Exogenous strigolactones impact metabolic profiles and phosphate starvation signalling in roots

Strigolactones (SLs) are important ex-planta signalling molecules in the rhizosphere, promoting the association with beneficial microorganisms, but also affecting plant interactions with harmful organisms. They are also plant hormones in-planta, acting as modulators of plant responses under nutrient-deficient conditions, mainly phosphate (Pi) starvation. In the present work, we investigate the potential role of SLs as regulators of early Pi starvation signalling in plants. A short-term pulse of the synthetic SL analogue 2′-epi-GR24 promoted SL accumulation and the expression of Pi starvation markers in tomato and wheat under Pi deprivation. 2′-epi-GR24 application also increased SL production and the expression of Pi starvation markers under normal Pi conditions, being its effect dependent on the endogenous SL levels. Remarkably, 2′-epi-GR24 also impacted the root metabolic profile under these conditions, promoting the levels of metabolites associated to plant responses to Pi limitation, thus partially mimicking the pattern observed under Pi deprivation. The results suggest an endogenous role for SLs as Pi starvation signals. In agreement with this idea, SL-deficient plants were less sensitive to this stress. Based on the results, we propose that SLs may act as early modulators of plant responses to P starvation.     

Canadian polar bear population structure using genome‐wide markers

Predicting the consequences of environmental changes, including human‐mediated climate change on species, requires that we quantify range‐wide patterns of genetic diversity and identify the ecological, environmental, and historical factors that have contributed to it. Here, we generate baseline data on polar bear population structure across most Canadian subpopulations (n = 358) using 13,488 genome‐wide single nucleotide polymorphisms (SNPs) identified with double‐digest restriction site‐associated DNA sequencing (ddRAD). Our ddRAD dataset showed three genetic clusters in the sampled Canadian range, congruent with previous studies based on microsatellites across the same regions; however, due to a lack of sampling in Norwegian Bay, we were unable to confirm the existence of a unique cluster in that subpopulation. These data on the genetic structure of polar bears using SNPs provide a detailed baseline against which future shifts in population structure can be assessed, and opportunities to develop new noninvasive tools for monitoring polar bears across their range.     

Mitochondrial morphodynamics alteration induced by influenza virus infection as a new antiviral strategy

Influenza virus infections are major public health threats due to their high rates of morbidity and mortality. Upon influenza virus entry, host cells experience modifications of endomembranes, including those used for virus trafficking and replication. Here we report that influenza virus infection modifies mitochondrial morphodynamics by promoting mitochondria elongation and altering endoplasmic reticulum-mitochondria tethering in host cells. Expression of the viral RNA recapitulates these modifications inside cells. Virus induced mitochondria hyper-elongation was promoted by fission associated protein DRP1 relocalization to the cytosol, enhancing a pro-fusion status. We show that altering mitochondrial hyper-fusion with Mito-C, a novel pro-fission compound, not only restores mitochondrial morphodynamics and endoplasmic reticulum-mitochondria contact sites but also dramatically reduces influenza replication. Finally, we demonstrate that the observed Mito-C antiviral property is directly connected with the innate immunity signaling RIG-I complex at mitochondria. Our data highlight the importance of a functional interchange between mitochondrial morphodynamics and innate immunity machineries in the context of influenza viral infection.     

Integrating systemic and molecular levels to infer key drivers sustaining metabolic adaptations

Metabolic adaptations to complex perturbations, like the response to pharmacological treatments in multifactorial diseases such as cancer, can be described through measurements of part of the fluxes and concentrations at the systemic level and individual transporter and enzyme activities at the molecular level. In the framework of Metabolic Control Analysis (MCA), ensembles of linear constraints can be built integrating these measurements at both systemic and molecular levels, which are expressed as relative differences or changes produced in the metabolic adaptation. Here, combining MCA with Linear Programming, an efficient computational strategy is developed to infer additional non-measured changes at the molecular level that are required to satisfy these constraints. An application of this strategy is illustrated by using a set of fluxes, concentrations, and differentially expressed genes that characterize the response to cyclin-dependent kinases 4 and 6 inhibition in colon cancer cells. Decreases and increases in transporter and enzyme individual activities required to reprogram the measured changes in fluxes and concentrations are compared with down-regulated and up-regulated metabolic genes to unveil those that are key molecular drivers of the metabolic response.     

Late‐in‐life treadmill training rejuvenates autophagy,protein aggregate clearance,and function in mouse hearts

Protein quality control mechanisms decline during the process of cardiac aging. This enables the accumulation of protein aggregates and damaged organelles that contribute to age‐associated cardiac dysfunction. Macroautophagy is the process by which post‐mitotic cells such as cardiomyocytes clear defective proteins and organelles. We hypothesized that late‐in‐life exercise training improves autophagy, protein aggregate clearance, and function that is otherwise dysregulated in hearts from old vs. adult mice. As expected, 24‐month‐old male C57BL/6J mice (old) exhibited repressed autophagosome formation and protein aggregate accumulation in the heart, systolic and diastolic dysfunction, and reduced exercise capacity vs. 8‐month‐old (adult) mice (all p < 0.05). To investigate the influence of late‐in‐life exercise training, additional cohorts of 21‐month‐old mice did (old‐ETR) or did not (old‐SED) complete a 3‐month progressive resistance treadmill running program. Body composition, exercise capacity, and soleus muscle citrate synthase activity improved in old‐ETR vs. old‐SED mice at 24 months (all p < 0.05). Importantly, protein expression of autophagy markers indicate trafficking of the autophagosome to the lysosome increased, protein aggregate clearance improved, and overall function was enhanced (all p < 0.05) in hearts from old‐ETR vs. old‐SED mice. These data provide the first evidence that a physiological intervention initiated late‐in‐life improves autophagic flux, protein aggregate clearance, and contractile performance in mouse hearts.     

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.

     

A new human growth hormone production process using a recombinant Bacillus subtilis strain.

We constructed a series of hybrid plasmids which directed the synthesis of different human growth hormone (hGH) precursor sequences in Bacillus subtilis. In addition to the 191 amino acids of the hormone, the precursors had in common an amino-terminal extension characterized by the presence of a methionine at position 1 and of the tetrapeptide Ile-Glu-Gly-Arg preceding the first residue (Phe) of hGH. The sequence between the methionine and the tetrapeptide was specific for each precursor and, because of the presence of charged residues, conferred particular properties to the molecules. Long homopolymeric tail-containing precursors such as MRRRRRRIILM-IEGR appeared insoluble whereas shorter sequences of the type MRR-IEGR and MEELM-IEGR augmented the solubility of the precursors with respect to Met-hGH. The soluble precursors could be easily purified from the bulk proteins taking advantage of the charged residues present on the N-terminal tail. After purification, the natural hGH was obtained by treating the precursors with the protease Factor Xa which cleaves after the arginine residue of the tetrapeptide IEGR. A protocol for the production and purification of authentic hGH from a strain expressing one of these soluble precursors is reported.     

Genetic and developmental analyses of chaetae pattern formation in Drosophila tergites

Summary The role of the achaete-scute complex and extramacrochaetae, Notch, Delta, Enhancer of split and Hairless genes in chaeta patterning in Drosophila tergites was studied in genetic mosaics and in mutant combinations. The mutant phenotypes of different alleles of each gene can be ordered in characteristic topographical seriations. These seriations are related to the pattern of proliferation of histoblasts and the time of singularization of sensory organ mother cells from surrounding epidermal cells. Genetic mosaics of lethal alleles show that these genes are fundamentally involved in this singularization and subsequent differentiation. The study of mutant combinations of alleles of these genes reveals specific relationships of epistasis and synergism between them. The results suggest that spatial and temporal variations in achaete-scute complex functional products in cells, modulated by the activity of other genes involved in signal transduction, define the patterned differentiation of sensory organs in tergites. Offprint requests to: A. García-Bellido     

Increasing in vitro germination of Musa balbisiana seed

Seeds of Musa balbisiana were soaked in water for five days prior to excision of embryos. Embryos with their longitudinal axis laid flat and half-way embedded on agar-solidified medium produced the highest germination and the most desirable plantlet characteristics. Germination in vitro was 94% within 7 days compared to 50% after 54 days for greenhouse-sown seeds.     

Fast efflux of Ca2+ mediated by the sarcoplasmic reticulum Ca2(+)-ATPase.

The Ca2(+)-ATPase found in the light fraction of sarcoplasmic reticulum vesicles can be phosphorylated by Pi, forming an acylphosphate residue at the catalytic site of the enzyme. This reaction was inhibited by the phenothiazines trifluoperazine, chlorpromazine, imipramine, and fluphenazine and by the beta-adrenergic blocking agents propranolol and alprenolol. The inhibition was reversed by raising either the Pi or the Mg2+ concentration in the medium and was not affected by the presence of K+. Phosphorylation of the Ca2(+)-ATPase by Pi was also inhibited by ruthenium red and spermidine. These compounds compete with Mg2+, but, unlike the phenothiazines, they did not compete with Pi at the catalytic site, and the inhibition was abolished when K+ was included in the assay medium. The efflux of Ca2+ from loaded vesicles was greatly increased by the phenothiazines and by propranolol and alprenolol. In the presence of 200 microM trifluoperazine, the rate of Ca2+ efflux was higher than 3 mumol of Ca2+/mg of protein/10 s. The activation of efflux by these drugs was antagonized by Pi, Mg2+, K+, Ca2+, ADP, dimethyl sulfoxide, ruthenium red, and spermidine. The increase of Ca2+ efflux caused by trifluoperazine was not correlated with binding of the drug to the membrane lipids. It is concluded that the Ca2+ pump can be uncoupled by different drugs, thereby greatly increasing the efflux of Ca2+ through the ATPase. Displacement of these drugs by the natural ligands of the ATPase blocks the efflux through the uncoupled pathway and limits it to a much smaller rate. Thus, the Ca2(+)-ATPase can operate either as a pump (coupled) or as a Ca2+ channel (uncoupled).