Engineered bacterial therapeutics for detecting and treating CRC.

Despite an overall decrease in occurrence, colorectal cancer (CRC) remains the third most common cause of cancer deaths in the USA. Detection of CRC is difficult in high-risk groups, including those with genetic predispositions, with disease traits, or from certain demographics. There is emerging interest in using engineered bacteria to identify early CRC development, monitor changes in the adenoma and CRC microenvironment, and prevent cancer progression. Novel genetic circuits for cancer therapeutics or functions to enhance existing treatment modalities have been tested and verified in vitro and in vivo. Inclusion of biocontainment measures would prepare strains to meet therapeutic standards. Thus, engineered bacteria present an opportunity for detection and treatment of CRC lesions in a highly sensitive and specific manner.

Single-sequence protein structure prediction using a language model and deep learning.

AlphaFold2 and related computational systems predict protein structure using deep learning and co-evolutionary relationships encoded in multiple sequence alignments (MSAs). Despite high prediction accuracy achieved by these systems, challenges remain in (1) prediction of orphan and rapidly evolving proteins for which an MSA cannot be generated; (2) rapid exploration of designed structures; and (3) understanding the rules governing spontaneous polypeptide folding in solution. Here we report development of an end-to-end differentiable recurrent geometric network (RGN) that uses a protein language model (AminoBERT) to learn latent structural information from unaligned proteins. A linked geometric module compactly represents Cα backbone geometry in a translationally and rotationally invariant way. On average, RGN2 outperforms AlphaFold2 and RoseTTAFold on orphan proteins and classes of designed proteins while achieving up to a 106-fold reduction in compute time. These findings demonstrate the practical and theoretical strengths of protein language models relative to MSAs in structure prediction.

A programmable encapsulation system improves delivery of therapeutic bacteria in mice.

Living bacteria therapies have been proposed as an alternative approach to treating a broad array of cancers. In this study, we developed a genetically encoded microbial encapsulation system with tunable and dynamic expression of surface capsular polysaccharides that enhances systemic delivery. Based on a small RNA screen of capsular biosynthesis pathways, we constructed inducible synthetic gene circuits that regulate bacterial encapsulation in Escherichia coli Nissle 1917. These bacteria are capable of temporarily evading immune attack, whereas subsequent loss of encapsulation results in effective clearance in vivo. This dynamic delivery strategy enabled a ten-fold increase in maximum tolerated dose of bacteria and improved anti-tumor efficacy in murine models of cancer. Furthermore, in situ encapsulation increased the fraction of microbial translocation among mouse tumors, leading to efficacy in distal tumors. The programmable encapsulation system promises to enhance the therapeutic utility of living engineered bacteria for cancer.

Tet2 Inactivation Enhances the Antitumor Activity of Tumor-Infiltrating Lymphocytes.

Inactivation of tumor-infiltrating lymphocytes (TIL) is one of the mechanisms mitigating antitumor immunity during tumor onset and progression. Epigenetic abnormalities are regarded as a major culprit contributing to the dysfunction of TILs within tumor microenvironments. In this study, we used a murine model of melanoma to discover that Tet2 inactivation significantly enhances the antitumor activity of TILs with an efficacy comparable to immune checkpoint inhibition imposed by anti-PD-L1 treatment. Single-cell RNA-sequencing analysis suggested that Tet2-deficient TILs exhibit effector-like features. Transcriptomic and ATAC-sequencing analysis showed that Tet2 ablation reshapes chromatin accessibility and favors binding of transcription factors geared toward CD8+ T-cell activation. Furthermore, the ETS family of transcription factors contributed to augmented CD8+ T-cell function following Tet2 depletion. Overall, our study establishes that Tet2 constitutes one of the epigenetic barriers that account for dysfunction of TILs and that Tet2 inactivation could promote antitumor immunity to suppress tumor growth. SIGNIFICANCE: This study suggests that ablation of TET2+ from TILs could promote their antitumor function by reshaping chromatin accessibility for key transcription factors and enhancing the transcription of genes essential for antitumor activity.

An Interscholastic Network To Generate LexA Enhancer Trap Lines in Drosophila.

Binary expression systems like the LexA-LexAop system provide a powerful experimental tool kit to study gene and tissue function in developmental biology, neurobiology, and physiology. However, the number of well-defined LexA enhancer trap insertions remains limited. In this study, we present the molecular characterization and initial tissue expression analysis of nearly 100 novel StanEx LexA enhancer traps, derived from the StanEx1 index line. This includes 76 insertions into novel, distinct gene loci not previously associated with enhancer traps or targeted LexA constructs. Additionally, our studies revealed evidence for selective transposase-dependent replacement of a previously-undetected KP element on chromosome III within the StanEx1 genetic background during hybrid dysgenesis, suggesting a molecular basis for the over-representation of LexA insertions at the NK7.1 locus in our screen. Production and characterization of novel fly lines were performed by students and teachers in experiment-based genetics classes within a geographically diverse network of public and independent high schools. Thus, unique partnerships between secondary schools and university-based programs have produced and characterized novel genetic and molecular resources in Drosophila for open-source distribution, and provide paradigms for development of science education through experience-based pedagogy.

Rapid screening of engineered microbial therapies in a 3D multicellular model.

Synthetic biology is transforming therapeutic paradigms by engineering living cells and microbes to intelligently sense and respond to diseases including inflammation, infections, metabolic disorders, and cancer. However, the ability to rapidly engineer new therapies far outpaces the throughput of animal-based testing regimes, creating a major bottleneck for clinical translation. In vitro approaches to address this challenge have been limited in scalability and broad applicability. Here, we present a bacteria-in-spheroid coculture (BSCC) platform that simultaneously tests host species, therapeutic payloads, and synthetic gene circuits of engineered bacteria within multicellular spheroids over a timescale of weeks. Long-term monitoring of bacterial dynamics and disease progression enables quantitative comparison of critical therapeutic parameters such as efficacy and biocontainment. Specifically, we screen Salmonella typhimurium strains expressing and delivering a library of antitumor therapeutic molecules via several synthetic gene circuits. We identify candidates exhibiting significant tumor reduction and demonstrate high similarity in their efficacies, using a syngeneic mouse model. Last, we show that our platform can be expanded to dynamically profile diverse microbial species including Listeria monocytogenes, Proteus mirabilis, and Escherichia coli in various host cell types. This high-throughput framework may serve to accelerate synthetic biology for clinical applications and for understanding the host-microbe interactions in disease sites.

Genome-wide maps of distal gene regulatory enhancers active in the human placenta.

Placental dysfunction is implicated in many pregnancy complications, including preeclampsia and preterm birth (PTB). While both these syndromes are influenced by environmental risk factors, they also have a substantial genetic component that is not well understood. Precisely controlled gene expression during development is crucial to proper placental function and often mediated through gene regulatory enhancers. However, we lack accurate maps of placental enhancer activity due to the challenges of assaying the placenta and the difficulty of comprehensively identifying enhancers. To address the gap in our knowledge of gene regulatory elements in the placenta, we used a two-step machine learning pipeline to synthesize existing functional genomics studies, transcription factor (TF) binding patterns, and evolutionary information to predict placental enhancers. The trained classifiers accurately distinguish enhancers from the genomic background and placental enhancers from enhancers active in other tissues. Genomic features collected from tissues and cell lines involved in pregnancy are the most predictive of placental regulatory activity. Applying the classifiers genome-wide enabled us to create a map of 33,010 predicted placental enhancers, including 4,562 high-confidence enhancer predictions. The genome-wide placental enhancers are significantly enriched nearby genes associated with placental development and birth disorders and for SNPs associated with gestational age. These genome-wide predicted placental enhancers provide candidate regions for further testing in vitro, will assist in guiding future studies of genetic associations with pregnancy phenotypes, and aid interpretation of potential mechanisms of action for variants found through genetic studies.