Regeneration Rates of Dendrobium Bobby Messina Plbs with Ascorbic Acid Using PVS2 Vitrification

Cryopreservation techniques using PVS2 vitrification was applied on PLBs of Dendrobium Bobby Messina, with survival monitored through observations of growth rate and the 2,3,5-triphenyltetrazolium chloride (TTC) analyses. The parameters optimized were PLBs size, preculture concentration, preculture duration, PVS2 incubation temperature and duration. The optimized parameters obtained were 3-4mm of PLBs precultured in 0.2M sucrose for 1 day, treated with a mixture of 2M glycerol and 0.4M sucrose supplemented with half strength liquid MS media at 25°C for 20 minutes and subsequently dehydrated with plant vitrification solution 2 (PVS2) at 0°C for 20 minutes prior storage in liquid nitrogen. Following rapid warming in a water bath at 40°C for 90 seconds, PLBs were washed with a half strength liquid MS media supplemented with 1.2M sucrose. Subsequently, PLBs were cultured on half strength semi-solid MS media supplemented with 2% (w/v) sucrose in the absence of growth regulator. The optimized vitrification method was successful in preserving this orchid as it produced growth recovery in cryopreserved PLBs up to 40%. Ascorbic acid was added in the media to evaluate the regeneration process of cryopreserved PLBs. However, growth recovery rate was only 10% at 0.6mM ascorbic acid. RAPD analysis using 6 primers indicated that cryopreserved and non-cryopreserved PLBs from vitrification method were genetically faithful to the mother plant. However, 3 primers showed polymorphism and 1 primers indicated partial polymorphism between the cryopreserved and non-cryopreserved PLBs in comparative to the mother plant

A Comparison between Four Analytical Methods for the Measurement of Fe(II) at Nanomolar Concentrations in Coastal Seawater

Dissolved Fe(II) in seawater is deemed an important micronutrient for microbial organisms, but its analysis is challenging due to its transient nature. We conducted a series of Fe(II) method comparison experiments, where spikes of 5 to 31 nM Fe(II) were added to manipulated seawaters with varying dissolved oxygen (37 to 156 μM) concentrations. The observed Fe(II) concentrations from four analytical methods were compared: spectrophotometry with ferrozine, stripping voltammetry, and flow injection analysis using luminol (with, and without, a pre-concentration column). Direct comparisons between the different methods were undertaken from the derived apparent Fe(II) oxidation rate constant (k1). Whilst the two luminol based methods produced the most similar concentrations throughout the experiments, k1 was still subject to a 20–30% discrepancy between them. Contributing factors may have included uncertainty in the calibration curves, and different responses to interferences from Co(II) and humic/fulvic organic material. The difference in measured Fe(II) concentrations between the luminol and ferrozine methods, from 10 min–2 h after the Fe(II) spikes were added, was always relatively large in absolute terms (>4 nM) and relative to the spike added (>20% of the initial Fe(II) concentration). k1 derived from ferrozine observed Fe(II) concentrations was 3–80%, and 4–16%, of that derived from luminol observed Fe(II) with, and without, pre-concentration respectively. The poorest comparability of k1 was found after humic/fulvic material was added to raise dissolved organic carbon to 120 μM. A luminol method without pre-concentration then observed Fe(II) to fall below the detection limit (<0.49 nM) within 10 min of a 17 nM Fe(II) spike addition, yet other methods still observed Fe(II) concentrations of 2.7 to 3.7 nM 30 min later. k1 also diverged accordingly with the ferrozine derived value 4% of that derived from luminol without pre-concentration. These apparent inconsistencies suggest that some inter-dataset differences in measured Fe(II) oxidation rates in natural waters may be attributable to differences in the analytical methods used rather than arising solely from substantial shifts in Fe(II) speciation

Differential sensing with arrays of de novo designed peptide assemblies

Differential sensing attempts to mimic the mammalian senses of smell and taste to identify analytes and complex mixtures. In place of hundreds of complex, membrane-bound G-protein coupled receptors, differential sensors employ arrays of small molecules. Here we show that arrays of computationally designed de novo peptides provide alternative synthetic receptors for differential sensing. We use self-assembling α-helical barrels (αHBs) with central channels that can be altered predictably to vary their sizes, shapes and chemistries. The channels accommodate environment-sensitive dyes that fluoresce upon binding. Challenging arrays of dye-loaded barrels with analytes causes differential fluorophore displacement. The resulting fluorimetric fingerprints are used to train machine-learning models that relate the patterns to the analytes. We show that this system discriminates between a range of biomolecules, drink, and diagnostically relevant biological samples. As αHBs are robust and chemically diverse, the system has potential to sense many analytes in various settings

Photodynamic treatment of human breast and prostate cancer cells using rose bengal-encapsulated nanoparticles

Cancer, a prominent cause of death, presents treatment challenges, including high dosage requirements, drug resistance, poor tumour penetration and systemic toxicity in traditional chemotherapy. Photodynamic therapy, using photosensitizers like rose bengal (RB) with a green laser, shows promise against breast cancer cells in vitro. However, the hydrophilic RB struggles to efficiently penetrate the tumour site due to the unique clinical microenvironment, aggregating around rather than entering cancer cells. In this study, we have synthesized and characterized RB-encapsulated chitosan nanoparticles with a peak particle size of ~200 nm. These nanoparticles are readily nternalized by cells and, in combination with a green laser (λ = 532 nm) killed 94–98% of cultured human breast cancer cells (MCF-7) and prostate cancer cells (PC3) at a low dosage (25 μg/mL RB-nanoparticles, fluence ~126 J/cm2, and irradiance ~0.21 W/cm2). Furthermore, these nanoparticles are not toxic to cultured human normal breast cells (MCF10A), which opens an avenue for translational applications

Uncovering an allosteric mode of action for a selective inhibitor of human Bloom syndrome protein

BLM (Bloom syndrome protein) is a RECQ-family helicase involved in the dissolution of complex DNA structures and repair intermediates. Synthetic lethality analysis implicates BLM as a promising target in a range of cancers with defects in the DNA damage response; however, selective small molecule inhibitors of defined mechanism are currently lacking. Here, we identify and characterise a specific inhibitor of BLM’s ATPase-coupled DNA helicase activity, by allosteric trapping of a DNA-bound translocation intermediate. Crystallographic structures of BLM-DNA-ADP-inhibitor complexes identify a hitherto unknown interdomain interface, whose opening and closing are integral to translocation of ssDNA, and which provides a highly selective pocket for drug discovery. Comparison with structures of other RECQ helicases provides a model for branch migration of Holliday junctions by BLM

Identifying xenobiotic metabolites with in silico prediction tools and LCMS suspect screening analysis

Understanding the metabolic fate of a xenobiotic substance can help inform its potential health risks and allow for the identification of signature metabolites associated with exposure. The need to characterize metabolites of poorly studied or novel substances has shifted exposure studies towards non-targeted analysis (NTA), which often aims to profile many compounds within a sample using high-resolution liquid-chromatography mass-spectrometry (LCMS). Here we evaluate the suitability of suspect screening analysis (SSA) liquid-chromatography mass-spectrometry to inform xenobiotic chemical metabolism. Given a lack of knowledge of true metabolites for most chemicals, predictive tools were used to generate potential metabolites as suspect screening lists to guide the identification of selected xenobiotic substances and their associated metabolites. Thirty-three substances were selected to represent a diverse array of pharmaceutical, agrochemical, and industrial chemicals from Environmental Protection Agency’s ToxCast chemical library. The compounds were incubated in a metabolically-active in vitro assay using primary hepatocytes and the resulting supernatant and lysate fractions were analyzed with high-resolution LCMS. Metabolites were simulated for each compound structure using software and then combined to serve as the suspect screening list. The exact masses of the predicted metabolites were then used to select LCMS features for fragmentation via tandem mass spectrometry (MS/MS). Of the starting chemicals, 12 were measured in at least one sample in either positive or negative ion mode and a subset of these were used to develop the analysis workflow. We implemented a screening level workflow for background subtraction and the incorporation of time-varying kinetics into the identification of likely metabolites. We used haloperidol as a case study to perform an in-depth analysis, which resulted in identifying five known metabolites and five molecular features that represent potential novel metabolites, two of which were assigned discrete structures based on in silico predictions. This workflow was applied to five additional test chemicals, and 15 molecular features were selected as either reported metabolites, predicted metabolites, or potential metabolites without a structural assignment. This study demonstrates that in some–but not all–cases, suspect screening analysis methods provide a means to rapidly identify and characterize metabolites of xenobiotic chemicals

FAIR Data Pipeline: provenance-driven data management for traceable scientific workflows

Modern epidemiological analyses to understand and combat the spread of disease depend critically on access to, and use of, data. Rapidly evolving data, such as data streams changing during a disease outbreak, are particularly challenging. Data management is further complicated by data being imprecisely identified when used. Public trust in policy decisions resulting from such analyses is easily damaged and is often low, with cynicism arising where claims of "following the science" are made without accompanying evidence. Tracing the provenance of such decisions back through open software to primary data would clarify this evidence, enhancing the transparency of the decision-making process. Here, we demonstrate a Findable, Accessible, Interoperable and Reusable (FAIR) data pipeline developed during the COVID-19 pandemic that allows easy annotation of data as they are consumed by analyses, while tracing the provenance of scientific outputs back through the analytical source code to data sources. Such a tool provides a mechanism for the public, and fellow scientists, to better assess the trust that should be placed in scientific evidence, while allowing scientists to support policy-makers in openly justifying their decisions. We believe that tools such as this should be promoted for use across all areas of policy-facing research

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy

Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12-16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI's magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57

Genome-wide meta-analysis associates HLA-DQA1/DRB1 and LPA and lifestyle factors with human longevity

Genomic analysis of longevity offers the potential to illuminate the biology of human aging. Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA). We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity. Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated. We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD. Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan.Variability in human longevity is genetically influenced. Using genetic data of parental lifespan, the authors identify associations at HLA-DQA/DRB1 and LPA and find that genetic variants that increase educational attainment have a positive effect on lifespan whereas increasing BMI negatively affects lifespan