Reproducible 3D bioprinting of <i>Streptococcus mutans</i> to create model oral biofilms
Bibliographic record
Abstract
ABSTRACT Novel approaches are needed to study relationships between oral biofilm strains, enable three-dimensional oral biofilm deposition, and hasten the rigor and pace of basic and translational biofilm studies. Previously, 3D-bioprinters were leveraged to deposit spatially patterned biofilms onto sugar-rich agar surfaces to study how the underlying spatial organization of various microbes impacts biofilm persistence and virulence. Herein, we have developed a new method to adapt this process from limited, soft agar surfaces to biomimetic solid substrates submerged in aqueous solutions for studying oral biofilms in vitro. Streptococcus mutans UA159 was used to compare standard in vitro biofilm development with our new 3D-printed bio-ink hydrogels on hydroxyapatite disks, which mimic tooth surfaces. Biofilms formed using the bio-ink methodology showed minimal quantitative differences in virulence factors, including environmental pH, biomass, and cell density, compared to biofilms formed using the standard in vitro methodology. The bio-ink technique resulted in higher exopolysaccharide deposition, a key virulence factor for biofilm cohesion and protection, as well as more homogeneous spatial distribution of bacterial microcolonies. Our newly developed technique produces 3D-printable model biofilms that match the virulence benchmarks of the standard method, opening possibilities to print biofilms onto any substrate and a new way to study multidimensional biofilm dynamics. IMPORTANCE Dental caries is the most common oral disease caused by biofilms in humans with cost limitations. Changes in the human diet have increased the exposure to sugar-rich processed food, increasing the incidence and severity of dental caries and creating greater rationale for understanding biofilm deposition, microbial interactions, and maintenance of quiescence of the oral microbiota. Recent 3D-printing techniques have been leveraged to develop the first model biofilms, providing spatial control over microbe deposition and enabling unprecedented investigation of the impact of cell-cell interactions and spatial organizationupon biofilm persistence, sensitivity to drugs, and virulence. Here, we have developed new methods to extend bioprinting to oral biofilms using cariogenic Streptococcus mutans . Our technique is an attempt to establish an alternative method for oral biofilm formation in vitro that uses 3D-printing tools, preserving the virulence of standard in vitro biofilms while amplifying the availability and versatility of methods for understanding the microbiome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".