008 Côté et al. 2012 Talbot et al. 2012). To our knowledge, this is the first empirical work that addresses movement of these two important rabies hosts in a spatially explicit landscape genetics framework and also the first attempt to quantify the effect of habitat composition on their dispersal. Such work is important to refine predictive models of rabies propagation that use rivers (or other discrete barriers such as mountain chains) and human density indices to predict the rate of propagation of rabies (e.g. Smith et al. 2002 Russell et al. 2005, 2006).


CRISPR-Cas9: Institut Curie becomes equipped with genetic scissors for screening

CRISPR-Cas9 works like a pair of scissors capable of cutting the genome precisely. This technology is based on a complex composed of a small RNA called “guide” and the nuclease Cas9. The complex thus formed binds to a specific DNA sequence, complementary to the guide RNA. This binding is followed by a double strand cut of the DNA by Cas9. DNA repair mechanisms can subsequently be used to introduce precise mutations. In this way, researchers can manipulate DNA to suppress the function of a gene or replace it with a modified gene (the so-called “homologous recombination” method).

CRISPR-Cas9, a new technology? The CRISPR system (Clustered Regularly Interspaced Short Palindromic Repeats) has been around for millions of years. Bacteria use it as an adaptive immune defense mechanism against viruses. The methodology is simple and extremely effective: upon entry into bacteria, the viral genome is recognized by this dedicated immune memory system. Bacteria then destroy the genome of their enemy by cutting it off.

The use of programmable nucleases for genome-modifying purposes has been underway for about 12 years in laboratories. However, first generation nucleases were very complex to implement. CRISPR-Cas9 is revolutionary in its ease of programming, speed of execution and lower cost compared to other nucleases. The high flexibility of the CRISPR-Cas9 system makes it possible to perform genetic screening experiments. This methodology entails mutating all known genes in a population of cells, each cell carrying a single mutation. It is then possible to determine which genes are involved in a biological process of interest (cell growth, resistance to treatments, etc.).

A new genetic screening platform based on CRISPR-Cas9 has just been created at the Institut Curie’s research center. The CRISPR’it platform has generated a lot of enthusiasm.” We are already working with 15 research teams encompassing various topics “, says Camille Fouassier, the platform manager.

The evolution of the technology
The CRISPR-Cas9 technology has still important limitations. It can only be used at certain sites in the DNA sequence. And the sgRNA/Cas9 complex can cut sequences that resemble the target sequence (so called ‘off targets”). In addition, DNA cutting can induce a state of cellular stress and therefore bias the analysis of the resulting phenotypes. Finally, the DNA repair process leads to highly variable type of mutations that do not always eliminate gene function.

To overcome these limitations, scientists are working on derivatives of the CRISPR-Cas9 system. Alternative approaches being studied include a more precise and less toxic spinoff that introduces DNA substitutions. In particular, this method has the ability to change a codon into a premature stop signal for protein synthesis (“stop” codon).

Other derivatives take advantage of the opportunity offered by the CRISPR-Cas9 system to target a DNA sequence of interest. Instead of using CAS9 nuclease activity to edit the DNA sequence, local CAS9 recruitment can be used to modulate the overlying biological processes. For example, it is possible to increase or decrease gene expression or to locally modify the chemical composition of DNA or of the histone proteins associated with it. These chemical changes in turn influence different biological processes such as DNA repair or gene expression.

Applications of CRISPR-Cas9 at the Institut Curie
The applications in research are immense, since biologists now have the ability to introduce all types of genetic modifications into cells or model organisms. In just a few years, CRISPR-Cas9 has become a central tool in many research programs.

In particular, genetic screening by CRISPR-Cas9 represents a key asset for cancer research. For example, it is possible to address the problem of resistance to anti-tumor treatments. Genetic screening can be used to identify genes whose mutation makes cells resistant to a therapeutic molecule. The technology is also used to identify the Achilles heel of each type of cancer using a synthetic lethality approach. This latter strategy is based on the assumption that mutations that cause cancer create new vulnerabilities that can be used to kill cancer cells. Although CRISPR-Cas9 screening is most often deployed in in vitro cell culture systems, the ambition of the new platform is to work as closely as possible to the natural tumor environment. In particular, 3D culture systems (known as organoids), which recreate in vitro part of the architectural properties of tumors, represent attractive model systems. An approach also under investigation is the use of genetic screens on patient-derived xenografts, which currently represent the best models of cancer for pre-clinical studies.

Another application of the technology is the study of recurrent mutations found in cancer. These mutations can lead to an increase in the activity of oncogenic proteins or, conversely, a loss of the function of proteins that inhibit tumor development. However, we still poorly understand the function of most mutated genes in cancer.” By reintroducing these genetic abnormalities into cell lines thanks to CRISPR-Cas9, researchers are able to evaluate their impact on tumor development”, said Michel Wassef, scientific co-director of the platform.

Applications for immunotherapy are also under development. T lymphocytes taken from patients are genetically modified to express a chimeric receptor programmed to recognize a tumor marker (CAR T cells) and thus destroy the diseased cells.


Results

Chemical space analysis

Initially, an activity threshold of 1 μM based on half maximal effective concentration (EC50) against P. falciparum was defined for discrimination between active and inactive compounds previously tested against asexual blood stages of P. falciparum. In addition, a threshold of 10 μM based on half-maximal cytotoxic concentration (CC50) for the NIH/3T3 cells was defined for discrimination between toxic and nontoxic compounds [42]. The analysis of chemical space was performed by using the curated datasets (see Materials and Methods) for erythrocytic stages of P. falciparum 3D7 strain (chloroquine sensitive) dataset containing 1,162 compounds (P. falciparum dataset) and cytotoxicity dataset tested against mouse embryonic fibroblasts (NIH/3T3 cell line) containing 1,270 compounds (cytotoxicity dataset). This analysis has been performed by clustering both datasets separately, which revealed that both are very structurally dissimilar, containing smaller clusters of similar compounds (Fig 2).

Cluster analysis of A) 1,162 compounds from P. falciparum dataset and B) 1,270 compounds from cytotoxicity dataset. Dendrogram and heatmap of the distance matrix are both colored according to structural similarity (orange/red = similar blue/violet = dissimilar). The x- and y-axis labels of the heatmap represent compounds.

The plot was obtained using barycentric coordinates from 2D RDKit descriptors showing active (blue dots) and inactive (black diamonds) compounds of P. falciparum dataset and toxic (red stars) and nontoxic (green triangles) from cytotoxicity dataset.

Although the datasets are structurally diverse, they share the same regions of chemical space. When analyzing the chemical space of both datasets together, by protting the two-dimensional barcentric coordinates [43] (Fig 3, see Materials and Methods for details), one can see that most of the active and inactive compounds from P. falciparum dataset overlap within the same regions of chemical space of toxic and nontoxic compounds from the cytotoxicity dataset. This analysis reveals that multiple compounds active against P. falciparum in the erythrocytic stage are potentially toxic in mouse embryonic fibroblasts. For this reason, we developed predictive computational models for both biological properties in order to select only compounds predicted as active for P. falciparum and nontoxic for mammalian cells.

Performance of binary QSAR models

Binary QSAR models were built to distinguish active vs. inactive compounds for P. falciparum and toxic vs. nontoxic compounds for NIH/3T3 cells. According to the statistical results of a 5-fold external cross-validation procedure (see Materials and Methods), the combination of Morgan and FeatMorgan fingerprints (radius 2: FeatMorgan_2, Morgan_2 radius 4: FeatMorgan_4, Morgan_4) with deep learning (see Materials and Methods for details) led to predictive binary QSAR models. Statistical characteristics of developed QSAR models estimated by 5-fold external cross-validation are reported in Table 1. Briefly, correct classification rate (CCR) values were ranging between 0.82–0.87 sensitivity (SE)– 0.82–0.87 specificity (SP)– 0.82–0.87, and a coverage– 0.77–0.87. Table 1 shows the detailed performances of the binary QSAR models. The model built using Morgan_2 demonstrated the best performance among all other models developed for P. falciparum (CCR = 0.84 SE = 0.82 SP = 0.86 and PPV = 0.86). On the other hand, the best model developed for prediction of cytotoxicity for mammalian fibroblasts was built using FeatMorgan_4 (CCR = 0.87 SE = 0.87 and SP = 0.87).

Performance of continuous QSAR models

We have developed continuous QSAR models aiming to predict negative logarithmic units of EC50 values (pEC50) against P. falciparum and CC50 values (pCC50) against NIH/3T3 cell line. According to the statistical results of a 5-fold external cross-validation procedure, the combination of Morgan and FeatMorgan fingerprints (radius 2: FeatMorgan_2, Morgan_2) with deep learning led to statistically predictive models (Table 2), with predictive squared correlation coefficient for the test set ( ) values ranging between 0.70–0.88, root mean square error of cross-validation (RMSECV) of 0.44–0.55, mean absolute error (MAE) of 0.31–0.43, and coverage of 0.79–0.81. The model built using Morgan_2 demonstrated the best performance among all other models developed for P. falciparum ( = 0.88, RMSECV = 0.49, and MAE = 0.43). On the other hand, the best model developed for prediction of cytotoxicity for mammalian fibroblasts was built using FeatMorgan_2 ( = 0.74, RMSECV = 0.44, and MAE = 0.31).

Model interpretation

To provide a mechanistic interpretation and shed some light from the structural and biological data used to build the continuous QSAR models, we plotted the predicted feature (fingerprint) importance to visualize how the fragments contributed for the antiplasmodial activity and the cytotoxicity (Fig 4 and S1 Fig). According to our results, atoms or fragments promoting positive contribution for the antiplasmodial activity are highlighted in red, while structural moieties decreasing the activity are highlighted in green.

Compounds experimentally tested in P. falciparum assay, extracted from the literature and used to build/validate our models. Fragments increasing the activity are colored in red structural moieties decreasing the activity are highlighted in green indifferent fragments are not highlighted. pEC50 exp = pEC50 experimental pEC50 pred = pEC50 predicted.

By analyzing the contribution maps generated for the P. falciparum dataset, we identified six major fragments with favorable contribution for antiplasmodial activity. Examples of favorable fragments are as follows: 1,2,4,5-tetraoxaspiro[5.5]undecane 7-chloroquinoline 2,5-dimethylhexa-1,5-diene pyridin-2-amine 1,4-dihydroquinolin-4-one and 1,3,5-triazine-2,4,6-triamine. We also identified six fragments with unfavorable contribution for antiplasmodial activity, such as: 1,2-dimethyl-1,4-dihydropyridin-4-one 2-methylfuran 5-guanidine N-ethylpropanamide 2,6-dimethylhepta-1,5-diene and 4H-pyrido[1,2-a]pyrimidin-4-one. Moreover, we also calculated the predicted influence of structural fragments on the cytotoxicity. A summarized list of atoms or fragments with favorable and unfavorable contribution for cytotoxicity on mammalian fibroblasts is available in S1 Fig. The structural and biological information provided by the QSAR models developed using deep learning could be useful for designing or optimizing potent and selective antiplasmodial compounds by replacing unfavorable fragments by favorable fragments, assuming true independence of physicochemical effects.

Virtual screening

The virtual screening (VS) was carried out following the workflow presented in Fig 5. Initially, 486,115 compounds available on EXPRESS-Pick collection of ChemBridge were downloaded and standardized for VS. Then, drug-likeness filters (Veber [44] and Lipinski’s rules [45]) were applied to prioritize molecules with good oral bioavailability, to ensure that the compound has basic properties of active drugs. In parallel, colloidal aggregation tool was used to filter out molecules that are known to aggregate in experimental assays [46,47] After these steps, 72,260 compounds were excluded. Afterwards, the remaining compounds were submitted to conservative binary and continuous QSAR models for prediction of the activity against blood stages of P. falciparum and cytotoxicity against mammalian cells. The final selection of candidate compounds can be summarized as follows: (i) the compounds predicted as active and non-cytotoxic by the binary QSAR models (ii) compounds with pEC50 ≥6.00 (i.e., EC50 ≤1 μM for P. falciparum) and pCC50 <5.00 (i.e., CC50 >10 μM for mammalian cells) predicted by the continuous QSAR models (iii) and compounds inside the applicability domain (AD) of the QSAR models. The combination of binary and continuous QSAR models was implemented to increase success rates in virtual screening campaign. In addition, the AD was determined in order to set “reliable” and “unreliable” predictions [48,49]. The predictions were considered reliable when they were within the chemical space used for training the models. Finally, we performed a chemical similarity analysis to select a subset of structurally diverse compounds. At the end of this process, five candidate compounds were selected for biological evaluation (Fig 6).

Veber and Lipinski rules were used to prioritize candidate compounds with good oral bioavailability, to ensure that the compound has basic properties of active drugs colloidal aggregation tool was used to filter out molecules that are known to aggregate in experimental assays chemical similarity analysis and visual inspection were performed to select a subset of structurally diverse candidate compounds.

Atoms or fragments promoting positive contribution for the antiplasmodial activity are highlighted in red.

Experimental validation

The five candidate compounds were evaluated in vitro against asexual blood stages of P. falciparum sensitive (3D7), and multi-drug-resistant (W2) strains. The EC50 for each compound (Table 3) indicate that two compounds, 2-(4,6-diphenyl-1,2-dihydro-1,3,5-triazin-2-yl)phenol (LabMol-149), 4-benzoic acid (LabMol-151) and N2-(3-fluorophenyl)-N4-[(oxolan-2-yl)methyl]quinazoline-2,4-diamine (LabMol-152), were potent at inhibiting the parasite growth, showing activities in submicromolar and low nanomolar range against both 3D7 and W2 strains. Moreover, the compound LabMol-152 (EC50 = 0.049 μM and 0.078 μM for 3D7 and W2, respectively) showed efficacy in the same range of activity of the reference drugs, chloroquine (EC50 = 0.011 μM) and pyrimethamine (EC50 = 0.037 μM). The candidate compounds were also evaluated for their cytotoxicity against fibroblast-like cell lines derived from monkey kidney (COS-7 cells). With respect to selectivity, LabMol-149 and LabMol-152 showed the most promising results (selectivity index, SI, ranging between 71.4–340.8, Table 3).


All Models Aren&apost Created Equal

By Ronald Hammond, PhD
Product Manager, Physiology and Laboratory Safety


Models are among the most traditional and useful items in a science educator’s toolbox. Whether the subject of study is leaf morphology, invertebrate zoology, general biology, or human anatomy, models are the perfect adjunct to texts and diagrams. They enable students to examine, in 3-dimensional perspective, the finest structural details of an organism or its components.

In many cases, models can be disassembled in a manner similar to performing a dissection. As an adjunct to dissection, models are an excellent reference tool for identifying small and indistinct structures. With the careful planning and execution of the master sculpture (from which the mold is made), high-quality models display the maximum number of anatomical structures.

Human anatomy models

No discipline of science relies more on the use of models than does human anatomy and physiology. Preserved human tissues are not readily available for educational purposes and, when they are, the condition of the specimens is usually not satisfactory to show detailed morphology. Many structures, such as the components of the middle and inner ear, are much too small for convenient study from natural specimens.

A thorough knowledge of the structure of cells and tissues is an absolute prerequisite for an understanding of the physiology, or function, of the various organ systems. An enlarged model is an indispensable teaching aid in such instances.

A complete human figure or torso model with head is actually a collection of models of individual organs, many of which can be separated and even opened for internal inspection. A human torso model of high quality is a valuable acquisition for any anatomy classroom. Generally, those with the largest number of removable components have the greatest teaching value.

Care of models

  1. Photocopy the identification key that accompanies each model and store the original key in a safe place.
  2. To avoid fading and deterioration of models, keep them in a reasonably cool area away from direct sunlight. Drape them with untreated cloth to prevent the accumulation of dirt and dust.
  3. Gently clean your models with a soft cloth and warm, soapy water to restore the surface without damaging the finish. Never use abrasives and solvents.
  • Molded from durable materials that guarantee long life and resistance to breakage
  • Painted with nontoxic, acrylic formulations bonded securely to the substrate to last the life of the model
  • Crafted carefully by a skilled artisan to assure that every minute detail is represented correctly
  • Mounted on attractive, reinforced display bases
  • Complete with identification keys
  • Backed by the unconditional Carolina guarantee

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Explanatory pluralism and integration

Above I have argued that, from a pragmatic point of view, psychopathological research should embrace research at multiple levels. In other words, if the aim is to make progress in treating and understanding mental disorder, explanatory pluralism is preferable to explanatory reductionism. Explanatory pluralism in psychopathology has been defended by many authors (e.g. Kendler, Reference Kendler 2005 Miller, Reference Miller 2010 Borsboom et al., Reference Borsboom, Cramer and Kalis 2019), and it is also inherent in the biopsychosocial model that has been influential in psychiatry at least since the 1970s and is still widely taught to students (Pilgrim, Reference Pilgrim 2002 Frisch, Reference Frisch 2016).

However, the basic idea that explanations need to be looked for at multiple levels is compatible with many different scenarios of how such multilevel research will actually play out (Marchionni, Reference Marchionni 2008 Sullivan, Reference Sullivan, Kincaid and Sullivan 2014, Reference Sullivan 2017 Gijsbers, Reference Gijsbers 2016 Love, Reference Love, Massimi, Romeijn and Schurz 2017). Most importantly, pluralism can lead to (1) a patchwork of explanations at different levels, or (2) integration of explanations at different levels. In the first scenario, the different perspectives or different levels that are needed for fully explaining psychopathology cannot be combined to one grand multilevel explanation, but are somehow incompatible or incongruent. In the second scenario, the different perspectives and different levels are compatible and complement each other in such a way that they can be combined to one grand harmonious multilevel explanation. These two scenarios should be seen as the extreme ends of a continuum, as the success of integration is not a yes-or-no thing but a matter of degree.

Most philosophers, researchers, and clinicians would probably agree that something like the integrative scenario is the more preferable and attractive alternative. Many authors have also explicitly advocated integrative pluralism for psychopathology (e.g. Kendler, Reference Kendler 2005 Mitchell, Reference Mitchell, Kendler and Parnas 2008 Miller, Reference Miller 2010). However, what has received less attention is how integration would work in practice, and what are the challenges and hurdles that hold back integration. In this section, I will focus on these questions, drawing from recent philosophy of biology where these issues have been more extensively discussed.

First of all, an important feature of integration that is often forgotten in theoretical discussions, also in psychopathology, is that it is case-specific. Philosophers and researchers often search for a general answer to the question of reduction and integration, arguing for example that mental disorders can be explained based on brain circuits, or that psychological explanations are always indispensable. In contrast, the degree to which multiple disciplines and levels are needed, and the degree to which the different perspectives can be integrated, can both vary from disorder to disorder (or even from symptom to symptom). The phenomenon that is studied or the problem that needs to be solved (the ‘problem agenda’, Love, Reference Love 2008) determines what fields and what levels are needed (Love, Reference Love 2008 Brigandt, Reference Brigandt 2010). For example, it is plausible that there will be satisfactory low-level biological explanations for disorders such as dementia, but that such explanations will be insufficient for depression or posttraumatic stress disorder (PTSD). Similarly, the integration of explanations or models of different levels might work out well and lead to new insights in one context, but face profound obstacles in another context (see below). In other words, accounts of pluralism and integration in psychopathology should not be overgeneralized, but should be case-specific and sensitive to the scientific details of each case.

Another crucial point is that integration should be seen as an active and dynamic process, and not just as the final goal or end result of pursuing research at different levels. As O'Malley and Soyer ( Reference O'Malley and Soyer 2012) show in the context of systems biology, integration has often led to new insights, or even to the emergence of new and flourishing research fields. They also emphasize that integration does not occur just by combining explanations or theories of different levels, but often involves importing and translating data and models of one discipline to another. For example, in the recently emerged field of evolutionary systems biology, insights and data from evolutionary biology are imported into the cellular and molecular models of systems biology (O'Malley and Soyer, Reference O'Malley and Soyer 2012). One recent example of this in psychopathology is the Ising model. This model represents a network of binary variables that interact with their neighbors, and was originally introduced in the 1920s to model the behavior of magnetic particles. However, it turns out that the same model can also be used to describe neural networks (Yuste, Reference Yuste 2015), and more recently, the Ising model has been shown to be mathematically equivalent to Item Response Theory models and binary symptom network models in psychology (Van Borkulo et al., Reference Van Borkulo, Borsboom, Epskamp, Blanken, Boschloo, Schoevers and Waldorp 2014 Kruis and Maris, Reference Kruis and Maris 2016 Marsman et al., Reference Marsman, Borsboom, Kruis, Epskamp, van Bork, Waldorp, Maas and Maris 2018). The fact that integrative multilevel research has been so fruitful in other fields should provide a strong incentive for pursuing such research in psychopathology as well.

However, although it is clear that there is much to be gained from integrating methods, data, and perspectives of different levels, there are several obstacles to such integration in psychopathology. First of all, in the biological sciences integration often occurs through the elaboration of multilevel mechanisms through constraints (Bechtel and Richardson, Reference Bechtel and Richardson 1993 Craver and Darden, Reference Craver and Darden 2013). The idea is that different fields impose different constraints on what the explanatory mechanism for a phenomenon could be, and in this way the space of possible mechanisms is narrowed down. Often researchers start with a sketch of a mechanism, and as more evidence is gathered, this sketch can be refined and black boxes are filled in. This can involve many different disciplines and perspectives. For example, the discovery and refinement of the model of protein synthesis in the 1950 and 1960s involved integrating knowledge from biochemistry (e.g. chemical reactions involving amino acids) and molecular biology, resulting in constraints on how the mechanism of protein synthesis could look like. Eventually these constraints and inputs from multiple fields resulted in the DNA–RNA theory of protein synthesis.

One obstacle to this kind of integration is the incommensurability of levels discussed in section 2. The part-whole hierarchies in psychology are different from the (mechanistic) part-whole hierarchies in biology, and it is not clear how the two can be integrated. Thus, the mechanistic picture needs to be complemented with an account of how psychological states can be integrated into mechanistic explanations. So far, this has been only done in the context of computational or functional states in psychology (e.g. Piccinini and Craver, Reference Piccinini and Craver 2011 Thomas and Sharp, Reference Thomas and Sharp 2019), but it is not clear how this kind of integration would work for phenomena such as affect states, beliefs, and symptoms. Such integration is also challenged by the fact that psychological processes often unfold and interact at different time scales than biological processes (section 2). Integrating models and explanations that pertain to different time scales are not impossible, but currently there is little understanding on how it should be done.

A more general problem for integration in psychopathology is descriptive complexity: different conceptual frameworks often do not carve phenomena in the same way, but result in mismatching and conflicting categorizations (Wimsatt, Reference Wimsatt, Schaffner and Cohen 1972 Sullivan, Reference Sullivan, Kincaid and Sullivan 2014, Reference Sullivan 2017 Tabb and Schaffner, Reference Tabb, Schaffner, Kendler and Parnas 2017). This can be vividly seen in schizophrenia research. As Sullivan ( Reference Sullivan, Kincaid and Sullivan 2014) points out, the cognitive deficits that are important to schizophrenia are studied both in cognitive neuroscience and in cognitive neurobiology, but from different perspectives. In cognitive neuroscience, the aim is to probe specific cognitive functions and to localize them in the brain with neuroimaging techniques. In cognitive neurobiology, the cognitive deficits underlying schizophrenia are studied through animal models (e.g. rats). In such experiments, the aim is to discover differences in the behavior of rats, which are taken to indicate a cognitive deficit, but no mapping to specific human cognitive functions is made. Thus, even though the same phenomenon is nominally being targeted, it is conceptualized in different ways. More generally, Tabb and Schaffner ( Reference Tabb, Schaffner, Kendler and Parnas 2017) point out that different state-of-the-art models of schizophrenia that focus on different levels do not even agree on what are the defining features or key symptoms of schizophrenia.

Issues like this abound in psychopathology. For example, fear extinction is studied in neurobiology with rodents based on freezing behavior after a foot shock. In humans, fear extinction is measured with more complex stimuli, and typically with skin conductance responses as the dependent variable (Lonsdorf et al., Reference Lonsdorf, Menz, Andreatta, Fullana, Golkar, Haaker and Drexler 2017). Recently doubts have been raised regarding this translation, as it is far from clear that the setups are measuring the same phenomena (Lonsdorf et al., Reference Lonsdorf, Merz and Fullana 2019 see also Glas, Reference Glas 2004 Khalidi, Reference Khalidi 2005). In psychopathology, descriptive complexity seems to be the rule rather than the exception.

However, this does not mean that there is no hope for integration. In biology, there are many success stories of integrating fields that seem to conceptualize phenomena in different ways (e.g. in the context of systems biology mentioned above O'Malley and Soyer, Reference O'Malley and Soyer 2012). Also in psychopathology, concentrated interdisciplinary efforts, involving both scientists from different fields and philosophers of science, can help to aligning concepts and models of different fields in the context of a specific problem or phenomenon (see also Love, Reference Love 2008 Sullivan, Reference Sullivan 2017 Laplane et al., Reference Laplane, Mantovani, Adolphs, Chang, Mantovani, McFall-Ngai and Pradeu 2019).


Funding

The authors would like to thank Brazilian funding agencies, CNPq, FAPEG, FAPESP, and CAPES for financial support and fellowships. FC, GC, and CA were supported by FAPESP (Grants #2012/16525-2, #2015/20774-6, and #2017/02353-9, respectively). DB was supported by FAPESP (Grant #2013/13119-6) and CNPq (Grant #405996/2016-0). JC was supported by FAPESP fellowship (#2016/16649-4). EM appreciates support from NIH (Grant 1U01CA207160 and GM5105946) and CNPq (Grant #400760/2014-2). CA, PC, and FC are CNPq research fellows. PC was partially supported by the Fundação Nacional de Desenvolvimento do Ensino Superior Particular – Funadesp, via UniEvangélica – Centro Universitário de Anápolis.


Methods

We recently sequenced five additional SAR11 genomes from three phylogenetically distinct clades, corresponding to the subgroups Ia, III and a new, distantly related subgroup (Group V) 46 (Figs. 6 & 7), which were annotated consistently with others used in this study by IMG 47 (http://img.jgi.doe.gov/cgi-bin/m/main.cgi). We made exclusive use of publicly available bacterial genome sequences from the IMG database to control for gene-calling methodological differences across databases. The 127 taxa used in the whole alphaproteobacterial dataset (Table S1) were a subset of the total available sequences chosen to represent all sequenced genera. Given that some species are highly overrepresented in the database, we omitted several taxa from these overrepresented groups. Datasets with mitochondrial sequences included most available genome sequences from the respective Alphaproteobacteria orders (Table S2). Mitochondrial sequences (Table S3) were obtained from NCBI (http://www.ncbi.nlm.nih.gov/). The sequences were analyzed using Hal, an automated pipeline for phylogenetic analysis of genomic protein sequence data.

The Hal pipeline 23,48 (http://aftol.org/pages/Halweb3.htm http://sourceforge.net/projects/bio-hal/) consists of a set of Perl scripts that automates a series of phylogenomic analyses using existing software and sequence analysis programs and was executed on a 64-bit Linux cluster operating Red Hat Linux 3.2.3, Linux version 2.4.21. For this study, the Hal pipeline directed the following analyses: Protein sequences were imported in fasta format from sequenced genomes listed in Table S1–3 and subjected to an all-vs-all BLASTP with the output E-values provided to the program MCL 49 . Using a Markov Clustering algorithm, MCL grouped proteins into orthologous clusters (OCs) as function of the all-vs-all BLASTP E-values. Clustering was executed across a range of stringencies (inflation parameters 1.1 – 5.0) and OCs were filtered for any redundant clusters (clusters found at more than one inflation parameter), clusters containing more than one protein per genome (multi-copy OC) and clusters containing proteins whose best reciprocal BLAST hit was outside of the cluster (clusters more likely to contain paralogs). Clusters were also filtered across a range of missing values settings (10–60%) whereby a cluster may contain only one protein per genome but a defined percentage of the genomes may be missing from the cluster. This function allows for the incorporation for more single-copy clusters when such proteins are either missing from a given genome or missing from a genome annotation.

Protein sequences for accepted OCs were extracted from their respective genome fasta sequence files and aligned using MUSCLE 50 with default settings. To accommodate for problematic regions of the alignments, three separate alignments were created for each protein alignment: one removing all gap-containing columns (remgaps) and two removing problematic regions of the alignments based on the default conservative (Glocks-con) and liberal (Gblocks-lib) options of the program GBlocks 51 . Best models of amino acid substitution for each protein alignment were estimated using ProtTest 52 . Individual protein alignments were concatenated into one super-alignment and analyzed using RAxML 53 with the PROTCAT setting for the rate model and with each protein partition of the super-alignment assigned its best model of amino acid substitution. Nodal support was estimated based on 100 bootstrap replications using the rapid bootstrapping option as implemented in RAxML. For each analysis, three phylogenetic trees were generated, representing the three super-alignments (remgaps, GBlocks-con and GBlocks-lib) produced as part of the automated alignment routine.


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