Host response to malaria during pregnancy: placental monocyte recruitment is associated with elevated beta chemokine expression

Malaria during pregnancy is associated with poor birth outcomes, particularly low birth weight. Recently, monocyte infiltration into the placental intervillous space has been identified as a key risk factor for low birth weight. However, the malaria-induced chemokines involved in recruiting and activating placental monocytes have not been identified. In this study, we determined which chemokines are elevated during placental malaria infection and the association between chemokine expression and placental monocyte infiltration. Placental malaria infection was associated with elevations in mRNA expression of three beta chemokines, macrophage-inflammatory protein 1 (MIP-1) alpha (CCL3), monocyte chemoattractant protein 1 (MCP-1; CCL2), and I-309 (CCL1), and one alpha chemokine, IL-8 (CXCL8); all correlated with monocyte density in the placental intervillous space. Placental plasma concentrations of MIP-1 alpha and IL-8 were increased in women with placental malaria and were associated with placental monocyte infiltration. By immunohistochemistry, we localized placental chemokine production in malaria-infected placentas: some but not all hemozoin-laden maternal macrophages produced MIP-1 beta and MCP-1, and fetal stromal cells produced MCP-1. In sum, local placental production of chemokines is increased in malaria, and may be an important trigger for monocyte accumulation in the placenta.     

Comparison of the capabilities of accelerated solvent extraction and sonication as extraction techniques for the quantification of kavalactones in Piper methysticum (Kava) roots by high performance liquid chromatography with ultra violet detection

A conventional extraction technique of sonication has been compared, in terms of extraction efficiency, extraction time and amount of solvent, with the more novel technique of accelerated solvent extraction for the extraction of kavain from the powdered roots of Piper methysticum (Kava) with acetone. The extracts were analysed using high-performance liquid chromatography with ultra violet detection. The effects of varying solvent volume and extraction time upon the quantity of kavain extracted with sonication, and the effects of varying temperature upon the kavain extraction efficiency by ASE, were investigated. ASE was found to be more efficient with respect to time and solvent volume required; however, a good agreement was found between the kavain concentration obtained using both extraction techniques.     

Inflorescence architecture: A developmental genetics approach

We are characterizing a suiteof Pisum sativum mutants that alter inflorescence architecture to construct a model for the genetic regulation of inflorescence development in a plant with a compound raceme. Such a model, when compared with those created forAntirrhinum majus andArabidopsis thaliana, both of which have simple racemes, should provide insight into the evolution of the development of inflorescence architecture. The highly conserved nature of cloned genes that regulate reproductive development in plants and the morphological similarities among our mutants and those identified inA. majus andA. thaliana enhance the probability that a developmental genetics approach will be fruitful. Here we describe sixP. sativum mutants that affect morphologically and architecturally distinct aspects of the inflorescence, and we analyze interactions among these genes. Both vegetative and inflorescence growth of the primary axis is affected byUNIFOLIA TA, which is necessary for the function ofDETERMINATE (DET).DET maintains indeterminacy in the first-order axis. In its absence, the meristem differentiates as a stub covered with epidermal hairs.DET interacts withVEGETATIVE1 (VEG1).VEG1 appears essential for second-order inflorescence (I₂) development.veg1 mutants fail to flower or differentiate the I₂ meristem into a rudimentary stub,det veg1 double mutants produce true terminal flowers with no stubs, indicating that two genes must be eliminated for terminal flower formation inP. sativum, whereas elimination of a single gene accomplishes this inA. thaliana andA. majus. NEPTUNE also affects I₂ development by limiting to two the number of flowers produced prior to stub formation. Its role is independent ofDET, as indicated by the additive nature of the double mutantdet nep. UNI, BROC, and PIM all play roles in assigning floral meristem identity to the third-order branch.pim mutants continue to produce inflorescence branches, resulting in a highly complex architecture and aberrant flowers.uni mutants initiate a whorl of sepals, but floral organogenesis is aberrant beyond that developmental point, and the double mutantuni pim lacks identifiable floral organs. A wild-type phenotype is observed inbroc plants, butbroc enhancesthe pim phenotype in the double mutant, producing inflorescences that resemble broccoli. Collectively these genes ensure that only the third-order meristem, not higher- or lower-order meristems, generates floral organs, thus precisely regulating the overall architecture of the plant. Gene symbols used in this article: For clarity a common symbolization is used for genes of all species discussed in this article. Genes are symbolized with italicized capital letters. Mutant alleles are represented by lowercase, italicized letters. In both cases, the number immediately following the gene symbol differentiates among genes with the same symbol. If there are multiple alleles, a hyphen followed by a number is used to distinguish alleles. Protein products are represented by capital letters without italics.     

Mammalian electron transferring flavoprotein.flavoprotein dehydrogenase complexes observed by microelectrospray ionization-mass spectrometry and surface plasmon resonance

Microelectrospray ionization-mass spectrometry was used to directly observe electron transferring flavoprotein.flavoprotein dehydrogenase interactions. When electron transferring flavoprotein and porcine dimethylglycine dehydrogenase or sarcosine dehydrogenase were incubated together in the absence of substrate, a relative molecular mass corresponding to the flavoprotein.electron transferring flavoprotein complex was observed, providing the first direct observation of these mammalian complexes. When an acyl-CoA dehydrogenase family member, human short chain acyl-CoA dehydrogenase, was incubated with dimethylglycine dehydrogenase and electron transferring flavoprotein, the microelectrospray ionization-mass spectrometry signal for the dimethylglycine dehydrogenase.electron transferring flavoprotein complex decreased, indicating that the acyl-CoA dehydrogenases have the ability to compete with the dimethylglycine dehydrogenase/sarcosine dehydrogenase family for access to electron transferring flavoprotein. Surface plasmon resonance solution competition experiments revealed affinity constants of 2.0 and 5.0 microm for the dimethylglycine dehydrogenase-electron transferring flavoprotein and short chain acyl-CoA dehydrogenase-electron transferring flavoprotein interactions, respectively, suggesting the same or closely overlapping binding motif(s) on electron transferring flavoprotein for dehydrogenase interaction.     

Extension of dynamic range of sensitive nanoparticle-based immunoassays

Nanoparticles have successfully been employed in immunometric assays that require high sensitivity. Certain analytes, however, require dynamic ranges (DRs) around a predetermined cut-off value. Here, we have studied the effects that antibody orientation and addition of free solid-phase and detection antibodies have on assay sensitivity and DR in traditional sandwich-type immunoassays. D-dimer and cardiac troponin I (cTnI), both routinely used in critical care testing, were applied as model analytes. The assays were performed in microtitration wells with preimmobilized solid-phase antibody. Inherently fluorescent nanoparticles coated with second antibody were used to detect the analyte. The selection of antibody orientation and addition of free solid-phase or detection antibody, with nanoparticles and calibrator, desensitized the assays and extended the DR. With D-dimer the upper limit of the DR was improved from 50 to 10,000 ng/ml, and with cTnI from 25 to 1000 ng/ml. Regression analysis with the Stago STA Liatest D-dimer assay yielded a slope (95% confidence interval) of 0.09 (0.07–0.11) and a y-intercept of −7.79 (−17.87–2.29) ng/L (n = 65, r = 0.906). Thus it is concluded that Europium(III)-chelate-doped nanoparticles can also be employed in immunoassays that require wide DRs around a certain cut-off limit.     

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.

     

Serum non-transferrin-bound iron and low-density lipoprotein oxidation in heterozygous hemochromatosis

Non-transferrin-bound iron (NTBI) is implicated in lipid peroxidation but the relation with oxidative modification of low-density lipoprotein (LDL) is not known. We assessed variables reflecting in vitro and in vivo LDL oxidation in two age- and sex-matched groups (n=23) of hereditary hemochromatosis heterozygotes (C282Y), characterized by a clear difference in mean serum NTBI (1.55+/-0.57 micromol/L vs 3.70+/-0.96 micromol/L). Plasma level of oxidized LDL (absolute and relative to plasma apolipoprotein B), and IgG and IgM antibodies to oxidized LDL, markers of in vivo LDL oxidation, did not differ between the groups with low and high serum NTBI. Mean lag-phase of in vitro LDL oxidation was also not significantly different between both study groups. Conclusion: these findings do not support the hypothesis that NTBI promotes oxidative modification of plasma LDL.     

Targeted proteomics of low-level proteins in human plasma by LC/MSn: using human growth hormone as a model system

This paper describes the profiling of human growth hormone (hGH) in human plasma in order to assess the dynamic range of the ion-trap mass spectrometer for proteomic studies of complex biological samples. Human growth hormone is an example of a low-level plasma protein in vivo, present at subfemtomole levels. This study was performed on a plasma sample in which hGH has been spiked at 10-fold above the natural level, that is approximately 16 pg/microL of plasma. Initially, the measurement was carried out without any sample enrichment and consisted of the following steps: the full set of plasma proteins were reduced, alkylated, and digested with trypsin, and the resulting peptides were separated on a capillary C-18 column and then detected by ion-trap mass spectrometry (1D LC/MS). In addition, this study provided a global view of the serum proteome with over 200 plasma proteins being preliminarily identified. In the MS/MS analysis, hGH was detected by characterization of the first tryptic peptide (T1). The initial identification was confirmed by alternative approaches, which also allowed the evaluation of different sample purification protocols. First, the plasma sample containing hGH was fractionated on a reversed-phase HPLC column and digested, and hGH could now be identified by MS/MS measurements of two tryptic peptides (T1 and T4) by the same 1D LC/MS protocol. In addition, the assignment of peptide identity was made with higher certainty (as measured by an algorithm score). The plasma sample was also fractionated by 1D and 2D gel electrophoresis, the selected bands were digested and analyzed again by the 1D LC/MS protocol. In both cases using the gel prepurifications, hGH was identified with additional peptides. Finally, the plasma sample was analyzed by 2D chromatography (ion exchange and reversed phase) on a new instrumental platform (ProteomeX), and hGH was identified by the observation of five tryptic peptides. In conclusion, these experiments were able to detect growth hormone in the low femtomole level with a dynamic range of 1 in 40 000 by several independent approaches. The amount of growth hormone, while 10-fold above normal in vivo levels, represents concentrations that may be present in disease states (such as acromegaly) and also in doping control measurements. These studies have demonstrated that shotgun sequencing approaches (LC/MS/MS) not only can profile high-abundance proteins in complex biological fluids but also have the potential to identify and quantitate low-level proteins present in such complex mixtures without extensive prepurification protocols. A key to such studies, however, is to use targeted approaches that reduce the complexity of the solute mixture that is presented to the mass spectrometer at a given time point. The various sample preparation protocols described here all improved the quality of the hGH measurement, although in this study the 2D chromatographic approach gave the greatest sequence coverage.     

RACK1 binds to a signal transfer region of G betagamma and inhibits phospholipase C beta2 activation

Receptor for Activated C Kinase 1 (RACK1), a novel G betagamma-interacting protein, selectively inhibits the activation of a subclass of G betagamma effectors such as phospholipase C beta2 (PLCbeta2) and adenylyl cyclase II by direct binding to G betagamma (Chen, S., Dell, E. J., Lin, F., Sai, J., and Hamm, H. E. (2004) J. Biol. Chem. 279, 17861-17868). Here we have mapped the RACK1 binding sites on G betagamma. We found that RACK1 interacts with several different G betagamma isoforms, including G beta1gamma1, Gbeta1gamma2, and Gbeta5gamma2, with similar affinities, suggesting that the conserved residues between G beta1 and G beta5 may be involved in their binding to RACK1. We have confirmed this hypothesis and shown that several synthetic peptides corresponding to the conserved residues can inhibit the RACK1/G betagamma interaction as monitored by fluorescence spectroscopy. Interestingly, these peptides are located at one side of G beta1 and have little overlap with the G alpha subunit binding interface. Additional experiments indicate that the G betagamma contact residues for RACK1, in particular the positively charged amino acids within residues 44-54 of G beta1, are also involved in the interaction with PLCbeta2 and play a critical role in G betagamma-mediated PLCbeta2 activation. These data thus demonstrate that RACK1 can regulate the activity of a G betagamma effector by competing for its binding to the signal transfer region of G betagamma.