BINDER: computationally inferring a gene regulatory network for Mycobacterium abscessus

Mycobacterium abscessus: insights from a bioinformatic perspective

Mycobacterium abscessus is a nontuberculous mycobacterium, associated with broncho-pulmonary infections in individuals suffering from cystic fibrosis, bronchiectasis, and pulmonary diseases. The risk factors for transmission include biofilms, contaminated water resources, fomites, and infected individuals. M. abscessus is extensively resistant to antibiotics. To date, there is no vaccine and combination antibiotic therapy is followed. However, drug toxicities, low cure rates, and high cost of treatment make it imperfect. Over the last 20 years, bioinformatic studies on M. abscessus have advanced our understanding of the pathogen. This review integrates knowledge from the analysis of genomes, microbiomes, genomic variations, phylogeny, proteome, transcriptome, secretome, antibiotic resistance, and vaccine design to further our understanding. The utility of genome-based studies in comprehending disease progression, surveillance, tracing transmission routes, and epidemiological outbreaks on a global scale has been highlighted. Furthermore, this review underlined the importance of using computational methodologies for pinpointing factors responsible for pathogen survival and resistance. We reiterate the significance of interdisciplinary research to fight M. abscessus. In a nutshell, the outcome of computational studies can go a long way in creating novel therapeutic avenues to control M. abscessus mediated pulmonary infections.

Curation, inference, and assessment of a globally reconstructed gene regulatory network for Streptomyces coelicolor

Streptomyces coelicolor A3(2) is a model microorganism for the study of Streptomycetes, antibiotic production, and secondary metabolism in general. Even though S. coelicolor has an outstanding variety of regulators among bacteria, little effort to globally study its transcription has been made. We manually curated 29 years of literature and databases to assemble a meta-curated experimentally-validated gene regulatory network (GRN) with 5386 genes and 9707 regulatory interactions (~ 41% of the total expected interactions). This provides the most extensive and up-to-date reconstruction available for the regulatory circuitry of this organism. Only ~ 6% (534/9707) are supported by experiments confirming the binding of the transcription factor to the upstream region of the target gene, the so-called “strong” evidence. While for the remaining interactions there is no confirmation of direct binding. To tackle network incompleteness, we performed network inference using several methods (including two proposed here) for motif identification in DNA sequences and GRN inference from transcriptomics. Further, we contrasted the structural properties and functional architecture of the networks to assess the reliability of the predictions, finding the inference from DNA sequence data to be the most trustworthy approach. Finally, we show two applications of the inferred and the curated networks. The inference allowed us to propose novel transcription factors for the key Streptomyces antibiotic regulatory proteins (SARPs). The curated network allowed us to study the conservation of the system-level components between S. coelicolor and Corynebacterium glutamicum. There we identified the basal machinery as the common signature between the two organisms. The curated networks were deposited in Abasy Atlas (https://abasy.ccg.unam.mx/) while the inferences are available as Supplementary Material.

Gene Regulatory Networks of Penicillium echinulatum 2HH and Penicillium oxalicum 114-2 Inferred by a Computational Biology Approach

Penicillium echinulatum 2HH and Penicillium oxalicum 114-2 are well-known cellulase fungal producers. However, few studies addressing global mechanisms for gene regulation of these two important organisms are available so far. A recent finding that the 2HH wild-type is closely related to P. oxalicum leads to a combined study of these two species. Firstly, we provide a global gene regulatory network for P. echinulatum 2HH and P. oxalicum 114-2, based on TF-TG orthology relationships, considering three related species with well-known regulatory interactions combined with TFBSs prediction. The network was then analyzed in terms of topology, identifying TFs as hubs, and modules. Based on this approach, we explore numerous identified modules, such as the expression of cellulolytic and xylanolytic systems, where XlnR plays a key role in positive regulation of the xylanolytic system. It also regulates positively the cellulolytic system by acting indirectly through the cellodextrin induction system. This remarkable finding suggests that the XlnR-dependent cellulolytic and xylanolytic regulatory systems are probably conserved in both P. echinulatum and P. oxalicum. Finally, we explore the functional congruency on the genes clustered in terms of communities, where the genes related to cellular nitrogen, compound metabolic process and macromolecule metabolic process were the most abundant. Therefore, our approach allows us to confer a degree of accuracy regarding the existence of each inferred interaction.