Combining ANOVA-PCA with POCHEMON to analyse micro-organism development in a polymicrobial environment

Arctangent normalization and principal-component analyses merge method to classify characteristics utilizing time-dependent material data

We propose a technique for classifying paints with time-dependent properties using a new method of merging principal-component analyses (the "PCA-merge" method) that utilizes shifting of the barycenter of the PCA score plot. To understand the molecular structure, elemental concentrations, and the concentrations in the evolved gaseous component of various paints, we performed comprehensive characterizations using Fourier transform infrared spectroscopy, inductively coupled plasma mass spectrometry, and head-space-gas chromatograph/mass spectrometry while drying the paint films for 1-48 h. As various detected intensity- and time-axis variables have different dimensions that cannot be handled equally, we normalized those data as an angle parameter (θ) using arctangent to reduce the influence of high/low intensity data and the various analytical instrument. We could classify the paints into suitable categories by applying multivariate analysis to this arctangent-normalized data set. In addition, we developed a new PCA-merge method to analyze data groups that include different time components. This method merges the PCA data groups of each time-component axis into that of specific-component axes and distinguishes each sample by utilizing the shift in the barycenter of the PCA score plot. The proposed method enables the simultaneous utilization of various data groups that contain information about static and dynamic properties. This provides further insight into the characteristics of the paint materials via shifts in the barycenter of the PCA scores without requiring numerous peak identifications.

Computation-aided studies related to the induction of specialized metabolite biosynthesis in microbial co-cultures: An introductory overview

Co-cultivation is an effective method of inducing the production of specialized metabolites (SMs) in microbial strains. By mimicking the ecological interactions that take place in natural environment, this approach enables to trigger the biosynthesis of molecules which are not formed under monoculture conditions. Importantly, microbial co-cultivation may lead to the discovery of novel chemical entities of pharmaceutical interest. The experimental efforts aimed at the induction of SMs are greatly facilitated by computational techniques. The aim of this overview is to highlight the relevance of computational methods for the investigation of SM induction via microbial co-cultivation. The concepts related to the induction of SMs in microbial co-cultures are briefly introduced by addressing four areas associated with the SM induction workflows, namely the detection of SMs formed exclusively under co-culture conditions, the annotation of induced SMs, the identification of SM producer strains, and the optimization of fermentation conditions. The computational infrastructure associated with these areas, including the tools of multivariate data analysis, molecular networking, genome mining and mathematical optimization, is discussed in relation to the experimental results described in recent literature. The perspective on the future developments in the field, mainly in relation to the microbiome-related research, is also provided.

Effects of the Coculture Initiation Method on the Production of Secondary Metabolites in Bioreactor Cocultures of Penicillium rubens and Streptomyces rimosus

Bioreactor cocultures involving Penicillium rubens and Streptomyces rimosus were investigated with regard to secondary metabolite production, morphological development, dissolved oxygen levels, and carbon substrate utilization. The production profiles of 22 secondary metabolites were analyzed, including penicillin G and oxytetracycline. Three inoculation approaches were tested, i.e., the simultaneous inoculation of P. rubens with S. rimosus and the inoculation of S. rimosus delayed by 24 or 48 h relative to P. rubens. The delayed inoculation of S. rimosus into the P. rubens culture did not prevent the actinomycete from proliferating and displaying its biosynthetic repertoire. Although a period of prolonged adaptation was needed, S. rimosus exhibited growth and the production of secondary metabolites regardless of the chosen delay period (24 or 48 h). This promising method of coculture initiation resulted in increased levels of metabolites tentatively identified as rimocidin B, 2-methylthio-cis-zeatin, chrysogine, benzylpenicilloic acid, and preaustinoid D relative to the values recorded for the monocultures. This study demonstrates the usefulness of the delayed inoculation approach in uncovering the metabolic landscape of filamentous microorganisms and altering the levels of secondary metabolites.

Expanding the chemical diversity through microorganisms co-culture: Current status and outlook

Natural products (NPs) are considered as a cornerstone for the generation of bioactive leads in drug discovery programs. However, one of the major limitations of NP drug discovery program is "rediscovery" of known compounds, thereby hindering the rate of drug discovery efficiency. Therefore, in recent years, to overcome these limitations, a great deal of attention has been drawn towards understanding the role of microorganisms' co-culture in inducing novel chemical entities. Such induction could be related to activation of genes which might be silent or expressed at very low levels (below detection limit) in pure-strain cultures under normal laboratory conditions. In this review, chemical diversity of compounds isolated from microbial co-cultures, is discussed. For this purpose, chemodiversity has been represented as a chemical-structure network based on the "Tanimoto Structural Similarity Index". This highlights the huge structural diversity induced by microbial co-culture. In addition, the current trends in microbial co-culture research are highlighted. Finally, the current challenges (1 - induction monitoring, 2 - reproducibility, 3 - growth time effect and 4 - up-scaling for isolation purposes) are discussed. The information in this review will support researchers to design microbial co-culture strategies for future research efforts. In addition, guidelines for co-culture induction reporting are also provided to strengthen future reporting in this NP field.