Evaluation of BG, NPR1, and PAL in cotton plants through Virus Induced gene silencing reveals their role in whitefly stress.

Whitefly is one of the most hazardous insect pests that infests a wide range of host plants and causes huge damage to crop worldwide. In order to engineer plants resilient to whitefly stress, it is important to identify and validate the responsive genes by exploring the molecular dynamics of plants under stress conditions. In this study three genes BG, NPR1, and PAL genes have been studied in cotton for elucidating their role in whitefly stress response. Initially, insilico approach was utilized to investigate the domains and phylogeny of BG, NPR1 and PAL genes and found out that these genes showed remarkable resemblance in four cotton species Gossypium hirsutum, G. barbadense, G. arboreum, and G. raimondii. In BG proteins the main functional domain was X8 belonging to glycohydro superfamily, in NPR1 two main functional domains were BTB_POZ at N terminal and NPR1_like_C at C terminal. In PAL functional domain PLN was found which belongs to Lyase class I superfamily. The promoter analysis of these genes displayed enrichment of hormone, stress and stimuli responsive cis elements. Through Virus Induced Gene Silencing (VIGS), these genes were targeted and kept under whitefly infestation. Overall, the whitefly egg and nymph production were observed 60-70% less on gene down regulated plants as compared to control plants. The qPCR-based expression analysis of certain stress-responsive genes showed that in BG down regulated plants the elevated expression of these whitefly responsive genes was detected, in NPR1 down regulated plants JAZ1 and HSP were found up regulated, ERF1 and WRKY40 didn't show significant differential expression, while MAPK6 was slightly down regulated. In PAL down regulated plants ERF1 and JAZ1 showed elevated expression while others didn't show significant alternation. Differential expression in gene down-regulated plants showed that whitefly responsive genes act in a complex inter signaling pathway and their expression impact each other. This study provides valuable insight into the structural and functional analysis of important whitefly responsive genes BG, NPR1, and PAL. The results will pave a path to future development of whitefly resilient crops.

Genome-Wide Analysis of WRKY Gene Family and Negative Regulation of GhWRKY25 and GhWRKY33 Reveal Their Role in Whitefly and Drought Stress Tolerance in Cotton.

The WRKY transcription factor family is marked by its significant responsiveness to both biotic and abiotic plant stresses. In the present study, the WRKY family of Gossypium hirsutum has been identified and classified into three groups based on the number of conserved WRKY domains and the type of zinc finger motif. This classification is further validated by conserved domain and phylogenetic analysis. Two members of the WRKY family, WRKY25 and WRKY33, have been targeted through VIGS in G. hirsutum. VIGS-infiltrated plants were evaluated under drought stress and whitefly infestation. It was observed that GhWRKY33-downregulated plants showed a decrease in whitefly egg and nymph population, and GhWRKY33 was found to be a strong negative regulator of whitefly and drought stress, while GhWRKY25 was found to be a moderate negative regulator of whitefly and drought stress. As the targeted genes are transcription factors influencing the expression of other genes, the relative expression of other stress-responsive genes, namely MPK6, WRKY40, HSP, ERF1, and JAZ1, was also analyzed through qRT-PCR. It was found elevated in GhWRKY33-downregulated plants, while GhWRKY25-downregulated plants through VIGS showed the elevated expression of ERF1 and WRKY40, a slightly increased expression of HSP, and a lower expression level of MPK6. Overall, this study provides an important insight into the WRKY TF family and the role of two WRKY TFs in G. hirsutum under drought stress and whitefly infestation. The findings will help to develop crops resilient to drought and whitefly stress.

Smart breeding approaches in post-genomics era for developing climate-resilient food crops.

Improving the crop traits is highly required for the development of superior crop varieties to deal with climate change and the associated abiotic and biotic stress challenges. Climate change-driven global warming can trigger higher insect pest pressures and plant diseases thus affecting crop production sternly. The traits controlling genes for stress or disease tolerance are economically imperative in crop plants. In this scenario, the extensive exploration of available wild, resistant or susceptible germplasms and unraveling the genetic diversity remains vital for breeding programs. The dawn of next-generation sequencing technologies and omics approaches has accelerated plant breeding by providing the genome sequences and transcriptomes of several plants. The availability of decoded plant genomes offers an opportunity at a glance to identify candidate genes, quantitative trait loci (QTLs), molecular markers, and genome-wide association studies that can potentially aid in high throughput marker-assisted breeding. In recent years genomics is coupled with marker-assisted breeding to unravel the mechanisms to harness better better crop yield and quality. In this review, we discuss the aspects of marker-assisted breeding and recent perspectives of breeding approaches in the era of genomics, bioinformatics, high-tech phonemics, genome editing, and new plant breeding technologies for crop improvement. In nutshell, the smart breeding toolkit in the post-genomics era can steadily help in developing climate-smart future food crops.

Self-Adaptation Resource Allocation for Continuous Offloading Tasks in Pervasive Computing.

Advancement in technology has led to an increase in data. Consequently, techniques such as deep learning and artificial intelligence which are used in deciphering data are increasingly becoming popular. Further, advancement in technology does increase user expectations on devices, including consumer interfaces such as mobile apps, virtual environments, or popular software systems. As a result, power from the battery is consumed fast as it is used in providing high definition display as well as in charging the sensors of the devices. Low latency requires more power consumption in certain conditions. Cloud computing improves the computational difficulties of smart devices with offloading. By optimizing the device's parameters to make it easier to find optimal decisions for offloading tasks, using a metaheuristic algorithm to transfer the data or offload the task, cloud computing makes it easier. In cloud servers, we offload the tasks and limit their resources by simulating them in a virtual environment. Then we check resource parameters and compare them using metaheuristic algorithms. When comparing the default algorithm FCFS to ACO or PSO, we find that PSO has less battery or makespan time compared to FCFS or ACO. The energy consumption of devices is reduced if their resources are offloaded, so we compare the results of metaheuristic algorithms to find less battery usage or makespan time, resulting in the PSO increasing battery life or making the system more efficient.