Antibacterial activity of green tea (Camellia sinensis) extracts against various bacteria isolated from environmental sources

Tea is cultivated in many countries of the world. India is largest tea (black tea) producer in world followed by Japan (green tea) and China. In the present study Camellia assamica (Green tea) leaves extracts were tested for antibacterial activity against various bacteria isolated from environmental sources. Different bacteria were isolated from sewage samples collected from different places at Solan Himachal Pradesh. Isolated bacteria were identified by Gram staining and biochemical tests. A total of six bacteria were identified at Department of Microbiology at SILB Solan (H.P) Green tea leaves extracts were tested for antibacterial activity. Tea leaves were collected from Palampur, Himachal Pradesh. Three different extracts were prepared by using standardized protocols. All the extracts were tested for antibacterial activity by disc diffusion method. Antibacterial assay was performed at 10µl, 20µl, and 30µl concentrations. Significant antibacterial activity was reported for all extracts with results. Aqueous extracts has shown little antibacterial activity against six bacteria isolated. Maximum antibacterial activity was found in methanolic extracts. Our study reflects the chemotherapeutic use of green tea.  Â

A prospective randomized study to compare dexmedetomidine and dexamethasone as an adjunct to bupivacaine in transversus abdominis plane block for post-operative analgesia in caesarean delivery

Background: Caesarean section is most frequently performed surgery worldwide. Patients experience moderate to severe pain in the first 48 hours post-operatively. Aim of this study was to evaluate the efficacy of dexmedetomidine and dexamethasone as an adjunct to bupivacaine in ultrasound guided TAP block for postoperative analgesia in patients of caesarean section.Methods: A total 120 ASA I and II patients undergoing elective and emergency caesarean section under subarachnoid block were randomly divided into three groups B, BDM, BDX to receive bupivacaine alone or dexmedetomidine or dexamethasone as an adjunct to bupivacaine in ultrasound guided TAP block. Postoperatively, the patients were evaluated for pain level at rest and on movement with a 10 cm visual analog scale (VAS) pain score (0 = no pain and 10 = worst pain), time to demand of first analgesic request, number of analgesic requirements, nausea or vomiting, sedation and patient satisfaction at 0 hours and at 2, 4, 6, 12, 18, and 24 hours.Results: VAS score was significantly higher in group B in comparison to BDM and BDX, and higher in BDX in comparison to group BDM. Mean duration of analgesia was significantly higher in group BDM in comparison to group B and BDX. Total number of rescue analgesic demands were significantly lower in group BDM in comparison to group B and BDX. Sedation score and satisfaction score was higher in group BDM as compared to group B and BDX.Conclusions: Addition of dexmedetomidine and dexamethasone as an adjunct to bupivacaine reduces postoperative pain, prolongs analgesia, decreases demand for additional analgesics and provides better maternal satisfaction as compared to plain bupivacaine group in TAP block in patients undergoing caesarean section under subarachnoid block. Among dexmedetomidine and dexamethasone, dexmedetomidine had prolonged analgesia as compared to dexamethasone group

Integration of text mining and biological network analysis: Identification of essential genes in sulfate-reducing bacteria

The growth and survival of an organism in a particular environment is highly depends on the certain indispensable genes, termed as essential genes. Sulfate-reducing bacteria (SRB) are obligate anaerobes which thrives on sulfate reduction for its energy requirements. The present study used Oleidesulfovibrio alaskensis G20 (OA G20) as a model SRB to categorize the essential genes based on their key metabolic pathways. Herein, we reported a feedback loop framework for gene of interest discovery, from bio-problem to gene set of interest, leveraging expert annotation with computational prediction. Defined bio-problem was applied to retrieve the genes of SRB from literature databases (PubMed, and PubMed Central) and annotated them to the genome of OA G20. Retrieved gene list was further used to enrich protein–protein interaction and was corroborated to the pangenome analysis, to categorize the enriched gene sets and the respective pathways under essential and non-essential. Interestingly, the sat gene (dde_2265) from the sulfur metabolism was the bridging gene between all the enriched pathways. Gene clusters involved in essential pathways were linked with the genes from seleno-compound metabolism, amino acid metabolism, secondary metabolite synthesis, and cofactor biosynthesis. Furthermore, pangenome analysis demonstrated the gene distribution, where 69.83% of the 116 enriched genes were mapped under “persistent,” inferring the essentiality of these genes. Likewise, 21.55% of the enriched genes, which involves specially the formate dehydrogenases and metallic hydrogenases, appeared under “shell.” Our methodology suggested that semi-automated text mining and network analysis may play a crucial role in deciphering the previously unexplored genes and key mechanisms which can help to generate a baseline prior to perform any experimental studies

Identification of AHL Synthase in <i>Desulfovibrio vulgaris</i> Hildenborough Using an In-Silico Methodology

Sulfate-reducing bacteria (SRB) are anaerobic bacteria that form biofilm and induce corrosion on various material surfaces. The quorum sensing (QS) system that employs acyl homoserine lactone (AHL)-type QS molecules primarily govern biofilm formation. Studies on SRB have reported the presence of AHL, but no AHL synthase have been annotated in SRB so far. In this computational study, we used a combination of data mining, multiple sequence alignment (MSA), homology modeling and docking to decode a putative AHL synthase in the model SRB, Desulfovibrio vulgaris Hildenborough (DvH). Through data mining, we shortlisted 111 AHL synthase genes. Conserved domain analysis of 111 AHL synthase genes generated a consensus sequence. Subsequent MSA of the consensus sequence with DvH genome indicated that DVU_2486 (previously uncharacterized protein from acetyltransferase family) is the gene encoding for AHL synthase. Homology modeling revealed the existence of seven α-helices and six β sheets in the DvH AHL synthase. The amalgamated study of hydrophobicity, binding energy, and tunnels and cavities revealed that Leu99, Trp104, Arg139, Trp97, and Tyr36 are the crucial amino acids that govern the catalytic center of this putative synthase. Identifying AHL synthase in DvH would provide more comprehensive knowledge on QS mechanism and help design strategies to control biofilm formation

Identification of AHL Synthase in Desulfovibrio vulgaris Hildenborough Using an In-Silico Methodology

Sulfate-reducing bacteria (SRB) are anaerobic bacteria that form biofilm and induce corrosion on various material surfaces. The quorum sensing (QS) system that employs acyl homoserine lactone (AHL)-type QS molecules primarily govern biofilm formation. Studies on SRB have reported the presence of AHL, but no AHL synthase have been annotated in SRB so far. In this computational study, we used a combination of data mining, multiple sequence alignment (MSA), homology modeling and docking to decode a putative AHL synthase in the model SRB, Desulfovibrio vulgaris Hildenborough (DvH). Through data mining, we shortlisted 111 AHL synthase genes. Conserved domain analysis of 111 AHL synthase genes generated a consensus sequence. Subsequent MSA of the consensus sequence with DvH genome indicated that DVU_2486 (previously uncharacterized protein from acetyltransferase family) is the gene encoding for AHL synthase. Homology modeling revealed the existence of seven &alpha;-helices and six &beta; sheets in the DvH AHL synthase. The amalgamated study of hydrophobicity, binding energy, and tunnels and cavities revealed that Leu99, Trp104, Arg139, Trp97, and Tyr36 are the crucial amino acids that govern the catalytic center of this putative synthase. Identifying AHL synthase in DvH would provide more comprehensive knowledge on QS mechanism and help design strategies to control biofilm formation

Transcriptomics and Functional Analysis of Copper Stress Response in the Sulfate-Reducing Bacterium Desulfovibrio alaskensis G20

Copper (Cu) is an essential micronutrient required as a co-factor in the catalytic center of many enzymes. However, excess Cu can generate pleiotropic effects in the microbial cell. In addition, leaching of Cu from pipelines results in elevated Cu concentration in the environment, which is of public health concern. Sulfate-reducing bacteria (SRB) have been demonstrated to grow in toxic levels of Cu. However, reports on Cu toxicity towards SRB have primarily focused on the degree of toxicity and subsequent elimination. Here, Cu(II) stress-related effects on a model SRB, Desulfovibrio alaskensis G20, is reported. Cu(II) stress effects were assessed as alterations in the transcriptome through RNA-Seq at varying Cu(II) concentrations (5 &micro;M and 15 &micro;M). In the pairwise comparison of control vs. 5 &micro;M Cu(II), 61.43% of genes were downregulated, and 38.57% were upregulated. In control vs. 15 &micro;M Cu(II), 49.51% of genes were downregulated, and 50.5% were upregulated. The results indicated that the expression of inorganic ion transporters and translation machinery was massively modulated. Moreover, changes in the expression of critical biological processes such as DNA transcription and signal transduction were observed at high Cu(II) concentrations. These results will help us better understand the Cu(II) stress-response mechanism and provide avenues for future research

Text-Mining to Identify Gene Sets Involved in Biocorrosion by Sulfate-Reducing Bacteria: A Semi-Automated Workflow

A significant amount of literature is available on biocorrosion, which makes manual extraction of crucial information such as genes and proteins a laborious task. Despite the fast growth of biology related corrosion studies, there is a limited number of gene collections relating to the corrosion process (biocorrosion). Text mining offers a potential solution by automatically extracting the essential information from unstructured text. We present a text mining workflow that extracts biocorrosion associated genes/proteins in sulfate-reducing bacteria (SRB) from literature databases (e.g., PubMed and PMC). This semi-automatic workflow is built with the Named Entity Recognition (NER) method and Convolutional Neural Network (CNN) model. With PubMed and PMCID as inputs, the workflow identified 227 genes belonging to several Desulfovibrio species. To validate their functions, Gene Ontology (GO) enrichment and biological network analysis was performed using UniprotKB and STRING-DB, respectively. The GO analysis showed that metal ion binding, sulfur binding, and electron transport were among the principal molecular functions. Furthermore, the biological network analysis generated three interlinked clusters containing genes involved in metal ion binding, cellular respiration, and electron transfer, which suggests the involvement of the extracted gene set in biocorrosion. Finally, the dataset was validated through manual curation, yielding a similar set of genes as our workflow; among these, hysB and hydA, and sat and dsrB were identified as the metal ion binding and sulfur metabolism genes, respectively. The identified genes were mapped with the pangenome of 63 SRB genomes that yielded the distribution of these genes across 63 SRB based on the amino acid sequence similarity and were further categorized as core and accessory gene families. SRB’s role in biocorrosion involves the transfer of electrons from the metal surface via a hydrogen medium to the sulfate reduction pathway. Therefore, genes encoding hydrogenases and cytochromes might be participating in removing hydrogen from the metals through electron transfer. Moreover, the production of corrosive sulfide from the sulfur metabolism indirectly contributes to the localized pitting of the metals. After the corroboration of text mining results with SRB biocorrosion mechanisms, we suggest that the text mining framework could be utilized for genes/proteins extraction and significantly reduce the manual curation time