Engineering Tunable, Low Latency Spatial Computation with Dual Input Quorum Sensing Promoters.

Quorum sensing signals have evolved for population-level signaling in bacterial communities and are versatile tools for engineering cell-cell signaling in synthetic biology projects. Here, we characterize the spatial diffusion of a palette of quorum sensing signals and find that their diffusion in agar can be predicted from their molecular weight with a simple power law. We also engineer novel dual- and multi-input promoters that respond to quorum-sensing diffusive signals for use in engineered genetic systems. We engineer a promoter scaffold that can be adapted for activation and repression by multiple diffusers simultaneously. Lastly, we combine the knowledge on diffusion dynamics with the novel genetic components to build a new generation of spatial, stripe-forming systems with a simplified design, improved robustness, tuneability, and response time.

Synthetic spatial patterning in bacteria: advances based on novel diffusible signals.

Engineering multicellular patterning may help in the understanding of some fundamental laws of pattern formation and thus may contribute to the field of developmental biology. Furthermore, advanced spatial control over gene expression may revolutionize fields such as medicine, through organoid or tissue engineering. To date, foundational advances in spatial synthetic biology have often been made in prokaryotes, using artificial gene circuits. In this review, engineered patterns are classified into four levels of increasing complexity, ranging from spatial systems with no diffusible signals to systems with complex multi-diffusor interactions. This classification highlights how the field was held back by a lack of diffusible components. Consequently, we provide a summary of both previously characterized and some new potential candidate small-molecule signals that can regulate gene expression in Escherichia coli. These diffusive signals will help synthetic biologists to successfully engineer increasingly intricate, robust and tuneable spatial structures.

Reducing metabolic burden in the PACEmid evolver system by remastering high-copy phagemid vectors.

Orthogonal or non-cross-reacting transcription factors are used in synthetic biology as components of genetic circuits. Brödel et al. (2016) engineered 12 such cIλ transcription factor variants using a directed evolution 'PACEmid' system. The variants operate as dual activator/repressors and expand gene circuit construction possibilities. However, the high-copy phagemid vectors carrying the cIλ variants imposed high metabolic burden upon cells. Here, the authors 'remaster' the phagemid backbones to relieve their burden substantially, exhibited by a recovery in Escherichia coli growth. The remastered phagemids' ability to function within the PACEmid evolver system is maintained, as is the cIλ transcription factors' activity within these vectors. The low-burden phagemid versions are more suitable for use in PACEmid experiments and synthetic gene circuits; the authors have, therefore, replaced the original high-burden phagemids on the Addgene repository. The authors' work emphasises the importance of understanding metabolic burden and incorporating it into design steps in future synthetic biology ventures.

Dynamical model fitting to a synthetic positive feedback circuit in E. coli.

Applying the principles of engineering to Synthetic Biology relies on the development of robust and modular genetic components, as well as underlying quantitative dynamical models that closely predict their behaviour. This study looks at a simple positive feedback circuit built by placing filamentous phage secretin pIV under a phage shock promoter. A single-equation ordinary differential equation model is developed to closely replicate the behaviour of the circuit, and its response to inhibition by TetR. A stepwise approach is employed to fit the model's parameters to time-series data for the circuit. This approach allows the dissection of the role of different parameters and leads to the identification of dependencies and redundancies between parameters. The developed genetic circuit and associated model may be used as a building block for larger circuits with more complex dynamics, which require tight quantitative control or tuning.

Phosphatidylethanolamine binding protein-4 (PEBP4) is increased in IgA nephropathy and is associated with IgA-positive B-cells in affected kidneys.

IgA nephropathy (IgAN) is the most common glomerulonephritis worldwide and a major cause of chronic kidney disease and failure. IgAN is driven by an autoimmune reaction against galactose-deficient IgA1 that results in the generation of autoantibodies and large IgG-IgA immune complexes. Immune complexes accumulate in the glomerular mesangium causing chronic inflammation and renal scarring. A significant proportion of IgAN patients develop end-stage kidney disease and require dialysis or transplantation. Currently, there are no approved specific therapies that can ameliorate the systemic autoimmune reaction in IgAN and no biomarkers that can predict renal inflammation and scarring. In this study, we used shotgun LC-MS/MS proteomics to compare small volumes of urine from healthy subjects and IgAN patients. We identified multiple urine proteins with unknown renal or IgAN function. Our attention was captured by the increase of phosphatidylethanolamine binding protein-4 (PEBP4) in IgAN urine. The function of PEBP4 in IgAN or renal disease is unknown. Increased levels of urine and serum PEBP4 were subsequently validated in different cohorts of IgAN patients and PEBP4 was linked to declining kidney function in IgAN. Strong PEBP4 staining was sporadically seen in IgAN kidney biopsies, colocalising with IgA in glomeruli and in the lumen of kidney tubules. In a small number of IgAN biopsies, PEBP4 colocalised with IgA and CD19 while the increased excretion of PEBP4 in IgAN urine was accompanied by increased excretion of classic B-cell factors BAFF, BCMA and TACI as well as IgA and IgG. PEBP4 is a new IgAN-related protein with unknown function and a likely renal disease marker in urine and serum.

Comparative Transcriptomics of Rat and Axolotl After Spinal Cord Injury Dissects Differences and Similarities in Inflammatory and Matrix Remodeling Gene Expression Patterns.

Following spinal cord injury in mammals, maladaptive inflammation, and matrix deposition drive tissue scarring and permanent loss of function. In contrast, axolotls regenerate their spinal cord after severe injury fully and without scarring. To explore previously unappreciated molecules and pathways that drive tissue responses after spinal cord injury, we performed a 4-way intersection of rat and axolotl transcriptomics datasets and isolated shared genes with similar or differential expression at days 1, 3, and 7 after spinal cord injury in both species. Systems-wide differences and similarities between the two species are described in detail using public-domain computational tools and key differentially regulated genes are highlighted. Amongst persistent differential expression in matching neuronal genes (upregulated in axolotls but downregulated in rats) and nucleic acid metabolism genes (downregulated in axolotls but upregulated in rats), we found multiple extracellular matrix genes that were upregulated in both species after spinal cord injury and all time-points (days 1, 3, and 7), indicating the importance of extracellular matrix remodeling in wound healing. Moreover, the archetypal transcription factor SP1, which was consistently upregulated in rats but was unchanged in axolotls, was predicted as a potential transcriptional regulator of classic inflammatory response genes in rats most of which were not regulated in regenerating axolotls. This analysis offers an extensive comparative platform between a non-regenerating mammal and a regenerating urodele after spinal cord injury. To better understand regeneration vs. scarring mechanisms it is important to understand consistent molecular differences as well as similarities after experimental spinal cord injury.

Combined Transcriptomics, Proteomics and Bioinformatics Identify Drug Targets in Spinal Cord Injury.

Spinal cord injury (SCI) causes irreversible tissue damage and severe loss of neurological function. Currently, there are no approved treatments and very few therapeutic targets are under investigation. Here, we combined 4 high-throughput transcriptomics and proteomics datasets, 7 days and 8 weeks following clinically-relevant rat SCI to identify proteins with persistent differential expression post-injury. Out of thousands of differentially regulated entities our combined analysis identified 40 significantly upregulated versus 48 significantly downregulated molecules, which were persistently altered at the mRNA and protein level, 7 days and 8 weeks post-SCI. Bioinformatics analysis was then utilized to identify currently available drugs with activity against the filtered molecules and to isolate proteins with known or unknown function in SCI. Our findings revealed multiple overlooked therapeutic candidates with important bioactivity and established druggability but with unknown expression and function in SCI including the upregulated purine nucleoside phosphorylase (PNP), cathepsins A, H, Z (CTSA, CTSH, CTSZ) and proteasome protease PSMB10, as well as the downregulated ATP citrate lyase (ACLY), malic enzyme (ME1) and sodium-potassium ATPase (ATP1A3), amongst others. This work reveals previously unappreciated therapeutic candidates for SCI and available drugs, thus providing a valuable resource for further studies and potential repurposing of existing therapeutics for SCI.