Abstract 4689: Co-treatment of MEKi and BRD4 inhibitors lead to synergistic repression of MYC-associated enhancer RNAs

Recent evidence suggests that co-treatment with BRD4 and MEK inhibitors can lead to synergistic suppression of MAPK signaling, however, little is known about the mechanism of action of this synergy. In a triple negative breast cancer cell line model, we harvested nascent RNA at 45 minutes following repression of BRD4 and MEK via a variant of the precision run-on followed by sequencing (PRO-seq) assay. We noted dose-dependent repression of enhancer RNAs over MYC binding sites as well as enhancer RNAs upstream known ERK targets such as DUSP6. Using new machine learning techniques, we were able to link synergy-specific enhancer RNAs to their target genes. This work will inform on new methods for patient stratification and biomarker selection of the BETi and MEKi treatment regimes.

Citation Format: Joseph Azofeifa, Joel Basken, Maria Lai, Ryan Langendorf, Laura Norris, Tim Read, Adam Robbins-Pianka. Co-treatment of MEKi and BRD4 inhibitors lead to synergistic repression of MYC-associated enhancer RNAs [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4689.

Abstract 1272: Enhancer RNA cell line database reveals new associations between TF activity and cancer subtype

Cell type and lineage is driven, in large part, by changes in transcription factor activity. Particularly in cancer, misregulation of transcription factor binding can lead to de-differentiated states and overproliferation. Recent work suggests that enhancer RNAs are a highly predictive readout of transcription factor activity, however, measuring them is an arduous process. We developed an automated strategy to measure nascent RNAs for over 50 different cancer cell line models. By a modified PRO-seq assay, we annotated enhancer RNAs and transcription factors unique to specific cancer backgrounds. Examples include: ESR1 activity in ER+ breast cancer models, AR activity in castration-sensitive prostate models and GATA3 in AML. This database is the first standardized collection of enhancer RNAs and and can be used in conjunction with the BROAD's CCLE and DepMap consortiums to help expedite treatment options.

Citation Format: Joseph Azofeifa, Joel Basken, Maria Lai, Ryan Langendorf, Laura Norris, Tim Read, Adam Robbins-Pianka. Enhancer RNA cell line database reveals new associations between TF activity and cancer subtype [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1272.

Abstract 5854: New therapeutic targets of ER positive breast cancer are revealed through nascent RNA transcription time series

Mechanistic understanding of Estrogen Receptor (ER) signaling will lead to new therapeutic targets for ER positive breast cancer. To this end, Arpeggio performed nascent RNA sequencing following estradiol (E2) treatment in MCF7 breast cancer cells every 15 minutes for 6 hours. To date, our study represents the densest time series of estrogen signaling ever reported. Arpeggio noted three transcriptional waves following E2 treatment: peaks at 30 minutes, 2.5 hours and 5 hours, respectively. We noted that the early response was enriched for canonical ER signaling, however, mid and late response were enriched for metabolic processes. Because our study was statistically powered with 24 time points, we could use delay-coordinate embedding to look for causal gene interactions. We implicated NME2 as the causal driver of the 5-hour transcriptional wave. Indeed, NME2 and Estrogen Receptor (ER) have been shown to co-localize where NME2 blocks ER effect. Our study represents a new methodology for target discovery in hormone-driven cancers.

Citation Format: Joseph Azofeifa, Joel Basken, Maria Lai, Ryan Langendorf, Laura Norris, Tim Read, Adam Robbins-Pianka. New therapeutic targets of ER positive breast cancer are revealed through nascent RNA transcription time series [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5854.

Qiita: rapid, web-enabled microbiome meta-analysis

Multi-omic insights into microbiome function and composition typically advance one study at a time. However, to understand relationships across studies, they must be aggregated into meta-analyses. This makes it possible to generate new hypotheses by finding features that are reproducible across biospecimens and data layers. Qiita dramatically accelerates such integration tasks in a web-based microbiome comparison platform, which we demonstrate with Human Microbiome Project and iHMP data.

Bacterial community changes in an industrial algae production system

While microalgae are a promising feedstock for production of fuels and other chemicals, a challenge for the algal bioproducts industry is obtaining consistent, robust algae growth. Algal cultures include complex bacterial communities and can be difficult to manage because specific bacteria can promote or reduce algae growth. To overcome bacterial contamination, algae growers may use closed photobioreactors designed to reduce the number of contaminant organisms. Even with closed systems, bacteria are known to enter and cohabitate, but little is known about these communities. Therefore, the richness, structure, and composition of bacterial communities were characterized in closed photobioreactor cultivations of Nannochloropsis salina in F/2 medium at different scales, across nine months spanning late summer-early spring, and during a sequence of serially inoculated cultivations. Using 16S rRNA sequence data from 275 samples, bacterial communities in small, medium, and large cultures were shown to be significantly different. Larger systems contained richer bacterial communities compared to smaller systems. Relationships between bacterial communities and algae growth were complex. On one hand, blooms of a specific bacterial type were observed in three abnormal, poorly performing replicate cultivations, while on the other, notable changes in the bacterial community structures were observed in a series of serial large-scale batch cultivations that had similar growth rates. Bacteria common to the majority of samples were identified, including a single OTU within the class Saprospirae that was found in all samples. This study contributes important information for crop protection in algae systems, and demonstrates the complex ecosystems that need to be understood for consistent, successful industrial algae cultivation. This is the first study to profile bacterial communities during the scale-up process of industrial algae systems.

Multiplex growth rate phenotyping of synthetic mutants in selection to engineer glucose and xylose co-utilization in Escherichia coli

Engineering the simultaneous consumption of glucose and xylose sugars is critical to enable the sustainable production of biofuels from lignocellulosic biomass. In most major industrial microorganisms glucose completely inhibits the uptake of xylose, limiting efficient sugar mixture conversion. In E. coli removal of the major glucose transporter PTS allows for glucose and xylose co-consumption but only after prolonged adaptation, which is an effective process but hard to control and prone to co-evolving undesired traits. Here we synthetically engineer mutants to target sugar co-consumption properties; we subject a PTS^- mutant to a short adaptive step and subsequently either delete or overexpress key genes previously suggested to affect sugar consumption. Screening the co-consumption properties of these mutants individually is very laborious. We show we can evaluate sugar co-consumption properties in parallel by culturing the mutants in selection and applying a novel approach that computes mutant growth rates in selection using chromosomal barcode counts obtained from Next-Generation Sequencing. We validate this multiplex growth rate phenotyping approach with individual mutant pure cultures, identify new instances of mutants cross-feeding on metabolic byproducts, and, importantly, find that the rates of glucose and xylose co-consumption can be tuned by altering glucokinase expression in our PTS^- background. Biotechnol. Bioeng. 2017;114: 885-893. © 2016 Wiley Periodicals, Inc.

Subsampled open-reference clustering creates consistent, comprehensive OTU definitions and scales to billions of sequences

We present a performance-optimized algorithm, subsampled open-reference OTU picking, for assigning marker gene (e.g., 16S rRNA) sequences generated on next-generation sequencing platforms to operational taxonomic units (OTUs) for microbial community analysis. This algorithm provides benefits over de novo OTU picking (clustering can be performed largely in parallel, reducing runtime) and closed-reference OTU picking (all reads are clustered, not only those that match a reference database sequence with high similarity). Because more of our algorithm can be run in parallel relative to “classic” open-reference OTU picking, it makes open-reference OTU picking tractable on massive amplicon sequence data sets (though on smaller data sets, “classic” open-reference OTU clustering is often faster). We illustrate that here by applying it to the first 15,000 samples sequenced for the Earth Microbiome Project (1.3 billion V4 16S rRNA amplicons). To the best of our knowledge, this is the largest OTU picking run ever performed, and we estimate that our new algorithm runs in less than 1/5 the time than would be required of “classic” open reference OTU picking. We show that subsampled open-reference OTU picking yields results that are highly correlated with those generated by “classic” open-reference OTU picking through comparisons on three well-studied datasets. An implementation of this algorithm is provided in the popular QIIME software package, which uses uclust for read clustering. All analyses were performed using QIIME’s uclust wrappers, though we provide details (aided by the open-source code in our GitHub repository) that will allow implementation of subsampled open-reference OTU picking independently of QIIME (e.g., in a compiled programming language, where runtimes should be further reduced). Our analyses should generalize to other implementations of these OTU picking algorithms. Finally, we present a comparison of parameter settings in QIIME’s OTU picking workflows and make recommendations on settings for these free parameters to optimize runtime without reducing the quality of the results. These optimized parameters can vastly decrease the runtime of uclust-based OTU picking in QIIME.

Amino termini of many yeast proteins map to downstream start codons

Comprehensive knowledge of proteome complexity is crucial to understanding cell function. Amino termini of yeast proteins were identified through peptide mass spectrometry on glutaraldehyde-treated cell lysates as well as a parallel assessment of publicly deposited spectra. An unexpectedly large fraction of detected amino-terminal peptides (35%) mapped to translation initiation at AUG codons downstream of the annotated start codon. Many of the implicated genes have suboptimal sequence contexts for translation initiation near their annotated AUG, and their ribosome profiles show elevated tag densities consistent with translation initiation at downstream AUGs as well as their annotated AUGs. These data suggest that a significant fraction of the yeast proteome derives from initiation at downstream AUGs, increasing significantly the repertoire of encoded proteins and their potential functions and cellular localizations.

The adjacent positioning of co-regulated gene pairs is widely conserved across eukaryotes

Background

Coordinated cell growth and development requires that cells regulate the expression of large sets of genes in an appropriate manner, and one of the most complex and metabolically demanding pathways that cells must manage is that of ribosome biogenesis. Ribosome biosynthesis depends upon the activity of hundreds of gene products, and it is subject to extensive regulation in response to changing cellular conditions. We previously described an unusual property of the genes that are involved in ribosome biogenesis in yeast; a significant fraction of the genes exist on the chromosomes as immediately adjacent gene pairs. The incidence of gene pairing can be as high as 24% in some species, and the gene pairs are found in all of the possible tandem, divergent, and convergent orientations.

Results

We investigated co-regulated gene sets in S. cerevisiae beyond those related to ribosome biogenesis, and found that a number of these regulons, including those involved in DNA metabolism, heat shock, and the response to cellular stressors were also significantly enriched for adjacent gene pairs. We found that as a whole, adjacent gene pairs were more tightly co-regulated than unpaired genes, and that the specific gene pairing relationships that were most widely conserved across divergent fungal lineages were correlated with those genes that exhibited the highest levels of transcription. Finally, we investigated the gene positions of ribosome related genes across a widely divergent set of eukaryotes, and found a significant level of adjacent gene pairing well beyond yeast species.

Conclusion

While it has long been understood that there are connections between genomic organization and transcriptional regulation, this study reveals that the strategy of organizing genes from related, co-regulated pathways into pairs of immediately adjacent genes is widespread, evolutionarily conserved, and functionally significant.