RAV1 family members function as transcriptional regulators and play a positive role in plant disease resistance.

Phytopathogens pose a severe threat to agriculture and strengthening the plant defense response is an important strategy for disease control. Here, we report that AtRAV1, an AP2 and B3 domain-containing transcription factor, is required for basal plant defense in Arabidopsis thaliana. The atrav1 mutant lines demonstrate hyper-susceptibility against fungal pathogens (Rhizoctonia solani and Botrytis cinerea), whereas AtRAV1 overexpressing lines exhibit disease resistance against them. Enhanced expression of various defense genes and activation of mitogen-activated protein kinases (AtMPK3 and AtMPK6) are observed in the R. solani infected overexpressing lines, but not in the atrav1 mutant plants. An in vitro phosphorylation assay suggests AtRAV1 to be a novel phosphorylation target of AtMPK3. Bimolecular fluorescence complementation and yeast two-hybrid assays support physical interactions between AtRAV1 and AtMPK3. Overexpression of the native as well as phospho-mimic but not the phospho-defective variant of AtRAV1 imparts disease resistance in the atrav1 mutant A. thaliana lines. On the other hand, overexpression of AtRAV1 fails to impart disease resistance in the atmpk3 mutant. These analyses emphasize that AtMPK3-mediated phosphorylation of AtRAV1 is important for the elaboration of the defense response in A. thaliana. Considering that RAV1 homologs are conserved in diverse plant species, we propose that they can be gainfully deployed to impart disease resistance in agriculturally important crop plants. Indeed, overexpression of SlRAV1 (a member of the RAV1 family) imparts disease tolerance against not only fungal (R. solani and B. cinerea), but also against bacterial (Ralstonia solanacearum) pathogens in tomato, whereas silencing of the gene enhances disease susceptibility.

FlavorDB: a database of flavor molecules.

Flavor is an expression of olfactory and gustatory sensations experienced through a multitude of chemical processes triggered by molecules. Beyond their key role in defining taste and smell, flavor molecules also regulate metabolic processes with consequences to health. Such molecules present in natural sources have been an integral part of human history with limited success in attempts to create synthetic alternatives. Given their utility in various spheres of life such as food and fragrances, it is valuable to have a repository of flavor molecules, their natural sources, physicochemical properties, and sensory responses. FlavorDB (http://cosylab.iiitd.edu.in/flavordb) comprises of 25,595 flavor molecules representing an array of tastes and odors. Among these 2254 molecules are associated with 936 natural ingredients belonging to 34 categories. The dynamic, user-friendly interface of the resource facilitates exploration of flavor molecules for divergent applications: finding molecules matching a desired flavor or structure; exploring molecules of an ingredient; discovering novel food pairings; finding the molecular essence of food ingredients; associating chemical features with a flavor and more. Data-driven studies based on FlavorDB can pave the way for an improved understanding of flavor mechanisms.

Tyrosine- and tryptophan-coated gold nanoparticles inhibit amyloid aggregation of insulin.

Here, we have strategically synthesized stable gold (AuNPs(Tyr), AuNPs(Trp)) and silver (AgNPs(Tyr)) nanoparticles which are surface functionalized with either tyrosine or tryptophan residues and have examined their potential to inhibit amyloid aggregation of insulin. Inhibition of both spontaneous and seed-induced aggregation of insulin was observed in the presence of AuNPs(Tyr), AgNPs(Tyr), and AuNPs(Trp) nanoparticles. These nanoparticles also triggered the disassembly of insulin amyloid fibrils. Surface functionalization of amino acids appears to be important for the inhibition effect since isolated tryptophan and tyrosine molecules did not prevent insulin aggregation. Bioinformatics analysis predicts involvement of tyrosine in H-bonding interactions mediated by its C=O, -NH2, and aromatic moiety. These results offer significant opportunities for developing nanoparticle-based therapeutics against diseases related to protein aggregation.

Capsaicin inhibits collagen fibril formation and increases the stability of collagen fibers.

Capsaicin is a versatile plant product which has been ascribed several health benefits and anti-inflammatory and analgesic properties. We have investigated the effect of capsaicin on the molecular stability, self-assembly, and fibril stability of type-I collagen. It was found that capsaicin suppresses collagen fibril formation, increases the stability of collagen fibers in tendons, and has no effect on the molecular stability of collagen. Turbidity assay data show that capsaicin does not promote disassembly of collagen fibrils. However, capsaicin moderately protects collagen fibrils from enzymatic degradation. Computational studies revealed the functions of the aromatic group and amide region of capsaicin in the collagen-capsaicin interaction. The results may have significant implications for capsaicin-based therapeutics that target excess collagen accumulation-linked pathology, for example thrombosis, fibrosis, and sclerosis.

SerpentinaDB: a database of plant-derived molecules of Rauvolfia serpentina.

BACKGROUND: Plant-derived molecules (PDMs) are known to be a rich source of diverse scaffolds that could serve as a basis for rational drug design. Structured compilation of phytochemicals from traditional medicinal plants can facilitate prospection for novel PDMs and their analogs as therapeutic agents. Rauvolfia serpentina is an important medicinal plant, endemic to Himalayan mountain ranges of Indian subcontinent, reported to be of immense therapeutic value against various diseases. DESCRIPTION: We present SerpentinaDB, a structured compilation of 147 R. serpentina PDMs, inclusive of their plant part source, chemical classification, IUPAC, SMILES, physicochemical properties, and 3D chemical structures with associated references. It also provides refined search option for identification of analogs of natural molecules against ZINC database at user-defined cut-off. CONCLUSION: SerpentinaDB is an exhaustive resource of R. serpentina molecules facilitating prospection for therapeutic molecules from a medicinally important source of natural products. It also provides refined search option to explore the neighborhood of chemical space against ZINC database to identify analogs of natural molecules obtained as leads. In a previous study, we have demonstrated the utility of this resource by identifying novel aldose reductase inhibitors towards intervention of complications of diabetes.

Machine learning models to predict sweetness of molecules.

Sweetness is a vital taste to which humans are innately attracted. Given the increasing prevalence of type-2 diabetes, it is highly relevant to build computational models to predict the sweetness of small molecules. Such models are valuable for identifying sweeteners with low calorific value. We present regression-based machine learning and deep learning algorithms for predicting sweetness. Toward this goal, we manually curated the most extensive dataset of 671 sweet molecules with known experimental sweetness values ranging from 0.2 to 22,500,000. Gradient Boost and Random Forest Regressors emerged as the best models for predicting the sweetness of molecules with a correlation coefficient of 0.94 and 0.92, respectively. Our models show state-of-the-art performance when compared with previously published studies. Besides making our dataset (SweetpredDB) available, we also present a user-friendly web server to return the predicted sweetness for small molecules, Sweetpred (https://cosylab.iiitd.edu.in/sweetpred).

Camouflage Detection and Its Association with Cognitive Style: A Functional Connectivity Study.

Background: Individual differences exist in performance in tasks that require visual search, such as camouflage detection (CD). Field dependence/independence (FD/I), as assessed using the Group Embedded Figures Test (GEFT), is an extensively studied dimension of cognitive style that classifies participants based on their visual perceptual styles. Materials and Methods: In the present study, we utilized fMRI on 46 healthy participants to investigate the underlying neural mechanisms specific to the cognitive styles of FD/FI while performing a CD task using both activation magnitude and an exploratory functional connectivity (FC) analysis. Group differences between high and low performers on the two extremes of the accuracy continuum of GEFT were studied. Results: No statistically significant group differences were observed using whole-brain voxel-wise comparison. However, the exploratory FC analysis revealed an enhanced communication between various regions subserving the cognitive traits required for visual search by FI participants over and above their FD counterparts. Conclusion: These enhanced connectivities suggest additional recruitment of cognitive functions to provide computational support that might facilitate superior performance in CD task by the participants who display a field-independent cognitive style.

Investigating the link between miR-34a-5p and TLR6 signaling in sepsis-induced ARDS.

Acute lung injury (ALI)/acute respiratory distress syndrome (ARDS) are lung complications diagnosed by impaired gaseous exchanges leading to mortality. From the diverse etiologies, sepsis is a prominent contributor to ALI/ARDS. In the present study, we retrieved sepsis-induced ARDS mRNA expression profile and identified 883 differentially expressed genes (DEGs). Next, we established an ARDS-specific weighted gene co-expression network (WGCN) and picked the blue module as our hub module based on highly correlated network properties. Later we subjected all hub module DEGs to form an ARDS-specific 3-node feed-forward loop (FFL) whose highest-order subnetwork motif revealed one TF (STAT6), one miRNA (miR-34a-5p), and one mRNA (TLR6). Thereafter, we screened a natural product library and identified three lead molecules that showed promising binding affinity against TLR6. We then performed molecular dynamics simulations to evaluate the stability and binding free energy of the TLR6-lead molecule complexes. Our results suggest these lead molecules may be potential therapeutic candidates for treating sepsis-induced ALI/ARDS. In-silico studies on clinical datasets for sepsis-induced ARDS indicate a possible positive interaction between miR-34a and TLR6 and an antagonizing effect on STAT6 to promote inflammation. Also, the translational study on septic mice lungs by IHC staining reveals a hike in the expression of TLR6. We report here that miR-34a actively augments the effect of sepsis on lung epithelial cell apoptosis. This study suggests that miR-34a promotes TLR6 to heighten inflammation in sepsis-induced ALI/ARDS. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-023-03700-1.

Computational gastronomy: A data science approach to food.

Cooking forms the core of our cultural identity other than being the basis of nutrition and health. The increasing availability of culinary data and the advent of computational methods for their scrutiny are dramatically changing the artistic outlook towards gastronomy. Starting with a seemingly simple question, 'Why do we eat what we eat?', data-driven research conducted in our lab has led to interesting explorations of traditional recipes, their flavor composition, and health associations. Our investigations have revealed 'culinary fingerprints' of regional cuisines across the world. Application of data-driven strategies for investigating the gastronomic data has opened up exciting avenues, giving rise to an all-new field of 'computational gastronomy'. This emerging interdisciplinary science asks questions of culinary origin to seek their answers via the compilation of culinary data and their analysis using methods of complex systems, statistics, computer science, and artificial intelligence. Along with complementary experimental studies, these endeavors have the potential to transform the food landscape by effectively leveraging data-driven food innovations for better health and nutrition.

RecipeDB: a resource for exploring recipes.

Cooking is the act of turning nature into the culture, which has enabled the advent of the omnivorous human diet. The cultural wisdom of processing raw ingredients into delicious dishes is embodied in their cuisines. Recipes thus are the cultural capsules that encode elaborate cooking protocols for evoking sensory satiation as well as providing nourishment. As we stand on the verge of an epidemic of diet-linked disorders, it is eminently important to investigate the culinary correlates of recipes to probe their association with sensory responses as well as consequences for nutrition and health. RecipeDB (https://cosylab.iiitd.edu.in/recipedb) is a structured compilation of recipes, ingredients and nutrition profiles interlinked with flavor profiles and health associations. The repertoire comprises of meticulous integration of 118 171 recipes from cuisines across the globe (6 continents, 26 geocultural regions and 74 countries), cooked using 268 processes (heat, cook, boil, simmer, bake, etc.), by blending over 20 262 diverse ingredients, which are further linked to their flavor molecules (FlavorDB), nutritional profiles (US Department of Agriculture) and empirical records of disease associations obtained from MEDLINE (DietRx). This resource is aimed at facilitating scientific explorations of the culinary space (recipe, ingredient, cooking processes/techniques, dietary styles, etc.) linked to taste (flavor profile) and health (nutrition and disease associations) attributes seeking for divergent applications. Database URL:  https://cosylab.iiitd.edu.in/recipedb.

BitterSweet: Building machine learning models for predicting the bitter and sweet taste of small molecules.

The dichotomy of sweet and bitter tastes is a salient evolutionary feature of human gustatory system with an innate attraction to sweet taste and aversion to bitterness. A better understanding of molecular correlates of bitter-sweet taste gradient is crucial for identification of natural as well as synthetic compounds of desirable taste on this axis. While previous studies have advanced our understanding of the molecular basis of bitter-sweet taste and contributed models for their identification, there is ample scope to enhance these models by meticulous compilation of bitter-sweet molecules and utilization of a wide spectrum of molecular descriptors. Towards these goals, our study provides a structured compilation of bitter, sweet and tasteless molecules and state-of-the-art machine learning models for bitter-sweet taste prediction (BitterSweet). We compare different sets of molecular descriptors for their predictive performance and further identify important features as well as feature blocks. The utility of BitterSweet models is demonstrated by taste prediction on large specialized chemical sets such as FlavorDB, FooDB, SuperSweet, Super Natural II, DSSTox, and DrugBank. To facilitate future research in this direction, we make all datasets and BitterSweet models publicly available, and present an end-to-end software for bitter-sweet taste prediction based on freely available chemical descriptors.

Network controllability analysis of intracellular signalling reveals viruses are actively controlling molecular systems.

In recent years control theory has been applied to biological systems with the aim of identifying the minimum set of molecular interactions that can drive the network to a required state. However, in an intra-cellular network it is unclear how control can be achieved in practice. To address this limitation we use viral infection, specifically human immunodeficiency virus type 1 (HIV-1) and hepatitis C virus (HCV), as a paradigm to model control of an infected cell. Using a large human signalling network comprised of over 6000 human proteins and more than 34000 directed interactions, we compared two states: normal/uninfected and infected. Our network controllability analysis demonstrates how a virus efficiently brings the dynamically organised host system into its control by mostly targeting existing critical control nodes, requiring fewer nodes than in the uninfected network. The lower number of control nodes is presumably to optimise exploitation of specific sub-systems needed for virus replication and/or involved in the host response to infection. Viral infection of the human system also permits discrimination between available network-control models, which demonstrates that the minimum dominating set (MDS) method better accounts for how the biological information and signals are organised during infection by identifying most viral proteins as critical driver nodes compared to the maximum matching (MM) method. Furthermore, the host driver nodes identified by MDS are distributed throughout the pathways enabling effective control of the cell via the high 'control centrality' of the viral and targeted host nodes. Our results demonstrate that control theory gives a more complete and dynamic understanding of virus exploitation of the host system when compared with previous analyses limited to static single-state networks.

Assortative mixing in Protein Contact Networks and protein folding kinetics.

MOTIVATION: Starting from linear chains of amino acids, the spontaneous folding of proteins into their elaborate 3D structures is one of the remarkable examples of biological self-organization. We investigated native state structures of 30 single-domain, two-state proteins, from complex networks perspective, to understand the role of topological parameters in proteins' folding kinetics, at two length scales--as 'Protein Contact Networks (PCNs)' and their corresponding 'Long-range Interaction Networks (LINs)' constructed by ignoring the short-range interactions. RESULTS: Our results show that, both PCNs and LINs exhibit the exceptional topological property of 'assortative mixing' that is absent in all other biological and technological networks studied so far. We show that the degree distribution of these contact networks is partly responsible for the observed assortativity. The coefficient of assortativity also shows a positive correlation with the rate of protein folding at both short- and long-contact scale, whereas, the clustering coefficients of only the LINs exhibit a negative correlation. The results indicate that the general topological parameters of these naturally evolved protein networks can effectively represent the structural and functional properties required for fast information transfer among the residues facilitating biochemical/kinetic functions, such as, allostery, stability and the rate of folding. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Data-driven analysis of biomedical literature suggests broad-spectrum benefits of culinary herbs and spices.

Spices and herbs are key dietary ingredients used across cultures worldwide. Beyond their use as flavoring and coloring agents, the popularity of these aromatic plant products in culinary preparations has been attributed to their antimicrobial properties. Last few decades have witnessed an exponential growth of biomedical literature investigating the impact of spices and herbs on health, presenting an opportunity to mine for patterns from empirical evidence. Systematic investigation of empirical evidence to enumerate the health consequences of culinary herbs and spices can provide valuable insights into their therapeutic utility. We implemented a text mining protocol to assess the health impact of spices by assimilating, both, their positive and negative effects. We conclude that spices show broad-spectrum benevolence across a range of disease categories in contrast to negative effects that are comparatively narrow-spectrum. We also implement a strategy for disease-specific culinary recommendations of spices based on their therapeutic tradeoff against adverse effects. Further by integrating spice-phytochemical-disease associations, we identify bioactive spice phytochemicals potentially involved in their therapeutic effects. Our study provides a systems perspective on health effects of culinary spices and herbs with applications for dietary recommendations as well as identification of phytochemicals potentially involved in underlying molecular mechanisms.

A hierarchical anatomical classification schema for prediction of phenotypic side effects.

Prediction of adverse drug reactions is an important problem in drug discovery endeavors which can be addressed with data-driven strategies. SIDER is one of the most reliable and frequently used datasets for identification of key features as well as building machine learning models for side effects prediction. The inherently unbalanced nature of this data presents with a difficult multi-label multi-class problem towards prediction of drug side effects. We highlight the intrinsic issue with SIDER data and methodological flaws in relying on performance measures such as AUC while attempting to predict side effects.We argue for the use of metrics that are robust to class imbalance for evaluation of classifiers. Importantly, we present a 'hierarchical anatomical classification schema' which aggregates side effects into organs, sub-systems, and systems. With the help of a weighted performance measure, using 5-fold cross-validation we show that this strategy facilitates biologically meaningful side effects prediction at different levels of anatomical hierarchy. By implementing various machine learning classifiers we show that Random Forest model yields best classification accuracy at each level of coarse-graining. The manually curated, hierarchical schema for side effects can also serve as the basis of future studies towards prediction of adverse reactions and identification of key features linked to specific organ systems. Our study provides a strategy for hierarchical classification of side effects rooted in the anatomy and can pave the way for calibrated expert systems for multi-level prediction of side effects.

Differential Network Analysis Reveals Evolutionary Complexity in Secondary Metabolism of Rauvolfia serpentina over Catharanthus roseus.

Comparative co-expression analysis of multiple species using high-throughput data is an integrative approach to determine the uniformity as well as diversification in biological processes. Rauvolfia serpentina and Catharanthus roseus, both members of Apocyanacae family, are reported to have remedial properties against multiple diseases. Despite of sharing upstream of terpenoid indole alkaloid pathway, there is significant diversity in tissue-specific synthesis and accumulation of specialized metabolites in these plants. This led us to implement comparative co-expression network analysis to investigate the modules and genes responsible for differential tissue-specific expression as well as species-specific synthesis of metabolites. Toward these goals differential network analysis was implemented to identify candidate genes responsible for diversification of metabolites profile. Three genes were identified with significant difference in connectivity leading to differential regulatory behavior between these plants. These genes may be responsible for diversification of secondary metabolism, and thereby for species-specific metabolite synthesis. The network robustness of R. serpentina, determined based on topological properties, was also complemented by comparison of gene-metabolite networks of both plants, and may have evolved to have complex metabolic mechanisms as compared to C. roseus under the influence of various stimuli. This study reveals evolution of complexity in secondary metabolism of R. serpentina, and key genes that contribute toward diversification of specific metabolites.

Control of Neuronal Network in Caenorhabditis elegans.

Caenorhabditis elegans, a soil dwelling nematode, is evolutionarily rudimentary and contains only ∼ 300 neurons which are connected to each other via chemical synapses and gap junctions. This structural connectivity can be perceived as nodes and edges of a graph. Controlling complex networked systems (such as nervous system) has been an area of excitement for mankind. Various methods have been developed to identify specific brain regions, which when controlled by external input can lead to achievement of control over the state of the system. But in case of neuronal connectivity network the properties of neurons identified as driver nodes is of much importance because nervous system can produce a variety of states (behaviour of the animal). Hence to gain insight on the type of control achieved in nervous system we implemented the notion of structural control from graph theory to C. elegans neuronal network. We identified 'driver neurons' which can provide full control over the network. We studied phenotypic properties of these neurons which are referred to as 'phenoframe' as well as the 'genoframe' which represents their genetic correlates. We find that the driver neurons are primarily motor neurons located in the ventral nerve cord and contribute to biological reproduction of the animal. Identification of driver neurons and its characterization adds a new dimension in controllability of C. elegans neuronal network. This study suggests the importance of driver neurons and their utility to control the behaviour of the organism.

Re-analysis of protein data reveals the germination pathway and up accumulation mechanism of cell wall hydrolases during the radicle protrusion step of seed germination in Podophyllum hexandrum- a high altitude plant.

Podophyllum hexandrum Royle is an important high-altitude plant of Himalayas with immense medicinal value. Earlier, it was reported that the cell wall hydrolases were up accumulated during radicle protrusion step of Podophyllum seed germination. In the present study, Podophyllum seed Germination protein interaction Network (PGN) was constructed by using the differentially accumulated protein (DAP) data set of Podophyllum during the radicle protrusion step of seed germination, with reference to Arabidopsis protein-protein interaction network (AtPIN). The developed PGN is comprised of a giant cluster with 1028 proteins having 10,519 interactions and a few small clusters with relevant gene ontological signatures. In this analysis, a germination pathway related cluster which is also central to the topology and information dynamics of PGN was obtained with a set of 60 key proteins. Among these, eight proteins which are known to be involved in signaling, metabolism, protein modification, cell wall modification, and cell cycle regulation processes were found commonly highlighted in both the proteomic and interactome analysis. The systems-level analysis of PGN identified the key proteins involved in radicle protrusion step of seed germination in Podophyllum.

Phenotypic side effects prediction by optimizing correlation with chemical and target profiles of drugs.

Despite technological progresses and improved understanding of biological systems, discovery of novel drugs is an inefficient, arduous and expensive process. Research and development cost of drugs is unreasonably high, largely attributed to the high attrition rate of candidate drugs due to adverse drug reactions. Computational methods for accurate prediction of drug side effects, rooted in empirical data of drugs, have the potential to enhance the efficacy of the drug discovery process. Identification of features critical for specifying side effects would facilitate efficient computational procedures for their prediction. We devised a generalized ordinary canonical correlation model for prediction of drug side effects based on their chemical properties as well as their target profiles. While the former is based on 2D and 3D chemical features, the latter enumerates a systems-level property of drugs. We find that the model incorporating chemical features outperforms that incorporating target profiles. Furthermore we identified the 2D and 3D chemical properties that yield best results, thereby implying their relevance in specifying adverse drug reactions.

Analysis of Food Pairing in Regional Cuisines of India.

Any national cuisine is a sum total of its variety of regional cuisines, which are the cultural and historical identifiers of their respective regions. India is home to a number of regional cuisines that showcase its culinary diversity. Here, we study recipes from eight different regional cuisines of India spanning various geographies and climates. We investigate the phenomenon of food pairing which examines compatibility of two ingredients in a recipe in terms of their shared flavor compounds. Food pairing was enumerated at the level of cuisine, recipes as well as ingredient pairs by quantifying flavor sharing between pairs of ingredients. Our results indicate that each regional cuisine follows negative food pairing pattern; more the extent of flavor sharing between two ingredients, lesser their co-occurrence in that cuisine. We find that frequency of ingredient usage is central in rendering the characteristic food pairing in each of these cuisines. Spice and dairy emerged as the most significant ingredient classes responsible for the biased pattern of food pairing. Interestingly while individual spices contribute to negative food pairing, dairy products on the other hand tend to deviate food pairing towards positive side. Our data analytical study highlighting statistical properties of the regional cuisines, brings out their culinary fingerprints that could be used to design algorithms for generating novel recipes and recipe recommender systems. It forms a basis for exploring possible causal connection between diet and health as well as prospection of therapeutic molecules from food ingredients. Our study also provides insights as to how big data can change the way we look at food.