Sexual signalling in Propithecus verreauxi: male ‘‘chest badge’’ and female mate choice

Communication, an essential prerequisite for sociality, involves the transmission of signals. A signal can be defined as any action or trait produced by one animal, the sender, that produces a change in the behaviour of another animal, the receiver. Secondary sexual signals are often used for mate choice because they may inform on a potential partner’s quality. Verreaux’s sifaka (Propithecus verreauxi) is characterized by the presence of two different morphs of males (bimorphism), which can show either a stained or clean chest. The chest becomes stained by secretions of the sternal gland during throat marking (rubbing throat and chest on a vertical substrate while smearing the scent deposition). The role of the chest staining in guiding female mate choice was previously hypothesized but never demonstrated probably due to the difficulty of observing sifaka copulations in the wild. Here we report that stained-chested males had a higher throat marking activity than clean-chested males during the mating season, but not during the birth season. We found that females copulated more frequently with stained-chested males than the clean-chested males. Finally, in agreement with the biological market theory, we found that clean-chested males, with a lower scent-releasing potential, offered more grooming to females. This ‘‘grooming for sex’’ tactic was not completely unsuccessful; in fact, half of the clean-chested males copulated with females, even though at low frequency. In conclusion, the chest stain, possibly correlated with different cues targeted by females, could be one of the parameters which help females in selecting mates

Identification of recurrent genetic patterns from targeted sequencing panels with advanced data science: a case-study on sporadic and genetic neurodegenerative diseases

open8noThis work is funded by the University of Bologna, the IRCCS Institute of Neurological sciences of Bologna, and by the European Grants H2020 GenoMed4All [AM1] (Grant N. 101017549) and H2020 MSCA-ITN IMforFUTURE (Grant N. 721815).Background Targeted Next Generation Sequencing is a common and powerful approach used in both clinical and research settings. However, at present, a large fraction of the acquired genetic information is not used since pathogenicity cannot be assessed for most variants. Further complicating this scenario is the increasingly frequent description of a poli/oligogenic pattern of inheritance showing the contribution of multiple variants in increasing disease risk. We present an approach in which the entire genetic information provided by target sequencing is transformed into binary data on which we performed statistical, machine learning, and network analyses to extract all valuable information from the entire genetic profile. To test this approach and unbiasedly explore the presence of recurrent genetic patterns, we studied a cohort of 112 patients affected either by genetic Creutzfeldt–Jakob (CJD) disease caused by two mutations in the PRNP gene (p.E200K and p.V210I) with different penetrance or by sporadic Alzheimer disease (sAD). Results Unsupervised methods can identify functionally relevant sources of variation in the data, like haplogroups and polymorphisms that do not follow Hardy–Weinberg equilibrium, such as the NOTCH3 rs11670823 (c.3837 + 21 T > A). Supervised classifiers can recognize clinical phenotypes with high accuracy based on the mutational profile of patients. In addition, we found a similar alteration of allele frequencies compared the European population in sporadic patients and in V210I-CJD, a poorly penetrant PRNP mutation, and sAD, suggesting shared oligogenic patterns in different types of dementia. Pathway enrichment and protein–protein interaction network revealed different altered pathways between the two PRNP mutations. Conclusions We propose this workflow as a possible approach to gain deeper insights into the genetic information derived from target sequencing, to identify recurrent genetic patterns and improve the understanding of complex diseases. This work could also represent a possible starting point of a predictive tool for personalized medicine and advanced diagnostic applications.openTarozzi, M.; Bartoletti-Stella, A.; Dall’Olio, D.; Matteuzzi, T.; Baiardi, S.; Parchi, P.; Castellani, G.; Capellari, S.Tarozzi, M.; Bartoletti-Stella, A.; Dall’Olio, D.; Matteuzzi, T.; Baiardi, S.; Parchi, P.; Castellani, G.; Capellari, S

Ab initio Hartree-Fock Born effective charges of LiH, LiF, LiCl, NaF, and NaCl

We use the Berry-phase-based theory of macroscopic polarization of dielectric crystals formulated in terms of Wannier functions, and state-of-the-art Gaussian basis functions, to obtain benchmark ab initio Hartree-Fock values of the Born effective charges of ionic compounds LiH, LiF, LiCl, NaF, and NaCl. We find excellent agreement with the experimental values for all the compounds except LiCl and NaCl, for which the disagreement with the experiments is close to 10% and 16%, respectively. This may imply the importance of many-body effects in those systems.Comment: 11 pages, Revtex, 2 figures (included), to appear in Phys. Rev. B April 15, 200

Authoritative subspecies diagnosis tool for European honey bees based on ancestryinformative SNPs

Background With numerous endemic subspecies representing four of its five evolutionary lineages, Europe holds a large fraction of Apis mellifera genetic diversity. This diversity and the natural distribution range have been altered by anthropogenic factors. The conservation of this natural heritage relies on the availability of accurate tools for subspecies diagnosis. Based on pool-sequence data from 2145 worker bees representing 22 populations sampled across Europe, we employed two highly discriminative approaches (PCA and F-ST) to select the most informative SNPs for ancestry inference. Results Using a supervised machine learning (ML) approach and a set of 3896 genotyped individuals, we could show that the 4094 selected single nucleotide polymorphisms (SNPs) provide an accurate prediction of ancestry inference in European honey bees. The best ML model was Linear Support Vector Classifier (Linear SVC) which correctly assigned most individuals to one of the 14 subspecies or different genetic origins with a mean accuracy of 96.2% +/- 0.8 SD. A total of 3.8% of test individuals were misclassified, most probably due to limited differentiation between the subspecies caused by close geographical proximity, or human interference of genetic integrity of reference subspecies, or a combination thereof. Conclusions The diagnostic tool presented here will contribute to a sustainable conservation and support breeding activities in order to preserve the genetic heritage of European honey bees.The SmartBees project was funded by the European Commission under its FP7 KBBE programme (2013.1.3-02, SmartBees Grant Agreement number 613960) https://ec.europa.eu/research/fp7.MP was supported by a Basque Government grant (IT1233-19). The funders provided the financial support to the research, but had no role in the design of the study, analysis, interpretations of data and in writing the manuscript

Glycobiology of cell death: when glycans and lectins govern cell fate

Although one typically thinks of carbohydrates as associated with cell growth and viability, glycosylation also has an integral role in many processes leading to cell death. Glycans, either alone or complexed with glycan-binding proteins, can deliver intracellular signals or control extracellular processes that promote initiation, execution and resolution of cell death programs. Herein, we review the role of glycans and glycan-binding proteins as essential components of the cell death machinery during physiologic and pathologic settings.Fil: Lichtenstein, Rachel. Ben-Gurion University of the Negev. Faculty of Engineering. Department of Biotechnology Engineering; IsraelFil: Rabinovich, Gabriel Adrian. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental (i); Argentina. Universidad de Buenos Aires. Facultad de Cs.exactas y Naturales. Departamento de Quimica Biologica; Argentin

Introducing evolutionary biologists to the analysis of big data: guidelines to organize extended bioinformatics training courses

Research in evolutionary biology has been progressively influenced by big data such as massive genome and transcriptome sequencing data, scalar measurements of several phenotypes on tens to thousands of individuals, as well as from collecting worldwide environmental data at an increasingly detailed scale. The handling and analysis of such data require computational skills that usually exceed the abilities of most traditionally trained evolutionary biologists. Here we discuss the advantages, challenges and considerations for organizing and running bioinformatics training courses of 2–3 weeks in length to introduce evolutionary biologists to the computational analysis of big data. Extended courses have the advantage of offering trainees the opportunity to learn a more comprehensive set of complementary topics and skills and allowing for more time to practice newly acquired competences. Many organizational aspects are common to any course, as the need to define precise learning objectives and the selection of appropriate and highly motivated instructors and trainees, among others. However, other features assume particular importance in extended bioinformatics training courses. To successfully implement a learning-by-doing philosophy, sufficient and enthusiastic teaching assistants (TAs) are necessary to offer prompt help to trainees. Further, a good balance between theoretical background and practice time needs to be provided and assured that the schedule includes enough flexibility for extra review sessions or further discussions if desired. A final project enables trainees to apply their newly learned skills to real data or case studies of their interest. To promote a friendly atmosphere throughout the course and to build a close-knit community after the course, allow time for some scientific discussions and social activities. In addition, to not exhaust trainees and TAs, some leisure time needs to be organized. Finally, all organization should be done while keeping the budget within fair limits. In order to create a sustainable course that constantly improves and adapts to the trainees’ needs, gathering short- and long-term feedback after the end of the course is important. Based on our experience we have collected a set of recommendations to effectively organize and run extended bioinformatics training courses for evolutionary biologists, which we here want to share with the community. They offer a complementary way for the practical teaching of modern evolutionary biology and reaching out to the biological community.Peer reviewe