Genetic and Neural Modularity Underlie the Evolution of Schooling Behavior in Threespine Sticklebacks

SummaryAlthough descriptions of striking diversity in animal behavior are plentiful, little is known about the mechanisms by which behaviors change and evolve between groups. To fully understand behavioral evolution, it will be necessary to identify the genetic mechanisms that mediate behavioral change in a natural context [1–3]. Genetic analysis of behavior can also reveal associations between behavior and morphological or neural phenotypes, providing insight into the proximate mechanisms that control behavior. Relatively few studies to date have successfully identified genes or genomic regions that contribute to behavioral variation among natural populations or species [2], particularly in vertebrates [4–8]. Here, we apply genetic approaches to dissect a complex social behavior that has long fascinated biologists, schooling behavior [9–13]. We performed quantitative trait locus (QTL) analysis of schooling in an F2 intercross between strongly schooling marine and weakly schooling benthic sticklebacks (Gasterosteus aculeatus) and found that distinct genetic modules control different aspects of schooling behavior. Two key components of the behavior, tendency to school and body position when schooling, are uncorrelated in hybrids and map to different genomic regions. Our results further point to a genetic link between one behavioral component, schooling position, and variation in the neurosensory lateral line

STOP-AD portal: Selecting the optimal pharmaceutical for preclinical drug testing in Alzheimer\u27s disease.

We propose an unbiased methodology to rank compounds for advancement into comprehensive preclinical testing for Alzheimer\u27s disease (AD). Translation of compounds to the clinic in AD has been hampered by poor predictive validity of models, compounds with limited pharmaceutical properties, and studies that lack rigor. To overcome this, MODEL-AD\u27s Preclinical Testing Core developed a standardized pipeline for assessing efficacy in AD mouse models. We hypothesize that rank-ordering compounds based upon pharmacokinetic, efficacy, and toxicity properties in preclinical models will enhance successful translation to the clinic. Previously compound selection was based solely on physiochemical properties, with arbitrary cutoff limits, making ranking challenging. Since no gold standard exists for systematic prioritization, validating a selection criteria has remained elusive. The STOP-AD framework evaluates the drug-like properties to rank compounds for in vivo studies, and uses an unbiased approach that overcomes the validation limitation by performing Monte-Carlo simulations. HIGHLIGHTS: Promising preclinical studies for AD drugs have not translated to clinical success. Systematic assessment of AD drug candidates may increase clinical translatability. We describe a well-defined framework for compound selection with clear selection metrics

Molecular estimation of neurodegeneration pseudotime in older brains.

The temporal molecular changes that lead to disease onset and progression in Alzheimer\u27s disease (AD) are still unknown. Here we develop a temporal model for these unobserved molecular changes with a manifold learning method applied to RNA-Seq data collected from human postmortem brain samples collected within the ROS/MAP and Mayo Clinic RNA-Seq studies. We define an ordering across samples based on their similarity in gene expression and use this ordering to estimate the molecular disease stage-or disease pseudotime-for each sample. Disease pseudotime is strongly concordant with the burden of tau (Braak score, P = 1.0 × 10-5), Aβ (CERAD score, P = 1.8 × 10-5), and cognitive diagnosis (P = 3.5 × 10-7) of late-onset (LO) AD. Early stage disease pseudotime samples are enriched for controls and show changes in basic cellular functions. Late stage disease pseudotime samples are enriched for late stage AD cases and show changes in neuroinflammation and amyloid pathologic processes. We also identify a set of late stage pseudotime samples that are controls and show changes in genes enriched for protein trafficking, splicing, regulation of apoptosis, and prevention of amyloid cleavage pathways. In summary, we present a method for ordering patients along a trajectory of LOAD disease progression from brain transcriptomic data

Analysis of ancestry heterozygosity suggests that hybrid incompatibilities in threespine stickleback are environment dependent

Hybrid incompatibilities occur when interactions between opposite ancestry alleles at different loci reduce the fitness of hybrids. Most work on incompatibilities has focused on those that are “intrinsic,” meaning they affect viability and sterility in the laboratory. Theory predicts that ecological selection can also underlie hybrid incompatibilities, but tests of this hypothesis using sequence data are scarce. In this article, we compiled genetic data for F(2) hybrid crosses between divergent populations of threespine stickleback fish (Gasterosteus aculeatus L.) that were born and raised in either the field (seminatural experimental ponds) or the laboratory (aquaria). Because selection against incompatibilities results in elevated ancestry heterozygosity, we tested the prediction that ancestry heterozygosity will be higher in pond-raised fish compared to those raised in aquaria. We found that ancestry heterozygosity was elevated by approximately 3% in crosses raised in ponds compared to those raised in aquaria. Additional analyses support a phenotypic basis for incompatibility and suggest that environment-specific single-locus heterozygote advantage is not the cause of selection on ancestry heterozygosity. Our study provides evidence that, in stickleback, a coarse—albeit indirect—signal of environment-dependent hybrid incompatibility is reliably detectable and suggests that extrinsic incompatibilities can evolve before intrinsic incompatibilities

Normal levels of p27Xic1 are necessary for somite segmentation and determining pronephric organ size

The Xenopus laevis cyclin dependent kinase inhibitor p27Xic1 has been shown to be involved in exit from the cell cycle and differentiation of cells into a quiescent state in the nervous system, muscle tissue, heart and retina. We show that p27Xic1 is expressed in the developing kidney in the nephrostomal regions. Using over-expression and morpholino oligonucleotide (MO) knock-down approaches we show normal levels of p27Xic1 regulate pronephros organ size by regulating cell cycle exit. Knock-down of p27Xic1 expression using a MO prevented myogenesis, as previously reported; an effect that subsequently inhibits pronephrogenesis. Furthermore, we show that normal levels of p27Xic1 are required for somite segmentation also through its cell cycle control function. Finally, we provide evidence to suggest correct paraxial mesoderm segmentation is not necessary for pronephric induction in the intermediate mesoderm. These results indicate novel developmental roles for p27Xic1, and reveal its differentiation function is not universally utilised in all developing tissues

Open drug discovery in Alzheimer\u27s disease.

Alzheimer\u27s disease (AD) drug discovery has focused on a set of highly studied therapeutic hypotheses, with limited success. The heterogeneous nature of AD processes suggests that a more diverse, systems-integrated strategy may identify new therapeutic hypotheses. Although many target hypotheses have arisen from systems-level modeling of human disease, in practice and for many reasons, it has proven challenging to translate them into drug discovery pipelines. First, many hypotheses implicate protein targets and/or biological mechanisms that are under-studied, meaning there is a paucity of evidence to inform experimental strategies as well as high-quality reagents to perform them. Second, systems-level targets are predicted to act in concert, requiring adaptations in how we characterize new drug targets. Here we posit that the development and open distribution of high-quality experimental reagents and informatic outputs-termed target enabling packages (TEPs)-will catalyze rapid evaluation of emerging systems-integrated targets in AD by enabling parallel, independent, and unencumbered research

Precision measurement of cardiac structure and function in cardiovascular magnetic resonance using machine learning

BACKGROUND: Measurement of cardiac structure and function from images (e.g. volumes, mass and derived parameters such as left ventricular (LV) ejection fraction [LVEF]) guides care for millions. This is best assessed using cardiovascular magnetic resonance (CMR), but image analysis is currently performed by individual clinicians, which introduces error. We sought to develop a machine learning algorithm for volumetric analysis of CMR images with demonstrably better precision than human analysis. METHODS: A fully automated machine learning algorithm was trained on 1923 scans (10 scanner models, 13 institutions, 9 clinical conditions, 60,000 contours) and used to segment the LV blood volume and myocardium. Performance was quantified by measuring precision on an independent multi-site validation dataset with multiple pathologies with n = 109 patients, scanned twice. This dataset was augmented with a further 1277 patients scanned as part of routine clinical care to allow qualitative assessment of generalization ability by identifying mis-segmentations. Machine learning algorithm ('machine') performance was compared to three clinicians ('human') and a commercial tool (cvi42, Circle Cardiovascular Imaging). FINDINGS: Machine analysis was quicker (20 s per patient) than human (13 min). Overall machine mis-segmentation rate was 1 in 479 images for the combined dataset, occurring mostly in rare pathologies not encountered in training. Without correcting these mis-segmentations, machine analysis had superior precision to three clinicians (e.g. scan-rescan coefficients of variation of human vs machine: LVEF 6.0% vs 4.2%, LV mass 4.8% vs. 3.6%; both P < 0.05), translating to a 46% reduction in required trial sample size using an LVEF endpoint. CONCLUSION: We present a fully automated algorithm for measuring LV structure and global systolic function that betters human performance for speed and precision

Delayed BCG immunization does not alter antibody responses to EPI vaccines in HIV-exposed and -unexposed South African infants.

BACKGROUND: Bacille Calmette-Guérin (BCG) is routinely given at birth in tuberculosis-endemic settings due to its protective effect against disseminated tuberculosis in infants. BCG is however contraindicated in HIV-infected infants. We investigated whether delaying BCG vaccination to 14 weeks of age affected vaccine-induced antibody responses to Haemophilus influenzae type b (Hib)-conjugate, pertussis, tetanus and Hepatitis B (HBV) vaccines, in HIV-exposed uninfected (HEU) and -unexposed uninfected (HUU) infants. METHODS: Infants were randomized to receive BCG at birth or at 14 weeks of age. Blood was taken at 14, 24, and 52 weeks of age and analyzed for Hib, pertussis, tetanus and HBV specific antibodies. RESULTS: BCG was given either at birth (106 infants, 51 HEU) or at 14 weeks of age (74 infants, 50 HEU). The timing of BCG vaccination did not influence the antibody response to any antigen studied. However, in a non-randomized comparison, HEU infants had higher Hib antibody concentrations at weeks 14 and 24 (p=0.001 and <0.001, respectively) and pertussis at week 24 (p=0.003). Conversely, HEU infants had lower antibody concentrations to HBV at 14 and 52 weeks (p=0.032 and p=0.031) with no differences in tetanus titres. CONCLUSIONS: HIV exposure, but not the timing of BCG vaccination, was associated with antibody concentrations to Hib, pertussis, HBV and tetanus primary immunization. CLINICAL TRIAL REGISTRATION: DOH-27-1106-1520

New Approach Using the Real-Time PCR Method for Estimation of the Toxic Marine Dinoflagellate Ostreopsis cf. ovata in Marine Environment

Background: We describe the development and validation of a new quantitative real time PCR (qrt-PCR) method for the enumeration of the toxic benthic dinoflagellate Ostreopsis cf. ovata in marine environment. The benthic Ostreopsis sp. has a world-wide distribution and is associated during high biomass proliferation with the production of potent palytoxin-like compounds affecting human health and environment. Species-specific identification, which is relevant for the complex of different toxins production, by traditional methods of microscopy is difficult due to the high morphological variability, and thus different morphotypes can be easily misinterpreted. Methodology/Findings: The method is based on the SYBR I Green real-time PCR technology and combines the use of a plasmid standard curve with a ‘‘gold standard’’ created with pooled crude extracts from environmental samples collected during a bloom event of Ostreopsis cf. ovata in the Mediterranean Sea. Based on their similar PCR efficiencies (95% and 98%, respectively), the exact rDNA copy number per cell was obtained in cultured and environmental samples. Cell lysates were used as the templates to obtain total recovery of DNA. The analytical sensitivity of the PCR was set at two rDNA copy number and 8.061024 cell per reaction for plasmid and gold standards, respectively; the sensitivity of the assay was of cells g21 fw or 121 in macrophyte and seawater samples, respectively. The reproducibility was determined on the total linear quantification range of both curves confirming the accuracy of the technical set-up in the complete ranges of quantification over time. Conclusions/Significance: We developed a qrt-PCR assay specific, robust and high sample throughput for the absolute quantification of the toxic dinoflagellate Ostreopsis cf. ovata in the environmental samples. This molecular approach may be considered alternative to traditional microscopy and applied for the monitoring of benthic toxic microalgal species in the marine ecosystems