Priya, P. | bioRxiv Science

bioRxiv2022

Stress Combinations and their Interactions inPlants Database (SCIPDb): A one-stop resource for understanding combined stress responses in plants

We have developed Stress Combinations and their Interactions in Plants Database (SCIPDb; http://www.nipgr.ac.in/scipdb.php), a compendium and interactive platform offering information on both morpho-physio-biochemical (phenome) and molecular (transcriptome) responses of plants to different combinations of stresses. To delineate the effects of various stress combinations/categories on yield in major agricultural crops, global phenome data from 939 studies was analyzed and results showed that yield was affected to the greatest extent under the abiotic-abiotic stress category, followed by the biotic-biotic and abiotic-biotic stress categories. In the abiotic-abiotic stress category, drought-heat, heat-salinity, and ozone-UV are the major stress combinations causing high yield loss in barley, wheat, soybean, and quinoa crops. In the abiotic-biotic stress category, the salinity-weed stress combination causes highest yield loss in rice crop. In the biotic-biotic stress category, the nematode-fungus combination was most detrimental, causing considerable yield losses in potato, groundnut, and sugar beet crops. Transcriptome datasets from 36 studies hosted in SCIPDb identified novel genes. Thus far, these genes have not been known to play a role in combined stress. Integretome analysis under combined drought-heat stress pinpointed carbohydrate, amino acid, and energy metabolism pathways as the crucial metabolic, proteomic, and transcriptional components in plant tolerance to combined stress. These examples illustrate the application of SCIPDb in identifying novel genes and pathways involved in combined stress tolerance. Further, we showed the application of this database in identifying novel candidate genes and pathways for combined drought and pathogen stress tolerance in Arabidopsis and chickpea. To our knowledge, SCIPDb is the only publicly available platform that provides extensive information and paves the way for advancing mechanistic understanding of plant responses to combined stresses.

plant biology↗

bioRxiv2022

A strategy to optimize the peptide-based inhibitors against different mutants of the spike protein of SARS-CoV-2

SARS-CoV-2 virus has caused high-priority health concerns at a global level. Vaccines have stalled the proliferation of viruses to some extent. Yet, the emergence of newer, potentially more infectious, and dangerous mutants such as delta and omicron are among the major challenges in finding a more permanent solution for this pandemic. The effectiveness of antivirals Molnupiravir and Paxlovid, authorized for emergency use by the FDA, are yet to be assessed at larger populations. Patients with a high risk of disease progression or hospitalization have received treatment with a combination of antibodies (antibody-cocktail). Most of the mutations leading to the new lineage of SARS-CoV-2 are found in the spike protein of this virus that plays a key role in facilitating host entry. The current study has investigated how to modify a promising peptide-based inhibitor of spike protein, LCB3, against common mutations in the target protein so that it retains its efficacy against the spike protein. LCB3 being a prototype for protein-based inhibitors is an ideal testing system to learn about protein-based inhibitors. Two common mutations N501Y and K417N are considered in this work. Using a structure-based approach that considers free energy decomposition of residues, distance, and the interactions between amino acids, we propose the substitutions of amino acid residues of LCB3 inhibitors. Our binding free energy calculations suggest a possible improvement in the binding affinity of existing inhibitor LCB3 to the mutant forms of the S-protein using simple substitutions at specific positions of the inhibitor. This approach, being general, can be used in different inhibitors and other mutations and help in fighting against SARS-CoV-2.

biophysics↗