Prediction of Phage Virion Proteins Using Machine Learning Methods.

Antimicrobial resistance (AMR) is a major problem and an immediate alternative to antibiotics is the need of the hour. Research on the possible alternative products to tackle bacterial infections is ongoing worldwide. One of the most promising alternatives to antibiotics is the use of bacteriophages (phage) or phage-driven antibacterial drugs to cure bacterial infections caused by AMR bacteria. Phage-driven proteins, including holins, endolysins, and exopolysaccharides, have shown great potential in the development of antibacterial drugs. Likewise, phage virion proteins (PVPs) might also play an important role in the development of antibacterial drugs. Here, we have developed a machine learning-based prediction method to predict PVPs using phage protein sequences. We have employed well-known basic and ensemble machine learning methods with protein sequence composition features for the prediction of PVPs. We found that the gradient boosting classifier (GBC) method achieved the best accuracy of 80% on the training dataset and an accuracy of 83% on the independent dataset. The performance on the independent dataset is better than other existing methods. A user-friendly web server developed by us is freely available to all users for the prediction of PVPs from phage protein sequences. The web server might facilitate the large-scale prediction of PVPs and hypothesis-driven experimental study design.

A network biology approach to identify crucial host targets for COVID-19.

The ongoing global pandemic of COVID-19, caused by SARS-CoV-2 has killed more than 5.9 million individuals out of ∼43 million confirmed infections. At present, several parts of the world are encountering the 3rd wave. Mass vaccination has been started in several countries but they are less likely to be broadly available for the current pandemic, repurposing of the existing drugs has drawn highest attention for an immediate solution. A recent publication has mapped the physical interactions of SARS-CoV-2 and human proteins by affinity-purification mass spectrometry (AP-MS) and identified 332 high-confidence SARS-CoV-2-human protein-protein interactions (PPIs). Here, we taken a network biology approach and constructed a human protein-protein interaction network (PPIN) with the above SARS-CoV-2 targeted proteins. We utilized a combination of essential network centrality measures and functional properties of the human proteins to identify the critical human targets of SARS-CoV-2. Four human proteins, namely PRKACA, RHOA, CDK5RAP2, and CEP250 have emerged as the best therapeutic targets, of which PRKACA and CEP250 were also found by another group as potential candidates for drug targets in COVID-19. We further found candidate drugs/compounds, such as guanosine triphosphate, remdesivir, adenosine monophosphate, MgATP, and H-89 dihydrochloride that bind the target human proteins. The urgency to prevent the spread of infection and the death of diseased individuals has prompted the search for agents from the pool of approved drugs to repurpose them for COVID-19. Our results indicate that host targeting therapy with the repurposed drugs may be a useful strategy for the treatment of SARS-CoV-2 infection.

Screening of Potential Vibrio cholerae Bacteriophages for Cholera Therapy: A Comparative Genomic Approach.

Cholera continues to be a major burden for developing nations, especially where sanitation, quality of water supply, and hospitalization have remained an issue. Recently, growing antimicrobial-resistant strains of Vibrio cholerae underscores alternative therapeutic strategies for cholera. Bacteriophage therapy is considered one of the best alternatives for antibiotic treatment. For the identification of potential therapeutic phages for cholera, we have introduced a comprehensive comparative analysis of whole-genome sequences of 86 Vibrio cholerae phages. We have witnessed extensive variation in genome size (ranging from 33 to 148 kbp), GC (G + C) content (varies from 34.5 to 50.8%), and the number of proteins (ranging from 15 to 232). We have identified nine clusters and three singletons using BLASTn, confirmed by nucleotide dot plot and sequence identity. A high degree of sequence and functional similarities in both the genomic and proteomic levels have been observed within the clusters. Evolutionary analysis confirms that phages are conserved within the clusters but diverse between the clusters. For each therapeutic phage, the top 2 closest phages have been identified using a system biology approach and proposed as potential therapeutic phages for cholera. This method can be applied for the classification of the newly isolated Vibrio cholerae phage. Furthermore, this systematic approach might be useful as a model for screening potential therapeutic phages for other bacterial diseases.

Cilnidipine and magnesium sulfate supplement ameliorates hyperglycemia, dyslipidemia and inhibits oxidative-stress in fructose-induced diabetic rats.

The study was designed to evaluate the safety and efficacy of cilnidipine (CLN) and Mg-supplementation in fructose-induced diabetic rats. Diabetes was induced into male Wister rats by feeding fructose (10% solution) in drinking water for 8 weeks. Diabetic rats were subjected for the oral administration of CLN1 (1 mg/kg/day) and CLN10 (10 mg/kg/day), and/or methyl cellulose (0.5%) as vehicle for 28 days. After 14 days of CLN treatment, MgSO4 (1%) was added to CLN1 and CLN10 groups for another 14 days. Age-matched healthy rats were used as normal control. After 28 days body weights were measured and organ weight to body ratio was calculated. Serum samples were analysed for fasting blood sugar (FBS), glycosylated hemoglobin (HbA1c), uric acid, lipid profiles, tri-iodothyronine (T3) and thyroid stimulating hormone (TSH), serum glutamic pyruvic transaminase (SGPT), serum glutamic oxaloacetic transaminase (SGOT), creatine phosphokinase myocardial-band (CK-MB), creatinine, albumin, electrolytes. Oral glucose tolerance tests (OGTT), liver histopathology and in-vivo antioxidant activities were also performed. The survival rate in diabetic rats was 100% after the oral administration of CLN, Mg-supplement and/or vehicle. A significant reduction in FBS levels and improvement in OGTT were observed in CLN10, CLN1+Mg and CLN10 + Mg groups after 28 days. Further, the treatment ameliorated serum lipid profile, uric acid, and albumin levels. The groups CLN10 and CLN10 + Mg improved HbA1c, liver glycogen, creatinine, T3, TSH levels and electrolytes in diabetic rats. Moreover, liver from CLN10 and CLN10 + Mg groups showed preservation of cellular architecture as evidenced by attenuation of inflammatory markers SGPT, SGOT and CK-MB; and the levels of superoxide dismutase (SOD), catalase (CAT), glutathione, malondialdehyde (MDA), markers of oxidative stress were significantly improved. CLN exerted prominent effects in the amelioration of hyperglycemia, dyslipidemia and reduced hepatic inflammation; and Mg-supplementation might have some beneficial effects on diabetic complications and oxidative stress in fructose-induced diabetic rats.

Pumpkin (Cucurbita maxima) seeds protect against formaldehyde-induced major organ damages.

Exposures to hazardous chemicals including formaldehyde are harmful to human health. In this study, the authors investigate the protective effects of pumpkin seed oil (PSO) extract against formaldehyde-induced major organ damages in mice. Administration of formaldehyde (FA) caused significant elevation of serum glutamic oxaloacetic transaminase (SGOT), serum glutamic pyruvic transaminase (SGPT), serum creatinine, etc. Histopathological examinations of liver, kidney, and brain tissues showed the degenerations of those organs. Mice pretreated with PSO extract significantly attenuated the FA-induced elevation of SGOT (39.0 ± 1.30 vs 20.5 ± 0.65 IU/L; FA-group vs PSO treatment group), SGPT (91.8 ± 1.65 vs 51.0 ± 1.29 IU/L), serum creatinine (1.05 ± 0.07 vs 0.65 ± 0.07 IU/L), and preserved the normal histology of organ tissues. The FA-induced elevation of malondialdehyde (MDA) in the brain, liver, and kidneys was suppressed by pretreatment with PSO extract. The extract also attenuated the FA-induced reduction of endogenous antioxidant pools. In vitro phytochemical analyses showed that PSO extract possesses free radical scavenging and total antioxidant activities due to the presence of phenolic and flavonoid compounds. Thus, PSO extract has significant protective effects against FA-induced organ toxicities by scavenging oxidative stress and inhibiting lipid peroxidation.

Pharmacological screening of fenofibrate-loaded solid dispersion in fructose-induced diabetic rat.

OBJECTIVES: Hyperlipidaemia is a common phenomenon in diabetes mellitus. Fenofibrate (FF) is a good candidate for the treatment of lipid abnormalities in patients with type 2 diabetes. But the bioavailability as well as therapeutic efficacy of this drug is limited to its dissolution behaviour. Here, the authors assess the therapeutic efficacy of a newly formulated solid dispersion of fenofibrate (SDF) having enhanced dissolution profiles in contrast to pure FF using fructose-induced diabetic rat model. METHODS: Fructose-induced diabetic rat model was developed to assess the pharmacological efficacy of the formulated SDF, and the results were compared with the effects of conventional FF therapy. KEY FINDINGS: The 14 days treatment showed better improvement in lipid-lowering potency of SDF than pure FF. SDF containing one-third dose of pure FF showed similar effect in terms of triglyceride, total cholesterol and low-density lipoprotein lowering efficacy, whereas increased high-density lipoprotein at same extent. The similar dose of SDF produced more prominent effect than FF. Histological studies also demonstrated the enhanced lipid clearance from liver by SDF than FF that was concordant with the biochemical results. CONCLUSIONS: This newly formulated SDF would be a promising alternative for conventional fenofibrate in treating hyperlipidaemia.

Identification of infectious disease-associated host genes using machine learning techniques.

BACKGROUND: With the global spread of multidrug resistance in pathogenic microbes, infectious diseases emerge as a key public health concern of the recent time. Identification of host genes associated with infectious diseases will improve our understanding about the mechanisms behind their development and help to identify novel therapeutic targets. RESULTS: We developed a machine learning techniques-based classification approach to identify infectious disease-associated host genes by integrating sequence and protein interaction network features. Among different methods, Deep Neural Networks (DNN) model with 16 selected features for pseudo-amino acid composition (PAAC) and network properties achieved the highest accuracy of 86.33% with sensitivity of 85.61% and specificity of 86.57%. The DNN classifier also attained an accuracy of 83.33% on a blind dataset and a sensitivity of 83.1% on an independent dataset. Furthermore, to predict unknown infectious disease-associated host genes, we applied the proposed DNN model to all reviewed proteins from the database. Seventy-six out of 100 highly-predicted infectious disease-associated genes from our study were also found in experimentally-verified human-pathogen protein-protein interactions (PPIs). Finally, we validated the highly-predicted infectious disease-associated genes by disease and gene ontology enrichment analysis and found that many of them are shared by one or more of the other diseases, such as cancer, metabolic and immune related diseases. CONCLUSIONS: To the best of our knowledge, this is the first computational method to identify infectious disease-associated host genes. The proposed method will help large-scale prediction of host genes associated with infectious-diseases. However, our results indicated that for small datasets, advanced DNN-based method does not offer significant advantage over the simpler supervised machine learning techniques, such as Support Vector Machine (SVM) or Random Forest (RF) for the prediction of infectious disease-associated host genes. Significant overlap of infectious disease with cancer and metabolic disease on disease and gene ontology enrichment analysis suggests that these diseases perturb the functions of the same cellular signaling pathways and may be treated by drugs that tend to reverse these perturbations. Moreover, identification of novel candidate genes associated with infectious diseases would help us to explain disease pathogenesis further and develop novel therapeutics.

An improved method for identification of small non-coding RNAs in bacteria using support vector machine.

Bacterial small non-coding RNAs (sRNAs) are not translated into proteins, but act as functional RNAs. They are involved in diverse biological processes like virulence, stress response and quorum sensing. Several high-throughput techniques have enabled identification of sRNAs in bacteria, but experimental detection remains a challenge and grossly incomplete for most species. Thus, there is a need to develop computational tools to predict bacterial sRNAs. Here, we propose a computational method to identify sRNAs in bacteria using support vector machine (SVM) classifier. The primary sequence and secondary structure features of experimentally-validated sRNAs of Salmonella Typhimurium LT2 (SLT2) was used to build the optimal SVM model. We found that a tri-nucleotide composition feature of sRNAs achieved an accuracy of 88.35% for SLT2. We validated the SVM model also on the experimentally-detected sRNAs of E. coli and Salmonella Typhi. The proposed model had robustly attained an accuracy of 81.25% and 88.82% for E. coli K-12 and S. Typhi Ty2, respectively. We confirmed that this method significantly improved the identification of sRNAs in bacteria. Furthermore, we used a sliding window-based method and identified sRNAs from complete genomes of SLT2, S. Typhi Ty2 and E. coli K-12 with sensitivities of 89.09%, 83.33% and 67.39%, respectively.

Antidiabetic, antihyperlipidemic and antioxidant properties of ethanol extract of Grewia asiatica Linn. bark in alloxan-induced diabetic rats.

BACKGROUND: Inspite of introduction of oral hypoglycemic agents, diabetes and its related complications remains to be a major clinical problem. The aim of this study was to investigate the antidiabetic, antihyperlipidemic and antioxidant activities of Grewia asiatica (Linn) stem bark in alloxan induced diabetic rats. METHODS: Diabetes was induced by a single dose of intraperitoneal injection of alloxan (110 mg/kg) in Norwegian Long Evans rats. Ethanol extract of barks from Grewia asiatica (GAE 200 and 400 mg/kg) and metformin (150 mg/kg) were orally administered once daily for 15 days. Blood glucose levels and body weights of rats were measured on 0, 5, 10 and 15 days of oral treatment. At the end of the experiment the rats were sacrificed and blood sample were collected for the measurement of total cholesterol (TC), triglycerides (TG), very low density lipoproteins (VLDL), low density lipoproteins (LDL), high density lipoproteins (HDL), SGOT and CK-MB. Analysis of liver glycogen content and histopathlogy of pancreas were carried out. In vitro DPPH free radical scavenging activity, total phenolic and total flavonoid content of GAE were also determined. RESULTS: After 15 days of oral administration of GAE at doses of 200 and 400 mg/kg increased survival rate and showed a significant attenuation in blood glucose and lipid profile in diabetic rats. Oral ingestion of GAE significantly reduced the SGOT and CK-MB levels and restored liver glycogen content when compared to diabetic control. The effects of GAE on SGOT, CK-MB and liver glycogen content were dose-dependent. The diabetic rats treated with GAE showed significant improvement in normal cellular population size of islets. Phytochemical screening of GAE revealed the presence of flavonoid, steroid, tannin and phenolic compounds. Total phenolic content was 44.65 ± 3.17 mg of gallic acid equivalent per gm of GAE extract and the total flavonoid content was 39.11 ± 4.65 mg of quercetin equivalent per gm of GAE extract. In DPPH scavenging assay, IC50 values of GAE and ascorbic acid were found 76.45 and 12.50 µg/ml, respectively. CONCLUSION: We demonstrated that ethanol extract of barks from G. asiatica possess glucose, lipid lowering efficacy, restored liver glycogen and protects pancreas from oxidative damage in alloxan-induced diabetic rats. Thus, the results of the present study provide a scientific rationale for the use of G. asiatica in the management of diabetes and its related complications.

Prediction of Intra-Species Protein-Protein Interactions in Enteropathogens Facilitating Systems Biology Study.

Protein-protein interactions in Escherichia coli (E. coli) has been studied extensively using high throughput methods such as tandem affinity purification followed by mass spectrometry and yeast two-hybrid method. This can in turn be used to understand the mechanisms of bacterial cellular processes. However, experimental characterization of such huge amount of interactions data is not available for other important enteropathogens. Here, we propose a support vector machine (SVM)-based prediction model using the known PPIs data of E. coli that can be used to predict PPIs in other enteropathogens, such as Vibrio cholerae, Salmonella Typhi, Shigella flexneri and Yersinia entrocolitica. Different features such as domain-domain association (DDA), network topology, and sequence information were used in developing the SVM model. The proposed model using DDA, degree and amino acid composition features has achieved an accuracy of 82% and 62% on 5-fold cross validation and blind E. coli datasets, respectively. The predicted interactions were validated by Gene Ontology (GO) semantic similarity measure and String PPIs database (experimental PPIs only). Finally, we have developed a user-friendly webserver named EnPPIpred to predict intra-species PPIs in enteropathogens, which will be of great help for the experimental biologists. The webserver EnPPIpred is freely available at http://bicresources.jcbose.ac.in/ssaha4/EnPPIpred/.

Antidiabetic and antioxidant activities of ethanolic extract of Semecarpus anacardium (Linn.) bark.

BACKGROUND: Diabetes mellitus is a global health problem and constantly increasing day by day. The number of diabetic people in world is expected to rise to 366 million in 2030. The available drugs for diabetes, insulin or oral hypoglycemic agents have one or more side effects and search for new antidiabetic drugs with minimal or no side effects from medicinal plants is a challenging for us. The present study was undertaken to investigate the antidiabetic and antioxidant activity of Semecarpus anacardium (Linn.) (abbreviated as SF). METHODS: The antidiabetic activity was determined by using alloxan-induced diabetic rats. After 15 days of treatment, serum biochemical parameters such as TC, TG, LDL, HDL, SGOT and SGPT were estimated. The survival rate, body weight, organ weight, liver glycogen and blood parameters (RBC and Hb) were also measured. The antioxidant activity was measured by DPPH free radical scavenging assay. Phytochemical screening, total phenolic and total flavonoid content were determined by using standard methods. RESULTS: The results showed that the survival rate was 100% in rats of Group SA 400. The effect of extract on blood glucose level in Groups SA 100, SA 200 and SA 400 were dose-dependent throughout the treatment period. No significant changes in organ weight to body weight ratio were observed, liver weights significantly improved in Groups SA 200 and SA 400. The bark extract exhibited significant (p < 0.05) anti-diabetic activity with lowering TC, TG, LDL level dose-dependently and protected liver which may be partially explained by attenuation of SGOT and SGPT levels and increases liver glycogen. The percentage of Hb and RBC counts were negatively correlated with the doses of extracts. In DPPH scavenging assay, IC50 values of SA extract and ascorbic acid were found 72.24 µg/ml and 17.81 µg/ml, respectively. Phytochemical screening showed the presence of steroids, triterpenoids, flavonoids, glycosides, saponins, and tannins that were contribute to biological activity. CONCLUSIONS: These results indicated that stem barks of S. anacardium possess strong anti-diabetic and antioxidant potentials and support traditional medicinal use for the treatment of diabetes mellitus and good source for natural antioxidants.

Prediction of interactions between viral and host proteins using supervised machine learning methods.

BACKGROUND: Viral-host protein-protein interaction plays a vital role in pathogenesis, since it defines viral infection of the host and regulation of the host proteins. Identification of key viral-host protein-protein interactions (PPIs) has great implication for therapeutics. METHODS: In this study, a systematic attempt has been made to predict viral-host PPIs by integrating different features, including domain-domain association, network topology and sequence information using viral-host PPIs from VirusMINT. The three well-known supervised machine learning methods, such as SVM, Naïve Bayes and Random Forest, which are commonly used in the prediction of PPIs, were employed to evaluate the performance measure based on five-fold cross validation techniques. RESULTS: Out of 44 descriptors, best features were found to be domain-domain association and methionine, serine and valine amino acid composition of viral proteins. In this study, SVM-based method achieved better sensitivity of 67% over Naïve Bayes (37.49%) and Random Forest (55.66%). However the specificity of Naïve Bayes was the highest (99.52%) as compared with SVM (74%) and Random Forest (89.08%). Overall, the SVM and Random Forest achieved accuracy of 71% and 72.41%, respectively. The proposed SVM-based method was evaluated on blind dataset and attained a sensitivity of 64%, specificity of 83%, and accuracy of 74%. In addition, unknown potential targets of hepatitis B virus-human and hepatitis E virus-human PPIs have been predicted through proposed SVM model and validated by gene ontology enrichment analysis. Our proposed model shows that, hepatitis B virus "C protein" binds to membrane docking protein, while "X protein" and "P protein" interacts with cell-killing and metabolic process proteins, respectively. CONCLUSION: The proposed method can predict large scale interspecies viral-human PPIs. The nature and function of unknown viral proteins (HBV and HEV), interacting partners of host protein were identified using optimised SVM model.

Dynamic modularity of host protein interaction networks in Salmonella Typhi infection.

BACKGROUND: Salmonella Typhi is a human-restricted pathogen, which causes typhoid fever and remains a global health problem in the developing countries. Although previously reported host expression datasets had identified putative biomarkers and therapeutic targets of typhoid fever, the underlying molecular mechanism of pathogenesis remains incompletely understood. METHODS: We used five gene expression datasets of human peripheral blood from patients suffering from S. Typhi or other bacteremic infections or non-infectious disease like leukemia. The expression datasets were merged into human protein interaction network (PIN) and the expression correlation between the hubs and their interacting proteins was measured by calculating Pearson Correlation Coefficient (PCC) values. The differences in the average PCC for each hub between the disease states and their respective controls were calculated for studied datasets. The individual hubs and their interactors with expression, PCC and average PCC values were treated as dynamic subnetworks. The hubs that showed unique trends of alterations specific to S. Typhi infection were identified. RESULTS: We identified S. Typhi infection-specific dynamic subnetworks of the host, which involve 81 hubs and 1343 interactions. The major enriched GO biological process terms in the identified subnetworks were regulation of apoptosis and biological adhesions, while the enriched pathways include cytokine signalling in the immune system and downstream TCR signalling. The dynamic nature of the hubs CCR1, IRS2 and PRKCA with their interactors was studied in detail. The difference in the dynamics of the subnetworks specific to S. Typhi infection suggests a potential molecular model of typhoid fever. CONCLUSIONS: Hubs and their interactors of the S. Typhi infection-specific dynamic subnetworks carrying distinct PCC values compared with the non-typhoid and other disease conditions reveal new insight into the pathogenesis of S. Typhi.

Development of highly stable nifedipine solid-lipid nanoparticles.

To improve the solubility of the drug nifedipine (NI), highly stabilized solid-lipid nanoparticles (SLNs) of nifedipine (NI-SLNs) were prepared by high pressure homogenization using two phospholipids, followed by lyophilization with individual sugar moieties (four monosaccharides and four disaccharides). The mean particle diameter, polydispersity index (PDI), zeta potential, drug loading, and the encapsulation efficiency of the NI-SLN suspension were determined to be 68.5 nm, 0.3, -62.1 mV, 2.7%, and 97.5%, respectively. In comparison with the NI-SLNs, the NI-SLNs lyophilized with trehalose (NI-SLN-Tre) showed a slight increase in the particle size from 68.5 to 107.7 nm, but the PDI decreased from 0.38 to 0.33, and no significant change in zeta potential was observed. Aqueous re-dispersibility study demonstrated that NI-SLNs lyophilized with trehalose had the maximum concentration (14.7 µg/mL) at 5 min, compared with lyophilized SLNs using other sugars; the use of other sugars also resulted in significant changes in the particle size, PDI, and zeta potential. A trehalose concentration of 2.5% w/v and a two-fold dilution of the SLN suspension were found to be the best conditions for lyophilization. Data from lyophilized SLNs using differential scanning calorimetry, powder X-ray diffraction, Fourier-transform infrared spectroscopy, and scanning electron microscopy indicated eventual transformation of NI-SLN-Tre from a crystalline to an amorphous state during the homogenization process. Finally, a stability study was performed with NI-SLN-Tre for up to 6 months at 30°C and 65% relative humidity, with no significant deterioration observed, suggesting that trehalose might be a useful cryoprotectant for NI-SLNs.

Simultaneous high-performance liquid chromatographic determination of atenolol and amlodipine in pharmaceutical-dosage form.

A simple, rapid and precise method is developed for the quantitative simultaneous determination of atenolol and amlodipine in a combined pharmaceutical-dosage form. The method is based on High Performance Liquid Chromatography (HPLC) on a reversed-phase column, shim-pack CLC, ODS (C18), 4.6 mmx25 cm & 0.5 mm, using a mobile phase of ammonium acetate buffer (the pH was adjusted to 4.5+/-0.05 with glacial acetic acid), acetonitrile and methanol (35::30:35 v/v). The buffer used in the mobile phase contains ammonium acetate in double-distilled water. The chromatographic conditions are- flow rate of 1.5 ml/min, column temperature at 40 degrees C and detector wavelength of 237 nm. Both the drugs were well resolved on the stationary phase and the retention times were around 1.5 minute for atenolol and 3.4 minute for amlodipine. The method was validated and shown to be linear for atenolol and amlodipine. The correlation coefficients for atenolol and amlodipine are 0.999963 and 0.999979, respectively. The relative standard deviations for six replicate measurements in two sets of each drug in the tablets is always less than 2% and mean % error of active recovery not more than +/-1.5%. The method was validated for precision and accuracy. The proposed method was successfully applied to the pharmaceutical dosage forms containing the above-mentioned drug combination without any interference by the excipients.