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Abstract DNA is the genetic code found inside all living cells and its molecular stability can also be utilized outside the cell. While extracellular DNA (eDNA) has been identified as a structural polymer in bacterial biofilms, whether it persists stably throughout development remains unclear. Here, we report that eDNA is temporarily invested in the biofilm matrix before being reclaimed later in development. Specifically, by imaging eDNA dynamics within undomesticatedBacillus subtilisbiofilms, we found eDNA is produced during biofilm establishment before being globally degraded in a spatiotemporally coordinated pulse. We identified YhcR, a secreted Ca²⁺-dependent nuclease, as responsible for eDNA degradation in pellicle biofilms. YhcR cooperates with two other nucleases, NucA and NucB, to reclaim eDNA for its phosphate content in colony biofilms. Our results identify extracellular nucleases that are crucial for eDNA reclamation during biofilm development and we therefore propose a new role for eDNA as a dynamic metabolic reservoir. 

ABSTRACT Biofilms provide individual bacteria with many advantages, yet dense cellular proliferation can also create intrinsic metabolic challenges including excessive acidification. Because such pH stress can be masked in buffered laboratory media—such as MSgg commonly used to studyBacillus subtilisbiofilms—it is not always clear how such biofilms cope with minimally buffered natural environments. Here, we report howB. subtilisbiofilms overcome this intrinsic metabolic challenge through an active pH regulation mechanism. Specifically, we find that these biofilms can modulate their extracellular pH to the preferred neutrophile range, even when starting from acidic and alkaline initial conditions, while planktonic cells cannot. We associate this behavior with dynamic interplay between acetate and acetoin biosynthesis and show that this mechanism is required to buffer against biofilm acidification. Furthermore, we find that buffering-deficient biofilms exhibit dysregulated biofilm development when grown in minimally buffered conditions. Our findings reveal an active pH regulation mechanism inB. subtilisbiofilms that could lead to new targets to control unwanted biofilm growth.IMPORTANCEpH is known to influence microbial growth and community dynamics in multiple bacterial species and environmental contexts. Furthermore, in many bacterial species, rapid cellular proliferation demands the use of overflow metabolism, which can often result in excessive acidification. However, in the case of bacterial communities known as biofilms, these acidification challenges can be masked when buffered laboratory media are employed to stabilize the pH environment for optimal growth. Our study reveals thatB. subtilisbiofilms use an active pH regulation mechanism to mitigate both growth-associated acidification and external pH challenges. This discovery provides new opportunities for understanding microbial communities and could lead to new methods for controlling biofilm growth outside of buffered laboratory conditions. 

Inflammatory bowel disease (IBD) is a spectrum of autoimmune diseases affecting the gastrointestinal tract characterized by a relapsing and remitting course of gut mucosal inflammation. Disease flares can be difficult to predict, and the current practice of IBD disease activity surveillance through endoscopy is invasive and requires medical expertise. Recent advancements in synthetic biology raise the possibility that symbiotic microbes can be engineered to selectively detect disease biomarkers used in current clinical practice. Here, we introduce an engineered probiotic capable of detecting the clinical gold standard IBD biomarker, calprotectin, with sensitivity and specificity in IBD patients. Specifically, we identified a bacterial promoter in the probiotic strainEscherichia coliNissle 1917 (EcN) which exhibits a specific expression increase in the presence of calprotectin. Using murine models of colitis, we show that the reporter signal is activated in vivo during transit of the GI tract following oral delivery. Furthermore, our engineered probiotic can successfully discriminate human patients with active IBD from those in remission and without IBD using patient stool samples, where the intensity of reporter signal quantitatively tracks with clinical laboratory–measured levels of calprotectin. Our pilot study sets the stage for probiotics that can be engineered to detect fecal calprotectin for precise noninvasive disease activity monitoring in IBD patients. 

Objective and Impact Statement . We propose an automated method of predicting Normal Pressure Hydrocephalus (NPH) from CT scans. A deep convolutional network segments regions of interest from the scans. These regions are then combined with MRI information to predict NPH. To our knowledge, this is the first method which automatically predicts NPH from CT scans and incorporates diffusion tractography information for prediction. Introduction . Due to their low cost and high versatility, CT scans are often used in NPH diagnosis. No well-defined and effective protocol currently exists for analysis of CT scans for NPH. Evans’ index, an approximation of the ventricle to brain volume using one 2D image slice, has been proposed but is not robust. The proposed approach is an effective way to quantify regions of interest and offers a computational method for predicting NPH. Methods . We propose a novel method to predict NPH by combining regions of interest segmented from CT scans with connectome data to compute features which capture the impact of enlarged ventricles by excluding fiber tracts passing through these regions. The segmentation and network features are used to train a model for NPH prediction. Results . Our method outperforms the current state-of-the-art by 9 precision points and 29 recall points. Our segmentation model outperforms the current state-of-the-art in segmenting the ventricle, gray-white matter, and subarachnoid space in CT scans. Conclusion . Our experimental results demonstrate that fast and accurate volumetric segmentation of CT brain scans can help improve the NPH diagnosis process, and network properties can increase NPH prediction accuracy.