PHLDA1 (pleckstrin homology-like domain,family A,member 1) knockdown promotes migration and invasion of MCF10A breast epithelial cells

PHLDA1 (pleckstrin homology-like domain, family A, member 1) is a multifunctional protein that plays distinct roles in several biological processes including cell death and therefore its altered expression has been identified in different types of cancer. Progressively loss of PHLDA1 was found in primary and metastatic melanoma while its overexpression was reported in intestinal and pancreatic tumors. Previous work from our group showed that negative expression of PHLDA1 protein was a strong predictor of poor prognosis for breast cancer disease. However, the function of PHLDA1 in mammary epithelial cells and the tumorigenic process of the breast is unclear. To dissect PHLDA1 role in human breast epithelial cells, we generated a clone of MCF10A cells with stable knockdown of PHLDA1 and performed functional studies. To achieve reduced PHLDA1 expression we used shRNA plasmid transfection and then changes in cell morphology and biological behavior were assessed. We found that PHLDA1 downregulation induced marked morphological alterations in MCF10A cells, such as changes in cell-to-cell adhesion pattern and cytoskeleton reorganization. Regarding cell behavior, MCF10A cells with reduced expression of PHLDA1 showed higher proliferative rate and migration ability in comparison with control cells. We also found that MCF10A cells with PHLDA1 knockdown acquired invasive properties, as evaluated by transwell Matrigel invasion assay and showed enhanced colony-forming ability and irregular growth in low attachment condition. Altogether, our results indicate that PHLDA1 downregulation in MCF10A cells leads to morphological changes and a more aggressive behavior.     

Examining the reaction between antioxidant compounds and 2,2-diphenyl-1-picrylhydrazyl (DPPH) through a computational investigation

In this work, we present a computational investigation on the reactions between two well-known antioxidants (quercetin and morin) and 2,2-diphenyl-1-picrylhydrazyl (DPPH). A density functional theory (DFT) approach with the B3LYP functional and the 6-31G(d,p) basis set was used for the simulations. The structural and energetic parameters (Gibbs free-energy, ΔG, and Gibbs free-energy of activation, ΔG⁺⁺) were determined to provide information on the antioxidant activity as well as to evaluate the contributions of each hydroxyl group to the referred property. According to the results obtained, quercetin presented three hydroxyls as being thermodynamically spontaneous in the reaction with DPPH (4\(^{\prime }\)-ArOH, 3\(^{\prime }\)-ArOH, and 3-ArOH, with ΔG = -4.93 kcal/mol, -2.89 kcal/mol, and -1.87 kcal/mol, respectively) against only two in the case of morin (2\(^{\prime }\)-ArOH and 3-ArOH, with ΔG = -7.56 kcal/mol and -4.57 kcal/mol, respectively). Hence, quercetin was found to be a more efficient antioxidant, which is in agreement with different experimental and computational investigations of bond dissociation enthalpies (BDEs). However, the order of contribution of the OH groups of each compound to the antioxidant potential present some differences when compared to what was seen in the previous investigations, especially for morin. These findings are in contrast to what was observed in studies based on the determinations of BDEs. Therefore, experimental investigations on the hydrogen-atom transfer mechanism (HAT) for both compounds are encouraged in order to clarify these observations.     

Metals and arsenic in marine fish commercialized in the NE Brazil: Risk to human health

Abstract

Arsenic, cadmium, lead, and mercury in fish is the result of long-term biomagnification in the food chain and is of public concern, due to the toxicity they engender. The objective of this research was to determine the concentrations of arsenic, cadmium, lead, and mercury in 13 species of marine fish broadly commercialized in Aracaju, SE, Brazil and to evaluate the risks of fish consumption associated with these trace elements, using the Target Hazard Quotient (THQ). As, Cd, and Pb levels were measured with inductively coupled plasma mass spectrometry (ICP-MS), and mercury was analyzed via cold vapor atomic absorption spectrometry. The results indicate a large variability in concentrations for arsenic (0.07–2.03?mg kg^–1) and mercury (0.01–1.44?mg kg^–1), associated with the animal dietary category. Cadmium (0.04–0.19?mg kg^–1) and lead (<0.01–0.45?mg kg^–1), on the other hand showed a mild variability. None of the evaluated specimens had As, Cd, and Pb THQ values higher than 1. The THQ values for mercury were higher but indicated no consumption risk, except for amberjack, and snook fish. Overall THQ indicates lower risk of consumption in fish that are at the base of the food chain, than in those that are top predators.     

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.

     

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.     

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