The human immunodeficiency virus antigen Nef forms protein bodies in leaves of transgenic tobacco when fused to zeolin

Protein bodies (PB) are stable polymers naturally formed by certain seed storage proteins within the endoplasmic reticulum (ER). The human immunodeficiency virus negative factor (Nef) protein, a potential antigen for the development of an anti-viral vaccine, is highly unstable when introduced into the plant secretory pathway, probably because of folding defects in the ER environment. The aim of this study was to promote the formation of Nef-containing PB in tobacco (Nicotiana tabacum) leaves by fusing the Nef sequence to the N-terminal domains of the maize storage protein γ-zein or to the chimeric protein zeolin (which efficiently forms PB and is composed of the vacuolar storage protein phaseolin fused to the N-terminal domains of γ-zein). Protein blots and pulse–chase indicate that fusions between Nef and the same γ-zein domains present in zeolin are degraded by ER quality control. Consistently, a mutated zeolin, in which wild-type phaseolin was substituted with a defective version known to be degraded by ER quality control, is unstable in plant cells. Fusion of Nef to the entire zeolin sequence instead allows the formation of PB detectable by electron microscopy and subcellular fractionation, leading to zeolin–Nef accumulation higher than 1% of total soluble protein, consistently reproduced in independent transgenic plants. It is concluded that zeolin, but not its γ-zein portion, has a positive dominant effect over ER quality control degradation. These results provide insights into the requirements for PB formation and avoidance of quality-control degradation, and indicate a strategy for enhancing foreign protein accumulation in plants

Anti-HIV-1 Activity of a New Scorpion Venom Peptide Derivative Kn2-7

For over 30 years, HIV/AIDS has wreaked havoc in the world. In the absence of an effective vaccine for HIV, development of new anti-HIV agents is urgently needed. We previously identified the antiviral activities of the scorpion-venom-peptide-derived mucroporin-M1 for three RNA viruses (measles viruses, SARS-CoV, and H5N1). In this investigation, a panel of scorpion venom peptides and their derivatives were designed and chosen for assessment of their anti-HIV activities. A new scorpion venom peptide derivative Kn2-7 was identified as the most potent anti-HIV-1 peptide by screening assays with an EC50 value of 2.76 µg/ml (1.65 µM) and showed low cytotoxicity to host cells with a selective index (SI) of 13.93. Kn2-7 could inhibit all members of a standard reference panel of HIV-1 subtype B pseudotyped virus (PV) with CCR5-tropic and CXCR4-tropic NL4-3 PV strain. Furthermore, it also inhibited a CXCR4-tropic replication-competent strain of HIV-1 subtype B virus. Binding assay of Kn2-7 to HIV-1 PV by Octet Red system suggested the anti-HIV-1 activity was correlated with a direct interaction between Kn2-7 and HIV-1 envelope. These results demonstrated that peptide Kn2-7 could inhibit HIV-1 by direct interaction with viral particle and may become a promising candidate compound for further development of microbicide against HIV-1

Candidate HIV-1 Tat vaccine development: from basic science to clinical trials.

Abstract not availabl

A NEW PERSPECTIVE FOR REGIONAL LANDSLIDE SUSCEPTIBILITY ASSESSMENT

Landslides pose a severe geohazard in many countries. The availability of inventories depicting the spatial and temporal distribution of landslides is crucial for assessing landslide susceptibility and risk in territorial planning or investigating landscape evolution. In the case of the Italian territory, several landslide hazard and risk maps were produced ranging from regional to national scale. This was made possible leveraging public domain data of the Italian Landslide Inventory (IFFI project; Trigila et alii, 2010), or other geodatabases spanning from local to regional scale. However, the practical utility of this inventory is often limited in many applications due to its spatial inhomogeneity or the use of different mapping methods and classification criteria. Despite the impressive advancements in techniques for assessing natural hazard susceptibility at a national scale over the past years, including statistical models, AI based models (i.e. Neural Networks) and others, the results are still limited by the quality of the data used. Specifically, the effectiveness of these models is closely tied to the quality of the landslide inventory utilized. Currently, recent regional landslide inventories could potentially enhance precision and accuracy compared to the national dataset, primarily owing to their finer resolution compared to the IFFI dataset. In this work, we present a new approach to assess landslide susceptibility at local scale, relying on regional landslide inventories. Using a data-driven technique, we propose to train a single model on a landslide inventory consisting of a composition of regional inventories selected to be representative of the national scenario. The weighted model is now capable of predicting landslide susceptibility in any study area across Italy. The entire analysis has been done using the SRT tool for Google Earth Engine and the SZ-plugin for QGIS. All the data used and processed are freely available and downloadable. The proposed approach has been tested in the framework of the PNRR RETURN project. The evaluation was conducted in two specific areas: the first one encompasses a section of the railway connecting Napoli to Bari (southern Italy), while the second focuses on areas impacted by the Marche region 2022 landslide event (central Italy). © Author(s). All rights reserved