Receptor-specific targeting with complementary peptide nucleic acids conjugated to peptide analogs and radionuclides

Summary Genomic sequencing makes it possible to identify all the genes of an organism, now including Homo sapiens. Yet measurement of the expression of each gene of interest still presents a daunting prospect. Northern blots, RNase protection assays, as well as microarrays and related technologies permit measurement of gene expression in total RNA extracted from cultured cells or tissue samples. It would be most valuable, however, to quantitate gene expression noninvasively in living cells and tissues. Unfortunately, no reliable method has been available to measure levels of specific mRNAs in vivo. Peptide nucleic acids (PNAs) display superior ruggedness and hybridization properties as a diagnostic tool for gene expression, and could be used for this purpose. On the down side, they are negligibly internalized by normal or malignant cells in the absence of conjugated ligands. Nevertheless, we have observed that Tc-99m-peptides can delineate tumors, and PNA-peptides designed to bind to IGF-1 receptors on malignant cells are taken up specifically and concentrated in nuclei. We have postulated that antisense Tc-99m-PNA-peptides will be taken up by human cancer cells, will hybridize to complementary mRNA targets, and will permit scintigraphic imaging of oncogene mRNAs in human cancer xenografts in a mouse model. The oncogenes cyclin D1, ERBB2, c-MYC, K-RAS, and tumor suppressor p53 are being probed initially. These experiments provide a proof-of-principle for noninvasive detection of oncogene expression in living cells and tissues. This scintigraphic imaging technique should be applicable to any particular gene of interest in a cell or tissue type with characteristic receptors.     

Decreased GABA receptor in the cerebral cortex of epileptic rats: effect of Bacopa monnieri and Bacoside-A

Abstact

Background

Gamma amino butyric acid (GABA), the principal inhibitory neurotransmitter in the cerebral cortex, maintains the inhibitory tones that counter balances neuronal excitation. When this balance is perturbed, seizures may ensue.

Methods

In the present study, alterations of the general GABA, GABA_A and GABA_B receptors in the cerebral cortex of the epileptic rat and the therapeutic application of Bacopa monnieri were investigated.

Results

Scatchard analysis of [³H]GABA, [³H]bicuculline and [³H]baclofen in the cerebral cortex of the epileptic rat showed significant decrease in B_max (P < 0.001) compared to control. Real Time PCR amplification of GABA receptor subunits such as GABA_Aά1, GABA_Aγ, GABA_Aδ, GABA_B and GAD where down regulated (P < 0.001) in epileptic rats. GABA_Aά5 subunit and Cyclic AMP responsible element binding protein were up regulated. Confocal imaging study confirmed the decreased GABA receptors in epileptic rats. Epileptic rats have deficit in radial arm and Y maze performance.

Conclusions

Bacopa monnieri and Bacoside-A treatment reverses epilepsy associated changes to near control suggesting that decreased GABA receptors in the cerebral cortex have an important role in epileptic occurrence; Bacopa monnieri and Bacoside-A have therapeutic application in epilepsy management.     

Respiratory Phenomics across Multiple Models of Protein Hyperacylation in Cardiac Mitochondria Reveals a Marginal Impact on Bioenergetics

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An integrative framework of coexistence mechanisms in competitive metacommunities

Species distribution in a metacommunity varies according to their traits, the distribution of environmental conditions and connectivity among localities. These ingredients contribute to coexistence across spatial scales via species sorting, patch dynamics, mass effects and neutral dynamics. These mechanisms however seldom act in isolation and the impact of landscape configuration on their relative importance remains poorly understood. We present a new model of metacommunity dynamics that simultaneously considers these four possible mechanisms over spatially explicit landscapes and propose a statistical approach to partition their contribution to species distribution. We find that landscape configuration can induce dispersal limitations that have negative consequences for local species richness. This result was more pronounced with neutral dynamics and mass effect than with species sorting or patch dynamics. We also find that the relative importance of the four mechanisms varies not only among landscape configurations, but also among species, with some species being mostly constrained by dispersal and/or drift and others by sorting. Changes in landscape properties might lead to a shift in coexistence mechanisms and, by extension, to a change in community composition. This confirms the importance of considering landscape properties for conservation and management. Our results illustrate the idea that ecological communities are the results of multiple mechanisms acting at the same time and complete our understanding of spatial processes in competitive metacommunities.     

Signal-to-signal neural networks for improved spike estimation from calcium imaging data

Spiking information of individual neurons is essential for functional and behavioral analysis in neuroscience research. Calcium imaging techniques are generally employed to obtain activities of neuronal populations. However, these techniques result in slowly-varying fluorescence signals with low temporal resolution. Estimating the temporal positions of the neuronal action potentials from these signals is a challenging problem. In the literature, several generative model-based and data-driven algorithms have been studied with varied levels of success. This article proposes a neural network-based signal-to-signal conversion approach, where it takes as input raw-fluorescence signal and learns to estimate the spike information in an end-to-end fashion. Theoretically, the proposed approach formulates the spike estimation as a single channel source separation problem with unknown mixing conditions. The source corresponding to the action potentials at a lower resolution is estimated at the output. Experimental studies on the spikefinder challenge dataset show that the proposed signal-to-signal conversion approach significantly outperforms state-of-the-art-methods in terms of Pearson’s correlation coefficient, Spearman’s rank correlation coefficient and yields comparable performance for the area under the receiver operating characteristics measure. We also show that the resulting system: (a) has low complexity with respect to existing supervised approaches and is reproducible; (b) is layer-wise interpretable, and (c) has the capability to generalize across different calcium indicators.     

Production of lignosulfonate in NSSC‐based biorefinery

The spent liquor (SL) of a neutral sulfite semichemical (NSSC) pulping process contains a considerable amount of lignocelluloses and is treated in wastewater systems. The lignocelluloses, however, can be used for producing value‐added products if they are isolated from the SL. In this article, solvent treatment (mixing acetone, ethanol, or isopropyl with SL) was used as a method for isolating lignosulfonate from SL. The maximum lignosulfonate removal was obtained via mixing isopropyl alcohol with SL at the weight ratio of 20/80, room temperature, and 5.7 pH. The results also showed that the molecular weight and anionic charge density of the precipitates were in the range of 5,000–70,000 g/mol and 0.2–1.8 meq/g, respectively. Based on these results, a process was proposed for isolating lignosulfonate from SL and converting the NSSC process to an NSSC‐based biorefinery. © 2015 American Institute of Chemical Engineers Biotechnol. Prog., 31:1508–1514, 2015     

Bayesian spatial modeling of moose count data: increasing estimator efficiency and exploring ecological hypotheses

Moose management throughout much of Alaska and Canada relies on aerial count data, which are commonly collected using the geospatial population estimator (GSPE) protocol. The GSPE uses a model-based analytical approach and finite-population block kriging to estimate abundance from a collection of sampled survey units. Widespread implementation and well-documented analytical software have resulted in reliable estimates of moose abundance, density, and composition across a large proportion of their range. Analysis is conducted almost exclusively using the GSPE software, which fits a fixed model structure to data collected within a single year. The downside of this approach to analysis is that the fixed model structure is inefficient for estimation, leading to more field effort than would otherwise be necessary to achieve a desired level of estimator precision. We developed a more easily modified and flexible Bayesian spatial general additive model approach (BSG) that accommodates spatial and temporal covariates (e.g., habitat characteristics, trend), multiple survey events, prior information, and incomplete detection. Using a series of 6 GSPE surveys conducted in Yukon-Charley Rivers National Preserve, Alaska, USA, from 2003–2019, we established the equivalence of the 2 approaches under similar model structures. We then extended the BSG to demonstrate how a more comprehensive approach to analysis can affect estimator precision and be used to assess ecological relationships. The precision of annual abundance estimators from the BSG were improved by an average of 43% over those based on the standard GSPE analysis, highlighting the very real costs of assuming a fixed (i.e., suboptimal) model structure. The population increased at a rate of 2.3%/year (95% CrI = 0.8–3.8%), and the increase was largely explained by a parallel increase in wildfire extent (i.e., high quality moose habitat). These results suggest that our approach could be used to increase estimator efficiency or decrease future survey costs without any modifications to the basic protocol. While modification of the GSPE software is possible, practitioners may find the BSG approach more convenient for quickly developing model structures for a particular application, thereby allowing them to extract more information from existing and future datasets.