Comparison of B-Cell Lupus and Lymphoma Using a Novel Immune Imbalance Transcriptomics Algorithm Reveals Potential Therapeutic Targets

Abstract

Background/objectives: Systemic lupus erythematosus (lupus) and B-cell lymphoma (lymphoma) co-occur at higher-than-expected rates and primarily depend on B cells for their pathology. These observations implicate shared inflammation-related B cell molecular mechanisms as a potential cause of co-occurrence.

Methods: We consequently implemented a novel Immune Imbalance Transcriptomics (IIT) algorithm and applied IIT to lupus, lymphoma, and healthy B cell RNA-sequencing (RNA-seq) data to find shared and contrasting mechanisms that are potential therapeutic targets.

Results: We observed 7143 significantly dysregulated genes in both lupus and lymphoma. Of those genes, we found 5137 to have a significant immune imbalance, defined as a significant dysregulation by both diseases, as analyzed by IIT. Gene Ontology (GO) term and pathway enrichment of the IIT genes yielded immune-related "Neutrophil Degranulation" and "Adaptive Immune System", which validates that the IIT algorithm isolates biologically relevant genes in immunity and inflammation. We found that 344 IIT gene products are known targets for established and/or repurposed drugs. Among our results, we found 48 known and 296 novel lupus targets, along with 151 known and 193 novel lymphoma targets. Known disease drug targets in our IIT results further validate that IIT isolates genes with disease-relevant mechanisms.

Conclusions: We anticipate the IIT algorithm, together with the shared and contrasting gene mechanisms uncovered here, will contribute to the development of immune-related therapeutic options for lupus and lymphoma patients.

Keywords: B-cell lymphoma; RNA-Seq; algorithm; autoimmune disease; cancer; drug discovery; immune imbalance; immune imbalance transcriptomics (IIT); systemic lupus erythematosus (SLE).

Figure 1

Rationale and Workflow Visualization for the Immune Imbalance Transcriptomics (IIT) algorithm. (A) Using cancer, autoimmune, and healthy control data from the same tissue type, IIT seeks to identify differentially expressed genes from four quadrant profiles, which can be described in two distinct groupings: (1) two profiles that both indicate similar gene expression, including genes upregulated in both cancer and autoimmunity (C+A+) and genes downregulated in both cancer and autoimmunity (C−A−); and (2) two profiles that show opposite gene expression, that is, genes upregulated in cancer but downregulated in autoimmunity (C+A−) and genes downregulated in cancer but upregulated in autoimmunity (C−A+). The results and discussion refer to these four immune imbalance quadrant profiles. (B) A visual representation of the IIT algorithm workflow (see Section 2, Materials and Methods for additional detail).

Figure 2

Lupus–Lymphoma IIT Calculation Increases Statistical Stringency through Multiple Filtering Steps. (A) Summation of unaltered log2FCs ignores some gene results from three of four quadrants. (B) Weighting log2FCs by −log10FDR (mlog10FDR) acknowledges statistical nuance; * represents multiplication in this panel heading. (C) Z-scoring log2FC distributions diminishes the bias toward more extreme cancer log2FCs in final summation results. (D) Squaring all log2FC values before summation allows gathering of results from all four quadrants. (E) Square rooting all values diminishes outlier values. (F) Ratio weighting of each summed value includes more results with extreme lupus expression. (G) Histogram visualization of gene frequency counts from the IIT-significant portion of the null distribution. The frequency gate threshold, as determined by the p-value test, is marked in red on the histogram. (H) The differentially expressed genes (DEGs) that were significant in both the lupus vs. healthy and the lymphoma vs. healthy results. The lupus log₂FCs are along the x-axis, while lymphoma log₂FCs are on the y-axis, with each dot representing one gene. Genes with significant IIT scores are plotted in purple. Genes not significant based on the null distribution are plotted in black. Genes that were removed by the noise-detecting frequency gate are plotted in gray. Quadrants in the graph are divided by the axes.

Figure 3

Principal Component Analysis (PCA) of Analyzed Samples. Principal Component Analysis was performed on the count tables of all samples, with lupus samples represented in shades of red, lymphoma samples represented in shades of blue, and healthy samples represented in shades of yellow. (A) All samples are colored by disease status. We expected to see samples of the same color grouping together in panel 2A, due to each color representing a distinct disease phenotype (red for lupus, blue for lymphoma, and yellow for healthy). (B) Healthy samples colored by the study of origin. (C) Lymphoma samples were colored by their study of origin. (D) Lupus samples colored by the study of origin.

Figure 4

Lupus–Lymphoma IIT Gene Quadrants and GO Enrichment. (A) Gene Ontology (GO) term enrichment, by quadrant, against the Biological Process GO library. (B) Gene Ontology (GO) term enrichment, by quadrant, against the Cellular Component GO library. (C) Gene Ontology (GO) term enrichment, by quadrant, against the Molecular Function GO library. (D) Upset chart (an alternative to a five-way Venn diagram) showing the overlapping GO Results from all included GO libraries between individual quadrant enrichment lists and the enrichment list of the entire significant gene dataset.