Effect of genotype and season on gynogenesis efficiency in Gerbera

The effects of season, genotype and their interaction on haploid production were evaluated on four genotypes responsive to gynogenesis. Naked mature unfertilised ovules, collected from April to October, were cultivated on modified MS basal medium plus 0.88 μM 6-benzyladenine and 0.57 μM indol-3-acetic acid. After 30–60 days, recovered calli were transferred to a MS basal medium with 8.8 μM BA and 0.57 μM IAA for regeneration. Genotype and season of ovule collection substantially interact in the recovery of haploid calli. Between the four genotypes analysed, two gave more calli in the spring, one in the autumn and the fourth showed only weak differences between seasons. Shoot recovery depended upon both the season of ovule collection and the genotype but no significant interaction was shown by our data. The ability to produce haploid callus is not predictive for efficient shoot regeneration. This revised version was published online in June 2006 with corrections to the Cover Date.     

Characterization of the biological conversion of naphthalene to (+)-cis-(1R,2S)-dihydroxy-1,2-dihydronaphthalene in direct micellar systems

The whole cell biological conversion of naphthalene to (+)-cis-(1R,2S)-dihydroxy-1,2-dihydronaphthalene by the E. coli JM109(pPS1778) recombinant strain carrying the naphthalene dioxygenase and regulatory genes cloned from Pseudomonas fluorescens N3 in micellar systems has been investigated using biochemical and chemico-physical techniques. Reverse and direct micellar systems have been tested. Non-ionic surfactants (Tween and Triton X series) were found not to inhibit either the growth of the bacteria and the expression of the hydroxylating dioxygenase enzyme in such systems and were utilized in order to speed up the naphthalene conversion by increasing its solubility and also its bioavailability. The phase behavior of the direct micellar system was characterized through light scattering and other chemico-physical techniques. Further addition of isopropyl-palmitate 1–2% v/v to the micellar systems resulted in an increase of the apparent substrate concentration in solution and particularly its bioavailability thus allowing faster catalytic conversions resulting in an increase in productivity for the process. Since the cis-dihydrodiols are acquiring considerable potential as chiral pool synthons in asymmetric synthesis for a variety of industrial processes, possible applications for efficient small and large-scale production of such compounds are discussed.     

A field guide to cultivating computational biology

Evolving in sync with the computation revolution over the past 30 years, computational biology has emerged as a mature scientific field. While the field has made major contributions toward improving scientific knowledge and human health, individual computational biology practitioners at various institutions often languish in career development. As optimistic biologists passionate about the future of our field, we propose solutions for both eager and reluctant individual scientists, institutions, publishers, funding agencies, and educators to fully embrace computational biology. We believe that in order to pave the way for the next generation of discoveries, we need to improve recognition for computational biologists and better align pathways of career success with pathways of scientific progress. With 10 outlined steps, we call on all adjacent fields to move away from the traditional individual, single-discipline investigator research model and embrace multidisciplinary, data-driven, team science.

Do you want to attract computational biologists to your project or to your department? Despite the major contributions of computational biology, those attempting to bridge the interdisciplinary gap often languish in career advancement, publication, and grant review. Here, sixteen computational biologists around the globe present "A field guide to cultivating computational biology," focusing on solutions.

Biology in the digital era requires computation and collaboration. A modern research project may include multiple model systems, use multiple assay technologies, collect varying data types, and require complex computational strategies, which together make effective design and execution difficult or impossible for any individual scientist. While some labs, institutions, funding bodies, publishers, and other educators have already embraced a team science model in computational biology and thrived [1–7], others who have not yet fully adopted it risk severely lagging behind the cutting edge. We propose a general solution: “deep integration” between biology and the computational sciences. Many different collaborative models can yield deep integration, and different problems require different approaches (Fig 1).Open in a separate windowFig 1Supporting interdisciplinary team science will accelerate biological discoveries.Scientists who have little exposure to different fields build silos, in which they perform science without external input. To solve hard problems and to extend your impact, collaborate with diverse scientists, communicate effectively, recognize the importance of core facilities, and embrace research parasitism. In biologically focused parasitism, wet lab biologists use existing computational tools to solve problems; in computationally focused parasitism, primarily dry lab biologists analyze publicly available data. Both strategies maximize the use and societal benefit of scientific data.In this article, we define computational science extremely broadly to include all quantitative approaches such as computer science, statistics, machine learning, and mathematics. We also define biology broadly, including any scientific inquiry pertaining to life and its many complications. A harmonious deep integration between biology and computer science requires action—we outline 10 immediate calls to action in this article and aim our speech directly at individual scientists, institutions, funding agencies, and publishers in an attempt to shift perspectives and enable action toward accepting and embracing computational biology as a mature, necessary, and inevitable discipline (Box 1).Box 1. Ten calls to action for individual scientists, funding bodies, publishers, and institutions to cultivate computational biology. Many actions require increased funding support, while others require a perspective shift. For those actions that require funding, we believe convincing the community of need is the first step toward agencies and systems allocating sufficient support

1. Respect collaborators’ specific research interests and motivationsProblem: Researchers face conflicts when their goals do not align with collaborators. For example, projects with routine analyses provide little benefit for computational biologists.Solution: Explicit discussion about interests/expertise/goals at project onset.Opportunity: Clearly defined expectations identify gaps, provide commitment to mutual benefit.
2. Seek necessary input during project design and throughout the project life cycleProblem: Modern research projects require multiple experts spanning the project’s complexity.Solution: Engage complementary scientists with necessary expertise throughout the entire project life cycle.Opportunity: Better designed and controlled studies with higher likelihood for success.
3. Provide and preserve budgets for computational biologists’ workProblem: The perception that analysis is “free” leads to collaborator budget cuts.Solution: When budget cuts are necessary, ensure that they are spread evenly.Opportunity: More accurate, reproducible, and trustworthy computational analyses.
4. Downplay publication author order as an evaluation metric for computational biologistsProblem: Computational biologist roles on publications are poorly understood and undervalued.Solution: Journals provide more equitable opportunities, funding bodies and institutions improve understanding of the importance of team science, scientists educate each other.Opportunity: Engage more computational biologist collaborators, provide opportunities for more high-impact work.
5. Value software as an academic productProblem: Software is relatively undervalued and can end up poorly maintained and supported, wasting the time put into its creation.Solution: Scientists cite software, and funding bodies provide more software funding opportunities.Opportunity: More high-quality maintainable biology software will save time, reduce reimplementation, and increase analysis reproducibility.
6. Establish academic structures and review panels that specifically reward team scienceProblem: Current mechanisms do not consistently reward multidisciplinary work.Solution: Separate evaluation structures to better align peer review to reward indicators of team science.Opportunity: More collaboration to attack complex multidisciplinary problems.
7. Develop and reward cross-disciplinary training and mentoringProblem: Academic labs and institutions are often insufficiently equipped to provide training to tackle the next generation of biological problems, which require computational skills.Solution: Create better training programs aligned to necessary on-the-job skills with an emphasis on communication, encourage wet/dry co-mentorship, and engage younger students to pursue computational biology.Opportunity: Interdisciplinary students uncover important insights in their own data.
8. Support computing and experimental infrastructure to empower computational biologistsProblem: Individual computational labs often fund suboptimal cluster computing systems and lack access to data generation facilities.Solution: Institutions can support centralized compute and engage core facilities to provide data services.Opportunity: Time and cost savings for often overlooked administrative tasks.
9. Provide incentives and mechanisms to share open data to empower discovery through reanalysisProblem: Data are often siloed and have untapped potential.Solution: Provide institutional data storage with standardized identifiers and provide separate funding mechanisms and publishing venues for data reuse.Opportunity: Foster new breed of researchers, “research parasites,” who will integrate multimodal data and enhance mechanistic insights.
10. Consider infrastructural, ethical, and cultural barriers to clinical data accessProblem: Identifiable health data, which include sensitive information that must be kept hidden, are distributed and disorganized, and thus underutilized.Solution: Leadership must enforce policies to share deidentifiable data with interoperable metadata identifiers.Opportunity: Derive new insights from multimodal data integration and build datasets with increased power to make biological discoveries.

     

Diversity of European habitat types is correlated with geography more than climate and human pressure

Habitat richness, that is, the diversity of ecosystem types, is a complex, spatially explicit aspect of biodiversity, which is affected by bioclimatic, geographic, and anthropogenic variables. The distribution of habitat types is a key component for understanding broad‐scale biodiversity and for developing conservation strategies. We used data on the distribution of European Union (EU) habitats to answer the following questions: (i) how do bioclimatic, geographic, and anthropogenic variables affect habitat richness? (ii) Which of those factors is the most important? (iii) How do interactions among these variables influence habitat richness and which combinations produce the strongest interactions? The distribution maps of 222 terrestrial habitat types as defined by the Natura 2000 network were used to calculate habitat richness for the 10 km × 10 km EU grid map. We then investigated how environmental variables affect habitat richness, using generalized linear models, generalized additive models, and boosted regression trees. The main factors associated with habitat richness were geographic variables, with negative relationships observed for both latitude and longitude, and a positive relationship for terrain ruggedness. Bioclimatic variables played a secondary role, with habitat richness increasing slightly with annual mean temperature and overall annual precipitation. We also found an interaction between anthropogenic variables, with the combination of increased landscape fragmentation and increased population density strongly decreasing habitat richness. This is the first attempt to disentangle spatial patterns of habitat richness at the continental scale, as a key tool for protecting biodiversity. The number of European habitats is related to geography more than climate and human pressure, reflecting a major component of biogeographical patterns similar to the drivers observed at the species level. The interaction between anthropogenic variables highlights the need for coordinated, continental‐scale management plans for biodiversity conservation.     

Characterization of sulfonylurea receptors in isolated human pancreatic islets

Current information on pancreatic islet sulfonylurea receptors has been obtained with laboratory animal pancreatic β cells or stable β-cell lines. In the present study, we evaluated the properties of sulfonylurea receptors of human islets of Langherans, prepared by collagenase digestion and density-gradient purification. The binding characterisitics of labeled glibenclamide to pancreatic islet membrane preparations were analyzed, displacement studies with several oral hypoglycemic agents were performed, and these latter compounds were tested as for their insulinotropic action on intact human islets. [³H]glibenclamide saturable binding was shown to be linear at ≤0.25 mg/ml protein; it was both temperature and time dependent. Scatchard analysis of the equilibrium binding data at 25°C indicated the presence of a single class of saturable, high-affinity binding sites with a K_d value of 1.0 ± 0.07 nM and a Bmax value of 657 ± 48 fmol/mg of proteins. The displacement experiments showed the following rank order of potency of the oral hypoglycemic agents we tested: glibenclamide = glimepiride > tolbutamide > chlorpropamide ≫ metformin. This binding potency order was parallel with the insulinotropic potency of the evaluated compounds. J. Cell. Biochem. 71:182–188, 1998. © 1998 Wiley-Liss, Inc.     

Conformational sampling of bioactive conformers: a low-temperature NMR study of 15N-Leu–enkephalin

Conformational studies of enkephalins are hampered by their high flexibility which leads to mixtures of quasi-isoenergetic conformers in solution and makes NOEs very difficult to detect in NMR spectra. In order to improve the quality of the NMR data, Leu–enkephalin was synthesized with ¹⁵N-labelled uniformly on all amide nitrogens and examined in a viscous solvent medium at low temperature. HMQC NOESY spectra of the labelled Leu–enkephalin in a DMSO_d6/H₂O mixture at 275 K do show numerous NOEs, but these are not consistent with a single conformer and are only sufficient to describe the conformational state as a mixture of several conformers. Here a different approach to the structure–activity relationships of enkephalins is presented: it is possible to analyse the NMR data in terms of limiting canonical structures (i.e. β- and γ-turns) and finally to select only those consistent with the requirements of δ selective agonists and antagonists. This strategy results in the prediction of a family of conformers that may be useful in the design of new δ selective opioid peptides. © 1998 European Peptide Society and John Wiley & Sons, Ltd.     

Reversible binding of ethacrynic acid to human serum albumin: Difference circular dichroism study

The reversible binding of ethacrynic acid was characterized by a difference circular dichroism method. A 2/1 stoichiometry was determined for the [drug]/[HSA] (human serum albumin) complex. The reversible binding of ethacrynic acid to HSA determines direct competition with ligands that selectivity bind to site II and to the fatty acid site. Furthermore, indirect competition was shown for ligands for site I (anticooperative) and to site III (cooperative). Chirality 11:33–38, 1999. © 1999 Wiley‐Liss, Inc.     

Discovery of 5-substituent-N-arylbenzamide derivatives as potent,selective and orally bioavailable LRRK2 inhibitors

Leucine-rich repeat kinase 2 (LRRK2) has been suggested as a potential therapeutic target for Parkinson’s disease. Herein we report the discovery of 5-substituent-N-arylbenzamide derivatives as novel LRRK2 inhibitors. Extensive SAR study led to the discovery of compounds 8e, which demonstrated potent LRRK2 inhibition activity, high selectivity across the kinome, good brain exposure, and high oral bioavailability.