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Undiscovered Physiology of Transcript and Protein Networks

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ABSTRACT

The past two decades have witnessed a rapid evolution in our ability to measure RNA and protein from biological systems. As a result, new principles have arisen regarding how information is processed in cells, how decisions are made, and the role of networks in biology. This essay examines this technological evolution, reviewing (and critiquing) the conceptual framework that has emerged to explain how RNA and protein networks control cellular function. We identify how future investigations into transcriptomes, proteomes, and other cellular networks will enable development of more robust, quantitative models of cellular behavior whilst also providing new avenues to use knowledge of biological networks to improve human health. © 2016 American Physiological Society. Compr Physiol 6:1851‐1872, 2016.

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Figure 1. Figure 1. Evolution of RNA quantitation techniques toward a more comprehensive catalogue of RNA species. (A) The first microarray was published in 1995 by Patrick Brown and quantified 45 mRNA species simultaneously using hybridization to DNA probes [reprinted with permission ()]. (B) Microarrays have since advanced to measure tens of thousands of RNAs, including noncoding RNA. Shown is a heatmap of 768 lncRNAs found by array to exhibit altered abundances in the blood between patients with and without left ventricular remodeling following myocardial infarction [reprinted with permission ()]. In this case, transcriptome measurements enabled unbiased identification of disease progression biomarkers. (C) Due to the development of RNA‐sequencing and subsequent advances in the library preparation, sequencing and data analysis, quantification a greater diversity of RNA species on a transcriptome‐wide scale is now routine. Shown is a Sashimi plot displaying the relative abundances of different exons in an example measured from the hearts of wild‐type mice (red) and mice with a knockout of a splicing factor. y‐axis represents normalized RNA‐seq reads (expression), x‐axis represents genomic coordinates. The arcs are numbered to indicate the raw number of junction reads. Arcs with greater values bridge two exons that are more often spliced next to each other [reprinted with permission ()]. While these data were acquired from mice that were experimentally manipulated to disrupt splicing, many studies find exon usage is an important component to the transcriptome regulation of cell‐type specificity, development, and disease.
Figure 2. Figure 2. RNA abundance and protein abundance both correlate better with ribosome occupancy than they do with each other. Expression analysis was performed on lymphoblastoid cell lines of diverse genetic backgrounds taken from the HapMap project. Genes were clustered into modules or neurons (hexagon, right panels) within a self‐organizing map based on similar expression profiles across four different measurements (protein abundance, translation efficiency, RNA abundance, and ribosome occupancy; left panel). The right panel displays the same self‐organizing map colored to portray the mean expression of the genes within the module based on the four different datasets. The authors ask if hexagons with similar mean expression by one measurement (either both colored red or both colored blue) also show similar expression when using an alternate measure of expression. Ribosome occupancy correlates with RNA expression and protein level better than RNA expression and protein level correlate with each other. Note, ribosome occupancy is defined by the total read counts for an RNA after ribosome profiling, while translation efficiency takes into account the total pool of RNA (RNA‐seq) in addition to the ribosome occupancy [reprinted with permission ()].
Figure 3. Figure 3. Evolution of proteomics toward network analysis. (A) Two‐dimensional protein gels were published in 1975 (top panel, [reprinted with permission ()]), and remained a common tool for identifying proteome‐level quantitative differences between samples up into the late 1990s (bottom panel [reprinted with permission ()]). Bottom panel is a computer‐processed image of a silver‐stained 2D gel from a human dilated cardiomyopathy sample. Spots represent protein isoforms identifiable by their position in the gel (number indicates database protein ID). Note that PTM can shift a protein's location in the gel, providing additional information. Red spots indicate isoforms which were less abundant (weaker signal; similar to Western blot analysis) across the dilated cardiomyopathy patients as compared to ischemic cardiomyopathy samples run in a separate gel, and analyzed together using computer software. Note that this analysis reveals on average 1282 spots per sample, in the same general scale as LC/MS/MS analyses; however, the identification of the individual spots, when not coupled to mass spectrometry, remains imprecise. (B) By contrast, advances in mass spectrometry and sample preparation pipeline have enabled quantification of PTMs across entire signaling cascades from multiple conditions. Shown here is the known insulin signaling pathway curated from multiple databases, overlaid with phosphorylation quantitation (expressed as fold‐change) from a mass spectrometry analysis performed on liver samples from mice treated with PBS or insulin at two time points [reprinted with permission ()]. These techniques are optimized for a focused subproteome, thus enabled thorough, dynamic measurements of the system, which go beyond identifying proteins into the realm of mapping biological processes within a network. (C) Shown is a protein‐protein interaction network from HeLa cells generated through combining coimmunoprecipitation followed by mass spectrometry for 1125 different proteins [reprinted with permission ()]. Red indicates edges previously annotated in CORUM. On its own, this network represents a database to inform other protein interaction studies. However, the authors took this study a step further to compare their interaction network with the relative abundance of the proteins to infer complex stability. Thus, by comparing across networks, the omics datasets are able to generate new understanding of properties of the proteome.
Figure 4. Figure 4. Mass spectrometry techniques for building protein networks. (A) Peptides (circles; size indicates relative abundance) elute from the LC column into the mass spectrometer. In shotgun/bottom‐up proteomics, peptides are scanned in the MS1 and the most abundant ions selected for fragmentation and identification via multiple MS2 scans. In MRM, both the MS1 and MS2 scan are performed on predetermined m/z ratios set by the user to precisely quantify peptides of interest, including low abundant peptides. SWATH by contrast fragments all ions from the MS1 scan, resulting in many more MS2 scans, each containing spectra from many parent ions. (B) Upstream techniques can be used in conjunction with mass spectrometry to enable protein and PTM identification, quantitation, and spatial localization information used to build protein networks.
Figure 5. Figure 5. The role of genetics in gene expression is organ specific. To test the relationship between genetics, gene expression, and phenotype, we examined data from a panel of 37 genetically diverse, inbred mouse strains with microarray data from multiple organs: Macrophages with and without LPS stimulation (unpublished), striatum (), hippocampus (), bone marrow (), and heart with and without isoproterenol (ISO) stimulation to induce heart failure (). Strains were clustered based on expression of all genes on the microarray (All) or a class of genes known as the “fetal gene program” (Fetal), whose cardiac expression are considered to be biomarkers of heart failure. The relatedness between each strain‐by‐strain comparison (Euclidean) was compared across organs. If the relative similarity in expression between two strains is similar across two organs, those two organs cluster closer together on the dendrogram. We also incorporated genetic relatedness based on kinship matrix derived from SNPs (Genetics). Macrophages cluster according to genetics, suggesting that strains with similar genetics also show similar expression patterns in macrophages regardless of if we examine all genes, or the cardiac fetal genes, and even when examining expression after LPS stimulation. By contrast, other organs, such as bone marrow, have expression relationships that less closely match genetic relationships. For context, we compared the relationships between genetics versus mRNA expression to that of genetics versus cardiac phenotype [ejection fraction (EF) and heart weight/body weight (HW/BW), two indices which change in heart failure]. In some cases, the genetic relationship more closely matched the phenotype than the expression (basal EF), but in other cases it did not (EF after ISO). We hypothesized that the “fetal gene program” was an intermediate between genetics and phenotype, but found that it no more closely matched the phenotypic relationships than when we examined all genes together. These analyses indicate that the relationship between genetic variation, mRNA expression, and ultimately phenotype is buffered at each level. For example, complex SNP interactions and chromatin features may buffer the relationship between genetic variation and mRNA expression, while posttranscriptional and posttranslational processing as well as compartmentalization may buffer the relationship between mRNA and protein levels, with the relationship between protein and phenotype in turn buffered by protein network properties and interaction with other classes of molecules.
Figure 6. Figure 6. Spectrum of cognitive bias in basic and translational research. Implementation of discovery science and hypothesis‐driven research comprise a spectrum analogous to the “opportunity cost” principle. Points along the curve represent experiments where the opportunity cost is minimized, because some perfect balance between discovery and hypothesis is struck. Point A defines a species of research with very high uncertainty and little or no theoretical underpinning, but with the potential to be very novel. Point B defines another type in which highly focused and inherently biased research reaches full potential by maximizing prior knowledge. Studies that lie under the curve, due to shoddy or underexplored data or an experimental design that builds only incrementally on precedent, fail to meet the ideal balance of discovery and hypothesis [reprinted with permission ()].


Figure 1. Evolution of RNA quantitation techniques toward a more comprehensive catalogue of RNA species. (A) The first microarray was published in 1995 by Patrick Brown and quantified 45 mRNA species simultaneously using hybridization to DNA probes [reprinted with permission ()]. (B) Microarrays have since advanced to measure tens of thousands of RNAs, including noncoding RNA. Shown is a heatmap of 768 lncRNAs found by array to exhibit altered abundances in the blood between patients with and without left ventricular remodeling following myocardial infarction [reprinted with permission ()]. In this case, transcriptome measurements enabled unbiased identification of disease progression biomarkers. (C) Due to the development of RNA‐sequencing and subsequent advances in the library preparation, sequencing and data analysis, quantification a greater diversity of RNA species on a transcriptome‐wide scale is now routine. Shown is a Sashimi plot displaying the relative abundances of different exons in an example measured from the hearts of wild‐type mice (red) and mice with a knockout of a splicing factor. y‐axis represents normalized RNA‐seq reads (expression), x‐axis represents genomic coordinates. The arcs are numbered to indicate the raw number of junction reads. Arcs with greater values bridge two exons that are more often spliced next to each other [reprinted with permission ()]. While these data were acquired from mice that were experimentally manipulated to disrupt splicing, many studies find exon usage is an important component to the transcriptome regulation of cell‐type specificity, development, and disease.


Figure 2. RNA abundance and protein abundance both correlate better with ribosome occupancy than they do with each other. Expression analysis was performed on lymphoblastoid cell lines of diverse genetic backgrounds taken from the HapMap project. Genes were clustered into modules or neurons (hexagon, right panels) within a self‐organizing map based on similar expression profiles across four different measurements (protein abundance, translation efficiency, RNA abundance, and ribosome occupancy; left panel). The right panel displays the same self‐organizing map colored to portray the mean expression of the genes within the module based on the four different datasets. The authors ask if hexagons with similar mean expression by one measurement (either both colored red or both colored blue) also show similar expression when using an alternate measure of expression. Ribosome occupancy correlates with RNA expression and protein level better than RNA expression and protein level correlate with each other. Note, ribosome occupancy is defined by the total read counts for an RNA after ribosome profiling, while translation efficiency takes into account the total pool of RNA (RNA‐seq) in addition to the ribosome occupancy [reprinted with permission ()].


Figure 3. Evolution of proteomics toward network analysis. (A) Two‐dimensional protein gels were published in 1975 (top panel, [reprinted with permission ()]), and remained a common tool for identifying proteome‐level quantitative differences between samples up into the late 1990s (bottom panel [reprinted with permission ()]). Bottom panel is a computer‐processed image of a silver‐stained 2D gel from a human dilated cardiomyopathy sample. Spots represent protein isoforms identifiable by their position in the gel (number indicates database protein ID). Note that PTM can shift a protein's location in the gel, providing additional information. Red spots indicate isoforms which were less abundant (weaker signal; similar to Western blot analysis) across the dilated cardiomyopathy patients as compared to ischemic cardiomyopathy samples run in a separate gel, and analyzed together using computer software. Note that this analysis reveals on average 1282 spots per sample, in the same general scale as LC/MS/MS analyses; however, the identification of the individual spots, when not coupled to mass spectrometry, remains imprecise. (B) By contrast, advances in mass spectrometry and sample preparation pipeline have enabled quantification of PTMs across entire signaling cascades from multiple conditions. Shown here is the known insulin signaling pathway curated from multiple databases, overlaid with phosphorylation quantitation (expressed as fold‐change) from a mass spectrometry analysis performed on liver samples from mice treated with PBS or insulin at two time points [reprinted with permission ()]. These techniques are optimized for a focused subproteome, thus enabled thorough, dynamic measurements of the system, which go beyond identifying proteins into the realm of mapping biological processes within a network. (C) Shown is a protein‐protein interaction network from HeLa cells generated through combining coimmunoprecipitation followed by mass spectrometry for 1125 different proteins [reprinted with permission ()]. Red indicates edges previously annotated in CORUM. On its own, this network represents a database to inform other protein interaction studies. However, the authors took this study a step further to compare their interaction network with the relative abundance of the proteins to infer complex stability. Thus, by comparing across networks, the omics datasets are able to generate new understanding of properties of the proteome.


Figure 4. Mass spectrometry techniques for building protein networks. (A) Peptides (circles; size indicates relative abundance) elute from the LC column into the mass spectrometer. In shotgun/bottom‐up proteomics, peptides are scanned in the MS1 and the most abundant ions selected for fragmentation and identification via multiple MS2 scans. In MRM, both the MS1 and MS2 scan are performed on predetermined m/z ratios set by the user to precisely quantify peptides of interest, including low abundant peptides. SWATH by contrast fragments all ions from the MS1 scan, resulting in many more MS2 scans, each containing spectra from many parent ions. (B) Upstream techniques can be used in conjunction with mass spectrometry to enable protein and PTM identification, quantitation, and spatial localization information used to build protein networks.


Figure 5. The role of genetics in gene expression is organ specific. To test the relationship between genetics, gene expression, and phenotype, we examined data from a panel of 37 genetically diverse, inbred mouse strains with microarray data from multiple organs: Macrophages with and without LPS stimulation (unpublished), striatum (), hippocampus (), bone marrow (), and heart with and without isoproterenol (ISO) stimulation to induce heart failure (). Strains were clustered based on expression of all genes on the microarray (All) or a class of genes known as the “fetal gene program” (Fetal), whose cardiac expression are considered to be biomarkers of heart failure. The relatedness between each strain‐by‐strain comparison (Euclidean) was compared across organs. If the relative similarity in expression between two strains is similar across two organs, those two organs cluster closer together on the dendrogram. We also incorporated genetic relatedness based on kinship matrix derived from SNPs (Genetics). Macrophages cluster according to genetics, suggesting that strains with similar genetics also show similar expression patterns in macrophages regardless of if we examine all genes, or the cardiac fetal genes, and even when examining expression after LPS stimulation. By contrast, other organs, such as bone marrow, have expression relationships that less closely match genetic relationships. For context, we compared the relationships between genetics versus mRNA expression to that of genetics versus cardiac phenotype [ejection fraction (EF) and heart weight/body weight (HW/BW), two indices which change in heart failure]. In some cases, the genetic relationship more closely matched the phenotype than the expression (basal EF), but in other cases it did not (EF after ISO). We hypothesized that the “fetal gene program” was an intermediate between genetics and phenotype, but found that it no more closely matched the phenotypic relationships than when we examined all genes together. These analyses indicate that the relationship between genetic variation, mRNA expression, and ultimately phenotype is buffered at each level. For example, complex SNP interactions and chromatin features may buffer the relationship between genetic variation and mRNA expression, while posttranscriptional and posttranslational processing as well as compartmentalization may buffer the relationship between mRNA and protein levels, with the relationship between protein and phenotype in turn buffered by protein network properties and interaction with other classes of molecules.


Figure 6. Spectrum of cognitive bias in basic and translational research. Implementation of discovery science and hypothesis‐driven research comprise a spectrum analogous to the “opportunity cost” principle. Points along the curve represent experiments where the opportunity cost is minimized, because some perfect balance between discovery and hypothesis is struck. Point A defines a species of research with very high uncertainty and little or no theoretical underpinning, but with the potential to be very novel. Point B defines another type in which highly focused and inherently biased research reaches full potential by maximizing prior knowledge. Studies that lie under the curve, due to shoddy or underexplored data or an experimental design that builds only incrementally on precedent, fail to meet the ideal balance of discovery and hypothesis [reprinted with permission ()].
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Emma Monte, Manuel Rosa‐Garrido, Thomas M. Vondriska, Jessica Wang. Undiscovered Physiology of Transcript and Protein Networks. Compr Physiol 2016, 6: 1851-1872. doi: 10.1002/cphy.c160003