Genome-based solutions for managing mucormycosis.

An uncommon opportunistic fungal infection known as mucormycosis is caused by a class of molds called mucoromycetes. Currently, antifungal therapy and surgical debridement are the primary treatment options for mucormycosis. Despite the importance of comprehensive knowledge on mucormycosis, there is a lack of well-annotated databases that provide all relevant information. In this study, we have gathered and organized all available information related to mucormycosis that include disease's genome, proteins, diagnostic methods. Furthermore, using the AlphaFold2.0 prediction tool, we have predicted the tertiary structures of potential drug targets. We have categorized the information into three major sections: "genomics/proteomics," "immunotherapy," and "drugs." The genomics/proteomics module contains information on different strains responsible for mucormycosis. The immunotherapy module includes putative sequence-based therapeutics predicted using established tools. Drugs module provides information on available drugs for treating the disease. Additionally, the drugs module also offers prerequisite information for designing computationally aided drugs, such as putative targets and predicted structures. In order to provide comprehensive information over internet, we developed a web-based platform MucormyDB (https://webs.iiitd.edu.in/raghava/mucormydb/).

Reducing options of ammonia volatilization and improving nitrogen use efficiency via organic and inorganic amendments in wheat (Triticum aestivum L.).

Background: This study investigates the effect of organic and inorganic supplements on the reduction of ammonia (NH3) volatilization, improvement in nitrogen use efficiency (NUE), and wheat yield. Methods: A field experiment was conducted following a randomized block design with 10 treatments i.e., T1-without nitrogen (control), T2-recommended dose of nitrogen (RDN), T3-(N-(n-butyl) thiophosphoric triamide) (NBPT @ 0.5% w/w of RDN), T4-hydroquinone (HQ @ 0.3% w/w of RDN), T5-calcium carbide (CaC2 @ 1% w/w of RDN), T6-vesicular arbuscular mycorrhiza (VAM @ 10 kg ha-1), T7-(azotobacter @ 50 g kg-1 seeds), T8-(garlic powder @ 0.8% w/w of RDN), T9-(linseed oil @ 0.06% w/w of RDN), T10-(pongamia oil @ 0.06% w/w of RDN). Results: The highest NH3 volatilization losses were observed in T2 at about 20.4 kg ha-1 per season. Significant reduction in NH3 volatilization losses were observed in T3 by 40%, T4 by 27%, and T8 by 17% when compared to the control treatment. Soil urease activity was found to be decreased in plots receiving amendments, T3, T4, and T5. The highest grain yield was observed in the T7 treated plot with 5.09 t ha-1, and straw yield of 9.44 t ha-1 in T4. Conclusion: The shifting towards organic amendments is a feasible option to reduce NH3 volatilization from wheat cultivation and improves NUE.

SalivaDB-a comprehensive database for salivary biomarkers in humans.

Saliva as a non-invasive diagnostic fluid has immense potential as a tool for early diagnosis and prognosis of patients. The information about salivary biomarkers is broadly scattered across various resources and research papers. It is important to bring together all the information on salivary biomarkers to a single platform. This will accelerate research and development in non-invasive diagnosis and prognosis of complex diseases. We collected widespread information on five types of salivary biomarkers-proteins, metabolites, microbes, micro-ribonucleic acid (miRNA) and genes found in humans. This information was collected from different resources that include PubMed, the Human Metabolome Database and SalivaTecDB. Our database SalivaDB contains a total of 15 821 entries for 201 different diseases and 48 disease categories. These entries can be classified into five categories based on the type of biomolecules; 6067, 3987, 2909, 2272 and 586 entries belong to proteins, metabolites, microbes, miRNAs and genes, respectively. The information maintained in this database includes analysis methods, associated diseases, biomarker type, regulation status, exosomal origin, fold change and sequence. The entries are linked to relevant biological databases to provide users with comprehensive information. We developed a web-based interface that provides a wide range of options like browse, keyword search and advanced search. In addition, a similarity search module has been integrated which allows users to perform a similarity search using Basic Local Alignment Search Tool and Smith-Waterman algorithm against biomarker sequences in SalivaDB. We created a web-based database-SalivaDB, which provides information about salivary biomarkers found in humans. A wide range of web-based facilities have been integrated to provide services to the scientific community. https://webs.iiitd.edu.in/raghava/salivadb/.

Prediction of celiac disease associated epitopes and motifs in a protein.

Introduction: Celiac disease (CD) is an autoimmune gastrointestinal disorder causes immune-mediated enteropathy against gluten. Gluten immunogenic peptides have the potential to trigger immune responses which leads to damage the small intestine. HLA-DQ2/DQ8 are major alleles that bind to epitope/antigenic region of gluten and induce celiac disease. There is a need to identify CD associated epitopes in protein-based foods and therapeutics. Methods: In this study, computational tools have been developed to predict CD associated epitopes and motifs. Dataset used for training, testing and evaluation contain experimentally validated CD associated and non-CD associate peptides. We perform positional analysis to identify the most significant position of an amino acid residue in the peptide and checked the frequency of HLA alleles. We also compute amino acid composition to develop machine learning based models. We also developed ensemble method that combines motif-based approach and machine learning based models. Results and Discussion: Our analysis support existing hypothesis that proline (P) and glutamine (Q) are highly abundant in CD associated peptides. A model based on density of P&Q in peptides has been developed for predicting CD associated peptides which achieve maximum AUROC 0.98 on independent data. We discovered motifs (e.g., QPF, QPQ, PYP) which occurs specifically in CD associated peptides. We also developed machine learning based models using peptide composition and achieved maximum AUROC 0.99. Finally, we developed ensemble method that combines motif-based approach and machine learning based models. The ensemble model-predict CD associated motifs with 100% accuracy on an independent dataset, not used for training. Finally, the best models and motifs has been integrated in a web server and standalone software package "CDpred". We hope this server anticipate the scientific community for the prediction, designing and scanning of CD associated peptides as well as CD associated motifs in a protein/peptide sequence (https://webs.iiitd.edu.in/raghava/cdpred/).

DMPPred: a tool for identification of antigenic regions responsible for inducing type 1 diabetes mellitus.

There are a number of antigens that induce autoimmune response against ß-cells, leading to type 1 diabetes mellitus (T1DM). Recently, several antigen-specific immunotherapies have been developed to treat T1DM. Thus, identification of T1DM associated peptides with antigenic regions or epitopes is important for peptide based-therapeutics (e.g. immunotherapeutic). In this study, for the first time, an attempt has been made to develop a method for predicting, designing, and scanning of T1DM associated peptides with high precision. We analysed 815 T1DM associated peptides and observed that these peptides are not associated with a specific class of HLA alleles. Thus, HLA binder prediction methods are not suitable for predicting T1DM associated peptides. First, we developed a similarity/alignment based method using Basic Local Alignment Search Tool and achieved a high probability of correct hits with poor coverage. Second, we developed an alignment-free method using machine learning techniques and got a maximum AUROC of 0.89 using dipeptide composition. Finally, we developed a hybrid method that combines the strength of both alignment free and alignment-based methods and achieves maximum area under the receiver operating characteristic of 0.95 with Matthew's correlation coefficient of 0.81 on an independent dataset. We developed a web server 'DMPPred' and stand-alone server for predicting, designing and scanning T1DM associated peptides (https://webs.iiitd.edu.in/raghava/dmppred/).

Mitigation of yield-scaled greenhouse gas emissions from irrigated rice through Azolla, Blue-green algae, and plant growth-promoting bacteria.

Irrigated transplanted flooded rice is a major source of methane (CH4) emission. We carried out experiments for 2 years in irrigated flooded rice to study if interventions like methane-utilizing bacteria, Blue-green algae (BGA), and Azolla could mitigate the emission of CH4 and nitrous oxide (N2O) and lower the yield-scaled global warming potential (GWP). The experiment included nine treatments: T1 (120 kg N ha-1 urea), T2 (90 kg N ha-1 urea + 30 kg N ha-1 fresh Azolla), T3 (90 kg N ha-1 urea + 30 kg N ha-1 Blue-green algae (BGA), T4 (60 kg N ha-1 urea + 30 kg N ha-1 BGA + 30 kg N ha-1 Azolla, T5 (120 kg N ha-1 urea + Hyphomicrobium facile MaAL69), T6 (120 kg N ha-1 by urea + Burkholderia vietnamiensis AAAr40), T7 (120 kg N ha-1 by urea + Methylobacteruim oryzae MNL7), T8 (120 kg N ha-1 urea + combination of Burkholderia AAAr40, Hyphomicrobium facile MaAL69, Methylobacteruim oryzae MNL7), and T9 (no N fertilizer). Maximum decrease in cumulative CH4 emission was observed with the application of Methylobacteruim oryzae MNL7 in T7 (19.9%), followed by Azolla + BGA in T4 (13.2%) as compared to T1 control. N2O emissions were not significantly affected by the application of CH4-oxidizing bacteria. However, significantly lower (P<0.01) cumulative N2O emissions was observed in T4 (40.7%) among the fertilized treatments. Highest yields were observed in Azolla treatment T2 with 25% less urea N application. The reduction in yield-scaled GWP was at par in T4 (Azolla and BGA) and T7 (Methylobacteruim oryzae MNL7) treatments and reduced by 27.4% and 15.2% in T4 and T7, respectively, as compared to the T1 (control). K-means clustering analysis showed that the application of Methylobacteruim oryzae MNL7, Azolla, and Azolla + BGA can be an effective mitigation option to reduce the global warming potential while increasing the yield.

Effect of elevated ozone and carbon dioxide interaction on growth, yield, nutrient content and wilt disease severity in chickpea grown in Northern India.

Wilt caused by Fusarium oxysporum, sp. Ciceris (FOC) is an important disease causing losses up to 10% in chickpea yield. Experiments were conducted growing chickpea in free air ozone and carbon dioxide enrichment rings under four treatments of elevated ozone (O3) (EO:60 ± 10 ppb), elevated carbon dioxide (CO2) (ECO2:550 ± 25 ppm), combination of elevated CO2 and O3 (EO + ECO2) and ambient control for quantifying the effect on growth, yield, biochemical and nutrient content of chickpea. For studying the impact on wilt disease, chickpea was grown additionally in pots with soil containing FOC in these rings. The incidence of Fusarium wilt reduced significantly (p < 0.01) under EO as compared to ambient and ECO2. The activities of pathogenesis-related proteins chitinase and ß-1,3- glucanase, involved in plant defense mechanism were enhanced under EO. The aboveground biomass and pod weight declined by 18.7 and 15.8% respectively in uninnoculated soils under EO, whereas, in FOC inoculated soil (diseased plants), the decline under EO was much less at 8.6 and 9.9% as compared to the ambient. Under EO, the activity of super oxide dismutase increased significantly (p < 0.5, 40%) as compared to catalase (12.5%) and peroxidase (17.5%) without any significant increase under EO + ECO2. The proline accumulation was significantly (p < 0.01) higher in EO as compared to EO + ECO2, and ECO2. The seed yield declined under EO due to significant reduction (p < 0.01) in the number of unproductive pods and seed weight. No change in the protein, total soluble sugars, calcium and phosphorus content was observed in any of the treatments, however, a significant decrease in potassium (K) content was observed under EO + ECO2. Elevated CO2 (554ppm) countered the impacts of 21.1 and 14.4 ppm h (AOT 40) O3 exposure on the seed yield and nutrient content (except K) in the EO + CO2 treatment and reduced the severity of wilt disease in the two years' study.

Methane production, oxidation and mitigation: A mechanistic understanding and comprehensive evaluation of influencing factors.

Methane is one of the critical greenhouse gases, which absorb long wavelength radiation, affects the chemistry of atmosphere and contributes to global climate change. Rice ecosystem is one of the major anthropogenic sources of methane. The anaerobic waterlogged soil in rice field provides an ideal environment to methanogens for methanogenesis. However, the rate of methanogenesis differs according to rice cultivation regions due to a number of biological, environmental and physical factors like carbon sources, pH, Eh, temperature etc. The interplay between the different conditions and factors may also convert the rice fields into sink from source temporarily. Mechanistic understanding and comprehensive evaluation of these variations and responsible factors are urgently required for designing new mitigation options and evaluation of reported option in different climatic conditions. The objective of this review paper is to develop conclusive understanding on the methane production, oxidation, and emission and methane measurement techniques from rice field along with its mitigation/abatement mechanism to explore the possible reduction techniques from rice ecosystem.

Synergistic action of tropospheric ozone and carbon dioxide on yield and nutritional quality of Indian mustard (Brassica juncea (L.) Czern.).

Field experiments were conducted in open top chamber during rabi seasons of 2009-10 and 2010-11 at the research farm of the Indian Agricultural Research Institute, New Delhi to study the effect of tropospheric ozone (O3) and carbon dioxide (CO2) interaction on yield and nutritional quality of Indian mustard (Brassica juncea (L.) Czern.). Mustard plants were grown from emergence to maturity under different treatments: charcoal-filtered air (CF, 80-85 % less O3 than ambient O3 and ambient CO2), nonfiltered air (NF, 5-10 % less O3 than ambient O3 and ambient CO2 ), nonfiltered air with elevated carbon dioxide (NF + CO2, NF air and 550 ± 50 ppm CO2), elevated ozone (EO, NF air and 25-35 ppb elevated O3), elevated ozone along with elevated carbon dioxide (EO + CO2, NF air, 25-35 ppb O3 and 550 ± 50 ppm CO2), and ambient chamber less control (AC, ambient O3 and CO2). Elevated O3 exposure led to reduced photosynthesis and leaf area index resulting in decreased seed yield of mustard. Elevated ozone significantly decreased the oil and micronutrient content in mustard. Thirteen to 17 ppm hour O3 exposure (accumulated over threshold of 40 ppm, AOT 40) reduced the oil content by 18-20 %. Elevated CO2 (500 ± 50 ppm) along with EO was able to counter the decline in oil content in the seed, and it increased by 11 to 13 % over EO alone. Elevated CO2, however, decreased protein, calcium, zinc, iron, magnesium, and sulfur content in seed as compared to the nonfiltered control, whereas removal of O3 from air in the charcoal-filtered treatment resulted in a significant increase in the same.

Greenhouse gas mitigation in rice-wheat system with leaf color chart-based urea application.

Conventional blanket application of nitrogen (N) fertilizer results in more loss of N from soil system and emission of nitrous oxide, a greenhouse gas (GHG). The leaf color chart (LCC) can be used for real-time N management and synchronizing N application with crop demand to reduce GHG emission. A 1-year study was carried out to evaluate the impact of conventional and LCC-based urea application on emission of nitrous oxide, methane, and carbon dioxide in a rice-wheat system of the Indo-Gangetic Plains of India. Treatments consisted of LCC scores of ≤4 and 5 for rice and wheat and were compared with conventional fixed-time N splitting schedule. The LCC-based urea application reduced nitrous oxide emission in rice and wheat. Application of 120 kg N per hectare at LCC ≤ 4 decreased nitrous oxide emission by 16% and methane by 11% over the conventional split application of urea in rice. However, application of N at LCC ≤ 5 increased nitrous oxide emission by 11% over the LCC ≤ 4 treatment in rice. Wheat reduction of nitrous oxide at LCC ≤ 4 was 18% as compared to the conventional method. Application of LCC-based N did not affect carbon dioxide emission from soil in rice and wheat. The global warming potential (GWP) were 12,395 and 13,692 kg CO(2) ha(-1) in LCC ≤ 4 and conventional urea application, respectively. Total carbon fixed in conventional urea application in rice-wheat system was 4.89 Mg C ha(-1) and it increased to 5.54 Mg C ha(-1) in LCC-based urea application (LCC ≤ 4). The study showed that LCC-based urea application can reduce GWP of a rice-wheat system by 10.5%.