Post-fast refeeding enhances intestinal stem cell-mediated regeneration and tumourigenesis through mTORC1-dependent polyamine synthesis.

For more than a century, fasting regimens have improved health, lifespan, and tissue regeneration in diverse organisms, including humans. However, how fasting and post-fast refeeding impact adult stem cells and tumour formation has yet to be explored in depth. Here, we demonstrate that post-fast refeeding increases intestinal stem cell (ISC) proliferation and tumour formation: Post-fast refeeding augments the regenerative capacity of Lgr5+ intestinal stem cells (ISCs), and loss of the tumour suppressor Apc in ISCs under post-fast refeeding leads to a higher tumour incidence in the small intestine and colon than in the fasted or ad libitum (AL) fed states. This demonstrates that post-fast refeeding is a distinct state. Mechanistically, we discovered that robust induction of mTORC1 in post-fast-refed ISCs increases protein synthesis via polyamine metabolism to drive these changes, as inhibition of mTORC1, polyamine metabolite production, or protein synthesis abrogates the regenerative or tumourigenic effects of post-fast refeeding. Thus, fast-refeeding cycles must be carefully considered when planning diet-based strategies for regeneration without increasing cancer risk, as post-fast refeeding leads to a burst not only in stem cell-driven regeneration but also in tumourigenicity.

NExtSEEK: Extending SEEK for Active Management of Interoperable Metadata.

Data management is a critical challenge required to improve the rigor and reproducibility of large projects. Adhering to Findable, Accessible, Interoperable, and Reusable (FAIR) standards provides a baseline for meeting these requirements. Although many existing repositories handle data in a FAIR-compliant manner, there are limited tools in the public domain to handle the metadata burden required to connect data from multi-omic projects that span multiple institutions and are deposited in diverse repositories. One promising approach is the SEEK platform, which allows for diverse metadata and provides an established repository. SEEK is challenged by the assumption of single deposition events where a sample is immutable once entered in the database. This is structured for published data but presents a limitation for ongoing studies where multiple sequential events may occur in a single sample at different sites. To address this issue, we have created a modified wrapper around the SEEK platform that allows for active data management by establishing more discrete sample types that are mutable to permit the expansion of the types of metadata, allowing researchers to track additional information. The use of discrete nodes also converts assays from nodes to edges, creating a network model of the study and more accurately representing the experimental process. With these changes to SEEK, users are able to collect and organize the information that researchers need to improve reusability and reproducibility as well as make data and metadata available to the scientific community through public repositories.

Reprogramming NK cells and macrophages via combined antibody and cytokine therapy primes tumors for elimination by checkpoint blockade.

Treatments aiming to augment immune checkpoint blockade (ICB) in cancer often focus on T cell immunity, but innate immune cells may have important roles to play. Here, we demonstrate a single-dose combination treatment (termed AIP) using a pan-tumor-targeting antibody surrogate, half-life-extended interleukin-2 (IL-2), and anti-programmed cell death 1 (PD-1), which primes tumors to respond to subsequent ICB and promotes rejection of large established tumors in mice. Natural killer (NK) cells and macrophages activated by AIP treatment underwent transcriptional reprogramming; rapidly killed cancer cells; governed the recruitment of cross-presenting dendritic cells (DCs) and other leukocytes; and induced normalization of the tumor vasculature, facilitating further immune infiltration. Thus, innate cell-activating therapies can initiate critical steps leading to a self-sustaining cycle of T cell priming driven by ICB.

Implications of non-uniqueness in phylogenetic deconvolution of bulk DNA samples of tumors.

BACKGROUND: Tumors exhibit extensive intra-tumor heterogeneity, the presence of groups of cellular populations with distinct sets of somatic mutations. This heterogeneity is the result of an evolutionary process, described by a phylogenetic tree. In addition to enabling clinicians to devise patient-specific treatment plans, phylogenetic trees of tumors enable researchers to decipher the mechanisms of tumorigenesis and metastasis. However, the problem of reconstructing a phylogenetic tree T given bulk sequencing data from a tumor is more complicated than the classic phylogeny inference problem. Rather than observing the leaves of T directly, we are given mutation frequencies that are the result of mixtures of the leaves of T. The majority of current tumor phylogeny inference methods employ the perfect phylogeny evolutionary model. The underlying Perfect Phylogeny Mixture (PPM) combinatorial problem typically has multiple solutions. RESULTS: We prove that determining the exact number of solutions to the PPM problem is #P-complete and hard to approximate within a constant factor. Moreover, we show that sampling solutions uniformly at random is hard as well. On the positive side, we provide a polynomial-time computable upper bound on the number of solutions and introduce a simple rejection-sampling based scheme that works well for small instances. Using simulated and real data, we identify factors that contribute to and counteract non-uniqueness of solutions. In addition, we study the sampling performance of current methods, identifying significant biases. CONCLUSIONS: Awareness of non-uniqueness of solutions to the PPM problem is key to drawing accurate conclusions in downstream analyses based on tumor phylogenies. This work provides the theoretical foundations for non-uniqueness of solutions in tumor phylogeny inference from bulk DNA samples.