Efficient in vitro propagation of Agave parrasana Berger

An efficient method for the in vitro propagation of Agave parrasana Berger, an important ornamental plant species native to the state of Coahuila, México, was developed. Proliferation of good quality shoots was achieved on agar-solidified basal MS medium supplemented with L₂ vitamins and 13.3 μM benzyladenine. Rooting was successful in the basal medium with no growth regulators; however, a light intensity of 100 μmol m^-2 s^-1 was found to promote better rooting than 25 μmol m^-2 s^-1. This revised version was published online in June 2006 with corrections to the Cover Date.     

Traditional fermented beverages from Mexico as a potential probiotic source

Fermentation is one of the oldest ways of processing food. Some fermented food is produced industrially, but can also be produced in an artisanal way by certain ethnic groups, called traditional fermented foods. In Mexico, there are a variety of traditional fermented beverages which are produced in an artisanal way. They include those made with maize (atole agrio, pozol, and tesgüino), fruit (tepache and colonche), and obtained by plant fermentation (pulque, tuba, and taberna). These beverages have been used since ancient times for religious and medicinal purposes. The medicinal effect may be due to fermented microorganisms. The presence of beneficial microorganisms known as probiotics provides beneficial effects to consumer health, improving the balance of intestinal host, and reducing the risk of gastrointestinal diseases, mainly. Most probiotics belong to the genus Lactobacillus, but Bifidobacterium, Bacillus, and yeast are also found. Therefore, it is important that the microbiological diversity of the beverages is studied and documented. This review includes information on the microbial diversity and probiotic potential of the most important traditional fermented beverages from Mexico.     

Enzymatic,immunological and phylogenetic characterization of <Emphasis Type="Italic">Brucella suis</Emphasis> urease

Background  

The sequenced genomes of the Brucella spp. have two urease operons, ure-1 and ure-2, but there is evidence that only one is responsible for encoding an active urease. The present work describes the purification and the enzymatic and phylogenomic characterization of urease from Brucella suis strain 1330. Additionally, the urease reactivity of sera from patients diagnosed with brucellosis was examined.     

Quantitative Proteome-Property Relationships (QPPRs). Part 1: finding biomarkers of organic drugs with mean Markov connectivity indices of spiral networks of blood mass spectra

Numerical parameters of the molecular networks, also referred as Topological Indices or Connectivity Indices (CIs), have been used in Bioorganic and Medicinal Chemistry to find Quantitative Structure-Activity, Property or Toxicity Relationship (QSAR, QSPR and QSTR) models. QSPR models generally use CIs as inputs to predict the biological activity of compounds. However, the literature does not evidence a great effort to find QSAR-like models for other biologically and chemically relevant systems. For instance, blood proteome constitutes a protein-rich information reservoir, since the serum proteome Mass Spectra (MS) represents a potential information source for the early detection of Biomarkers for diseases and/or drug-induced toxicities. The concept of mass spectrum network (MS network) for a single protein is already well-known. However, there are no reported results on the use of CIs for a MS network of a whole proteome to explore MS patterns. In this work, we introduced for the first time a novel network representation and the CIs for the MS of blood proteome samples. The new network bases on Randic's Spiral network have been previously introduced for protein sequences. The new MS CIs, called here Spiral Markov Connectivity (SMC(k)) of the MS Spiral graph can be calculated with the software MARCH-INSIDE, combining network and Markov model theory. The SMC(k) values could be used to seek QSAR-like models, called in this work Quantitative Proteome-Property Relationships (QPPRs). We calculate the SMC(k) values for 62 blood samples and fit a QPPR model by discriminating proteome MS, typical of individuals susceptible to suffer drug-induced cardiotoxicity from control samples. The accuracy, sensitivity, and specificity values of the QPPR model were between 73.08% and 87.5% in training and validation series. This work points to QPPR models as a powerful tool for MS detection of biomarkers in proteomics.     

Intracameral bevacizumab and mitomycin C Trabeculectomy for eyes with neovascular glaucoma: a case series

The purpose of this study was to describe the surgical outcomes and safety of intracameral bevacizumab during trabeculectomy in eyes with neovascular glaucoma. Pilot study included four eyes (four patients) with refractory neovascular glaucoma submitted to fornix-based trabeculectomy with adjunctive use of bevacizumab in the anterior chamber during the procedure. Patients were previously treated with panretinal photocoagulation as standard therapy. Variables evaluated were intraocular pressure, bleb appearance, iris neovascularization, intraoperative/postoperative complications, and visual outcomes. No intraoperative complication was observed. The mean follow-up period was 12.75 (range, 12–15 months). All eyes showed significant intraocular pressure control postoperatively. Iris neovascularization reduced significantly within 1 month after surgery. Mild anterior chamber inflammation was observed during follow-up in all eyes. No significant postoperative complication was observed, and no patient presented visual acuity deterioration. Intracameral bevacizumab may be used as an adjunctive therapy during trabeculectomy in eyes with neovascular glaucoma.     

Unified QSAR approach to antimicrobials. 4. Multi-target QSAR modeling and comparative multi-distance study of the giant components of antiviral drug–drug complex networks

One limitation of almost all antiviral Quantitative Structure–Activity Relationships (QSAR) models is that they predict the biological activity of drugs against only one species of virus. Consequently, the development of multi-tasking QSAR models (mt-QSAR) to predict drugs activity against different species of virus is of the major vitally important. These mt-QSARs offer also a good opportunity to construct drug–drug Complex Networks (CNs) that can be used to explore large and complex drug-viral species databases. It is known that in very large CNs we can use the Giant Component (GC) as a representative sub-set of nodes (drugs) and but the drug–drug similarity function selected may strongly determines the final network obtained. In the three previous works of the present series we reported mt-QSAR models to predict the antimicrobial activity against different fungi [Gonzalez-Diaz, H.; Prado-Prado, F. J.; Santana, L.; Uriarte, E. Bioorg. Med. Chem. 2006, 14, 5973], bacteria [Prado-Prado, F. J.; Gonzalez-Diaz, H.; Santana, L.; Uriarte E. Bioorg. Med. Chem. 2007, 15, 897] or parasite species [Prado-Prado, F.J.; González-Díaz, H.; Martinez de la Vega, O.; Ubeira, F.M.; Chou K.C. Bioorg. Med. Chem. 2008, 16, 5871]. However, including these works, we do not found any report of mt-QSAR models for antivirals drug, or a comparative study of the different GC extracted from drug–drug CNs based on different similarity functions. In this work, we used Linear Discriminant Analysis (LDA) to fit a mt-QSAR model that classify 600 drugs as active or non-active against the 41 different tested species of virus. The model correctly classifies 143 of 169 active compounds (specificity = 84.62%) and 119 of 139 non-active compounds (sensitivity = 85.61%) and presents overall training accuracy of 85.1% (262 of 308 cases). Validation of the model was carried out by means of external predicting series, classifying the model 466 of 514, 90.7% of compounds. In order to illustrate the performance of the model in practice, we develop a virtual screening recognizing the model as active 92.7%, 102 of 110 antivirus compounds. These compounds were never use in training or predicting series. Next, we obtained and compared the topology of the CNs and their respective GCs based on Euclidean, Manhattan, Chebychey, Pearson and other similarity measures. The GC of the Manhattan network showed the more interesting features for drug–drug similarity search. We also give the procedure for the construction of Back-Projection Maps for the contribution of each drug sub-structure to the antiviral activity against different species.     

AdDLP, a bacterial defensin-like peptide, exhibits anti-Plasmodium activity

Antimicrobial defensins with the cysteine-stabilized α-helical and β-sheet (CSαβ) motif are widely distributed in three eukaryotic kingdoms. However, recent work suggests that bacteria could possess defensin-like peptides (DLPs). Here, we report recombinant expression, in vitro folding, structural and functional characterization of a DLP from the myxobacterium Anaeromyxobacter dehalogenans (AdDLP). Circular dichroism analysis indicates that recombinant AdDLP adopts a typical structural feature of eukaryotic defensins, which is also consistent with an ab initio structure model predicted using I-TASSER algorithm. We found that AdDLP is an antimalarial peptide that led to more than 50% growth inhibition on sexual stages of Plasmodium berghei at micromolar concentrations and killed 100% intraerythrocytic Plasmodium falciparum at 10 μM in a time-dependent manner. These results provide functional evidence for myxobacterial origin of eukaryotic defensins. High-level production of the pure anti-Plasmodium peptide without harming mammalian red blood cells in Escherichia coli makes AdDLP an interesting candidate for antimalarial drug design.