Use of Flow Cytometry for Rapid,Quantitative Detection of Poliovirus-Infected Cells via TAT Peptide-Delivered Molecular Beacons

Rapid and efficient detection of viral infection is crucial for the prevention of disease spread during an outbreak and for timely clinical management. In this paper, the utility of Tat peptide-modified molecular beacons (MBs) as a rapid diagnostic tool for the detection of virus-infected cells was demonstrated. The rapid intracellular delivery mediated by the Tat peptide enabled the detection of infected cells within 30 s, reaching saturation in signal in 30 min. This rapid detection scheme was coupled with flow cytometry (FC), resulting in an automated, high-throughput method for the identification of virus-infected cells. Because of the 2-order-of-magnitude difference in fluorescence intensity between infected and uninfected cells, as few as 1% infected cells could be detected. Because of its speed and sensitivity, this approach may be adapted for the practical diagnosis of multiple viral infections.     

Peptide nanowires for coordination and signal transduction of peroxidase biosensors to carbon nanotube electrode arrays

A strategy of metallizing peptides to serve as conduits of electronic signals that bridge between a redox enzyme and a carbon-nanotube electrode has been developed with enhanced results. In conjunction, a protocol to link the biological elements to the tips of carbon nanotubes has been developed to optimize contact and geometry between the redox enzyme and the carbon nanotube electrode array. A peptide nanowire of 33 amino acids, comprised of a leucine zipper motif, was mutated to bind divalent metals, conferring conductivity into the peptide. Reaction between a thiolate of the peptide with the sulfenic acid of the NADH peroxidase enzyme formed a peptide-enzyme assembly that are fully primed to transduce electrons out of the enzyme active site to an electrode. Scanning electron microscopy shows immobilization and linking of the assembly specifically to the tips of carbon nanotube electrodes, as designed. Isothermal titration calorimetry and mass spectrometry indicate a binding stoichiometry of at least three metals bound per peptide strand. Overall, these results highlight the gain that can be achieved when the signal tranducing units of a biosensor are aligned through directed peptide chemistry.     

Diabetes reduces aortic endothelial gap junctions in ApoE-deficient mice: simvastatin exacerbates the reduction.

We examined the endothelial gap junctions in diabetic hyperlipidemic mice. Male apolipoprotein E (apoE)-deficient mice were made diabetic by streptozotocin. Three weeks later, the animals were treated with simvastatin for 2 weeks. The expression of aortic gap junctions in the non-diabetic (n=10), untreated diabetic (n=10), and simvastatin-treated diabetic animals (n=6) was analyzed. There was a >4-fold increase in serum cholesterol level and >50% increase in plaque areas in the diabetic mice, regardless of simvastatin treatment. Western blotting of aortae showed reduced expression of connexin37 (Cx37) and Cx40 in the diabetic mice, which were further decreased in the simvastatin-treated diabetic mice. Immunoconfocal microscopy showed that endothelial gap junctions made of Cx37 and Cx40 were both reduced in the untreated diabetic mice compared with the non-diabetic mice (decrease: Cx37, 41%; Cx40, 42%; both p<0.01). The reduction was greater in the simvastatin-treated mice (decrease in treated diabetic vs non-diabetic: Cx37, 61%; Cx40, 79%; both p<0.01; decrease in treated diabetic vs untreated diabetic: Cx37, 34%; Cx40, 63%; both p<0.01). Cx37 and Cx40 were decreased in the endothelium of plaque surface. Cx43 appeared in the medial layer and inner layer of the intima. All three connexins were rarely expressed in monocytes/macrophages inside the plaques. In conclusion, in apoE-deficient mice, streptozotocin-induced diabetes is associated with downregulation of endothelial Cx37 and Cx40 gap junctions. Short-term treatment with simvastatin exacerbates the downregulation.     

Contrasting Patterns in the Early Stage of SARS-CoV-2 Evolution between Humans and Minks

One of the unique features of SARS-CoV-2 is its apparent neutral evolution during the early pandemic (before February 2020). This contrasts with the preceding SARS-CoV epidemics, where viruses evolved adaptively. SARS-CoV-2 may exhibit a unique or adaptive feature which deviates from other coronaviruses. Alternatively, the virus may have been cryptically circulating in humans for a sufficient time to have acquired adaptive changes before the onset of the current pandemic. To test the scenarios above, we analyzed the SARS-CoV-2 sequences from minks (Neovision vision) and parental humans. In the early phase of the mink epidemic (April to May 2020), nonsynonymous to synonymous mutation ratio per site in the spike protein is 2.93, indicating a selection process favoring adaptive amino acid changes. Mutations in the spike protein were concentrated within its receptor-binding domain and receptor-binding motif. An excess of high-frequency derived variants produced by genetic hitchhiking was found during the middle (June to July 2020) and late phase I (August to September 2020) of the mink epidemic. In contrast, the site frequency spectra of early SARS-CoV-2 in humans only show an excess of low-frequency mutations, consistent with the recent outbreak of the virus. Strong positive selection in the mink SARS-CoV-2 implies that the virus may not be preadapted to a wide range of hosts and illustrates how a virus evolves to establish a continuous infection in a new host. Therefore, the lack of positive selection signal during the early pandemic in humans deserves further investigation.     

Integrating across neuroimaging modalities boosts prediction accuracy of cognitive ability

Variation in cognitive ability arises from subtle differences in underlying neural architecture. Understanding and predicting individual variability in cognition from the differences in brain networks requires harnessing the unique variance captured by different neuroimaging modalities. Here we adopted a multi-level machine learning approach that combines diffusion, functional, and structural MRI data from the Human Connectome Project (N = 1050) to provide unitary prediction models of various cognitive abilities: global cognitive function, fluid intelligence, crystallized intelligence, impulsivity, spatial orientation, verbal episodic memory and sustained attention. Out-of-sample predictions of each cognitive score were first generated using a sparsity-constrained principal component regression on individual neuroimaging modalities. These individual predictions were then aggregated and submitted to a LASSO estimator that removed redundant variability across channels. This stacked prediction led to a significant improvement in accuracy, relative to the best single modality predictions (approximately 1% to more than 3% boost in variance explained), across a majority of the cognitive abilities tested. Further analysis found that diffusion and brain surface properties contribute the most to the predictive power. Our findings establish a lower bound to predict individual differences in cognition using multiple neuroimaging measures of brain architecture, both structural and functional, quantify the relative predictive power of the different imaging modalities, and reveal how each modality provides unique and complementary information about individual differences in cognitive function.     

Specific PCR product primer design using memetic algorithm

To provide feasible primer sets for performing a polymerase chain reaction (PCR) experiment, many primer design methods have been proposed. However, the majority of these methods require a relatively long time to obtain an optimal solution since large quantities of template DNA need to be analyzed. Furthermore, the designed primer sets usually do not provide a specific PCR product size. In recent years, evolutionary computation has been applied to PCR primer design and yielded promising results. In this article, a memetic algorithm (MA) is proposed to solve primer design problems associated with providing a specific product size for PCR experiments. The MA is compared with a genetic algorithm (GA) using an accuracy formula to estimate the quality of the primer design and test the running time. Overall, 50 accession nucleotide sequences were sampled for the comparison of the accuracy of the GA and MA for primer design. Five hundred runs of the GA and MA primer design were performed with PCR product lengths of 150–300 bps and 500–800 bps, and two different methods of calculating T_m for each accession nucleotide sequence were tested. A comparison of the accuracy results for the GA and MA primer design showed that the MA primer design yielded better results than the GA primer design. The results further indicate that the proposed method finds optimal or near‐optimal primer sets and effective PCR products in a dry dock experiment. Related materials are available online at http://bio.kuas.edu.tw/ma‐pd/ . © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009