Circular dichroism detection in HPLC

A circular dichroism-based detection system presents several advantages in the HPLC analysis of chiral compounds because of the selective monitoring of optically active molecules. Its use allows reliable determination of enantiomeric excesses and elution order. To this end, the application of empirical, semiempirical, and nonempirical methods to get stereochemical information from the CD signal is reported. Furthermore, recording the CD spectra on line and evaluation of the dissymetry factor make the CD detection very powerful in characterizing the stereochemistry of chiral eluates.     

Putative spermine synthases from Thalassiosira pseudonana and Arabidopsis thaliana synthesize thermospermine rather than spermine

Polyamines are involved in many fundamental cellular processes. Common polyamines are putrescine, spermidine and spermine. Spermine is synthesized by transfer of an aminopropyl residue derived from decarboxylated S-adenosylmethionine to spermidine. Thermospermine is an isomer of spermine and assumed to be synthesized by an analogous mechanism. However, none of the recently described spermine synthases was investigated for their possible activity as thermospermine synthases. In this work, putative spermine synthases from the diatom Thalassiosira pseudonana and from Arabidopsis thaliana could be identified as thermospermine synthases. These findings may explain the previous result that two putative spermine synthase genes in Arabidopsis produce completely different phenotypes in knock-out experiments. Likely, part of putative spermine synthases identifiable by sequence comparisons represents in fact thermospermine synthases.     

Macular Microcysts in Mitochondrial Optic Neuropathies: Prevalence and Retinal Layer Thickness Measurements

Purpose

To investigate the thickness of the retinal layers and to assess the prevalence of macular microcysts (MM) in the inner nuclear layer (INL) of patients with mitochondrial optic neuropathies (MON).

Methods

All patients with molecularly confirmed MON, i.e. Leber’s Hereditary Optic Neuropathy (LHON) and Dominant Optic Atrophy (DOA), referred between 2010 and 2012 were enrolled. Eight patients with MM were compared with two control groups: MON patients without MM matched by age, peripapillary retinal nerve fiber layer (RNFL) thickness, and visual acuity, as well as age-matched controls. Retinal segmentation was performed using specific Optical coherence tomography (OCT) software (Carl Zeiss Meditec). Macular segmentation thickness values of the three groups were compared by one-way analysis of variance with Bonferroni post hoc corrections.

Results

MM were identified in 5/90 (5.6%) patients with LHON and 3/58 (5.2%) with DOA. The INL was thicker in patients with MON compared to controls regardless of the presence of MM [133.1±7μm vs 122.3±9μm in MM patients (p<0.01) and 128.5±8μm vs. 122.3±9μm in no-MM patients (p<0.05)], however the outer nuclear layer (ONL) was thicker in patients with MM (101.4±1mμ) compared to patients without MM [77.5±8mμ (p<0.001)] and controls [78.4±7mμ (p<0.001)]. ONL thickness did not significantly differ between patients without MM and controls.

Conclusion

The prevalence of MM in MON is low (5-6%), but associated with ONL thickening. We speculate that in MON patients with MM, vitreo-retinal traction contributes to the thickening of ONL as well as to the production of cystic spaces.     

Role of PGE2 on gallbladder muscle cytoprotection of guinea pigs

H2O2 and taurochenodeoxycholic acid (TCDC) impair the contraction induced by CCK-8, ACh, and KCl without affecting the actions of PGE2 and damage functions of membrane proteins except for PGE2 receptors. The aim of this study was to examine whether the preserved PGE2 actions contribute to cytoprotective mechanisms against reactive oxygen species. Muscle cells from guinea pig gallbladder were obtained by enzymatic digestion. Levels of lipid peroxidation and activities of SOD and catalase were determined by spectrophotometry. Pretreatment with PGE2 prevented the inhibition of H2O2 or TCDC on agonist (CCK-8, ACh, and KCl)-induced contraction and reduced the expected increase in lipid peroxidation and activities of catalase and SOD caused by H2O2 and TCDC. Incubation with CCK-8 for 60 min desensitized CCK-1 receptors up to 30 min, whereas no receptor desensitization was observed after PGE2 pretreatment. Cholesterol-rich liposome treatment enhanced the inhibition of H2O2 and TCDC on agonists-induced contraction, including that of PGE2. Pretreatment with PGE2 before H2O2 and TCDC did not completely block their inhibition on agonist-induced contraction. Cholesterol-rich liposome treatment impaired the expected increase in catalase activities in response to PGE2. We conclude that pretreatment with PGE2 prevents the muscle cell damage caused by H2O2 and TCDC due to the resistance of PGE2 receptors to agonist-induced desensitization. The preservation of PGE2 receptors may be designed to conserve these cytoprotective functions that are, however, impaired by the presence of excess cholesterol in the plasma membrane.     

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.

     

Age-Related Differences in the Accuracy of Web Query-Based Predictions of Influenza-Like Illness

BackgroundWeb queries are now widely used for modeling, nowcasting and forecasting influenza-like illness (ILI). However, given that ILI attack rates vary significantly across ages, in terms of both magnitude and timing, little is known about whether the association between ILI morbidity and ILI-related queries is comparable across different age-groups. The present study aimed to investigate features of the association between ILI morbidity and ILI-related query volume from the perspective of age.MethodsSince Google Flu Trends is unavailable in Italy, Google Trends was used to identify entry terms that correlated highly with official ILI surveillance data. All-age and age-class-specific modeling was performed by means of linear models with generalized least-square estimation. Hold-out validation was used to quantify prediction accuracy. For purposes of comparison, predictions generated by exponential smoothing were computed.ResultsFive search terms showed high correlation coefficients of > .6. In comparison with exponential smoothing, the all-age query-based model correctly predicted the peak time and yielded a higher correlation coefficient with observed ILI morbidity (.978 vs. .929). However, query-based prediction of ILI morbidity was associated with a greater error. Age-class-specific query-based models varied significantly in terms of prediction accuracy. In the 0–4 and 25–44-year age-groups, these did well and outperformed exponential smoothing predictions; in the 15–24 and ≥ 65-year age-classes, however, the query-based models were inaccurate and highly overestimated peak height. In all but one age-class, peak timing predicted by the query-based models coincided with observed timing.ConclusionsThe accuracy of web query-based models in predicting ILI morbidity rates could differ among ages. Greater age-specific detail may be useful in flu query-based studies in order to account for age-specific features of the epidemiology of ILI.