Synthetic Epigenetic Reprogramming of Mesenchymal to Epithelial States Using the CRISPR/dCas9 Platform in Triple Negative Breast Cancer

Epithelial-mesenchymal transition (EMT) is a reversible transcriptional program invoked by cancer cells to drive cancer progression. Transcription factor ZEB1 is a master regulator of EMT, driving disease recurrence in poor-outcome triple negative breast cancers (TNBCs). Here, this work silences ZEB1 in TNBC models by CRISPR/dCas9-mediated epigenetic editing, resulting in highly-specific and nearly complete suppression of ZEB1 in vivo, accompanied by long-lasting tumor inhibition. Integrated "omic" changes promoted by dCas9 linked to the KRAB domain (dCas9-KRAB) enabled the discovery of a ZEB1-dependent-signature of 26 genes differentially-expressed and -methylated, including the reactivation and enhanced chromatin accessibility in cell adhesion loci, outlining epigenetic reprogramming toward a more epithelial state. In the ZEB1 locus transcriptional silencing is associated with induction of locally-spread heterochromatin, significant changes in DNA methylation at specific CpGs, gain of H3K9me3, and a near complete erasure of H3K4me3 in the ZEB1 promoter. Epigenetic shifts induced by ZEB1-silencing are enriched in a subset of human breast tumors, illuminating a clinically-relevant hybrid-like state. Thus, the synthetic epi-silencing of ZEB1 induces stable "lock-in" epigenetic reprogramming of mesenchymal tumors associated with a distinct and stable epigenetic landscape. This work outlines epigenome-engineering approaches for reversing EMT and customizable precision molecular oncology approaches for targeting poor outcome breast cancers.

Short tandem repeat expansions in sporadic amyotrophic lateral sclerosis and frontotemporal dementia

Pathogenic short tandem repeat (STR) expansions cause over 20 neurodegenerative diseases. To determine the contribution of STRs in sporadic amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), we used ExpansionHunter, REviewer, and polymerase chain reaction validation to assess 21 neurodegenerative disease-associated STRs in whole-genome sequencing data from 608 patients with sporadic ALS, 68 patients with sporadic FTD, and 4703 matched controls. We also propose a data-derived outlier detection method for defining allele thresholds in rare STRs. Excluding C9orf72 repeat expansions, 17.6% of clinically diagnosed ALS and FTD cases had at least one expanded STR allele reported to be pathogenic or intermediate for another neurodegenerative disease. We identified and validated 162 disease-relevant STR expansions in C9orf72 (ALS/FTD), ATXN1 [spinal cerebellar ataxia type 1 (SCA1)], ATXN2 (SCA2), ATXN8 (SCA8), TBP (SCA17), HTT (Huntington's disease), DMPK [myotonic dystrophy type 1 (DM1)], CNBP (DM2), and FMR1 (fragile-X disorders). Our findings suggest clinical and pathological pleiotropy of neurodegenerative disease genes and highlight their importance in ALS and FTD.

An intronic GAA repeat expansion in FGF14 causes the autosomal-dominant adult-onset ataxia SCA50/ATX-FGF14

Adult-onset cerebellar ataxias are a group of neurodegenerative conditions that challenge both genetic discovery and molecular diagnosis. In this study, we identified an intronic (GAA) repeat expansion in fibroblast growth factor 14 (FGF14). Genetic analysis of 95 Australian individuals with adult-onset ataxia identified four (4.2%) with (GAA)_>300 and a further nine individuals with (GAA)_>250. PCR and long-read sequence analysis revealed these were pure (GAA) repeats. In comparison, no control subjects had (GAA)_>300 and only 2/311 control individuals (0.6%) had a pure (GAA)_>250. In a German validation cohort, 9/104 (8.7%) of affected individuals had (GAA)_>335 and a further six had (GAA)_>250, whereas 10/190 (5.3%) control subjects had (GAA)_>250 but none were (GAA)_>335. The combined data suggest (GAA)_>335 are disease causing and fully penetrant (p = 6.0 × 10^-8, OR = 72 [95% CI = 4.3-1,227]), while (GAA)_>250 is likely pathogenic with reduced penetrance. Affected individuals had an adult-onset, slowly progressive cerebellar ataxia with variable features including vestibular impairment, hyper-reflexia, and autonomic dysfunction. A negative correlation between age at onset and repeat length was observed (R² = 0.44, p = 0.00045, slope = -0.12) and identification of a shared haplotype in a minority of individuals suggests that the expansion can be inherited or generated de novo during meiotic division. This study demonstrates the power of genome sequencing and advanced bioinformatic tools to identify novel repeat expansions via model-free, genome-wide analysis and identifies SCA50/ATX-FGF14 as a frequent cause of adult-onset ataxia.

A founder event causing a dominant childhood epilepsy survives 800 years through weak selective pressure

Genetic epilepsy with febrile seizures plus (GEFS+) is an autosomal dominant familial epilepsy syndrome characterized by distinctive phenotypic heterogeneity within families. The SCN1B c.363C>G (p.Cys121Trp) variant has been identified in independent, multi-generational families with GEFS+. Although the variant is present in population databases (at very low frequency), there is strong clinical, genetic, and functional evidence to support pathogenicity. Recurrent variants may be due to a founder event in which the variant has been inherited from a common ancestor. Here, we report evidence of a single founder event giving rise to the SCN1B c.363C>G variant in 14 independent families with epilepsy. A common haplotype was observed in all families, and the age of the most recent common ancestor was estimated to be approximately 800 years ago. Analysis of UK Biobank whole-exome-sequencing data identified 74 individuals with the same variant. All individuals carried haplotypes matching the epilepsy-affected families, suggesting all instances of the variant derive from a single mutational event. This unusual finding of a variant causing an autosomal dominant, early-onset disease in an outbred population that has persisted over many generations can be attributed to the relatively mild phenotype in most carriers and incomplete penetrance. Founder events are well established in autosomal recessive and late-onset disorders but are rarely observed in early-onset, autosomal dominant diseases. These findings suggest variants present in the population at low frequencies should be considered potentially pathogenic in mild phenotypes with incomplete penetrance and may be more important contributors to the genetic landscape than previously thought.

Measuring pathway database coverage of the phosphoproteome

Protein phosphorylation is one of the best known post-translational mechanisms playing a key role in the regulation of cellular processes. Over 100,000 distinct phosphorylation sites have been discovered through constant improvement of mass spectrometry based phosphoproteomics in the last decade. However, data saturation is occurring and the bottleneck of assigning biologically relevant functionality to phosphosites needs to be addressed. There has been finite success in using data-driven approaches to reveal phosphosite functionality due to a range of limitations. The alternate, more suitable approach is making use of prior knowledge from literature-derived databases. Here, we analysed seven widely used databases to shed light on their suitability to provide functional insights into phosphoproteomics data. We first determined the global coverage of each database at both the protein and phosphosite level. We also determined how consistent each database was in its phosphorylation annotations compared to a global standard. Finally, we looked in detail at the coverage of each database over six experimental datasets. Our analysis highlights the relative strengths and weaknesses of each database, providing a guide in how each can be best used to identify biological mechanisms in phosphoproteomic data.

A Scalable Permutation Approach Reveals Replication and Preservation Patterns of Network Modules in Large Datasets

Network modules-topologically distinct groups of edges and nodes-that are preserved across datasets can reveal common features of organisms, tissues, cell types, and molecules. Many statistics to identify such modules have been developed, but testing their significance requires heuristics. Here, we demonstrate that current methods for assessing module preservation are systematically biased and produce skewed p values. We introduce NetRep, a rapid and computationally efficient method that uses a permutation approach to score module preservation without assuming data are normally distributed. NetRep produces unbiased p values and can distinguish between true and false positives during multiple hypothesis testing. We use NetRep to quantify preservation of gene coexpression modules across murine brain, liver, adipose, and muscle tissues. Complex patterns of multi-tissue preservation were revealed, including a liver-derived housekeeping module that displayed adipose- and muscle-specific association with body weight. Finally, we demonstrate the broader applicability of NetRep by quantifying preservation of bacterial networks in gut microbiota between men and women.

An interaction map of circulating metabolites, immune gene networks, and their genetic regulation

BACKGROUND

Immunometabolism plays a central role in many cardiometabolic diseases. However, a robust map of immune-related gene networks in circulating human cells, their interactions with metabolites, and their genetic control is still lacking. Here, we integrate blood transcriptomic, metabolomic, and genomic profiles from two population-based cohorts (total N = 2168), including a subset of individuals with matched multi-omic data at 7-year follow-up.

RESULTS

We identify topologically replicable gene networks enriched for diverse immune functions including cytotoxicity, viral response, B cell, platelet, neutrophil, and mast cell/basophil activity. These immune gene modules show complex patterns of association with 158 circulating metabolites, including lipoprotein subclasses, lipids, fatty acids, amino acids, small molecules, and CRP. Genome-wide scans for module expression quantitative trait loci (mQTLs) reveal five modules with mQTLs that have both cis and trans effects. The strongest mQTL is in ARHGEF3 (rs1354034) and affects a module enriched for platelet function, independent of platelet counts. Modules of mast cell/basophil and neutrophil function show temporally stable metabolite associations over 7-year follow-up, providing evidence that these modules and their constituent gene products may play central roles in metabolic inflammation. Furthermore, the strongest mQTL in ARHGEF3 also displays clear temporal stability, supporting widespread trans effects at this locus.

CONCLUSIONS

This study provides a detailed map of natural variation at the blood immunometabolic interface and its genetic basis, and may facilitate subsequent studies to explain inter-individual variation in cardiometabolic disease.

Metabolomics in epidemiology: from metabolite concentrations to integrative reaction networks

Metabolomics is becoming feasible for population-scale studies of human disease. In this review, we survey epidemiological studies that leverage metabolomics and multi-omics to gain insight into disease mechanisms. We outline key practical, technological and analytical limitations while also highlighting recent successes in integrating these data. The use of multi-omics to infer reaction rates is discussed as a potential future direction for metabolomics research, as a means of identifying biomarkers as well as inferring causality. Furthermore, we highlight established analysis approaches as well as simulation-based methods currently used in single- and multi-cell levels in systems biology.

The Biomarker GlycA Is Associated with Chronic Inflammation and Predicts Long-Term Risk of Severe Infection

The biomarker glycoprotein acetylation (GlycA) has been shown to predict risk of cardiovascular disease and all-cause mortality. Here, we characterize biological processes associated with GlycA by leveraging population-based omics data and health records from >10,000 individuals. Our analyses show that GlycA levels are chronic within individuals for up to a decade. In apparently healthy individuals, elevated GlycA corresponded to elevation of myriad inflammatory cytokines, as well as a gene coexpression network indicative of increased neutrophil activity, suggesting that individuals with high GlycA may be in a state of chronic inflammatory response. Accordingly, analysis of infection-related hospitalization and death records showed that increased GlycA increased long-term risk of severe non-localized and respiratory infections, particularly septicaemia and pneumonia. In total, our work demonstrates that GlycA is a biomarker for chronic inflammation, neutrophil activity, and risk of future severe infection. It also illustrates the utility of leveraging multi-layered omics data and health records to elucidate the molecular and cellular processes associated with biomarkers.

Extracting reaction networks from databases-opening Pandora's box

Large quantities of information describing the mechanisms of biological pathways continue to be collected in publicly available databases. At the same time, experiments have increased in scale, and biologists increasingly use pathways defined in online databases to interpret the results of experiments and generate hypotheses. Emerging computational techniques that exploit the rich biological information captured in reaction systems require formal standardized descriptions of pathways to extract these reaction networks and avoid the alternative: time-consuming and largely manual literature-based network reconstruction. Here, we systematically evaluate the effects of commonly used knowledge representations on the seemingly simple task of extracting a reaction network describing signal transduction from a pathway database. We show that this process is in fact surprisingly difficult, and the pathway representations adopted by various knowledge bases have dramatic consequences for reaction network extraction, connectivity, capture of pathway crosstalk and in the modelling of cell-cell interactions. Researchers constructing computational models built from automatically extracted reaction networks must therefore consider the issues we outline in this review to maximize the value of existing pathway knowledge.

PATHLOGIC-S: a scalable Boolean framework for modelling cellular signalling

Curated databases of signal transduction have grown to describe several thousand reactions, and efficient use of these data requires the development of modelling tools to elucidate and explore system properties. We present PATHLOGIC-S, a Boolean specification for a signalling model, with its associated GPL-licensed implementation using integer programming techniques. The PATHLOGIC-S specification has been designed to function on current desktop workstations, and is capable of providing analyses on some of the largest currently available datasets through use of Boolean modelling techniques to generate predictions of stable and semi-stable network states from data in community file formats. PATHLOGIC-S also addresses major problems associated with the presence and modelling of inhibition in Boolean systems, and reduces logical incoherence due to common inhibitory mechanisms in signalling systems. We apply this approach to signal transduction networks including Reactome and two pathways from the Panther Pathways database, and present the results of computations on each along with a discussion of execution time. A software implementation of the framework and model is freely available under a GPL license.