LipidSIM: Inferring mechanistic lipid biosynthesis perturbations from lipidomics with a flexible, low-parameter, Markov modeling framework.

Lipid metabolism is a complex and dynamic system involving numerous enzymes at the junction of multiple metabolic pathways. Disruption of these pathways leads to systematic dyslipidemia, a hallmark of many pathological developments, such as nonalcoholic steatohepatitis and diabetes. Recent advances in computational tools can provide insights into the dysregulation of lipid biosynthesis, but limitations remain due to the complexity of lipidomic data, limited knowledge of interactions among involved enzymes, and technical challenges in standardizing across different lipid types. Here, we present a low-parameter, biologically interpretable framework named Lipid Synthesis Investigative Markov model (LipidSIM), which models and predicts the source of perturbations in lipid biosynthesis from lipidomic data. LipidSIM achieves this by accounting for the interdependency between the lipid species via the lipid biosynthesis network and generates testable hypotheses regarding changes in lipid biosynthetic reactions. This feature allows the integration of lipidomics with other omics types, such as transcriptomics, to elucidate the direct driving mechanisms of altered lipidomes due to treatments or disease progression. To demonstrate the value of LipidSIM, we first applied it to hepatic lipidomics following Keap1 knockdown and found that changes in mRNA expression of the lipid pathways were consistent with the LipidSIM-predicted fluxes. Second, we used it to study lipidomic changes following intraperitoneal injection of CCl4 to induce fast NAFLD/NASH development and the progression of fibrosis and hepatic cancer. Finally, to show the power of LipidSIM for classifying samples with dyslipidemia, we used a Dgat2-knockdown study dataset. Thus, we show that as it demands no a priori knowledge of enzyme kinetics, LipidSIM is a valuable and intuitive framework for extracting biological insights from complex lipidomic data.

Steric Inhibition of 5' UTR Regulatory Elements Results in Upregulation of Human CFTR.

Cystic fibrosis (CF) is an autosomal recessive monogenic disease caused by mutations in the CFTR gene. Therapeutic approaches that are focused on correcting CFTR protein face the challenge of the heterogeneity in CFTR mutations and resulting defects. Thus, while several small molecules directed at CFTR show benefit in the clinic for subsets of CF patients, these drugs cannot treat all CF patients. Additionally, the clinical benefit from treatment with these modulators could be enhanced with novel therapies. To address this unmet need, we utilized an approach to increase CFTR protein levels through antisense oligonucleotide (ASO)-mediated steric inhibition of 5' UTR regulatory elements. We identified ASOs to upregulate CFTR protein expression and confirmed the regulatory role of the sites amenable to ASO-mediated upregulation. Two ASOs were investigated further, and both increased CFTR protein expression and function in cell lines and primary human bronchial epithelial cells with distinct CF genotypes. ASO treatment further increased CFTR function in almost all CF genotypes tested on top of treatment with the FDA approved drug Symdeko (ivacaftor and tezacaftor). Thus, we present a novel approach to CFTR therapeutic intervention, through ASO-mediated modulation of translation.

A novel 'social contract' - An attempt to harmonize a sponsor's exploratory research with a clinical study participant's data rights.

BACKGROUND: Pharmaceutical drug development rarely addresses a study participant's control of their genomic data, how to return individual incidental findings, and how to make use of genomic data more efficiently for exploratory research purposes. Mutually beneficial solutions to these issues are needed, as whole genome sequencing (WGS) is increasingly adopted in human research and as access to such information could provide impactful health-related information for a participant. METHODS: In this paper, we offer a novel framework to align a trial sponsor's need for broad exploratory research of the human genome with the study participant's right to data access and access control. The Exploratory Genetic Research Project (EGRP) aims to gather WGS on all participants of a sponsor's clinical trials. It is set up as a separate umbrella protocol to facilitate the consenting process, as well as the delineation between clinical trial endpoints versus exploratory future research. CONCLUSION: This concept establishes a participant's autonomy regarding access to genomic data and the disclosure of actionable incidental findings. The feasibility of EGRP will be tested and reassessed as it is deployed over the next few years.

Nucleic Acid Therapies for Cystic Fibrosis.

Nucleic acid therapeutics are an established class of drugs that enable specific targeting of a gene of interest. This diverse family of drugs includes antisense oligonucleotides, siRNAs, and mRNA replacement therapies, which can elicit both gene repression and activation, primarily at the RNA level. Recent advances in medicinal chemistry have increased drug potency and enhanced delivery and distribution to a broad array of tissue and cell types. A key advantage of nucleic acid therapeutics is in their application to monogenic diseases. Cystic fibrosis (CF) is one such disease that affects ∼70,000 people globally. This severe disease is an excellent candidate for nucleic acid therapies, as it is due to a genetic defect in a single epithelial chloride channel. Although CF affects many tissues, the primary cause of patient mortality is lung disease. Here we review the various nucleic acid therapeutic modalities and their mechanisms of action, the opportunities and challenges associated with application of nucleic acid drugs to the lung pathology of CF, and the current state and prospects for nucleic acid drugs for the treatment of CF.