Stress Combinations and their Interactions in Plants (SCIP) Database

Abiotic stress conditions like heat, drought and salinity stress majorly affect plant diseases. Temperature extremes negatively affect several viral and bacterial pathogen infections. Ozone treatment suppresses important diseases like Phytophthora root rot in soybean and BCMV infection in pinto beans. Likewise, shade treatment reduces the severity of Coffee berry disease in coffee but enhances Asian soybean rust in soybean. Heavy metals like manganese and aluminum treatment suppress Powdery mildew and Fusarium wilt in grapevine and pigeon pea, respectively. Nickel treatment, however, enhanced the susceptibility of the nickel hyperaccumulator Jewel flower to TUMV infection. Planting at different densities also affects disease development. A glimpse of the research on the effect of abiotic factors on plant development is provided in the figure.

The figure shows an overall picture of the research on biotic-biotic stress combinations. We re-analyzed research articles on nine biotic stress combinations and found that the nematode and fungus stress combination is the most extensively studied and had the most deleterious effect on plants among all stress combinations studied. Some biotic stress combinations were less deleterious (for example, combination of the oomycete Phytophthora infestans and Potato Virus S), while some were more damaging (for example, a combination of fungus Rhizoctonia solani and nematode Pratylenchus penetrans) than individual stresses. The red and green lines indicate the number of studies reporting negative and positive interactions between the pathogens, respectively. Refer to individual data pages in the database for more information. As analyzed from several research articles, we found that a total of 22 fungi, 13 nematodes, 7 oomycetes, 6 bacteria, and 5 viral pathogens are involved in various biotic stress combinations. Among them, the most commonly found pathogen in disease complexes is Meloidogyne incognita, followed by Rhizoctonia solani and Fusarium sp. Some biotic stressors like viruses reduced the infection by other pathogens/pests and benefited plants by reducing the overall damage.

As analyzed from several research articles, we found that a total of 19 plants are affected by nine biotic stress combinations. Potato was found to be the most effected followed by cotton, wheat and maize. The most deleterious combination reported is nematode and fungal pathogens. Refer to individual data pages for more information.

The analysis of various stress combination studies revealed a total of 23 major traits associated with structure, morphology, growth and yield. The traits were grouped in three major categories related to plant structure and growth, biomass and yield. The analysis revealed that plant biomass is the most studied trait across all stress combinations followed by yield.

Rice, an important food crop is significantly affected by various stress combinations. Among the different stress combinations, a combination of two abiotic stresses seems to incur maximum damage on the plants as reflected by the fact that all studies show an overall negative impact of the stress combinations. Some abiotic stress conditions like flooding, UV , temperature, and drought reduce the infection of some pathogens in rice. The combination of two pathogens or pests is more detrimental to rice except for the combination of Rice yellow mottle virus and Xanthomonas oryzae, wherein an increase in plant growth was observed under combined stress (compared to virus alone stress).

Representation indicating the effect of different stress combinations on wheat yield. Sunburst diagram comprises of three layers; the innermost layer represents the name of the stress combinations, the middle layer represents the stress treatments and the outer most layer depicts the calculated percent value. Percent change in parameter value was calculated as percent change under stress over the control. In the outer layer, the size of the box is directly proportional to the percent value, i.e., higher the percent value, bigger the box. Similarly, data for other plant species can be accessed using multiple questions listed under each group. Traits included in the plant performance group are plant height, root length, biomass, leaf number, leaf area, and yield. The treatment marked with star represents simultaneous stress imposition while all other are sequential stress. Nematode and fungus stress combination has two different studies which are colored in different shades of green for intuitive comparison.

A total of 39 different stress combinations (10 abiotic- abiotic, 20 abiotic-biotic, 9 biotic-biotic) were found to affect legumes. The maximum number of stress interactions were reported under biotic-biotic stress combinations with maximum number of studies reported under the nematode and fungus stress combination. Among the different legumes, soybean was found to be studied the most with regard to the effect of abiotic-abiotic and abiotic-biotic combinations.

We analyzed the impact of 22 stress combinations on growth and yield related traits (like biomass,root and shoot length, plant height and seed number, weight and yield) of different plants. The percent change under stress (over control) was calculated and was calculated and the results were visualized as a hierarchical radial tree. Stress combinations like drought and heat, and drought and salt cause highest reduction in the growth and yield related traits of plants whereas heat coupled with salt is found to be less deleterious than the individual stresses. Among different plants, wheat’s growth and yield was found to be affected severely by several combined stresses like drought-heat, salt-drought and drought-cold, fungus-fungus and fungus-waterlogging, the reductions being significantly higher under combined drought-heat and heat-highlight stress as compared to the individual stresses. The details about the visualizations can be found in the user guide (How to navigate and understand the visualizations of phenomics data?)

We analyzed the effect of 22 stress combinations on several physiological traits (like photosynthesis, stomatal conductance, photochemical efficiency, Fv/Fm, and chlorophyll content) of different plants. The percent change under stress (over control) was calculated and the results were visualized as a hierarchical radial tree. The analysis shows that the effect of stress combinations on the physiological traits varies highly with the plant type. Overall, the combination causing most deleterious effect on physiological traits is drought and heat. On the other hand, stress combination like ozone and low light, did not affect the physiological traits more than the individual stresses. In general, abiotic-abiotic stress combinations were found to cause more reductions in plant physiological traits as compared to abiotic-biotic and biotic- biotic stress combinations. Among different plants, wheat’s physiological traits were found to be affected severely by several combined stresses like drought-heat, heat-highlight, salt-drought and drought-highlight, the reductions being significantly higher under combined drought-heat and heat-highlight stress as compared to the individual stresses. The details about the visualizations can be found in the user guide (How to navigate and understand the visualizations of phenomics data?)

SCIPDb hosts an interactive global map (generated using the longitude and latitude of the place where the study was conducted for each article) of the distribution of various combined stress across the countries. The global distribution map, thereby, provides a account of the places where the research on the respective combined stresses is being conducted. In case of abiotic-biotic stress combinations, mapping the geographical distribution of the combined stresses provides a hint of the places where environmental conditions are aggravating important plant diseases. The geographical distribution of the abiotic-abiotic stress combinations, on the other hand highlights the emerging complex environmental conditions negatively affecting plant performance. Similarly, the global distribution of biotic-biotic stress combinations provides an account of the places where new disease complexes are emerging or being aggravated by the environmental conditions.

The workflow demonstrates a generic pipeline to mine and filter gene of interest from SCIPDb. The transcriptome datasets hosted in SCIPDb can be mined using four different approaches to filter gene of interest. 1. SCIPDb FTP server: can be used to directly download the analysed combined stress transcriptomes (unique genes and common genes between individual and combined stress treatments). 2. Combined stress category: provides list of differentially expressed genes (DEGs) in the pre-computed comparisons based on three step dropdown based selection of plant, followed by stress combination and finally category of desired genes. It is also possible to visualize the differentially expressed genes (DEGs) interactively on KEGG pathways and download several other metadata using KEGG genes link. 3. BLAST Search: SCIPDb hosts standalone BLAST server and integrated database of unique combined stress genes identified in 8 different plant species. Users can do sequence based search using the BLAST server in batch mode, to identify potential homologs or orthologs. 4. Keyword search: The transcriptome search section accepts input in six different categories namely i) Gene name, ii) Gene id iii) Pathway iv) Stress combination v) Plant and vi) User defined keywords. The final result is depicted in the form of interactive bootstrap table which presents lists of DEGs, gene name and other annotations, expression values, KEGG pathway and genes information. Multiple interactive visualizations, like Heat map, Venn diagrams, GO enrichment results in the form of Manhattan plots and co-functional networks have been provided to aid the users to further identify gene of interest based on functional categorization.

SCIPDb FTP server was used to identify genes of interest by a five step workflow shown in figure below, starting from i) Downloading combined stress DEGs from SCIPDb FTP server. This step has been further detailed and demonstrated using screenshots from the SCIPDb and steps a-d, which was used to download the datasets. a) SCIPDb FTP server was browsed to navigate to the transcriptome datasets section of database (Path: home/downloads/transcriptome_data). The server provides a hierarchical organization of analyzed combined stress transcriptomes from multiple plants. As an example case study b) Plant of interest was selected (Arabidopsis) c) Stress combination was selected (Abiotic-biotic), d) finally dataset was selected and downloaded locally. ii) 8243 DEGs from all the abiotic-biotic stress combinations were downloaded followed by iii) filtering of overlapping genes. This step fetched 5855 genes, from which iv) 28 genes were selected based on expression values and functional categorization. Finally v) Mutant analysis was done to experimentally validate function of these genes under drought and pathogen combined stress.