In another set of simulations, we sampled over values of a new ratio describing how expression varies between A’1-directed and A’2-directed B-active states. We wondered whether genes involved in any specific biological processes or contexts were overrepresented in the set of genes whose paralogs were significantly upregulated. Therefore, we performed gene set enrichment analysis to check for over-enrichment of any Gene Ontology—Biological Process terms, comparing the following sets of hits against their respective background sets of tested CRISPR targets. Cost depletion is an accounting method by which costs of natural resources are allocated to depletion over the period that make up the life of the asset. Cost depletion is computed by estimating the total quantity of mineral or other resources acquired and assigning a proportionate amount of the total resource cost to the quantity extracted in the period.
Several knockout target and paralog features are not associated with paralog upregulation
- As natural resources are extracted, they are counted and taken out from the property’s basis.
- We show that transcription factors that display potential transcriptional adaptation have more stable downstream regulatory targets after mutation.
- We present several analyses centered on the question of when an expression distribution can remain robust to the mutation of an upstream regulator.
Techniques such as CRISPRi, already being used in pooled screens [74, 75], or other methods of engineering knockdowns, could be helpful. Alternatively, if knockout is a requirement of the experimental design, engineering whole-gene deletion alleles could help decouple effects of transcriptional adaptation from that of specific gene knockouts. Another opportunity is presented by recently reported combinatorial CRISPR screens (e.g., [24, 74]), which include paired knockout of two or more genes in the same cells, which could identify gene sets for which transcriptional adaptation confounds the outcomes.
We and our partners process data to provide:
We searched for overlapping regulons between a knockout target and the paralog gene of interest in DoRothEA, only considering downstream genes with annotation confidence level A, B, or C (out of a possible range of A-E, see original source for evidence level descriptions) [63, 64]. There were 55 annotated regulon genes for the three target-paralog pairs with transcriptional adaptation versus 439 annotated regulon genes for all target-paralog pairs without transcriptional adaptation. The fact that transcriptional adaptation occurred across a wide range of processes and for gene sets not necessarily belonging to a single regulatory module or signaling pathway highlights the need to consider their implications when screening for any phenotypic outcomes. One way to address this concern is to perform screens with perturbation methods that avoid nonsense mutations.
Gene expression distribution shape varies widely across the parameter search space
For example, if $10 million of oil is extracted and the fixed percentage is 15%, $1.5 million of capitalized costs to extract the natural resource are depleted. Depletion is an accrual accounting technique used to allocate the cost of extracting natural resources such as timber, minerals, and oil from the earth. The objective of depletion is to match the cost of the natural resources that were sold with the revenues from the natural resources that were sold. In accounting, depletion refers to the expensing of a company’s cost of a natural resource.
Gene expression distribution robustness to mutation is dependent on model parameters
Like depreciation and amortization, depletion is a non-cash expense that lowers the cost value of an asset incrementally through scheduled charges to income. Where depletion differs is that it refers to the gradual exhaustion of natural resource reserves, as opposed to the wearing out of depreciable assets or aging life of intangibles. Examples of depletion involve the logical expensing of a company’s cost of natural resources such as oil, natural gas, coal, metals, stone, etc. Units are considered sold in the year the proceeds are taxable under the taxpayer’s accounting method. Depletion, for both accounting purposes and United States tax purposes, is a method of recording the gradual expense or use of natural resources over time. Because the percentage depletion looks at the property’s gross income and taxable income limit, as opposed to the amount of the natural resource extracted, it is not an acceptable reporting method for certain natural resources.
Cost depletion
In principle, our bioinformatic pipeline can be generalized to include other animal systems to reveal both species-specific and universal gene targets displaying transcriptional compensation [14, 15]. Cells and tissues have a remarkable ability to adapt to genetic perturbations via a variety of molecular mechanisms. Nonsense-induced transcriptional compensation, a form of transcriptional adaptation, has recently emerged as one such mechanism, in which nonsense mutations in a gene trigger upregulation of related genes, possibly conferring robustness at cellular and organismal levels. However, beyond a handful of developmental contexts and curated sets of genes, no comprehensive genome-wide investigation of this behavior has been undertaken for mammalian cell types and conditions. How the regulatory-level effects of inherently stochastic compensatory gene networks contribute to phenotypic penetrance in single cells remains unclear. We sought to describe the variability in gene expression emerging from gene regulatory networks with transcriptional adaptation and to quantify differences in aspects of variability between network outputs given different parameter values.
If producer X has capitalized costs on property A of $40,000, originally consisting of the lease bonus, capitalized exploration costs, and some capitalized carrying costs, and the lease has been producing for several years and during this time, X has claimed $10,000 of allowable depletion. In 2009, X’s share of production sold was 40,000 barrels and an engineer’s report indicated that 160,000 barrels could be recovered after December 31, 2009. We used the clusterProfiler R package v3.12.0 for gene ontology over-representation testing [55]. My Accounting Course is a world-class educational resource developed by experts to simplify accounting, finance, & investment analysis topics, so students and professionals can learn and propel their careers. Adjusted basis is the basis at end of year adjusted for prior years depletion in cost or percentage. The percentage depletion method requires a lot of estimates and is, therefore, not a heavily relied upon or accepted method of depletion.
When available, we used author-provided gene expression change calculations based on DESeq2 (for results from [58]). For all remaining datasets, for which DESeq2 results were not already available, the authors did provide mapped count data on GEO and/or they were available on GREIN. For these count-based results, we implemented DESeq2 ourselves, using the PyDESeq2 package, using default settings, comparing knockout samples against the matched controls from their respective studies [119]. In the model expanded to consider multiple paralogs, we conducted simulations with and without basal paralog expression. In one set of simulations, we fixed the effects of both paralogs, A’1 and A’2, on B, to be equal.
All content on this website, including dictionary, thesaurus, literature, geography, and other reference data is for informational purposes only. This information should not be considered complete, up to date, and is not intended to be used in place of a visit, consultation, or advice of a legal, medical, or any other professional.
The breadth of genes that appear to have transcriptional compensation also invites study of potential negative consequences of nonsense-induced paralog—or other related gene—upregulation. Might some compensatory changes be deleterious and, if so, could such deleterious changes explain select negative phenotypes previously ascribed to haploinsufficiency or gene dosage effects [80]? In a similar vein, our framework could be extended to analyze cases where paralogs are downregulated upon Cas9-induced nonsense mutations, potentially revealing new biology. Genes are nodes, and regulatory relationships are edges (e.g., A stimulates B leads to an edge from node A to node B; Fig. 5). The biological mechanism presented in recent studies on nonsense-induced transcriptional compensation implies a minimum set of regulatory relationships between an ancestral regulator, its paralog genes, and a downstream target gene [14, 15].
Lastly, simulations of a gene regulatory network with transcriptional adaptation produce a variety of expression distributions of downstream targets upon compensation, recapitulating observed diverse regulon expression changes after transcription factor mutation. Altogether, our work provides a strong foundation for future mechanistic experimental and computational studies of transcriptional adaptation. For Perturb-seq data-derived gene expression distribution analyses, we chose to focus on human transcription factor genes, as defined by the most recent version of AnimalTFDB3, last accessed July 28, 2023 [120].
For example, assume Big Texas Oil, Co. had discovered a large reserve of oil and estimates that the oil well will produce 200,000 barrels of oil. If the company invests $100,000 to extract the oil and extracts 10,000 barrels the first year, the depletion deduction is $5,000 ($100,000 X 10,000/200,000). Cost depletion is calculated by taking the property’s basis, total recoverable reserves and number of units sold into account. As natural resources are extracted, they are counted and taken out from the property’s basis.
Therefore, we calculated several summary statistics related to distribution shape to highlight important features of gene expression distributions. We present several analyses centered on the question of when an expression distribution can remain robust to the mutation of an upstream regulator. Therefore, we built an algorithm for classifying distribution shapes to reflect plausibly important differences. We were particularly interested in a robust method for identifying whether a distribution was unimodal and symmetric, suggesting a degree of homogeneity in expression. For distributions that were bimodal (or multimodal), one could imagine different emergent properties in a population of cells, e.g., with bistability or other kinds of functional diversity. For distributions that were unimodal but not symmetric, i.e., skewed, one could imagine a bias toward low-frequency diversity in behavior, either being very high expressors or very low expressors.
The mapping between simulation and wet-lab experiment can uncover plausible network and parameter constraints for individual compensating genes and could provide evidence for particular compensating gene regulatory steps affected by transcriptional adaptation. For example, one study used single-molecule approaches to study the effect of nonsense-mediated decay in U2OS cells with and without nonsense immunoglobulin-μ genes. They showed that UPF1 depletion increased the speed of transcriptional elongation in the wild-type but not in the nonsense immunoglobulin-μ gene [79]. Furthermore, regulatory network mappings at a single-cell level could also help explain incomplete phenotypic penetrance reported in association with transcriptional adaptation. Another set of questions center around whether gene length, number of introns and exons, chromosomal locations, and chromatin landscape play a role in which gene families exhibit nonsense-induced transcriptional compensation. Additionally, such mappings can help with the design and interpretation of functional genetic screens by taking into account genes known to be exhibiting transcriptional adaptation and the extent of its impact.
We model gene regulatory networks with, for each gene, two alleles with transcriptional burst activity independent of each other, consistent with observations of transcriptional burst regulation [123]. The edges between a given regulator gene product and the target gene alleles are set at equal weight, reflecting no regulatory differences at the allele level. For these studies, we implemented filters to consider knockout targets that we would a priori expect to have some detectable loss of gene dosage that would need to be compensated for by transcriptional adaptation. We first confirmed that the average library size of considered samples was at least approximately 1 million reads per sample.
We then included genes only if they were expressed at a level of 10 raw counts or higher across all samples. We chose to classify paralogs as upregulated if DESeq2 reported an adjusted p-value ≤ 0.05 and a log2 fold-change ≥ 0.5. In supplementary analyses, we also show results when paralogs are classified as upregulated using either (1) only the adjusted p-value ≤ 0.05 filter or (2) adjusted p-value ≤ 0.05, log2 fold-change ≥ 0.5, and basemean ≥ 10 filters. The final analysis included all knockout target genes with any significant paralog differential expression, up or down, irrespective of log2 fold-change. One limitation of our work is that a majority of the analysis was performed on datasets from bulk RNA sequencing studies, limiting a quantitative single-cell mapping with simulations. Another limitation of our framework is that we focused primarily on mice and human datasets given the breadth of available datasets.