Cancer and healthy cells have distinct distributions of molecular properties and thus respond differently to drugs. Cancer drugs ideally kill cancer cells while limiting harm to healthy cells. ...However, the inherent variance among cells in both cancer and healthy cell populations increases the difficulty of selective drug action. Here we formalize a classification framework based on the idea that an ideal cancer drug should maximally discriminate between cancer and healthy cells. More specifically, this discrimination should be performed on the basis of measurable cell markers. We divide the problem into three parts which we explore with examples. First, molecular markers should discriminate cancer cells from healthy cells at the single-cell level. Second, the effects of drugs should be statistically predicted by these molecular markers. Third, drugs should be optimized for classification performance. We find that expression levels of a handful of genes suffice to discriminate well between individual cells in cancer and healthy tissue. We also find that gene expression predicts the efficacy of some cancer drugs, suggesting that these cancer drugs act as suboptimal classifiers using gene profiles. Finally, we formulate a framework that defines an optimal drug, and predicts drug cocktails that may target cancer more accurately than the individual drugs alone. Conceptualizing cancer drugs as solving a discrimination problem in the high-dimensional space of molecular markers promises to inform the design of new cancer drugs and drug cocktails.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Pancreatic islet (dys)function and failure is central to both type 2 diabetes (T2D) genetic risk and pathogenesis. Islet cellular heterogeneity has impeded precise understanding of the specific ...gene(s) and pathway(s) in each cell type that are altered by and/or may contribute to pathophysiology in the prediabetic (PD) and type 2 diabetic (T2D) states. To identify cellular and molecular features of islet dysfunction in PD and T2D states, we completed single cell transcriptome profiling of 137k human islet cells from 11 ND, 6 PD, and 11 T2D donors (⋃5k/individual).
Single cell transcriptomes aggregated into distinct clusters of endocrine (alpha, beta, delta, PP/gamma), exocrine (acinar, stellate, ductal), endothelial, and immune cell types. Targeted analysis of each endocrine cell type identified a proliferative alpha cell sub-population comprising ∼0.15-0.3% of endocrine cells and variable insulin production and endoplasmic reticulum stress beta cell states. We compared the proportion of islet endocrine cell type (sub)populations sampled between T2D, PD, and ND donors and found that T2D samples contained significantly fewer beta cells and more alpha cells than ND and, to a lesser degree, PD samples. Delta and PP/gamma cell proportions were similar between disease states. Importantly, no (sub)populations were ND, PD, or T2D-specific.
We compared aggregate T2D, PD, and ND cell type-specific transcriptomes to identify differentially expressed genes in each cell type. Genes related to exocytic granule assembly/organization, including those induced by T2D risk variants (DGKB) or T2D state (GAP43), were among 99 genes induced in T2D beta cells. 103 repressed genes included those associated with glucose transport and implicated as T2D GWAS effector genes such as SLC2A2, SLC5A1, and STARD10, a gene whose deletion in mice impairs islet insulin secretion. Studies are ongoing to assess the functional role(s) of these and additional genes in islet (dys)function.
Disclosure
N. Lawlor: None. R. Kursawe: None. M.L. Stitzel: None.
Funding
American Diabetes Association/Pathway to Stop Diabetes (1-18-ACE-15 to M.L.S.)
Type 2 diabetes (T2D) is a complex disorder in which both genetic and environmental risk factors contribute to islet dysfunction and failure. Genome-wide association studies (GWAS) have linked single ...nucleotide polymorphisms (SNPs), most of which are noncoding, in >200 loci to islet dysfunction and T2D. Identification of the putative causal variants and their target genes and whether they lead to gain or loss of function remains challenging. Here, we profiled chromatin accessibility in pancreatic islet samples from 19 genotyped individuals and identified 2,949 SNPs associated with in vivo
-regulatory element use (i.e., chromatin accessibility quantitative trait loci caQTL). Among the caQTLs tested (
= 13) using luciferase reporter assays in MIN6 β-cells, more than half exhibited effects on enhancer activity that were consistent with in vivo chromatin accessibility changes. Importantly, islet caQTL analysis nominated putative causal SNPs in 13 T2D-associated GWAS loci, linking 7 and 6 T2D risk alleles, respectively, to gain or loss of in vivo chromatin accessibility. By investigating the effect of genetic variants on chromatin accessibility in islets, this study is an important step forward in translating T2D-associated GWAS SNP into functional molecular consequences.
Abstract
Summary
Single-cell RNA-sequencing (scRNA-seq) technology enables studying gene expression programs from individual cells. However, these data are subject to diverse sources of variation, ...including ‘unwanted’ variation that needs to be removed in downstream analyses (e.g. batch effects) and ‘wanted’ or biological sources of variation (e.g. variation associated with a cell type) that needs to be precisely described. Surrogate variable analysis (SVA)-based algorithms, are commonly used for batch correction and more recently for studying ‘wanted’ variation in scRNA-seq data. However, interpreting whether these variables are biologically meaningful or stemming from technical reasons remains a challenge. To facilitate the interpretation of surrogate variables detected by algorithms including IA-SVA, SVA or ZINB-WaVE, we developed an R Shiny application Visual Surrogate Variable Analysis (V-SVA) that provides a web-browser interface for the identification and annotation of hidden sources of variation in scRNA-seq data. This interactive framework includes tools for discovery of genes associated with detected sources of variation, gene annotation using publicly available databases and gene sets, and data visualization using dimension reduction methods.
Availability and implementation
The V-SVA Shiny application is publicly hosted at https://vsva.jax.org/ and the source code is freely available at https://github.com/nlawlor/V-SVA.
Contact
leed13@miamioh.edu or duygu.ucar@jax.org
Supplementary information
Supplementary data are available at Bioinformatics online.
Single cell RNA-sequencing (scRNA-seq) precisely characterizes gene expression levels and dissects variation in expression associated with the state (technical or biological) and the type of the ...cell, which is averaged out in bulk measurements. Multiple and correlated sources contribute to gene expression variation in single cells, which makes their estimation difficult with the existing methods developed for batch correction (e.g., surrogate variable analysis (SVA)) that estimate orthogonal transformations of these sources. We developed iteratively adjusted surrogate variable analysis (IA-SVA) that can estimate hidden factors even when they are correlated with other sources of variation by identifying a set of genes associated with each hidden factor in an iterative manner. Analysis of scRNA-seq data from human cells showed that IA-SVA could accurately capture hidden variation arising from technical (e.g., stacked doublet cells) or biological sources (e.g., cell type or cell-cycle stage). Furthermore, IA-SVA delivers a set of genes associated with the detected hidden source to be used in downstream data analyses. As a proof of concept, IA-SVA recapitulated known marker genes for islet cell subsets (e.g., alpha, beta), which improved the grouping of subsets into distinct clusters. Taken together, IA-SVA is an effective and novel method to dissect multiple and correlated sources of variation in scRNA-seq data.
Alpha TC1 (αTC1) and Beta-TC-6 (βTC6) mouse islet cell lines are cellular models of islet (dys)function and type 2 diabetes (T2D). However, genomic characteristics of these cells, and their ...similarities to primary islet alpha and beta cells, are undefined. Here, we report the epigenomic (ATAC-seq) and transcriptomic (RNA-seq) landscapes of αTC1 and βTC6 cells. Each cell type exhibits hallmarks of its primary islet cell counterpart including cell-specific expression of beta (e.g., Pdx1) and alpha (e.g., Arx) cell transcription factors (TFs), and enrichment of binding motifs for these TFs in αTC1/βTC6 cis-regulatory elements. αTC1/βTC6 transcriptomes overlap significantly with the transcriptomes of primary mouse/human alpha and beta cells. Our data further indicate that ATAC-seq detects cell-specific regulatory elements for cell types comprising ≥ 20% of a mixed cell population. We identified αTC1/βTC6 cis-regulatory elements orthologous to those containing type 2 diabetes (T2D)-associated SNPs in human islets for 33 loci, suggesting these cells' utility to dissect T2D molecular genetics in these regions. Together, these maps provide important insights into the conserved regulatory architecture between αTC1/βTC6 and primary islet cells that can be leveraged in functional (epi)genomic approaches to dissect the genetic and molecular factors controlling islet cell identity and function.
Abstract
Transcription factor (TF) footprinting uncovers putative protein-DNA binding via combined analyses of chromatin accessibility patterns and their underlying TF sequence motifs. TF footprints ...are frequently used to identify TFs that regulate activities of cell/condition-specific genomic regions (target loci) in comparison to control regions (background loci) using standard enrichment tests. However, there is a strong association between the chromatin accessibility level and the GC content of a locus and the number and types of TF footprints that can be detected at this site. Traditional enrichment tests (e.g. hypergeometric) do not account for this bias and inflate false positive associations. Therefore, we developed a novel post-processing method, Bias-free Footprint Enrichment Test (BiFET), that corrects for the biases arising from the differences in chromatin accessibility levels and GC contents between target and background loci in footprint enrichment analyses. We applied BiFET on TF footprint calls obtained from EndoC-βH1 ATAC-seq samples using three different algorithms (CENTIPEDE, HINT-BC and PIQ) and showed BiFET's ability to increase power and reduce false positive rate when compared to hypergeometric test. Furthermore, we used BiFET to study TF footprints from human PBMC and pancreatic islet ATAC-seq samples to show its utility to identify putative TFs associated with cell-type-specific loci.
Mitochondrial function is pivotal to β-cell competence. The mitochondrial life cycle balances mitochondrial biogenesis and turnover (mitophagy) to ensure optimal metabolic function. Human type 2 ...diabetic (T2D) β-cells are known to develop mitochondrial structural/functional defects, suggestive of a defective mitochondrial life cycle. However, it is unclear if these defects are a cause or consequence of T2D. Here, we observed that human T2D β-cells had reduced mitophagic flux, mtDNA content, and expression of mitochondrially encoded genes. To test the importance of the mitochondrial life cycle to drive β-cell failure in T2D, we developed 2 distinct β-cell specific mouse models: βTfamKO (to deplete mtDNA) and βClec16aΚΟ (to impair mitophagy). We observed age dependent loss of glucose tolerance, glucose stimulated insulin secretion and β-cell mass in both models, which was exacerbated by obesity in βClec16aΚΟ mice. Loss of β-cell mass was largely independent of changes in proliferation or apoptosis but rather, due to an induction of β-cell immaturity. Using lineage tracing approaches, we confirmed that βClec16aKO and βTfamKO mice induce formation of both insulin-negative immature β-cells and β-to-α cell transdifferentiation. Further, high-throughput gene expression profiling, biochemical, and metabolic assays highlighted that either Clec16a or Tfam deficiency induces an aberrant retrograde signaling program, manifested by reductions in cellular ATP and activation of the integrated stress response (ISR). Importantly, inhibition of the ISR in vivo ameliorated glucose intolerance, defective insulin secretion, β-cell immaturity, and loss of β-cell mass, suggesting that aberrant retrograde signaling may directly lead to loss of β-cell identity. Taken together, our studies illustrate that a unified mitochondrial lifecycle is necessary to maintain β-cell mass and identity and may be targeted to prevent β-cell failure in T2D.
Disclosure
G. Pearson: None. N. Lawlor: None. J. Zhu: None. E. M. Walker: None. E. C. Reck: None. M. L. Stitzel: None. S. Soleimanpour: None.
Funding
American Diabetes Association (1-19-PDF-063 to G.P.)
Genome-wide association studies (GWASs) and functional genomics approaches implicate enhancer disruption in islet dysfunction and type 2 diabetes (T2D) risk. We applied genetic fine-mapping and ...functional (epi)genomic approaches to a T2D- and proinsulin-associated 15q22.2 locus to identify a most likely causal variant, determine its direction of effect, and elucidate plausible target genes. Fine-mapping and conditional analyses of proinsulin levels of 8,635 non-diabetic individuals from the METSIM study support a single association signal represented by a cluster of 16 strongly associated (p < 10−17) variants in high linkage disequilibrium (r2 > 0.8) with the GWAS index SNP rs7172432. These variants reside in an evolutionarily and functionally conserved islet and β cell stretch or super enhancer; the most strongly associated variant (rs7163757, p = 3 × 10−19) overlaps a conserved islet open chromatin site. DNA sequence containing the rs7163757 risk allele displayed 2-fold higher enhancer activity than the non-risk allele in reporter assays (p < 0.01) and was differentially bound by β cell nuclear extract proteins. Transcription factor NFAT specifically potentiated risk-allele enhancer activity and altered patterns of nuclear protein binding to the risk allele in vitro, suggesting that it could be a factor mediating risk-allele effects. Finally, the rs7163757 proinsulin-raising and T2D risk allele (C) was associated with increased expression of C2CD4B, and possibly C2CD4A, both of which were induced by inflammatory cytokines, in human islets. Together, these data suggest that rs7163757 contributes to genetic risk of islet dysfunction and T2D by increasing NFAT-mediated islet enhancer activity and modulating C2CD4B, and possibly C2CD4A, expression in (patho)physiologic states.
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