Testing the impact of calibration on molecular divergence times using a fossil-rich group: the case of Nothofagus (Fagales)

Although temporal calibration is widely recognized as critical for obtaining accurate divergence-time estimates using molecular dating methods, few studies have evaluated the variation resulting from different calibration strategies. Depending on the information available, researchers have often used primary calibrations from the fossil record or secondary calibrations from previous molecular dating studies. In analyses of flowering plants, primary calibration data can be obtained from macro- and mesofossils (e.g., leaves, flowers, and fruits) or microfossils (e.g., pollen). Fossil data can vary substantially in accuracy and precision, presenting a difficult choice when selecting appropriate calibrations. Here, we test the impact of eight plausible calibration scenarios for Nothofagus (Nothofagaceae, Fagales), a plant genus with a particularly rich and well-studied fossil record. To do so, we reviewed the phylogenetic placement and geochronology of 38 fossil taxa of Nothofagus and other Fagales, and we identified minimum age constraints for up to 18 nodes of the phylogeny of Fagales. Molecular dating analyses were conducted for each scenario using maximum likelihood (RAxML + r8s) and Bayesian (BEAST) approaches on sequence data from six regions of the chloroplast and nuclear genomes. Using either ingroup or outgroup constraints, or both, led to similar age estimates, except near strongly influential calibration nodes. Using "early but risky" fossil constraints in addition to "safe but late" constraints, or using assumptions of vicariance instead of fossil constraints, led to older age estimates. In contrast, using secondary calibration points yielded drastically younger age estimates. This empirical study highlights the critical influence of calibration on molecular dating analyses. Even in a best-case situation, with many thoroughly vetted fossils available, substantial uncertainties can remain in the estimates of divergence times. For example, our estimates for the crown group age of Nothofagus varied from 13 to 113 Ma across our full range of calibration scenarios. We suggest that increased background research should be made at all stages of the calibration process to reduce errors wherever possible, from verifying the geochronological data on the fossils to critical reassessment of their phylogenetic position.     

Modulation of the mitogenic response of an epidermal growth factor-dependent keratinocyte cell line by dexamethasone, insulin, and transforming growth factor-beta

The stimulation of DNA synthesis by epidermal growth factor (EGF) has been studied for a cell line having properties useful for investigating the mechanism of action of EGF in epithelial cell populations. These studies employ a mouse keratinocyte cell line (MK), isolated by Weissman and Aaronson (1983), which is stringently dependent on exogenous EGF for growth in serum containing medium. The studies reported here characterize the compliment of EGR receptors present on the surface of MK cells and demonstrate the regulatory influence of other hormones on the capacity of EGF to stimulate DNA synthesis. Up-regulated MK cells contain approximately 22,000 EGF receptors per cell, but when the cells are grown in the presence of EGF the receptor number is reduced to about 4,000. It is estimated that only a small number of high-affinity receptors (less than 500) are required for EGF-dependent cell proliferation. In contrast to its action in fibroblastic cells, dexamethasone is a strong inhibitor of EGF-stimulated DNA synthesis of MK cells. Insulin at high concentrations, or insulin-like growth factors I or II (IGF-I, IGF-II) at physiological concentrations, synergistically enhance the EGF response. Interestingly, insulin or IGF-I or II are also able to reverse most of the dexamethasone inhibition of DNA synthesis. Transforming growth factor-beta (TGF-beta) inhibits, in reversible manner, the EGF stimulation of DNA synthesis and this inhibition is not overcome by insulin. TGF-beta receptors have been measured in MK cells and Scatchard analysis indicates approximately 20,000 receptors per cell. None of the modulatory hormones (insulin, dexamethasone, TGF-beta) significantly altered 125I-EGF binding characteristics in MK cells, suggesting a point of action distal to 125I-EGF binding.     

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.

     

Resilience indicators: prospects and limitations for early warnings of regime shifts

In the vicinity of tipping points—or more precisely bifurcation points—ecosystems recover slowly from small perturbations. Such slowness may be interpreted as a sign of low resilience in the sense that the ecosystem could easily be tipped through a critical transition into a contrasting state. Indicators of this phenomenon of ‘critical slowing down (CSD)’ include a rise in temporal correlation and variance. Such indicators of CSD can provide an early warning signal of a nearby tipping point. Or, they may offer a possibility to rank reefs, lakes or other ecosystems according to their resilience. The fact that CSD may happen across a wide range of complex ecosystems close to tipping points implies a powerful generality. However, indicators of CSD are not manifested in all cases where regime shifts occur. This is because not all regime shifts are associated with tipping points. Here, we review the exploding literature about this issue to provide guidance on what to expect and what not to expect when it comes to the CSD-based early warning signals for critical transitions.     

Detecting microRNA activity from gene expression data

Background  

MicroRNAs (miRNAs) are non-coding RNAs that regulate gene expression by binding to the messenger RNA (mRNA) of protein coding genes. They control gene expression by either inhibiting translation or inducing mRNA degradation. A number of computational techniques have been developed to identify the targets of miRNAs. In this study we used predicted miRNA-gene interactions to analyse mRNA gene expression microarray data to predict miRNAs associated with particular diseases or conditions.     

Identifying key biodiversity areas as marine conservation priorities in the greater Caribbean

Increasing rates of Anthropocene biodiversity extinctions suggest a possible sixth mass extinction event. Conservation planners are seeking effective ways to protect species, hotspots of biodiversity, and dynamic ecosystems to reduce and eventually eliminate the degradation and loss of diversity at the scale of genes, species, and ecosystems. While well-established, adequately enforced protected areas (PAs) increase the likelihood of preserving species and habitats, traditional placement methods are frequently inadequate in protecting biodiversity most at risk. Consequently, the Key Biodiversity Area (KBA) Partnership developed a set of science-based criteria and thresholds that iteratively identify sites where biodiversity is most in need of protection. KBA methodology has been rarely applied in the marine realm, where data are often extremely limited. We tested the feasibility of KBA population metrics in the Greater Caribbean marine region using occurrence and population data and threat statuses for 1669 marine vertebrates. These data identified areas where site-specific conservation measures can effectively protect biodiversity. Using KBA criteria pertaining to threatened and irreplaceable biodiversity, we identified 90 geographically unique potential KBAs, 34 outside and 56 within existing PAs. These provide starting points for local conservation managers to verify that KBA thresholds are met and to delineate site boundaries. Significant data gaps, such as population sizes, life history characteristics, and extent of habitats, prevent the full application of the KBA criteria to data-poor marine species. Increasing the rate and scope of marine sampling programs and digital availability of occurrence datasets will improve identification and delineation of KBAs in the marine environment.

     

A novel L-fuco-4-O-methyl-D-glucurono-D-xylan from Hyptis suaveolens

The acidic polysaccharide from the seed-coat mucilage of Hyptis suaveolens is a highly branched L-fuco-4-O-methyl-D-glucurono-D-xylan for which a structure is proposed having a 4-linked beta-D-xylan backbone carrying side chains of single 4-O-methyl-alpha-D-glucuronic acid residues at O-2 and 2-O-L-fucopyranosyl-D-xylopyranose units at O-3. The structural analysis involves base-catalyzed beta-elimination of uronic acid residues from the methylated glycan followed by degradation using a modified Svensson oxidation-elimination sequence.     

Substituted N-{3-[(1,1-dioxido-1,2-benzothiazol-3-yl)(phenyl)amino]propyl}benzamide analogs as potent Kv1.3 ion channel blockers. Part 2

We report the synthesis and in vitro activity of a series of novel substituted N-{3-[(1,1-dioxido-1,2-benzothiazol-3-yl)(phenyl)amino]propyl}benzamide analogs. These analogs showed potent inhibitory activity against Kv1.3. Several demonstrated similar potency to the known Kv1.3 inhibitor PAP-1 when tested under the IonWorks patch clamp assay conditions. Two compounds 13i and 13rr were advanced further as potential tool compounds for in vivo validation studies.     

Responses of nonstructural carbohydrates to shoot removal and soil moisture treatments in <Emphasis Type="Italic">Salix nigra</Emphasis>

Aboveground disturbances are common in dynamic riparian environments, and Salix nigra is well adapted with a vigorous resprouting response. Soil moisture stresses are also common, and S. nigra is flood tolerant and drought sensitive. The objective of this study was to quantify nonstructural carbohydrate (NSC) reserves in S. nigra following shoot removal and soil moisture treatments. NSC reserves provide energy for regeneration of shoot tissue until new functional leaves are developed. Three soil moisture treatments: well-watered (W), periodic flooding (F) and drought (D); and three shoot removal treatments: no shoots removed (R0), partial shoot removal (R1), and complete shoot removal (R2) were applied. Plants were harvested when new shoot development was observed (day 13). Statistical significance in the 3 × 3-factorial design was determined in two-factor ANOVA at P < 0.05. Both roots and cuttings were important reservoirs for NSC during resprouting response, with decreases in root (31%) and cutting (14%) biomass in R2 compared to R0. Rapid recovery of photosynthetic surface area (from 15 to 37% of R0) was found in R1. A clear pattern of starch mobilization was found in roots in R0, R1 and R2, with lowest root starch concentration in W, F higher than W, and D higher than F. Shoot starch concentration was lower in F and D compared to W in R0, however, in R1 shoot starch was reduced in W compared to F and D, possibly indicating reduced rates of translocation during soil moisture stress. Evidence of osmotic adjustment was found in roots and shoots with higher total ethanol-soluble carbohydrates (TESC) during soil moisture stress in F and D treatments. Total plant NSC pool was greater in F and D treatments compared to W, and progressively reduced from R0 to R1 to R2. Results indicated negative effects of drought, and to a lesser extent periodic flooding on resprouting response in S. nigra, with implications for reduced survival when exposed to combined stresses of aboveground disturbance and soil moisture.