Multi-omics tools for studying microbial biofilms: current perspectives and future directions

The advent of omics technologies has greatly improved our understanding of microbial biology, particularly in the last two decades. The field of microbial biofilms is, however, relatively new, consolidated in the 1980s. The morphogenic switching by microbes from planktonic to biofilm phenotype confers numerous survival advantages such as resistance to desiccation, antibiotics, biocides, ultraviolet radiation, and host immune responses, thereby complicating treatment strategies for pathogenic microorganisms. Hence, understanding the mechanisms governing the biofilm phenotype can result in efficient treatment strategies directed specifically against molecular markers mediating this process. The application of omics technologies for studying microbial biofilms is relatively less explored and holds great promise in furthering our understanding of biofilm biology. In this review, we provide an overview of the application of omics tools such as transcriptomics, proteomics, and metabolomics as well as multi-omics approaches for studying microbial biofilms in the current literature. We also highlight how the use of omics tools directed at various stages of the biological information flow, from genes to metabolites, can be integrated via multi-omics platforms to provide a holistic view of biofilm biology. Following this, we propose a future artificial intelligence-based multi-omics platform that can predict the pathways associated with different biofilm phenotypes.

Combination of synchrotron radiation-based Fourier transforms infrared microspectroscopy and confocal laser scanning microscopy to understand spatial heterogeneity in aquatic multispecies biofilms

Understanding the spatial heterogeneity within environmental biofilms can provide an insight into compartmentalization of different functions in biofilm communities. We used a non-destructive and label-free method by combining Synchrotron Radiation-based Fourier Transform Infrared Microspectroscopy (SR-FTIR) with Confocal Laser Scanning Microscopy (CLSM) to distinguish the spatial chemical changes within multispecies biofilms grown from natural storm waters in flow cells. Among the different surfaces tested for biofilm growth and optimal imaging, mylar membranes were most suited and it enabled successful spatial infrared imaging of natural biofilms for obtaining reliable and interpretable FTIR spectra. Time series analysis of biofilm growth showed that influx of water during biofilm growth, results in significant changes in biofilm formation. Early biofilms showed active nutrient acquisition and desiccation tolerance mechanisms corresponding with accumulation of secreted proteins. Statistical approach used for the evaluation of chemical spectra allowed for clustering and classification of various regions of the biofilm. Microheterogeneity was observed in the polymeric components of the biofilm matrix, including cellulose, glycocalyx and dextran-like molecules. Fructan and glycan-rich regions were distinguishable and glycocalyx was abundant in the strongly adhering peripheral regions of biofilms. Inner core showed coexistence of oxygen dimers and ferrihydrite that will likely support growth of Fe (II)-oxidising bacteria. The combined SR-FTIR microspectroscopy and CSLM approach for complex natural biofilms described here will be useful both in understanding heterogeneity of matrix components and in correlating functions of juxtaposed microbial species in complex natural biofilms with physicochemical microenvironment to which they are exposed.

Plant metabolomics: from experimental design to knowledge extraction

Metabolomics is one of the most recent additions to the functional genomics approaches. It involves the use of analytical chemistry techniques to provide high-density data of metabolic profiles. Data is then analyzed using advanced statistics and databases to extract biological information, thus providing the metabolic phenotype of an organism. Large variety of metabolites produced by plants through the complex metabolic networks and their dynamic changes in response to various perturbations can be studied using metabolomics. Here, we describe the basic features of plant metabolic diversity and analytical methods to describe this diversity, which includes experimental workflows starting from experimental design, sample preparation, hardware and software choices, combined with knowledge extraction methods. Finally, we describe a scenario for using these workflows to identify differential metabolites and their pathways from complex biological samples.