Comprehensive molecular interaction map of TGFß induced epithelial to mesenchymal transition in breast cancer.

Breast cancer is one of the prevailing cancers globally, with a high mortality rate. Metastatic breast cancer (MBC) is an advanced stage of cancer, characterised by a highly nonlinear, heterogeneous process involving numerous singling pathways and regulatory interactions. Epithelial-mesenchymal transition (EMT) emerges as a key mechanism exploited by cancer cells. Transforming Growth Factor-ß (TGFß)-dependent signalling is attributed to promote EMT in advanced stages of breast cancer. A comprehensive regulatory map of TGFß induced EMT was developed through an extensive literature survey. The network assembled comprises of 312 distinct species (proteins, genes, RNAs, complexes), and 426 reactions (state transitions, nuclear translocations, complex associations, and dissociations). The map was developed by following Systems Biology Graphical Notation (SBGN) using Cell Designer and made publicly available using MINERVA ( http://35.174.227.105:8080/minerva/?id=Metastatic_Breast_Cancer_1 ). While the complete molecular mechanism of MBC is still not known, the map captures the elaborate signalling interplay of TGFß induced EMT-promoting MBC. Subsequently, the disease map assembled was translated into a Boolean model utilising CaSQ and analysed using Cell Collective. Simulations of these have captured the known experimental outcomes of TGFß induced EMT in MBC. Hub regulators of the assembled map were identified, and their transcriptome-based analysis confirmed their role in cancer metastasis. Elaborate analysis of this map may help in gaining additional insights into the development and progression of metastatic breast cancer.

Molecular insights on bioactive compounds againstCovid-19: A Network pharmacological and computational study.

BACKGROUND: Network pharmacology based identification of phytochemicals in the form of cocktails against off-targets can play a significant role in inhibition of SARS_CoV2 viral entry and its propagation. This study includes network pharmacology, virtual screening, docking and molecular dynamics to investigate the distinct antiviral mechanisms of effective phytochemicals against SARS_CoV2. METHODS: SARS_CoV2 human-protein interaction network was explored from the BioGRID database and analysed using Cytoscape. Further analysis was performed to explore biological function, protein-phytochemical/drugs network and up-down regulation of pathological host target proteins. This lead to understand the antiviral mechanism of phytochemicals against SARS_CoV2. The network was explored through g:Profiler, EnrichR, CTD, SwissTarget, STITCH, DrugBank, BindingDB, STRING and SuperPred. Virtual screening of phytochemicals against potential antiviral targets such as M-Pro, NSP1, Receptor binding domain, RNA binding domain, and ACE2 discloses the effective interaction between them. Further, the binding energy calculations through simulation of the docked complex explains the efficiency and stability of the interactions. RESULTS: The network analysis identified quercetin, genistein, luteolin, eugenol, berberine, isorhamnetin and cinnamaldehyde to be interacting with host proteins ACE2, DPP4, COMT, TUBGCP3, CENPF, BRD2 and HMOX1 which are involved in antiviral mechanisms such as viral entry, viral replication, host immune response, and antioxidant activity. Thus indicating that herbal cocktails can effectively tackle the viral hijacking of the crucial biological functions of human host. Further exploration through Virtual screening, docking and molecular dynamics recognizes the effective interaction of phytochemicals such as punicalagin, scutellarin, and solamargine with their respective potential targets. CONCLUSION: This work illustrates probable strategy for identification of phytochemical based cocktails and off-targets which are effective against SARS_CoV 2.

A Hybrid Protocol for Identifying Comorbidity-Based Potential Drugs for COVID-19 Using Biomedical Literature Mining, Network Analysis, and Deep Learning.

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) has spread on an unprecedented scale around the globe. Despite of 141,975 published papers on COVID-19 and several hundreds of new studies carried out every day, this pandemic remains as a global challenge. Biomedical literature mining helps the researchers to understand the etiology of the disease and to gain an in-depth knowledge of the disease, potential drugs, vaccines developed and novel therapies. In addition to the available treatments, there is a huge need to address the comorbidity-based disease mortality in case of COVID-19 patients with type 2 diabetes mellitus (T2D), hypertension and cardiovascular disease (CVD). In this chapter, we provide a hybrid protocol based on biomedical literature mining, network analysis of omics data, and deep learning for the identification of most potential drugs for COVID-19.

SNAIL driven by a feed forward loop motif promotes TGFßinduced epithelial to mesenchymal transition.

Epithelial to Mesenchymal Transition (EMT) plays an important role in tissue regeneration, embryonic development, and cancer metastasis. Several signaling pathways are known to regulate EMT, among which the modulation of TGFß(Transforming Growth Factor-ß) induced EMT is crucial in several cancer types. Several mathematical models were built to explore the role of core regulatory circuit of ZEB/miR-200, SNAIL/miR-34 double negative feedback loops in modulating TGFßinduced EMT. Different emergent behavior including tristability, irreversible switching, existence of hybrid EMT states were inferred though these models. Some studies have explored the role of TGFßreceptor activation, SMADs nucleocytoplasmic shuttling and complex formation. Recent experiments have revealed that MDM2 along with SMAD complex regulates SNAIL expression driven EMT. Encouraged by this, in the present study we developed a mathematical model for p53/MDM2 dependent TGFßinduced EMT regulation. Inclusion of p53 brings in an additional mechanistic perspective in exploring the EM transition. The network formulated comprises a C1FFL moderating SNAIL expression involving MDM2 and SMAD complex, which functions as a noise filter and persistent detector. The C1FFL was also observed to operate as a coincidence detector driving the SNAIL dependent downstream signaling into phenotypic switching decision. Systems modelling and analysis of the devised network, displayed interesting dynamic behavior, systems response to various inputs stimulus, providing a better understanding of p53/MDM2 dependent TGF-ßinduced Epithelial to Mesenchymal Transition.

Development and extraction optimization of baicalein and pinostrobin from Scutellaria violacea through response surface methodology.

OBJECTIVE: To develop a process that involves optimization of the amount of baicalein and pinostrobin from the hydro-methanolic extract of the leaves of Scutellaria violacea by response surface methodology (RSM). MATERIALS AND METHODS: The combinatorial influence of various extraction parameters on the extraction yield was investigated by adopting Box-Behnken experimental design. Preliminary experiments carried out based on the traditional one variable at a time optimization revealed four such operational parameters to play a crucial role by influencing the yield. These four process parameters at three levels were considered to obtain the Box-Behnken experimental design. RESULTS: RSM based model fitted to the resulting experimental data suggested that 52.3% methanol/water, 12.46:1 solvent-solid ratio, 285 rpm agitation and 6.07 h of extraction time are the optimal conditions which yielded a maximized amount of baicalein and pinostrobin of 2.9 and 4.05 mg/g DM. Analysis of variance revealed a high correlation coefficient (R (2) = 0.999 for baicalein and 0.994 for pinostrobin), signifying a good fit between the regression model (second order) and the experimental observations. CONCLUSION: The present study signifies that both the metabolites have been extracted from S. violacea for the first time. Further, this study developed an optimized extraction procedure to obtain maximum yield of the metabolites, which is unique and better than conventional extraction methodology. The operational parameters under optimized conditions accounts for the lowest cost in extraction process thus, providing an efficient, rapid and cost-effective method for isolation and scale up of these commercially vital flavonoids.

Lipase catalyzed ester synthesis for food processing industries

Lipases are one of the most important industrial biocatalyst which catalyzes the hydrolysis of lipids. It can also reverse the reaction at minimum water activity. Because of this pliable nature, it is widely exploited to catalyze the diverse bioconversion reactions, such as hydrolysis, esterification, interesterification, alcoholysis, acidolysis and aminolysis. The property to synthesize the esters from the fatty acids and glycerol promotes its use in various ester synthesis. The esters synthesized by lipase finds applications in numerous fields such as biodiesel production, resolution of the recemic drugs, fat and lipid modification, flavour synthesis, synthesis of enantiopure pharmaceuticals and nutraceuticals. It plays a crucial role in the food processing industries since the process is unaffected by the unwanted side products. Lipase modifications such as the surfactant coating, molecular imprinting to suit for the non-aqueous ester synthesis have also been reported. This review deals with lipase catalyzed ester synthesis, esterification strategies, optimum conditions and their applications in food processing industries.

Lipases são catalizadores industriais dos mais importantes, os quais catalizam a hidrólise de lipídeos. Também podem reverter a reação a um mínimo de atividade de água. Devido sua natureza flexível, é amplamente explorada para catalizar uma diversidade de reações de bioconversão como hidrólise, esterificação, interesterificação, alcoólise, acidólise e aminólise. A propriedade de síntese de esteres a partir de ácidos graxos e glicerol promoveu seu uso em várias sínteses de esteres. Os esteres sintetizados por lipases encontram aplicação em numerosos campos como a produção de biodiesel, resolução de drogas racêmicas, modificação de gorduras e lipídios, sintese de aromas, síntese de produtos farmacêuticos enantiopuro e nutracêuticos. As lipases possuem um papel crucial nas indústrias de processamento de alimentos, pois os processos não são afetados por subprodutos indesejáveis. Modificações nas lipases como revestimento tensoativo, impressão molecular, para permitir a síntese de esteres não aquosos também são reportados. Esta revisão trata da síntese de éster catalizada por lipase, estratégia de esterificação, condições ótimas e suas aplicações em indústrias de processamento de alimento.

Evaluation of medium components by Plackett-Burman statistical design for lipase production by Candida rugosa and kinetic modeling.

Lipase production by Candida rugosa was carried out in submerged fermentation. Plackett-Burman statistical experimental design was applied to evaluate the fermentation medium components. The effect of twelve medium components was studied in sixteen experimental trials. Glucose, olive oil, peptone and FeCl3.6H2O were found to have more significance on lipase production by Candida rugosa. Maximum lipase activity of 3.8 u mL(-1) was obtained at 50 h of fermentation period. The fermentation was carried out at optimized temperature of 30 degrees C, initial pH of 6.8 and shaking speed of 120 r/min. Unstructured kinetic models were used to simulate the experimental data. Logistic model, Luedeking-Piret model and modified Luedeking-Piret model were found suitable to efficiently predict the cell mass, lipase production and glucose consumption respectively with high determination coefficient(R2). From the estimated values of the Luedeking-Piret kinetic model parameters, alpha and beta, it was found that the lipase production by Candida rugosa is growth associated.