Differential gene expression analysis in germinating and dormant teliospores of Tilletia indica using RNA seq approach

Karnal bunt of wheat is an important quarantine disease that interrupts India’s wheat trade in the international market. The whole transcriptome of germinating and dormant teliospores of Tilletia indica was performed using the RNA Seq approach to identify germination-related genes. Approximately 63 million reads were generated using the RNA sequencing by the Illumina NextSeq500 platform. The high-quality reads were deposited in NCBI SRA database (accession: PRJNA522347). The unigenes from the pooled teliospores were 16,575 having unigenes length of 28,998,753 bases. The high-quality reads of germinating teliospores mapped on to 21,505 predicted CDSs. 9,680 CDSs were common between dormant and germinating teliospores of T. indica. 11,825 CDSs were found to be in germinating teliospores while only 91 were unique in dormant spores of pathogen. The pathway analysis showed the highest number of pathways was found in germinating spores than dormant spores. The highest numbers of CDSs were found to be associated with translation (431 in number), transport and catabolism (340), signal transduction (326), and carbohydrate metabolism (283). The differential expression analysis (DESeq) of germinating and dormant teliospores showed that 686 CDS were up-regulated and 114 CDS were down-regulated in the germinating teliospores. Significant germination-related genes in the spores were validated using qPCR analysis. Ten genes viz. Ti3931, Ti6828, Ti7098, Ti7462, Ti7522, Ti 9289, Ti 8670, Ti 7959, Ti 7809,and Ti10095 were highly up-regulated in germinated teliospores which may have role in germination of spores.Further, these differentially expressed genes provide insights into the molecular events. This first study of transcriptome will be helpful to devise better management strategies to manage Karnal bunt disease

Chemo-profiling of Purpureocillium lilacinum and Paecilomyces variotii isolates using GC-MS analysis, and evaluation of their metabolites against M. incognita.

Nematophagous fungi are the best alternatives to chemical nematicides for managing nematodes considering environmental health. In the current study, activity of metabolites from ten isolates of Purpureocillium lilacinum (Thom) Luangsa-ard (Hypocreales: Ophiocordycipitaceae) and two isolates of Paecilomyces variotii Bainier (Eurotiales: Trichocomaceae), were examined to inhibit the hatching of Meloidogyne incognita (Kofoid & White) Chitwood (Tylenchida: Heteroderidae) eggs. At 100%, 50%, and 25% concentrations, respectively, the culture filtrate of the isolate P. lilacinum 6887 prevented 97.55%, 90.52%, and 62.97% of egg hatching. Out of all the isolates, Pl 6887, Pl 6553, and Pl 2362 showed the greatest results in the hatching inhibition experiment.Gas chromatography-mass spectrometry (GC-MS) analysis revealed a variety of nematicidal compounds from different isolates. A total of seven nematicidal compounds, including four very potent nematicidal fatty acids were found in the isolate Pl 6553. Secondary metabolites of the same isolate possess the highest M. incognita juvenile mortality, i.e., 43.33% and 92% after 48 hrs of treatment at 100 and 200 ppm concentrations, respectively. Significant difference was observed in juvenile mortality percentage among the isolate having highest and lowest nematicidal compounds. Nematicidal fatty acids like myristic and lauric acid were found for the first time in P. lilacinum. Multiple vacuole-like droplets were found inside the unhatched eggs inoculated with the culture filtrate of isolate Pl 6887, and also in the juveniles that perished in the ethyl acetate extract of isolate Pl 6553

Fig 4 -

M. incognita juvenile after 48 hours of incubation in (A) control where juveniles werefreely moving (scale bar 200 μm) (B) a dead juvenile in metabolites of P. lilacinum (Pl 6553) with vacuoles-like droplets (black arrow) after 48 hours of incubation (scale bar 50 μm).</p

GC-MS chromatogram of secondary metabolites from <i>P</i>. <i>lilacinum</i> 6948 isolate (PNG).

GC-MS chromatogram of secondary metabolites from P. lilacinum 6948 isolate (PNG).</p

GC-MS chromatogram of secondary metabolites from <i>P</i>. <i>lilacinum</i> 4483 isolate (PNG).

GC-MS chromatogram of secondary metabolites from P. lilacinum 4483 isolate (PNG).</p

GC-MS chromatogram of secondary metabolites from <i>P</i>. <i>lilacinum</i> 4899 isolate (PNG).

GC-MS chromatogram of secondary metabolites from P. lilacinum 4899 isolate (PNG).</p

GC-MS chromatogram of secondary metabolites from <i>P</i>. <i>lilacinum</i> 6064 isolate (PNG).

GC-MS chromatogram of secondary metabolites from P. lilacinum 6064 isolate (PNG).</p

Phylogenetic relationship between isolates of <i>Purpureocillium lilacinum</i> and <i>Paecilomyces variotii</i> based on ITS sequences constructed using the maximum likelihood method.

The percentage of trees in which the associated taxa clustered together is shown next to the branches and are based on 1000 bootsrap replications.</p

GC-MS chromatogram of secondary metabolites from <i>P</i>. <i>lilacinum</i> 2362 isolate (PNG).

GC-MS chromatogram of secondary metabolites from P. lilacinum 2362 isolate (PNG).</p

GC-MS chromatogram of secondary metabolites from <i>P</i>. <i>lilacinum</i> 5596 isolate (PNG).

GC-MS chromatogram of secondary metabolites from P. lilacinum 5596 isolate (PNG).</p