Pregnant women and their babies face significant risks from three vaccine-preventable diseases: COVID-19, influenza and pertussis. However, despite these vaccines' proven safety and effectiveness, uptake during pregnancy remains low.

Reactive oxygen species (ROS), which are a byproduct of oxidative metabolism, serve as signaling molecules in a number of physiological settings. However, if their levels are not tightly maintained, excess ROS lead to potentially cytotoxic oxidative stress. Accordingly, several transcriptional regulatory networks have evolved to include components that are highly ROS-responsive. Depending on the context, these regulatory networks can leverage ROS to respond to nutrient conditions, metabolism, or other physiological signals, or to respond to oxidative stress. However, ROS signaling is complex, so regulatory interactions between various ROS-responsive transcription factors are still being mapped out. Here we show that the transcription factor NRF2, a key regulator of the adaptive response to oxidative stress, directly regulates expression of HIF1A, which encodes HIF1α, a key transcriptional regulator of the adaptive response to hypoxia. We used an integrative genomics approach to identify HIF1A as a ROS-responsive transcript and we found an NRF2-bound antioxidant response element (ARE) approximately 30 kilobases upstream of HIF1A. This ARE sequence is deeply conserved, and we verified that it is directly bound and activated by NRF2. In addition, we found that HIF1A is upregulated in breast and bladder tumors with high NRF2 activity. Taken together, our results demonstrate that NRF2 targets a functional ARE at the HIF1A locus, and reveal a direct regulatory connection between two important oxygen responsive transcription factors.

Y-box binding protein 1 (YB-1) is a regulatory protein associated with oncogenesis and poor prognosis in patients with cancer. In the cell, YB-1 functions as a DNA and RNA binding protein that promotes or suppresses expression of target genes. The cancer-promoting activity of YB-1 is mediated through its activation of oncogenes and repression of tumor suppressor genes. Lipogenic enzyme stearoyl-CoA desaturase (SCD1) drives the production of endogenous monounsaturated fatty acids (MUFAs) in cells and protects against toxic buildup of saturated fatty acids. Clear cell renal cell carcinoma (ccRCC) is often characterized by aberrantly high SCD1 expression and cytosolic accumulation of unsaturated fatty acids. In the present study, a proteomics screen of cells treated with inhibitors of SCD1 supported a potential relationship between YB-1 and SCD1. It was revealed that the presence of MUFAs led to increased protein synthesis and increased expression of high molecular weight forms of YB-1 in ccRCC cells, but not in non-tumorigenic cells. Ectopic expression of YB-1 led to decreased expression levels of SCD1 protein and mRNA in ccRCC cell lines. Conversely, targeted knockdown of YB-1 increased SCD1 mRNA abundance. Analysis of ccRCC patient data from The Cancer Proteome Atlas database showed YB-1 expression was negatively associated with survival, whereas SCD1 was associated with improved survival. These data suggested an antagonistic relationship between YB-1 and SCD1 that may influence survival of patients with ccRCC.

Circulating tumor DNA (ctDNA) offers minimally invasive means to repeatedly interrogate tumor genomes, providing opportunities to monitor clonal dynamics induced by metastasis and therapeutic selective pressures. In metastatic cancers, ctDNA profiling allows for simultaneous analysis of both local and distant sites of recurrence. Despite the promise of ctDNA sampling, its utility in real-time genetic monitoring remains largely unexplored.

Analysis of DNA methylation in cell-free DNA (cfDNA) reveals clinically relevant biomarkers but requires specialized protocols and sufficient input material that limits its applicability. Millions of cfDNA samples have been profiled by genomic sequencing. To maximize the gene regulation information from the existing dataset, we developed FinaleMe, a non-homogeneous Hidden Markov Model (HMM), to predict DNA methylation of cfDNA and, therefore, tissues-of-origin directly from plasma whole-genome sequencing (WGS). We validated the performance with 80 pairs of deep and shallow-coverage WGS and whole-genome bisulfite sequencing (WGBS) data.