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Type-2 diabetes (T2D) is a rapidly growing multifactorial metabolic disorder that induces the onset of various diseases in the human body. The compositional and metabolic shift of the gut microbiota is a crucial factor behind T2D. Hence, gaining insight into the metabolic profile of the gut microbiota is essential for revealing their role in regulating the metabolism of T2D patients. Here, we have focused on the genome-scale community metabolic model reconstruction of crucial T2D-associated gut microbes. The model-based analysis of biochemical flux in T2D and healthy gut conditions showed distinct biochemical signatures and diverse metabolic interactions in the microbial community. The metabolic interactions encompass cross-feeding of short-chain fatty acids, amino acids, and vitamins among individual microbes within the community. In T2D conditions, a reduction in the metabolic flux of acetate, butyrate, vitamin B5, and bicarbonate was observed in the microbial community model, which can impact carbohydrate metabolism. The decline in butyrate levels is correlated with both insulin resistance and diminished glucose metabolism in T2D patients. Compared to the healthy gut, an overall reduction in glucose consumption and SCFA production flux was estimated in the T2D gut environment. Moreover, the decreased consumption profiles of branch chain amino acids (BCAAs) and aromatic amino acids (AAAs) in the T2D gut microbiota can be a distinct biomarker for T2D. Hence, the flux-level analysis of the microbial community model can provide insights into the metabolic reprogramming in diabetic gut microbiomes, which may be helpful in personalized therapeutics and diet design against T2D. [ABSTRACT FROM AUTHOR]

Sonneratia caseolaris (L.) is a common mangrove plant which has significant medicinal value in traditional medicine. Ethanol extract from the fruits of S. caseolaris (SCE) was used in this project to explore its different pharmacological effects considering its traditional usage. In the castor oil-induced diarrheal method, SCE significantly lengthened the latency of the first defecation period up to 95.8 and 119.4 min as well as lowering stool count by 43.3% and 64.4% at the doses of 250 and 500 mg/kg, respectively. In evaluating the neuropharmacological effect using the open-field model, a significant central nervous system (CNS) depressant nature was observed after a reduction in the no. of squares crossed by mice at various time intervals. In evaluating the blood coagulation effect, SCE significantly reduced blood clotting time at 5.86, 5.52, and 5.01 min at 25, 50, and 100 mg/ml doses, respectively. In the assessment of the anthelmintic effect, SCE significantly killed Paramphistomum cervi (P. cervi) where the death times of the nematodes were 40.3, 36.8, and 29.9 min at 12.5, 25, and 50 mg/ml doses, respectively. The extract showed a very poor cytotoxic effect in brine shrimp lethality bioassay. In molecular docking analysis, maslinic acid, oleanolic acid, luteolin, luteolin 7-O-β-glucoside, myricetin, ellagic acid, and R-nyasol showed the best binding affinities with the selected proteins which might be the credible reasons for eliciting pharmacological responses. Among these seven compounds, only luteolin 7-O-β-glucoside had two violations in Lipinski's rule of five. [ABSTRACT FROM AUTHOR]

Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment. [Display omitted] • Advancements in ML-based GEM reconstruction tools and strategies. • Application of ML in analyzing real-world omics data has been briefly discussed. • ML contributes greatly in refining & structuring heterogeneous biological big data. • Case studies on ML-guided CBM in biomedical and bioengineering applications. • Limitations and challenges of omics data handling and integration in GEMs. [ABSTRACT FROM AUTHOR]

Sonneratia caseolaris is a widely distributed mangrove plant having much therapeutic importance in traditional medicine. This plant is reported for possessing numerous compounds that are already used for many therapeutic purposes. After finding the presence of antioxidant components in the qualitative antioxidative assay, we went to conduct quantitative tests where the total contents of phenolics, flavonoids, and tannins were estimated as 122 mg GAE/gm, 613 mg QE/gm, and 30 mg GAE/gm, respectively. In DPPH free radical, H2O2, and superoxide radical scavenging assay, the SC50 values were found to be 87, 66, and 192 μg/ml, respectively. In FeCl3 reducing power assay, the RC50 of SC extract and ascorbic acid were 80 and 28 μg/ml, respectively. This extract revealed a significant peripheral analgesic effect in the acetic acid-induced writhing model in mice by reducing the writhing impulse by about 21% and 39% at 250 and 500 mg/kg doses, respectively, and a central analgesic effect in the tail immersion method by elongating the time up to about 22% and 37% at the same doses. In the anti-inflammatory test in mice, this extract reduced the paw edema size over the observed period in a dose-dependent manner. It also showed a significant reduction in the elevated rectal temperature of mice in the observing period in Brewer's yeast-induced pyrexia model. In silico analysis revealed better binding characteristics of ellagic acid and luteolin among other compounds with various receptors that might be responsible for antioxidative and anti-inflammatory properties. From our observation, we suppose that SC fruits might be a potential source of drug leads for various inflammatory disorders. [ABSTRACT FROM AUTHOR]

The project work was designed to investigate the phytochemical and selected pharmacological activities (antidiarrheal, analgesic, neuropharmacological behavior and anthelmintic) of leaves of Sanchezia nobilis. Hook. F. (Family: Acanthaceae). From its phytochemical analysis we found the presence of reducing sugar, combined reducing sugar, phenolic compounds, tannins, flavonoids, carbohydrates, gums, steroids, alkaloids, glycosides and terpenoids. In vivo antidiarrheal activity was substantiated by prolongation of latent period and decrease in total number of stools. The extract produced 62.49% and 74.01% decrease in stool count at the doses of 250 and 500 mg/kg body weight respectively while the standard drug Loperamide decrease in stool count was found to be 87.05% at a dose of 3 mg/kg body weight. The leaves extract produced 32.7% and 41.78% inhibition of writhing at the doses of 250 mg/kg and 500 mg/kg body weight respectively while the standard drug Diclofenac Na was found to be 74.23% at a dose of 25 mg/kg body weight. The extract showed dose dependent CNS depressant activity by reducing the locomotors activity. Higher dose of this extract (500 mg/kg body weight) comparing with standard Diazepam exposed sedative effect potentially. The extract exhibited concentration dependent anthelmintic activity against Paramphistomum cervi using Albendazole (15 mg/mL) as standard. The paralysis occurred between 9.00 to 83.91 min and death occurred between 11.33 to 86.33 min which were comparable to standard drug Albendazole. So, the present study concluded that the extract is fortified with significant antidiarrheal, CNS depression and anthelmintic activity and moderate analgesic activity. [ABSTRACT FROM AUTHOR]

Fungus-cultivating termite Odontotermes badius developed a mutualistic association with Termitomyces fungi for the plant material decomposition and providing a food source for the host survival. The mutualistic relationship sifted the microbiome composition of the termite gut and Termitomyces fungal comb. Symbiotic bacterial communities in the O. badius gut and fungal comb have been studied extensively to identify abundant bacteria and their lignocellulose degradation capabilities. Despite several metagenomic studies, the species-wide metabolic interaction patterns of bacterial communities in termite gut and fungal comb remains unclear. The bacterial species metabolic interaction network (BSMIN) has been constructed with 230 bacteria identified from the O. badius gut and fungal comb microbiota. The network portrayed the metabolic map of the entire microbiota and highlighted several inter-species biochemical interactions like cross-feeding, metabolic interdependency, and competition. Further, the reconstruction and analysis of the bacterial influence network (BIN) quantified the positive and negative pairwise influences in the termite gut and fungal comb microbial communities. Several key macromolecule degraders and fermentative microbial entities have been identified by analyzing the BIN. The mechanistic interplay between these influential microbial groups and the crucial glycoside hydrolases (GH) enzymes produced by the macromolecule degraders execute the community-wide functionality of lignocellulose degradation and subsequent fermentation. The metabolic interaction pattern between the nine influential microbial species has been determined by considering them growing in a synthetic microbial community. Competition (30%), parasitism (47%), and mutualism (17%) were predicted to be the major mode of metabolic interaction in this synthetic microbial community. Further, the antagonistic metabolic effect was found to be very high in the metabolic-deprived condition, which may disrupt the community functionality. Thus, metabolic interactions of the crucial bacterial species and their GH enzyme cocktail identified from the O. badius gut and fungal comb microbiota may provide essential knowledge for developing a synthetic microcosm with efficient lignocellulolytic machinery. • Metabolic interaction between termite gut and fungal comb microbiota. • Crucial lignocellulolytic and fermentative bacteria identified through BIN. • Assessment of pair-wise bacterial influence pattern in termite gut and fungal comb. • Plant biomass degradation with bacterial GH enzymes were represented in MDN. [ABSTRACT FROM AUTHOR]

Flagellaria indica (Family: Flagellariaceae) is a common climbing plant found primarily in tropical regions of many countries. The plant has various traditional uses, although most of them lack scientific published reports. The crude ethanolic extract of F. indica leaves was fractionated based on polarity using water, ethyl acetate, and n-hexane. Biological screening was conducted on the anthelmintic, blood coagulation, diuretic, and laxative activities of the water, ethyl acetate, and n-hexane fractions of F. indica leaves. In the anthelmintic test, the n-hexane fraction showed a moderate effect with paralysis times of 16.79 and 13.62 minutes and death times of 27.34 and 21.81 minutes, respectively, at doses of 25 and 50 mg/mL.In the blood coagulant test, only the water fraction showed a notable effect. The clotting times were 4.33, 6.02, 7.68, and 8.32 minutes, respectively, at doses of 200, 100, 50, and 25 mg/mL. Diuretic activity was performed to determine the increase in the volume of excreted urine, and electrolyte analysis of urine was performed to determine pH, density, conductance, and Na+, Cl-, K+ levels, as well as natriuretic, kaliuretic, saluretic, and CAI indexes. The ethyl acetate fraction showed better diuretic activity than the n-hexane fraction, while the water fraction did not reveal a notable diuretic effect. The Na+, Cl-, K+, natriuretic, and saluretic indexes were found satisfactory in the ethyl acetate fraction, and the CAI index was better in the n-hexane fraction. In the laxative test, the n-hexane fraction showed the best laxative properties, with an increase in stool weight of 38% and 54% at doses of 250 and 500 mg/kg, respectively. These results suggest that different fractions of F. indica leaves contain distinct phytochemicals that may be responsible for these biological effects. The isolation of bioactive compounds could help justify its traditional uses in modern medicine. [ABSTRACT FROM AUTHOR]

In recent years, polymer-supported magnetic iron oxide nanoparticles (MIO-NPs) have gained a lot of attention in biomedical and healthcare applications due to their unique magnetic properties, low toxicity, cost-effectiveness, biocompatibility, and biodegradability. In this study, waste tissue papers (WTP) and sugarcane bagasse (SCB) were utilized to prepare magnetic iron oxide (MIO)-incorporated WTP/MIO and SCB/MIO nanocomposite particles (NCPs) based on in situ co-precipitation methods, and they were characterized using advanced spectroscopic techniques. In addition, their anti-oxidant and drug-delivery properties were investigated. Field emission scanning electron microscopy (FESEM) and X-ray diffraction (XRD) analyses revealed that the shapes of the MIO-NPs, SCB/MIO-NCPs, and WTP/MIO-NCPs were agglomerated and irregularly spherical with a crystallite size of 12.38 nm, 10.85 nm, and 11.47 nm, respectively. Vibrational sample magnetometry (VSM) analysis showed that both the NPs and the NCPs were paramagnetic. The free radical scavenging assay ascertained that the WTP/MIO-NCPs, SCB/MIO-NCPs, and MIO-NPs exhibited almost negligible antioxidant activity in comparison to ascorbic acid. The swelling capacities of the SCB/MIO-NCPs and WTP/MIO-NCPs were 155.0% and 159.5%, respectively, which were much higher than the swelling efficiencies of cellulose-SCB (58.3%) and cellulose-WTP (61.6%). The order of metronidazole drug loading after 3 days was: cellulose-SCB < cellulose-WTP < MIO-NPs < SCB/MIO-NCPs < WTP/MIO-NCPs, whereas the sequence of the drug-releasing rate after 240 min was: WTP/MIO-NCPs < SCB/MIO-NCPs < MIO-NPs < cellulose-WTP < cellulose-SCB. Overall, the results of this study showed that the incorporation of MIO-NPs in the cellulose matrix increased the swelling capacity, drug-loading capacity, and drug-releasing time. Therefore, cellulose/MIO-NCPs obtained from waste materials such as SCB and WTP can be used as a potential vehicle for medical applications, especially in a metronidazole drug delivery system. [ABSTRACT FROM AUTHOR]

Metabolic engineering encompasses several widely-used strategies, which currently hold a high seat in the field of biotechnology when its potential is manifesting through a plethora of research and commercial products with a strong societal impact. The genomic revolution that occurred almost three decades ago has initiated the generation of large omics-datasets which has helped in gaining a better understanding of cellular behavior. The itinerary of metabolic engineering that has occurred based on these large datasets has allowed researchers to gain detailed insights and a reasonable understanding of the intricacies of biosystems. However, the existing trail-and-error approaches for metabolic engineering are laborious and time-intensive when it comes to the production of target compounds with high yields through genetic manipulations in host organisms. Machine learning (ML) coupled with the available metabolic engineering test instances and omics data brings a comprehensive and multidisciplinary approach that enables scientists to evaluate various parameters for effective strain design. This vast amount of biological data should be standardized through knowledge engineering to train different ML models for providing accurate predictions in gene circuits designing, modification of proteins, optimization of bioprocess parameters for scaling up, and screening of hyper-producing robust cell factories. This review briefs on the premise of ML, followed by mentioning various ML methods and algorithms alongside the numerous omics datasets available to train ML models for predicting metabolic outcomes with high-accuracy. The combinative interplay between the ML algorithms and biological datasets through knowledge engineering have guided the recent advancements in applications such as CRISPR/Cas systems, gene circuits, protein engineering, metabolic pathway reconstruction, and bioprocess engineering. Finally, this review addresses the probable challenges of applying ML in metabolic engineering which will guide the researchers toward novel techniques to overcome the limitations. [Display omitted] • Synergism of metabolic engineering and machine learning captures cell metabolism. • Machine learning towards exploring CRISPR/Cas systems have been reviewed. • Application of machine learning for robust gene circuit design has been provided. • Machine learning contributes greatly in targeted pathway and protein engineering. • Bioprocess Digital Twin helps in an improved predictability of physical processes. [ABSTRACT FROM AUTHOR]

Microbial bioproduction of chemicals, proteins, and primary metabolites from cheap carbon sources is currently an advancing area in industrial research. The model yeast, Saccharomyces cerevisiae , is a well-established biorefinery host that has been used extensively for commercial manufacturing of bioethanol from myriad carbon sources. However, its Crabtree-positive nature often limits the use of this organism for the biosynthesis of commercial molecules that do not belong in the fermentative pathway. To avoid extensive strain engineering of S. cerevisiae for the production of metabolites other than ethanol, non-conventional yeasts can be selected as hosts based on their natural capacity to produce desired commodity chemicals. Non-conventional yeasts like Kluyveromyces marxianus , K. lactis , Yarrowia lipolytica , Pichia pastoris, Scheffersomyces stipitis , Hansenula polymorpha, and Rhodotorula toruloides have been considered as potential industrial eukaryotic hosts owing to their desirable phenotypes such as thermotolerance, assimilation of a wide range of carbon sources, as well as ability to secrete high titers of protein and lipid. However, the advanced metabolic engineering efforts in these organisms are still lacking due to the limited availability of systems and synthetic biology methods like in silico models, well-characterised genetic parts, and optimized genome engineering tools. This review provides an insight into the recent advances and challenges of systems and synthetic biology as well as metabolic engineering endeavours towards the commercial usage of non-conventional yeasts. Particularly, the approaches in emerging non-conventional yeasts for the production of enzymes, therapeutic proteins, lipids, and metabolites for commercial applications are extensively discussed here. Various attempts to address current limitations in designing novel cell factories have been highlighted that include the advances in the fields of genome-scale metabolic model reconstruction, flux balance analysis, 'omics'-data integration into models, genome-editing toolkit development, and rewiring of cellular metabolisms for desired chemical production. Additionally, the understanding of metabolic networks using 13C-labelling experiments as well as the utilization of metabolomics in deciphering intracellular fluxes and reactions have also been discussed here. Application of cutting-edge nuclease-based genome editing platforms like CRISPR/Cas9, and its optimization towards efficient strain engineering in non-conventional yeasts have also been described. Additionally, the impact of the advances in promising non-conventional yeasts for efficient commercial molecule synthesis has been meticulously reviewed. In the future, a cohesive approach involving systems and synthetic biology will help in widening the horizon of the use of unexplored non-conventional yeast species towards industrial biotechnology. • Metabolic modeling approaches for non-conventional yeasts are reviewed. • Application of metabolomics studies to produce commercial metabolites is discussed. • Improvement of homologous recombination in non-conventional yeasts is provided. • Strategies for carbon machinery rewiring for metabolite overproduction are described. • Commercial scenarios of production using non-conventional yeasts are highlighted. [ABSTRACT FROM AUTHOR]