In situ spatiotemporal biochemical characterization of the activity of multicellular biofilms under external stimuli remains a significant challenge. Surface-enhanced Raman spectroscopy (SERS), combining the molecular fingerprint specificity of vibrational spectroscopy with the hotspot sensitivity of plasmonic nanostructures, has emerged as a promising noninvasive bioanalysis technique for living systems. However, most SERS devices do not allow reliable long-term spatiotemporal SERS measurements of multicellular systems because of challenges in producing spatially uniform and mechanically stable SERS hotspot arrays to interface large cellular networks. Further, very few studies have been done for multivariable analysis of spatiotemporal SERS datasets to extract spatially and temporally correlated biological information from multicellular systems. Here, we demonstrate the in situ label-free spatiotemporal SERS measurements and multivariate analysis of biofilm development processes and their viral responses to bacteriophage Phi6 infection by employing nanolaminate plasmonic crystal SERS devices to interface mechanically stable, uniform, and spatially dense hotspot arrays with P. syringae biofilms. We exploited unsupervised multivariate machine learning methods to resolve the spatiotemporal evolution and Phi6 dose-dependent changes of major Raman peaks originating from biochemical components in P. syringae biofilms, including cellular components, extracellular polymeric substances (EPS), metabolite molecules, and cell lysate enriched extracellular media. We then employed supervised multivariate analysis for multiclass classification of Phi6 dose-dependent biofilm response, demonstrating the potential for viral infection diagnosis. We envision extending the in situ spatiotemporal SERS method to monitor interactions between other viruses and bacterial networks for applications such as phage-based anti-biofilm therapy development and continuous pathogenic virus detection.

Biological filters in drinking water treatment are populated by highly diverse microbial communities which can contribute to eliminating natural organic and inorganic contaminations. Meanwhile, as a natural harbor of microbes, they significantly affect the bacterial community composition in the post-filtration water and in the distribution system. One of the primary concerns with biofilter-mediated downstream seeding of bacterial communities is the potential for the release of pathogenic microorganisms and particularly pathogens that may be disinfectant resistant. In this study, we evaluated the impact of influent nutrient conditions, particularly inorganic nitrogen species (i.e., reduced, and oxidized forms) on the biofilter community and its seeding potential. The choice of nitrogen, as the driver of biofilter seeding dynamics was driven by preliminary data from a full-scale drinking water treatment and distribution system. Three lab-scale bioreactors were fed with synthetic groundwater amended with low levels of ammonia, nitrate, or ammonia and nitrate for a period of nine months at constant and equal total inorganic nitrogen concentrations. We observed a distinctive biofilm community from the suspended community under different feeding strategies via 16S rRNA sequencing. Analysis of compositional differences suggested ammonia absence played a deterministic role in shaping and mediating biofilm microbial communities whereas planktonic communities were weakly influenced by it. Based on the temporal abundance-occupancy distribution of populations, a core-satellite model allowed us to determine that ammonium limitation can select populations with stronger seeding potential. Overall, our findings suggested that ammonium limitation can significantly shape the biofilter and microbial communities’ post-filtration.

Engineered nanoparticles (NPs), particularly nanoplastics, are emerging environmental contaminant of increasing global concern. Many studies have shown that biofilm, a community of bacteria, interacts with nanoparticles in the environment, thereby influencing the impact, and the broader fate and transport phenomena. However, fundamental understanding is still lacking on the exact mechanisms involved in these actions. The development of nanoparticle -biofilm models to uncover the complexity of nanoparticle diffusion patterns requires a thorough understanding of how the chosen nanoparticles will interact with the biofilm itself, and in particular with the biofilm self-produced extracellular polymeric matrix (EPS). This study explore the effect of biofilm architecture on nanoparticle diffusion in a model biofilm. With advanced analysis of fluorescence microscopy data, including image mean square displacement, 2D paired correlation function and single particle tracking; we characterized the heterogeneous diffusion of 20, 100, and 1000 nm carboxylated-polystyrene nanoparticles in a calcium cross- linked alginate matrix. Our results showed that cross-linking causes structural changes that can restrict and alter the diffusive behavior of NPs, but the significance of the cross-linking effect on NP diffusion depends on NP size. Additionally, the mobility of the nanoparticles through alginate was found to be anisotropic due to the spatial heterogeneities and micro-domains present in the EPS local structure. Our findings suggest that the size of the accessible area for diffusion in relation to the size of the nanoparticles have an important implication for understanding the diffusion patterns of nanoparticles in a heterogeneous biofilm matrix.

Biofilms are the workhorses of many environmental biotechnology systems and the compositions of their bacterial communities affects performance. Those communities are shaped by a shifting balance between deterministic forces (e.g., kinetic advantage) and stochastic forces (e.g., drift). Consequently, it is desirable to understand the nature of that balance, but it is frustratingly difficult to directly experimentally manipulate (rather than control for) random processes driving stochastic assembly.

We were able to manipulate ‘luck’ within an agent-based model of biofilm formation (NUFEB) by selecting seed values controlling random number generation. We identified the worst performing lineages in initial simulations which contained identical, evenly-spaced founders. Those lineages were then assigned altered kinetics, and the simulations were re-run with the same seed values (130680 simulations). In this manner we quantified the kinetic advantage required to overcome drift-driven failure.

Either the maximum specific growth rate or half-saturation constant had to improve by a non-trivial amount (10-20%) to ensure 50-50 odds for a failing lineage to become a thriving one. Further, crowding intensity affected the range of kinetic values over which both drift and selection influenced success, with the greatest range at moderate spacings between founding organisms (5 bacterial diameters).

There were strong interactions between experimental factors, underscoring the complexity of environmental systems. The results of the simulations could not be adequately described by simple linear models but could be reproduced with generalized additive models.

We believe these results can explain, in part, why a wastewater treatment plant undergoing high taxa turnover can retain stable functionality.

Biofilms cause over $3 trillion annually in damage ranging from harboring pathogens, biocorrosion to medical infection. As an alternative to chemical control strategies, germicidal UV-C irradiation using LEDs presents new opportunities for inhibiting biofilm on surfaces. UV-C light launched from LEDs into flexible, thin side emitting optical fibers (SEOFs) can deliver germicidal light to nearly any surface geometry from basins to small diameter tubing. Using fully submerged surfaces in flowing water spiked with Pseudomonas aeruginosa, a SEOF delivered a UV-C gradient to the surface. Biofilm growth over time was monitored in-situ using optical conference tomography. Biofilm formation was inhibited to below 0.05 mm³/mm² everywhere the 275 nm UV-C intensity was above 9 μW/cm². Biofilm samples were collected from multiple regions on the surface: 1) inhibition zone where biofilm formation gets inhibited, 2) low intensity zone where biofilm inhibition failed, 3) control area without UV-C irradiation, and 4) thick biofilm area followed with excessive (400 mJ/cm²) UV-C irradiation. RNA sequencing of these samples observed that excessive UV-C intensities inhibit functional genes expression related to general metabolism, DNA repairing, mobility and biofilm formation compared to dark control. However, with non-lethal UV-C exposure, the SOS response and quorum sensing related genes are upregulated to defend the stress. And the transcriptional responses reveal that Pseudomonas aeruginosa behaves unique survival strategies (e.g., DNA repairing, increasing mobility and biofilm formation) at places where inhibition failed with relatively lower UV-C stress. These results imply the importance of maintaining enough UV-C exposure onto surface for microbial control in many scenarios.