Streptomyces natalensis programmed cell death and morphological differentiation are dependenton oxidative stress

Streptomyces are aerobic Gram-positive bacteria characterized by a complex life cycle that includes hyphae differentiation and spore formation. Morphological differentiation is triggered by stressful conditions and takes place in a pro-oxidant environment, which sets the basis for an involvement of the oxidative stress response in this cellular process. Characterization of the phenotypic traits of Streptomycesnatalensis ΔkatA1 (mono-functional catalase) and ΔcatR (Fur-like repressor of katA1 expression) strains in solid medium revealed that both mutants had an impaired morphological development process. The sub-lethal oxidative stress caused by the absence of KatA1 resulted in the formation of a highly proliferative and undifferentiated vegetative mycelium, whereas de-repression of CatR regulon, from which KatA1 is the only known representative, resulted in the formation of scarce aerial mycelium. Both mutant strains had the transcription of genes associated with aerial mycelium formation and biosynthesis of the hyphae hydrophobic layer down-regulated. The first round of the programmed cell death (PCD) was inhibited in both strains which caused the prevalence of the transient primary mycelium (MI) over secondary mycelium (MII). Our data shows that the first round of PCD and morphological differentiation in S. natalensis is dependent on oxidative stress in the right amount at the right time.This work was funded by: "NORTE-07-0124-FEDER-000003 - Cell homeostasis tissue organization and organism biology" project co-funded by FEDER funds through the Operational North Region Programme (ON.2 - O Novo Norte) under National Strategic Reference Framework (QREN) and by National funds through FCT - Fundacao para a Ciencia e Tecnologia/MEC - Ministerio da Educacao e Ciencia and when applicable co-funded by FEDER funds within the partnership agreement PT2020 related with the research unit number 4293. TB was supported by a post-doctoral fellowship under the PEst-C/SAU/LA0002/2013 (FCOMP-01-0124-FEDER-037277) project; PO, MVM and SDSP were supported by the FCT fellowships SFRH/BPD/74894/2010, SFRH/BPD/95683/2013 and SFRH/BD/66367/2009, respectively. We thank Rui Fernandes, Hugo Osorio and Catarina Santos for excellent technical assistance in the preparation of samples for confocal microscopy, protein identification and in silico analysis of the S. natalensis genome, respectively

Natural computation meta-heuristics for the in silico optimization of microbial strains

<p>Abstract</p> <p>Background</p> <p>One of the greatest challenges in Metabolic Engineering is to develop quantitative models and algorithms to identify a set of genetic manipulations that will result in a microbial strain with a desirable metabolic phenotype which typically means having a high yield/productivity. This challenge is not only due to the inherent complexity of the metabolic and regulatory networks, but also to the lack of appropriate modelling and optimization tools. To this end, Evolutionary Algorithms (EAs) have been proposed for <it>in silico </it>metabolic engineering, for example, to identify sets of gene deletions towards maximization of a desired physiological objective function. In this approach, each mutant strain is evaluated by resorting to the simulation of its phenotype using the Flux-Balance Analysis (FBA) approach, together with the premise that microorganisms have maximized their growth along natural evolution.</p> <p>Results</p> <p>This work reports on improved EAs, as well as novel Simulated Annealing (SA) algorithms to address the task of <it>in silico </it>metabolic engineering. Both approaches use a variable size set-based representation, thereby allowing the automatic finding of the best number of gene deletions necessary for achieving a given productivity goal. The work presents extensive computational experiments, involving four case studies that consider the production of succinic and lactic acid as the targets, by using <it>S. cerevisiae </it>and <it>E. coli </it>as model organisms. The proposed algorithms are able to reach optimal/near-optimal solutions regarding the production of the desired compounds and presenting low variability among the several runs.</p> <p>Conclusion</p> <p>The results show that the proposed SA and EA both perform well in the optimization task. A comparison between them is favourable to the SA in terms of consistency in obtaining optimal solutions and faster convergence. In both cases, the use of variable size representations allows the automatic discovery of the approximate number of gene deletions, without compromising the optimality of the solutions.</p

A miRNA-Target Prediction Case Study

Giansanti, V., Castelli, M., Beretta, S., & Merelli, I. (2019). Comparing Deep and Machine Learning Approaches in Bioinformatics: A miRNA-Target Prediction Case Study. In V. V. Krzhizhanovskaya, M. H. Lees, P. M. A. Sloot, J. J. Dongarra, J. M. F. Rodrigues, P. J. S. Cardoso, J. Monteiro, ... R. Lam (Eds.), Computational Science – ICCS 2019: 19th International Conference, Faro, Portugal, June 12–14, 2019, Proceedings, Part III (pp. 31-44). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11538 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-22744-9_3MicroRNAs (miRNAs) are small non-coding RNAs with a key role in the post-transcriptional gene expression regularization, thanks to their ability to link with the target mRNA through the complementary base pairing mechanism. Given their role, it is important to identify their targets and, to this purpose, different tools were proposed to solve this problem. However, their results can be very different, so the community is now moving toward the deployment of integration tools, which should be able to perform better than the single ones. As Machine and Deep Learning algorithms are now in their popular years, we developed different classifiers from both areas to verify their ability to recognize possible miRNA-mRNA interactions and evaluated their performance, showing the potentialities and the limits that those algorithms have in this field. Here, we apply two deep learning classifiers and three different machine learning models to two different miRNA-mRNA datasets, of predictions from 3 different tools: TargetScan, miRanda, and RNAhybrid. Although an experimental validation of the results is needed to better confirm the predictions, deep learning techniques achieved the best performance when the evaluation scores are taken into account.authorsversionpublishe

To be or not to be a pseudogene: a molecular epidemiological approach to the mclx genes and its impact in tuberculosis

Tuberculosis presents a myriad of symptoms, progression routes and propagation patterns not yet fully understood. Whereas for a long time research has focused solely on the patient immunity and overall susceptibility, it is nowadays widely accepted that the genetic diversity of its causative agent, Mycobacterium tuberculosis, plays a key role in this dynamic. This study focuses on a particular family of genes, the mclxs (Mycobacterium cyclase/LuxR-like genes), which codify for a particular and nearly mycobacterial-exclusive combination of protein domains. mclxs genes were found to be pseudogenized by frameshift-causing insertion(s)/deletion(s) in a considerable number of M. tuberculosis complex strains and clinical isolates. To discern the functional implications of the pseudogenization, we have analysed the pattern of frameshift-causing mutations in a group of M. tuberculosis isolates while taking into account their microbial-, patient- and disease-related traits. Our logistic regression-based analyses have revealed disparate effects associated with the transcriptional inactivation of two mclx genes. In fact, mclx2 (Rv1358) pseudogenization appears to be primarily driven by the microbial phylogenetic background, being mainly related to the Euro-American (EAm) lineage; on the other hand, mclx3 (Rv2488c) presents a higher tendency for pseudogenization among isolates from patients born on the Western Pacific area, and from isolates causing extra-pulmonary infections. These results contribute to the overall knowledge on the biology of M. tuberculosis infection, whereas at the same time launch the necessary basis for the functional assessment of these so far overlooked genes.This work was supported by Fundacao para a Ciencia e Tecnologia (FCT), Portugal, and cofunded by Programa Operacional Regional do Norte (ON.2-O Novo Norte), Quadro de Referencia Estrategico Nacional (QREN), through the Fundo Europeu de Desenvolvimento Regional (FEDER), and from Projeto Estrategico - LA 26 - 2013-2014 (PEst-C/SAU/LA0026/2013). H.N.-G. received a personal FCT Grant (SFRH/BD/33902/2209). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Correlation of exhaled breath temperature with bronchial blood flow in asthma

In asthma elevated rates of exhaled breath temperature changes (Δe°T) and bronchial blood flow (Q(aw)) may be due to increased vascularity of the airway mucosa as a result of inflammation. We investigated the relationship of Δe°T with Q(aw )and airway inflammation as assessed by exhaled nitric oxide (NO). We also studied the anti-inflammatory and vasoactive effects of inhaled corticosteroid and β(2)-agonist. Δe°T was confirmed to be elevated (7.27 ± 0.6 Δ°C/s) in 19 asthmatic subjects (mean age ± SEM, 40 ± 6 yr; 6 male, FEV(1 )74 ± 6 % predicted) compared to 16 normal volunteers (4.23 ± 0.41 Δ°C/s, p < 0.01) (30 ± 2 yr) and was significantly increased after salbutamol inhalation in normal subjects (7.8 ± 0.6 Δ°C/ s, p < 0.05) but not in asthmatic patients. Q(aw), measured using an acetylene dilution method was also elevated in patients with asthma compared to normal subjects (49.47 ± 2.06 and 31.56 ± 1.6 μl/ml/min p < 0.01) and correlated with exhaled NO (r = 0.57, p < 0.05) and Δe°T (r = 0.525, p < 0.05). In asthma patients, Q(aw )was reduced 30 minutes after the inhalation of budesonide 400 μg (21.0 ± 2.3 μl/ml/min, p < 0.05) but was not affected by salbutamol. Δe°T correlates with Q(aw )and exhaled NO in asthmatic patients and therefore may reflect airway inflammation, as confirmed by the rapid response to steroids