α-MELANOCYTE-STIMULATING HORMONE: A POSSIBLE NEURONAL MARKER OF THE AGEING BRAIN

Abstract— The amount of α-melanocyte-stimulating hormone (α-MSH) in the entire hypothalamus as well as the amount of α-MSH in free granule and synaptosome fractions of hypothalamic homogenates was investigated throughout the lifespan of female rats (1-24 months). A 900 g supernatant fluid was prepared from hypothalami following homogenization in an iso-osmotic sucrose solution, and free granules and synaptosomes containing α-MSH were fractionated by means of continuous sucrose density gradient centrifugation. α-MSH was quantified by radioimmunoassay. The total amount of α-MSH in the hypothalamus, as well as the amount in free granules and synaptosomes prepared from hypothalami increased progressively from the 1st to the 5th month of life, and this increase was more pronounced in the free granules than in the synaptosomes. On the other hand, the amount of α-MSH in the hypothalamus and the amount present in free granules and synaptosomes prepared from 5-24-month-old animals decreased with age, and this decrease appeared to proceed at similar rates in both subcellular compartments. Based on these results, it is suggested that ageing of α-MSH neurons in the hypothalamus is accompanied by a degeneration of the axons and/or an alteration in the biosynthetic and degradative activities of the neuron.     

Factors affecting the dormancy and germination of bleeding heart [Lamprocapnos spectabilis (L.) Fukuhara] seeds

• Information on the optimal conditions to promote the germination of Lamprocapnos spectabilis (L.) Fukuhara seeds is limited; consequently, this study was conducted to establish the requirements to break seed dormancy and promote germination.
• The selected seeds had morphophysiological dormancy and had not begun embryo development. To study the dormancy breaking and embryo development processes, seeds were subjected to constant or changing temperature treatments during moist stratification.
• High temperature and humidity resulted in vigorous embryo growth, with the longest embryos occurring after 1 month of incubation at 20 °C. At 4 °C, the seeds required incubation period of at least 3 months to germinate. Embryo growth and germination were higher with changing high and low temperatures than under a constant temperature, and changing temperatures also considerably changed the endogenous hormone levels, embryo development and germination. Bioactive gibberellin (GA) content was higher in seeds incubated at 20 °C for 1 month, then at 4 °C for 2 months. The content of endogenous abscisic acid in seeds subjected to the same treatment decreased by 97.6% compared with that of the untreated seeds.
• Embryo growth and seed germination require changing high and low temperatures; however, exogenous GA₃ could substitute for high temperatures, as it also causes accelerated germination. In this study, the seeds of L. spectabilis were identified as an intermediate simple type, a sub‐level of morphophysiologically dormant seeds.

     

Alteration of mitochondrial DNA sequence and copy number in nasal polyp tissue

This study was designed to investigate the possibility that mtDNA mutations might arise in inflammatory or chronically damaged nasal polyp tissue from 23 patients. Thirteen patients (57%) displayed nasal polyp tissue-specific mtDNA mutations in the hypervariable segment of the control region and cytochrome b gene, which were not found in the corresponding blood cells and/or adjacent normal tissue. Nasal polyp tissue-specific length heteroplasmic mutations were also detected in nucleotide position (np) 303–315 homopolymeric poly C track (39%), np 514–523 CA repeats (17%) and np 16184–16193 poly C track (30%). The average mtDNA copy number was about three times higher in nasal polyp tissue than in the corresponding peripheral blood cells and adjacent non-polyp tissues. The level of reactive oxygen species (ROS) was significantly higher in the nasal polyp tissues compared to those from the corresponding samples. High level of ROS in nasal polyp tissue may contribute to development of mtDNA mutations, which may play a crucial role in the vicious cycle of pathophysiology of nasal polyps.     

Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data

The analysis of single-cell genomics data presents several statistical challenges, and extensive efforts have been made to produce methods for the analysis of this data that impute missing values, address sampling issues and quantify and correct for noise. In spite of such efforts, no consensus on best practices has been established and all current approaches vary substantially based on the available data and empirical tests. The k-Nearest Neighbor Graph (kNN-G) is often used to infer the identities of, and relationships between, cells and is the basis of many widely used dimensionality-reduction and projection methods. The kNN-G has also been the basis for imputation methods using, e.g., neighbor averaging and graph diffusion. However, due to the lack of an agreed-upon optimal objective function for choosing hyperparameters, these methods tend to oversmooth data, thereby resulting in a loss of information with regard to cell identity and the specific gene-to-gene patterns underlying regulatory mechanisms. In this paper, we investigate the tuning of kNN- and diffusion-based denoising methods with a novel non-stochastic method for optimally preserving biologically relevant informative variance in single-cell data. The framework, Denoising Expression data with a Weighted Affinity Kernel and Self-Supervision (DEWÄKSS), uses a self-supervised technique to tune its parameters. We demonstrate that denoising with optimal parameters selected by our objective function (i) is robust to preprocessing methods using data from established benchmarks, (ii) disentangles cellular identity and maintains robust clusters over dimension-reduction methods, (iii) maintains variance along several expression dimensions, unlike previous heuristic-based methods that tend to oversmooth data variance, and (iv) rarely involves diffusion but rather uses a fixed weighted kNN graph for denoising. Together, these findings provide a new understanding of kNN- and diffusion-based denoising methods. Code and example data for DEWÄKSS is available at https://gitlab.com/Xparx/dewakss/-/tree/Tjarnberg2020branch.