Leukemia aggressiveness is driven by chromatin remodeling and expression changes of core regulators.

Molecular mechanisms driving clonal aggressiveness in leukemia are not fully understood. We tracked and analyzed two mouse MLL-rearranged leukemic clones independently evolving towards higher aggressiveness. More aggressive subclones lost their growth differential ex vivo but restored it upon secondary transplantation, suggesting molecular memory of aggressiveness. Development of aggressiveness was associated with clone-specific gradual modulation of chromatin states and expression levels across the genome, with a surprising preferential trend of reversing the earlier changes between normal and leukemic progenitors. To focus on the core aggressiveness program, we identified genes with consistent changes of expression and chromatin marks that were maintained in vivo and ex vivo in both clones. Overexpressing selected core genes (Smad1 as aggressiveness driver, Irx5 and Plag1 as suppressors) affected leukemic progenitor growth in the predicted way and had convergent downstream effects on central transcription factors and repressive epigenetic modifiers, suggesting a broader regulatory network of leukemic aggressiveness.

Advances in generative modeling methods and datasets to design novel enzymes for renewable chemicals and fuels.

Biotechnology has revolutionized the development of sustainable energy sources by harnessing biomass as a feedstock for energy production. However, challenges such as recalcitrant feedstocks and inefficient metabolic pathways hinder the large-scale integration of renewable energy systems. Enzyme engineering has emerged as a powerful tool to address these challenges by enhancing enzyme activity, specificity, and stability. Generative machine learning (ML) models have shown great promise in accelerating protein design, allowing for the generation of novel protein sequences with desired properties by navigating vast spaces. This review paper aims to summarize the state of the art in generative models for protein design and how they can be applied to bioenergy applications, including the underlying architectures and training strategies. Additionally, it highlights the importance of high-quality datasets for training and evaluating generative models, organizes available datasets for generative protein design, and discusses the potential of applying generative models to strain design for bioenergy production.

Prenylcysteine oxidase 1 like protein is required for neutrophil bactericidal activities.

The bactericidal function of neutrophils is dependent on a myriad of intrinsic and extrinsic stimuli. Using systems immunology approaches we identify microbiome- and infection-induced changes in neutrophils. We focus on investigating the Prenylcysteine oxidase 1 like (Pcyox1l) protein function. Murine and human Pcyox1l proteins share ninety four percent aminoacid homology revealing significant evolutionary conservation and implicating Pcyox1l in mediating important biological functions. Here we show that the loss of Pcyox1l protein results in significant reductions in the mevalonate pathway impacting autophagy and cellular viability under homeostatic conditions. Concurrently, Pcyox1l CRISPRed-out neutrophils exhibit deficient bactericidal properties. Pcyox1l knock-out mice demonstrate significant susceptibility to infection with the gram-negative pathogen Psuedomonas aeruginosa exemplified through increased neutrophil infiltrates, hemorrhaging, and reduced bactericidal functionality. Cumulatively, we ascribe a function to Pcyox1l protein in modulation of the prenylation pathway and suggest connections beween metabolic responses and neutrophil functionality.

KinMod database: a tool for investigating metabolic regulation.

The ability of current kinetic models to simulate the phenotypic behaviour of cells is limited since cell metabolism is regulated at different levels including enzyme regulation. The small molecule regulation network (SMRN) enables cells to respond rapidly to environmental fluctuations by controlling the activity of enzymes in metabolic pathways. However, SMRN is not as well studied relative to metabolic networks. The main contributor to the lack of knowledge on this regulatory system is the sparsity of experimental data and the absence of a standard framework for representing available information. In this paper, we introduce the KinMod database that encompasses more than 2 million data points on the metabolism and metabolic regulation network of 9814 organisms KinMod database employs a hierarchical data structure to: (i) signify relationships between kinetic information obtained through in-vitro experiments and proteins, with an emphasis on SMRN, (ii) provide a thorough insight into available kinetic parameters and missing experimental measurements of this regulatory network and (iii) facilitate machine learning approaches for parameter estimation and accurate kinetic model construction by providing a homogeneous list of linked omics data. The hierarchical ontology of the KinMod database allows flexible exploration of data attributes and investigation of metabolic relationships within- and cross-species. Identifying missing experimental values suggests additional experiments required for kinetic parameter estimation. Linking multi-omics data and providing data on SMRN encourages the development of novel machine learning techniques for predicting missing kinetic parameters and promotes accurate kinetic model construction of cells metabolism by providing a comprehensive list of available kinetic measurements. To illustrate the value of KinMod data, we develop six analyses to visualize associations between data classes belonging to separate sections of the metabolism. Through these analyses, we demonstrate that the KinMod database provides a unique framework for biologists and engineers to retrieve, evaluate and compare the functional metabolism of species, including the regulatory network, and discover the extent of available and missing experimental values of the metabolic regulation. Database URL: https://lmse.utoronto.ca/kinmod/KINMOD.sql.gz.

TLR Signaling Rescues Fungicidal Activity in Syk-Deficient Neutrophils.

An impaired neutrophil response to pathogenic fungi puts patients at risk for fungal infections with a high risk of morbidity and mortality. Acquired neutrophil dysfunction in the setting of iatrogenic immune modulators can include the inhibition of critical kinases such as spleen tyrosine kinase (Syk). In this study, we used an established system of conditionally immortalized mouse neutrophil progenitors to investigate the ability to augment Syk-deficient neutrophil function against Candida albicans with TLR agonist signaling. LPS, a known immunomodulatory molecule derived from Gram-negative bacteria, was capable of rescuing effector functions of Syk-deficient neutrophils, which are known to have poor fungicidal activity against Candida species. LPS priming of Syk-deficient mouse neutrophils demonstrates partial rescue of fungicidal activity, including phagocytosis, degranulation, and neutrophil swarming, but not reactive oxygen species production against C. albicans, in part due to c-Fos activation. Similarly, LPS priming of human neutrophils rescues fungicidal activity in the presence of pharmacologic inhibition of Syk and Bruton's tyrosine kinase (Btk), both critical kinases in the innate immune response to fungi. In vivo, neutropenic mice were reconstituted with wild-type or Syk-deficient neutrophils and challenged i.p. with C. albicans. In this model, LPS improved wild-type neutrophil homing to the fungal challenge, although Syk-deficient neutrophils did not persist in vivo, speaking to its crucial role on in vivo persistence. Taken together, we identify TLR signaling as an alternate activation pathway capable of partially restoring neutrophil effector function against Candida in a Syk-independent manner.