Synthetic biology: at the crossroads of genetic engineering and human therapeutics-a Keystone Symposia report.

Synthetic biology has the potential to transform cell- and gene-based therapies for a variety of diseases. Sophisticated tools are now available for both eukaryotic and prokaryotic cells to engineer cells to selectively achieve therapeutic effects in response to one or more disease-related signals, thus sparing healthy tissue from potentially cytotoxic effects. This report summarizes the Keystone eSymposium "Synthetic Biology: At the Crossroads of Genetic Engineering and Human Therapeutics," which took place on May 3 and 4, 2021. Given that several therapies engineered using synthetic biology have entered clinical trials, there was a clear need for a synthetic biology symposium that emphasizes the therapeutic applications of synthetic biology as opposed to the technical aspects. Presenters discussed the use of synthetic biology to improve T cell, gene, and viral therapies, to engineer probiotics, and to expand upon existing modalities and functions of cell-based therapies.

BioParts-A Biological Parts Search Portal and Updates to the ICE Parts Registry Software Platform.

Capturing, storing, and sharing biological DNA parts data are integral parts of synthetic biology research. Here, we detail updates to the ICE biological parts registry software platform that enable these processes, describe our implementation of the Web of Registries concept using ICE, and establish Bioparts, a search portal for biological parts available in the public domain. The Web of Registries enables standalone ICE installations to securely connect and form a distributed parts database. This distributed database allows users from one registry to query and access plasmid, strain, (DNA) part, plant seed, and protein entry types in other connected registries. Users can also transfer entries from one ICE registry to another or make them publicly accessible. Bioparts, the new search portal, combines the ease and convenience of modern web search engines with the capabilities of bioinformatics search tools such as BLAST. This portal, available at bioparts.org, allows anyone to search for publicly accessible biological part information (e.g., NCBI, iGEM, SynBioHub, Addgene), including parts publicly accessible through ICE Registries. Additionally, the portal offers a REST API that enables third-party applications and tools to access the portal's functionality programmatically.

Repurposing a microfluidic formulation device for automated DNA construction.

Microfluidic applications have expanded greatly over the past decade. For the most part, however, each microfluidics platform is developed with a specific task in mind, rather than as a general-purpose device with a wide-range of functionality. Here, we show how a microfluidic system, originally developed to investigate protein phase behavior, can be modified and repurposed for another application, namely DNA construction. We added new programable controllers to direct the flow of reagents across the chip. We designed the assembly of a combinatorial Golden Gate DNA library using TeselaGen DESIGN software and used the repurposed microfluidics platform to assemble the designed library from off-chip prepared DNA assembly pieces. Further experiments verified the sequences and function of the on-chip assembled DNA constructs.

Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism.

Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms. From a single data-generation cycle, this enables successful forward engineering of complex aromatic amino acid metabolism in yeast, with the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training. Thus, this study highlights the power of combining mechanistic and machine learning models to effectively direct metabolic engineering efforts.

Combinatorial-Hierarchical DNA Library Design Using the TeselaGen DESIGN Module with j5.

Modern DNA assembly techniques are known for their potential to link multiple large DNA fragments together into even larger constructs in single pot reactions that are easier to automate and work more reliably than traditional cloning methods. The simplicity of the chemistry is in contrast to the increased work needed to design optimal reactions that maximize DNA fragment reuse, minimize cost, and organize thousands of potential chemical reactions. Here we examine available DNA assembly methods and describe through example, the construction of a complex but not atypical combinatorial and hierarchical library using protocols that are generated automatically with the assistance of modern synthetic biology software.

The three-dimensional architecture of a bacterial genome and its alteration by genetic perturbation.

We have determined the three-dimensional (3D) architecture of the Caulobacter crescentus genome by combining genome-wide chromatin interaction detection, live-cell imaging, and computational modeling. Using chromosome conformation capture carbon copy (5C), we derive ~13 kb resolution 3D models of the Caulobacter genome. The resulting models illustrate that the genome is ellipsoidal with periodically arranged arms. The parS sites, a pair of short contiguous sequence elements known to be involved in chromosome segregation, are positioned at one pole, where they anchor the chromosome to the cell and contribute to the formation of a compact chromatin conformation. Repositioning these elements resulted in rotations of the chromosome that changed the subcellular positions of most genes. Such rotations did not lead to large-scale changes in gene expression, indicating that genome folding does not strongly affect gene regulation. Collectively, our data suggest that genome folding is globally dictated by the parS sites and chromosome segregation.

The essential genome of a bacterium.

Caulobacter crescentus is a model organism for the integrated circuitry that runs a bacterial cell cycle. Full discovery of its essential genome, including non-coding, regulatory and coding elements, is a prerequisite for understanding the complete regulatory network of a bacterial cell. Using hyper-saturated transposon mutagenesis coupled with high-throughput sequencing, we determined the essential Caulobacter genome at 8 bp resolution, including 1012 essential genome features: 480 ORFs, 402 regulatory sequences and 130 non-coding elements, including 90 intergenic segments of unknown function. The essential transcriptional circuitry for growth on rich media includes 10 transcription factors, 2 RNA polymerase sigma factors and 1 anti-sigma factor. We identified all essential promoter elements for the cell cycle-regulated genes. The essential elements are preferentially positioned near the origin and terminus of the chromosome. The high-resolution strategy used here is applicable to high-throughput, full genome essentiality studies and large-scale genetic perturbation experiments in a broad class of bacterial species.

Assembly of the Caulobacter cell division machine.

Cytokinesis in Gram-negative bacteria is mediated by a multiprotein machine (the divisome) that invaginates and remodels the inner membrane, peptidoglycan and outer membrane. Understanding the order of divisome assembly would inform models of the interactions among its components and their respective functions. We leveraged the ability to isolate synchronous populations of Caulobacter crescentus cells to investigate assembly of the divisome and place the arrival of each component into functional context. Additionally, we investigated the genetic dependence of localization among divisome proteins and the cell cycle regulation of their transcript and protein levels to gain insight into the control mechanisms underlying their assembly. Our results revealed a picture of divisome assembly with unprecedented temporal resolution. Specifically, we observed (i) initial establishment of the division site, (ii) recruitment of early FtsZ-binding proteins, (iii) arrival of proteins involved in peptidoglycan remodelling, (iv) arrival of FtsA, (v) assembly of core divisome components, (vi) initiation of envelope invagination, (vii) recruitment of polar markers and cytoplasmic compartmentalization and (viii) cell separation. Our analysis revealed differences in divisome assembly among Caulobacter and other bacteria that establish a framework for identifying aspects of bacterial cytokinesis that are widely conserved from those that are more variable.

Automated quantitative live cell fluorescence microscopy.

Advances in microscopy automation and image analysis have given biologists the tools to attempt large scale systems-level experiments on biological systems using microscope image readout. Fluorescence microscopy has become a standard tool for assaying gene function in RNAi knockdown screens and protein localization studies in eukaryotic systems. Similar high throughput studies can be attempted in prokaryotes, though the difficulties surrounding work at the diffraction limit pose challenges, and targeting essential genes in a high throughput way can be difficult. Here we will discuss efforts to make live-cell fluorescent microscopy based experiments using genetically encoded fluorescent reporters an automated, high throughput, and quantitative endeavor amenable to systems-level experiments in bacteria. We emphasize a quantitative data reduction approach, using simulation to help develop biologically relevant cell measurements that completely characterize the cell image. We give an example of how this type of data can be directly exploited by statistical learning algorithms to discover functional pathways.

High-throughput identification of protein localization dependency networks.

Bacterial cells are highly organized with many protein complexes and DNA loci dynamically positioned to distinct subcellular sites over the course of a cell cycle. Such dynamic protein localization is essential for polar organelle development, establishment of asymmetry, and chromosome replication during the Caulobacter crescentus cell cycle. We used a fluorescence microscopy screen optimized for high-throughput to find strains with anomalous temporal or spatial protein localization patterns in transposon-generated mutant libraries. Automated image acquisition and analysis allowed us to identify genes that affect the localization of two polar cell cycle histidine kinases, PleC and DivJ, and the pole-specific pili protein CpaE, each tagged with a different fluorescent marker in a single strain. Four metrics characterizing the observed localization patterns of each of the three labeled proteins were extracted for hundreds of cell images from each of 854 mapped mutant strains. Using cluster analysis of the resulting set of 12-element vectors for each of these strains, we identified 52 strains with mutations that affected the localization pattern of the three tagged proteins. This information, combined with quantitative localization data from epitasis experiments, also identified all previously known proteins affecting such localization. These studies provide insights into factors affecting the PleC/DivJ localization network and into regulatory links between the localization of the pili assembly protein CpaE and the kinase localization pathway. Our high-throughput screening methodology can be adapted readily to any sequenced bacterial species, opening the potential for databases of localization regulatory networks across species, and investigation of localization network phylogenies.

Caulobacter PopZ forms a polar subdomain dictating sequential changes in pole composition and function.

The bacterium Caulobacter crescentus has morphologically and functionally distinct cell poles that undergo sequential changes during the cell cycle. We show that the PopZ oligomeric network forms polar ribosome exclusion zones that change function during cell cycle progression. The parS/ParB chromosomal centromere is tethered to PopZ at one pole prior to the initiation of DNA replication. During polar maturation, the PopZ-centromere tether is broken, and the PopZ zone at that pole then switches function to act as a recruitment factor for the ordered addition of multiple proteins that promote the transformation of the flagellated pole into a stalked pole. Stalked pole assembly, in turn, triggers the initiation of chromosome replication, which signals the formation of a new PopZ zone at the opposite cell pole, where it functions to anchor the newly duplicated centromere that has traversed the long axis of the cell. We propose that pole-specific control of PopZ function co-ordinates polar development and cell cycle progression by enabling independent assembly and tethering activities at the two cell poles.

The External RNA Controls Consortium: a progress report.

Standard controls and best practice guidelines advance acceptance of data from research, preclinical and clinical laboratories by providing a means for evaluating data quality. The External RNA Controls Consortium (ERCC) is developing commonly agreed-upon and tested controls for use in expression assays, a true industry-wide standard control.

A method for detecting and correcting feature misidentification on expression microarrays.

BACKGROUND: Much of the microarray data published at Stanford is based on mouse and human arrays produced under controlled and monitored conditions at the Brown and Botstein laboratories and at the Stanford Functional Genomics Facility (SFGF). Nevertheless, as large datasets based on the Stanford Human array began to accumulate, a small but significant number of discrepancies were detected that required a serious attempt to track down the original source of error. Due to a controlled process environment, sufficient data was available to accurately track the entire process leading to up to the final expression data. In this paper, we describe our statistical methods to detect the inconsistencies in microarray data that arise from process errors, and discuss our technique to locate and fix these errors. RESULTS: To date, the Brown and Botstein laboratories and the Stanford Functional Genomics Facility have together produced 40,000 large-scale (10-50,000 feature) cDNA microarrays. By applying the heuristic described here, we have been able to check most of these arrays for misidentified features, and have been able to confidently apply fixes to the data where needed. Out of the 265 million features checked in our database, problems were detected and corrected on 1.3 million of them. CONCLUSION: Process errors in any genome scale high throughput production regime can lead to subsequent errors in data analysis. We show the value of tracking multi-step high throughput operations by using this knowledge to detect and correct misidentified data on gene expression microarrays.

Universal Reference RNA as a standard for microarray experiments.

BACKGROUND: Obtaining reliable and reproducible two-color microarray gene expression data is critically important for understanding the biological significance of perturbations made on a cellular system. Microarray design, RNA preparation and labeling, hybridization conditions and data acquisition and analysis are variables difficult to simultaneously control. A useful tool for monitoring and controlling intra- and inter-experimental variation is Universal Reference RNA (URR), developed with the goal of providing hybridization signal at each microarray probe location (spot). Measuring signal at each spot as the ratio of experimental RNA to reference RNA targets, rather than relying on absolute signal intensity, decreases variability by normalizing signal output in any two-color hybridization experiment. RESULTS: Human, mouse and rat URR (UHRR, UMRR and URRR, respectively) were prepared from pools of RNA derived from individual cell lines representing different tissues. A variety of microarrays were used to determine percentage of spots hybridizing with URR and producing signal above a user defined threshold (microarray coverage). Microarray coverage was consistently greater than 80% for all arrays tested. We confirmed that individual cell lines contribute their own unique set of genes to URR, arguing for a pool of RNA from several cell lines as a better configuration for URR as opposed to a single cell line source for URR. Microarray coverage comparing two separately prepared batches each of UHRR, UMRR and URRR were highly correlated (Pearson's correlation coefficients of 0.97). CONCLUSION: Results of this study demonstrate that large quantities of pooled RNA from individual cell lines are reproducibly prepared and possess diverse gene representation. This type of reference provides a standard for reducing variation in microarray experiments and allows more reliable comparison of gene expression data within and between experiments and laboratories.

Gene expression patterns in ovarian carcinomas.

We used DNA microarrays to characterize the global gene expression patterns in surface epithelial cancers of the ovary. We identified groups of genes that distinguished the clear cell subtype from other ovarian carcinomas, grade I and II from grade III serous papillary carcinomas, and ovarian from breast carcinomas. Six clear cell carcinomas were distinguished from 36 other ovarian carcinomas (predominantly serous papillary) based on their gene expression patterns. The differences may yield insights into the worse prognosis and therapeutic resistance associated with clear cell carcinomas. A comparison of the gene expression patterns in the ovarian cancers to published data of gene expression in breast cancers revealed a large number of differentially expressed genes. We identified a group of 62 genes that correctly classified all 125 breast and ovarian cancer specimens. Among the best discriminators more highly expressed in the ovarian carcinomas were PAX8 (paired box gene 8), mesothelin, and ephrin-B1 (EFNB1). Although estrogen receptor was expressed in both the ovarian and breast cancers, genes that are coregulated with the estrogen receptor in breast cancers, including GATA-3, LIV-1, and X-box binding protein 1, did not show a similar pattern of coexpression in the ovarian cancers.

Gene expression patterns in renal cell carcinoma assessed by complementary DNA microarray.

Renal cell carcinoma comprises several histological types with different clinical behavior. Accurate pathological characterization is important in the clinical management of these tumors. We describe gene expression profiles in 41 renal tumors determined by using DNA microarrays containing 22,648 unique cDNAs representing 17,083 different UniGene Clusters, including 7230 characterized human genes. Differences in the patterns of gene expression among the different tumor types were readily apparent; hierarchical cluster analysis of the tumor samples segregated histologically distinct tumor types solely based on their gene expression patterns. Conventional renal cell carcinomas with clear cells showed a highly distinctive pattern of gene expression. Papillary carcinomas formed a tightly clustered group, as did tumors arising from the distal nephron and the normal kidney samples. Surprisingly, conventional renal cell carcinomas with granular cytoplasm were heterogeneous, and did not resemble any of the conventional carcinomas with clear cytoplasm in their pattern of gene expression. Characterization of renal cell carcinomas based on gene expression patterns provides a revised classification of these tumors and has the potential to supply significant biological and clinical insights.

Expression array technology in the diagnosis and treatment of breast cancer.

The most common group of cancers among American women involves malignancies of the breast. Breast cancer is a complex disease, involving several different types of tissues and specific cells with various functions, that is categorized into many distinct subtypes. Microarray analysis has recently revealed that different biological subtypes of breast cancer are accompanied by differences in their specific gene expression profile. Because breast tissue (and breast cancer) is heterogeneous, microarray analysis may provide clinicians with a better understanding of how to treat each specific case. Thus, microarray analysis may translate basic research data into more confident diagnoses, specifically designed treatment regimens geared to each patient's needs, and better clinical prognoses.