Characterization of two GH5 endoglucanases from termite microbiome using synthetic metagenomics.

Here, we characterize two novel GH5 endoglucanases (GH5CelA and GH5CelB) from an uncultured bacterium identified in termite gut microbiomes. Both genes were codon-optimized, synthetized, cloned, and expressed as recombinant proteins in Escherichia coli for subsequent purification. Both enzymes showed activity on the pNPC and barley ß-glucan substrates, whereas GH5CelB also showed low activity on carboxymethyl cellulose. The optimum conditions for both enzymes were an acid pH (5) and moderate temperature (35 to 50 °C). The enzymes differed in the kinetic profiles and patterns of the generated hydrolysis products. A structural-based modeling analysis indicated that both enzymes possess a typical (ß/α)8-barrel fold characteristic of GH5 family, with some differential features in the active site cleft. Also, GH5CelB presents a putative secondary binding site. Furthermore, adjacent to the active site of GH5CelA and GH5CelB, a whole subdomain rarely found in GH5 family may participate in substrate binding and thermal stability.Therefore, GH5CelA may be a good candidate for the production of cello-oligosaccharides of different degrees of polymerization applicable for feed and food industries, including prebiotics. On the other hand, GH5CelB could be useful in an enzymatic cocktail for the production of lignocellulosic bioethanol, because of the production of glucose as a hydrolysis product. Key Points • Synthetic metagenomics is a powerful approach for discovering novel enzymes. • Two novel GH5 endoglucanases from nonculturable microorganisms were characterized. • Structural differences between them and other GH5 endoglucanases were observed. • The enzymes may be good candidates for feed, food, and/or bioethanol industries.

A Phenotypic Characterization of Two Isolates of a Multidrug-Resistant Outbreak Strain of Mycobacterium tuberculosis with Opposite Epidemiological Fitness.

Tuberculosis (TB) is an infectious disease, caused by Mycobacterium tuberculosis, primarily affecting the lungs. The M. tuberculosis strain of the Haarlem family named M was responsible for a large multidrug-resistant TB (MDR-TB) outbreak in Buenos Aires. This outbreak started in the early 1990s and in the mid 2000s still accounted for 29% of all MDR-TB cases in Argentina. By contrast, a clonal variant of strain M, named 410, has caused a single tuberculosis case since the onset of the outbreak. The molecular bases of the high epidemiological fitness of the M strain remain unclear. To assess its unique molecular properties, herein, we performed a comparative protein and lipid analysis of a representative clone of the M strain (Mp) and the nonprosperous M variant 410. We also evaluated their growth in low pH. The variant 410 had higher levels of latency proteins under standard conditions and delayed growth at low pH, suggesting that it is more sensitive to stress stimuli than Mp. Moreover, Mp showed higher levels of mycolic acids covalently attached to the cell wall and lower accumulation of free mycolic acids in the outer layer than the 410 strain. The low expression of latency proteins together with the reduced content of surface mycolic acids may facilitate Mp to evade the host immune responses.

ERAP1 and PDE8A Are Downregulated in Cattle Protected against Bovine Tuberculosis.

Bovine tuberculosis (bTB) is a zoonotic disease caused by Mycobacterium bovis that is responsible for significant economic losses worldwide. In spite of its relevance, the limited knowledge about the host immune responses that provide effective protection against the disease has long hampered the development of an effective vaccine. The identification of host proteins with an expression that correlates with protection against bTB would contribute to the understanding of the cattle defence mechanisms against M. bovis infection. In this study, we found that ERAP1 and PDE8A were downregulated in vaccinated cattle that were protected from experimental M. bovis challenge. Remarkably, both genes encode proteins that have been negatively associated with immune protection against bTB.

Single nucleotide polymorphisms may explain the contrasting phenotypes of two variants of a multidrug-resistant Mycobacterium tuberculosis strain.

Globally, about 4.5% of new tuberculosis (TB) cases are multi-drug-resistant (MDR), i.e. resistant to the two most powerful first-line anti-TB drugs. Indeed, 480,000 people developed MDR-TB in 2015 and 190,000 people died because of MDR-TB. The MDR Mycobacterium tuberculosis M family, which belongs to the Haarlem lineage, is highly prosperous in Argentina and capable of building up further drug resistance without impairing its ability to spread. In this study, we sequenced the whole genomes of a highly prosperous M-family strain (Mp) and its contemporary variant, strain 410, which produced only one recorded tuberculosis case in the last two decades. Previous reports have demonstrated that Mp induced dysfunctional CD8+ cytotoxic T cell activity, suggesting that this strain has the ability to evade the immune response against M. tuberculosis. Comparative analysis of Mp and 410 genomes revealed non-synonymous polymorphisms in eleven genes and five intergenic regions with polymorphisms between both strains. Some of these genes and promoter regions are involved in the metabolism of cell wall components, others in drug resistance and a SNP in Rv1861, a gene encoding a putative transglycosylase that produces a truncated protein in Mp. The mutation in Rv3787c, a putative S-adenosyl-l-methionine-dependent methyltransferase, is conserved in all of the other prosperous M strains here analysed and absent in non-prosperous M strains. Remarkably, three polymorphic promoter regions displayed differential transcriptional activity between Mp and 410. We speculate that the observed mutations/polymorphisms are associated with the reported higher capacity of Mp for modulating the host's immune response.

Identification of potential biomarkers of disease progression in bovine tuberculosis.

Bovine tuberculosis (bTB) remains an important animal and zoonotic disease in many countries. The diagnosis of bTB is based on tuberculin skin test and IFN-γ release assays (IGRA). Positive animals are separated from the herd and sacrificed. The cost of this procedure is difficult to afford for developing countries with high prevalence of bTB; therefore, the improvement of diagnostic methods and the identification of animals in different stages of the disease will be helpful to control the infection. To identify biomarkers that can discriminate between tuberculin positive cattle with and without tuberculosis lesions (ML+ and ML-, respectively), we assessed a group of immunological parameters with three different classification methods: lineal discriminant analysis (LDA), quadratic discriminant analysis (QDA) and K nearest neighbors (k-nn). For this purpose, we used data from 30 experimentally infected cattle. All the classifiers (LDA, QDA and k-nn) selected IL-2 and IL-17 as the most discriminatory variables. The best classification method was LDA using IL-17 and IL-2 as predictors. The addition of IL-10 to LDA improves the performance of the classifier to discriminate ML-individuals (93.3% vs. 86.7%). Thus, the expression of IL-17, IL-2 and, in some cases, IL-10 would serve as an additional tool to study disease progression in herds with a history of bTB.

Improvement of a two-stage fermentation process for docosahexaenoic acid production by Aurantiochytrium limacinum SR21 applying statistical experimental designs and data analysis.

Statistical screening experimental designs were applied to identify the significant culture variables for biomass production of Aurantiochytrium limacinum SR21 and their optimal levels were found using a combination of Artificial Neural Networks, genetic algorithms and graphical analysis. The biomass value obtained (40.3g cell dry weight l(-1)) employing the selected culture conditions agreed with that predicted by the model. Subsequently, two significant culture conditions for docosahexaenoic acid (DHA) production were determined, finding that an inoculum of 10% (v/v), obtained from the previous (statistically optimized) stage, should be used in a DHA production medium having a molar C:N ratio of 55:1, to reach a production of 7.8 g DHA l(-1) d(-1). The production step was thereafter scaled in a 3.5l bioreactor, and DHA productivity of 3.7 g l(-1) d(-1) was obtained. This two-stage strategy: statistically optimized inoculum production (fist step) and a DHA production step, is presented for the first time to optimize a bioprocess conducive to the obtention of microbial DHA.

A simulation study comparing the impact of experimental error on the performance of experimental designs and artificial neural networks used for process screening.

Many variables and their interactions can affect a biotechnological process. Testing a large number of variables and all their possible interactions is a cumbersome task and its cost can be prohibitive. Several screening strategies, with a relatively low number of experiments, can be used to find which variables have the largest impact on the process and estimate the magnitude of their effect. One approach for process screening is the use of experimental designs, among which fractional factorial and Plackett-Burman designs are frequent choices. Other screening strategies involve the use of artificial neural networks (ANNs). The advantage of ANNs is that they have fewer assumptions than experimental designs, but they render black-box models (i.e., little information can be extracted about the process mechanics). In this paper, we simulate a biotechnological process (fed-batch growth of baker's yeast) to analyze and compare the effect of random experimental errors of different magnitudes and statistical distributions on experimental designs and ANNs. Except for the situation in which the error has a normal distribution and the standard deviation is constant, it was not possible to determine a clear-cut rule for favoring one screening strategy over the other. Instead, we found that the data can be better analyzed using both strategies simultaneously.