Biophysically Inspired Rational Design of Structured Chimeric Substrates for DNAzyme Cascade Engineering

The development of large-scale molecular computational networks is a promising approach to implementing logical decision making at the nanoscale, analogous to cellular signaling and regulatory cascades. DNA strands with catalytic activity (DNAzymes) are one means of systematically constructing molecular computation networks with inherent signal amplification. Linking multiple DNAzymes into a computational circuit requires the design of substrate molecules that allow a signal to be passed from one DNAzyme to another through programmed biochemical interactions. In this paper, we chronicle an iterative design process guided by biophysical and kinetic constraints on the desired reaction pathways and use the resulting substrate design to implement heterogeneous DNAzyme signaling cascades. A key aspect of our design process is the use of secondary structure in the substrate molecule to sequester a downstream effector sequence prior to cleavage by an upstream DNAzyme. Our goal was to develop a concrete substrate molecule design to achieve efficient signal propagation with maximal activation and minimal leakage. We have previously employed the resulting design to develop high-performance DNAzyme-based signaling systems with applications in pathogen detection and autonomous theranostics.     

Modification of morphine-induced change in striatal (3H)-spiroperidol binding and stereotype behavior by cyclo(Leu-Gly)

Recent reports from our laboratories have indicated that the peptide cyclo(Leu-Gly), an analog of MIF (Pro-Leu-Gly-NH₂), administered prior to chronic exposure to morphine, prevents the development of both analgesic tolerance and some signs of physical dependence. The same peptide treatment also prevented the development of morphine-induced increases in certain behavioral responses to the dopamine agonist apomorphine. The present study investigated behavioral (stereotypy) and neurochemical receptor changes (specific (³H)-spiroperidol binding) occuring in the rat striatal dopamine (DA) system following chronic morphine treatment with and without prior cyclo(Leu-Gly)administration. While chronic morphine treatment (s.c. 5 pellet implant for 3 days, each pellet contained 65 mg morphine free base) did not alter the total number of high-affinity striatal (³H)-spiroperidol binding sites (28 fmol/mg tissue), it did increase the affinity of the receptor for the ligand (K_D decreased from 40 to 24 pM). Cyclo(Leu-Gly) (8 mg/kg) prevented the morphine induced increase in dopamine receptor affinity. In parallel, cyclo(Leu-Gly) prevented the increase in apomorphine-induced stereotypy which was observed in chronic morphine treated rats. The peptide alone did not alter any of the binding characteristics. These data suggest that the ability of the peptide to block the development of physical dependence induced by morphine may involve the ability of the peptide to interfere with morphine-induced changes in dopaminergic systems.     

Aligning extracted LC-MS peak lists via density maximization

Rapid improvements in mass spectrometry sensitivity and mass accuracy combined with improved liquid chromatography separation technologies allow acquisition of high throughput metabolomics data, providing an excellent opportunity to understand biological processes. While spectral deconvolution software can identify discrete masses and their associated isotopes and adducts, the utility of metabolomic approaches for many statistical analyses such as identifying differentially abundant ions depends heavily on data quality and robustness, especially, the accuracy of aligning features across multiple biological replicates. We have developed a novel algorithm for feature alignment using density maximization. Instead of a greedy iterative, hence local, merging strategy, which has been widely used in the literature and in commercial applications, we apply a global merging strategy to improve alignment quality. Using both simulated and real data, we demonstrate that our new algorithm provides high map (e.g. chromatogram) coverage, which is critically important for non-targeted comparative metabolite profiling of highly replicated biological datasets.

     

Bioinformatic perspectives in the neuronal ceroid lipofuscinoses

The neuronal ceroid lipofuscinoses (NCLs) are a group of rare genetic diseases characterised clinically by the progressive deterioration of mental, motor and visual functions and histopathologically by the intracellular accumulation of autofluorescent lipopigment – ceroid – in affected tissues. The NCLs are clinically and genetically heterogeneous and more than 14 genetically distinct NCL subtypes have been described to date (CLN1–CLN14) (Haltia and Goebel, 2012 [1]). In this review we will chronologically summarise work which has led over the years to identification of NCL genes, and outline the potential of novel genomic techniques and related bioinformatic approaches for further genetic dissection and diagnosis of NCLs. This article is part of a Special Issue entitled: The Neuronal Ceroid Lipofuscinoses or Batten Disease.     

Mechanoregulation modeling of bone healing in realistic fracture geometries

Biomechanics and Modeling in Mechanobiology - In bone fracture healing, new tissue gradually forms, ossifies, and eventually remodels itself to restore mechanical stiffness and strength across...