Transient expression of βC1 protein differentially regulates host genes related to stress response, chloroplast and mitochondrial functions

<p>Abstract</p> <p>Background</p> <p>Geminiviruses are emerging plant pathogens that infect a wide variety of crops including cotton, cassava, vegetables, ornamental plants and cereals. The geminivirus disease complex consists of monopartite begomoviruses that require betasatellites for the expression of disease symptoms. These complexes are widespread throughout the Old World and cause economically important diseases on several crops. A single protein encoded by betasatellites, termed βC1, is a suppressor of gene silencing, inducer of disease symptoms and is possibly involved in virus movement. Studies of the interaction of βC1 with hosts can provide useful insight into virus-host interactions and aid in the development of novel control strategies. We have used the differential display technique to isolate host genes which are differentially regulated upon transient expression of the βC1 protein of chili leaf curl betasatellite (ChLCB) in <it>Nicotiana tabacum</it>.</p> <p>Results</p> <p>Through differential display analysis, eight genes were isolated from <it>Nicotiana tabacum</it>, at two and four days after infitration with βC1 of ChLCB, expressed under the control of the <it>Cauliflower mosaic virus </it>35S promoter. Cloning and sequence analysis of differentially amplified products suggested that these genes were involved in ATP synthesis, and acted as electron carriers for respiration and photosynthesis processes. These differentially expressed genes (DEGs) play an important role in plant growth and development, cell protection, defence processes, replication mechanisms and detoxification responses. Kegg orthology based annotation system analysis of these DEGs demonstrated that one of the genes, coding for polynucleotide nucleotidyl transferase, is involved in purine and pyrimidine metabolic pathways and is an RNA binding protein which is involved in RNA degradation.</p> <p>Conclusion</p> <p>βC1 differentially regulated genes are mostly involved in chloroplast and mitochondrial functions. βC1 also increases the expression of those genes which are involved in purine and pyrimidine metabolism. This information gives a new insight into the interaction of βC1 with the host and can be used to understand host-virus interactions in follow-up studies.</p

CaMELS : In silicoprediction of calmodulin binding proteins and their binding sites

Due to Ca2+‐dependent binding and the sequence diversity of Calmodulin (CaM) binding proteins, identifying CaM interactions and binding sites in the wet‐lab is tedious and costly. Therefore, computational methods for this purpose are crucial to the design of such wet‐lab experiments. We present an algorithm suite called CaMELS (CalModulin intEraction Learning System) for predicting proteins that interact with CaM as well as their binding sites using sequence information alone. CaMELS offers state of the art accuracy for both CaM interaction and binding site prediction and can aid biologists in studying CaM binding proteins. For CaM interaction prediction, CaMELS uses protein sequence features coupled with a large‐margin classifier. CaMELS models the binding site prediction problem using multiple instance machine learning with a custom optimization algorithm which allows more effective learning over imprecisely annotated CaM‐binding sites during training. CaMELS has been extensively benchmarked using a variety of data sets, mutagenic studies, proteome‐wide Gene Ontology enrichment analyses and protein structures. Our experiments indicate that CaMELS outperforms simple motif‐based search and other existing methods for interaction and binding site prediction. We have also found that the whole sequence of a protein, rather than just its binding site, is important for predicting its interaction with CaM. Using the machine learning model in CaMELS, we have identified important features of protein sequences for CaM interaction prediction as well as characteristic amino acid sub‐sequences and their relative position for identifying CaM binding sites. Python code for training and evaluating CaMELS together with a webserver implementation is available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#camels

ISLAND: in-silico proteins binding affinity prediction using sequence information

Background: Determining binding affinity in protein-protein interactions is important in the discovery and design of novel therapeutics and mutagenesis studies. Determination of binding affinity of proteins in the formation of protein complexes requires sophisticated, expensive and time-consuming experimentation which can be replaced with computational methods. Most computational prediction techniques require protein structures that limit their applicability to protein complexes with known structures. In this work, we explore sequence-based protein binding affinity prediction using machine learning. Method: We have used protein sequence information instead of protein structures along with machine learning techniques to accurately predict the protein binding affinity. Results: We present our findings that the true generalization performance of even the state-of-the-art sequence-only predictor is far from satisfactory and that the development of machine learning methods for binding affinity prediction with improved generalization performance is still an open problem. We have also proposed a sequence-based novel protein binding affinity predictor called ISLAND which gives better accuracy than existing methods over the same validation set as well as on external independent test dataset. A cloud-based webserver implementation of ISLAND and its python code are available at https://sites.google.com/view/wajidarshad/software. Conclusion: This paper highlights the fact that the true generalization performance of even the state-of-the-art sequence-only predictor of binding affinity is far from satisfactory and that the development of effective and practical methods in this domain is still an open problem

Heterologous expression of βC1 of Chili leaf curl virus in Pichia pastoris

Chili pepper is naturally susceptible to a wide range of viruses in all south Asian countries including Pakistan. Chili leaf curl virus is a monopartite begomovirus having single stranded circular betasatellite. It has one open reading frame βC1, required for pathogenicity determined, symptom induction and viral accumulation. It produces viral symptoms like mosaic, mottling, leaf distortion, vein etching, yellowing, stunting and narrowing of leaves. This study was conducted on the basis of βC1 protein, whether it was expressed in prokaryotic and yeast expression system or not because many viral proteins are lethal for the host organism. For this study, specific set of primers for βC1 were designed and amplified product was inserted into pET32a(+) bacterial and pPIC3.5K Pichia vectors for its expression. βC1 was not expressed in BL21 Escherichia coli expression system, while it was expressed in Pichia pastoris, when it was integrated into the genome through electroporation, and expressed protein was identified by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDSPAGE). This is the first study demonstrating the possibility of expression of βC1protein using P. pastoris.Key words: Monopartite begomoviruses, chili leaf curl betasatellite, heterologous expression, Pichia pastoris, betasatellite

Cow dung putrefaction via vermicomposting using Eisenia fetida and its influence on seed sprouting and vegetative growth of Viola wittrockiana (pansy).

The current research was conducted at Vermi-tech Unit, Muzaffarabad in 2018 to evaluate the efficacy of cow dung and vermicompost on seed sprouting, seedlings, and vegetative developmental parameters of Viola x wittrokiana (pansy). In the current study, vermicompost was produced using Eisenia fetida. Physicochemical parameters of vermicompost and organic manure were recorded before each experimentation. The potting experiment was designed and comprised of eight germination mediums containing different combinations of soil, sand, cow dung, and various concentrations of vermicompost such as 10% VC, 15% VC, 20% VC, 25% VC, 30% VC, and 35% VC. Seed sprouting and seedling developmental parameters were observed for 28 days while vegetative plant growth parameters were recorded after 10 weeks of transplantation in various vermicompost amended germination media. Pre and post-physicochemical analysis of germination media were also recorded to check their quality and permanency. The current findings showed that 30% VC germination media was an effective dose for early seed germination initiation and all seed germination parameters. However, the significant vegetative plant growth and flowering parameters of pansy occurred at 35% VC. Findings revealed that vermicompost not only enhanced the seed germination and growth of pansy but also improved soil health. These results indicate that vermicompost can be exploited as a potent bio-fertilizer for ornamental plant production

In vitro bactericidal, antidiabetic, cytotoxic, anticoagulant, and hemolytic effect of green-synthesized silver nanoparticles using Allium sativum clove extract incubated at various temperatures

The current research aimed to evaluate in vitro biological activities of green-synthesized silver nanoparticles using the Allium sativum clove extract. The stability of green-synthesized silver nanoparticles was evaluated via storage at 4°C, room temperature (37°C), and calcined at 300°C, 500°C, and 700°C. The antibacterial effect was evaluated using agar well, spread plate, biofilm reduction, and cell proliferation inhibition assays. The cytotoxic and antidiabetic effects were determined via brine shrimp lethality, protein kinase inhibition, and α-amylase inhibition assays. DPPH scavenging, iron-chelating, anticoagulant, and hemolytic effects were evaluated. The highest inhibition of Klebsiella pneumoniae was observed when freshly prepared, calcined (300°C), and stored nanoparticles (4°C and 37°C) were applied (9.66, 9.55, 7.33, and 6.65 mm) against freshly prepared and calcined at 700°C which showed the highest inhibition of Pseudomonas aeruginosa (8.55 and 7.66 mm). Cell viability assay, biofilm reduction assay, and spread plate method showed a significant antibacterial effect of freshly prepared silver nanoparticles. Freshly prepared and calcined nanoparticles at 300°C and 500°C possessed strong antioxidant and iron-chelating activity. Among all the synthesized silver nanoparticles, freshly prepared and calcined nanoparticles (300°C and 500°C) increases the prothrombin time. Silver nanoparticles possessed significant anticoagulant properties and less toxic at least concentration toward human RBCs. In brine shrimp lethality assay, freshly prepared nanoparticles showed a stronger toxic effect and caused high mortality of larvae. Protein kinase inhibition assay revealed that freshly prepared nanoparticles had the highest zone of inhibition (18.0 mm) at 50 µg/disc. Green-synthesized nanoparticles would be used as potential therapeutic agents to overcome both infectious and noninfectious diseases