To investigate whether functional polymorphisms exist in the C-reactive protein (CRP) gene, i.e., ones that contribute directly to differences in baseline CRP among individuals, we sequenced a 1,156-nucleotide-long stretch of the CRP gene promoter in 287 ostensibly healthy people. We identified two single-nucleotide polymorphisms (SNPs), a bi-allelic one at nucleotide -409 (G-->A), and a tri-allelic one at -390 (C-->T-->A), both resident within the hexameric core of transcription factor binding E-box elements. Electrophoretic mobility shift assays confirmed that the SNP within the sequence (-412)CACGTG(-407) (E-box 1) modulates transcription factor binding, and that the one within (-394)CACTTG(-389) (E-box 2) supports transcription factor binding only when the -390 T allele is present. The commonest of four E-box 1/E-box 2 haplotypes (-409G/-390T) identified in the population supported highest promoter activity in luciferase reporter assays, and the rarest one (-409A/-390T) supported the least. Importantly, serum CRP in people with these haplotypes reproduced this rank order, i.e., people with the -409G/-390T haplotype had the highest baseline serum CRP (mean +/- SEM 10.9 +/- 2.25 microg/ml) and people with the -409A/-390T haplotype had the lowest (5.01 +/- 1.56 microg/ml). Furthermore, haplotype-associated differences in baseline CRP were not due to differences in age, sex, or race, and were still apparent in people with no history of smoking. At least two other SNPs in the CRP promoter lie within E-box elements (-198 C-->T, E-box 4, and -861 T-->C, E-box 3), indicating that not only is the quality of E-box sites in CRP a major determinant of baseline CRP level, but also that the number of E-boxes may be important. These data confirm that the CRP promoter does encode functional polymorphisms, which should be considered when baseline CRP is being used as an indicator of clinical outcome. Ultimately, development of genetic tests to screen for CRP expression variants could allow categorization of healthy people into groups at high versus low future risk of inflammatory disease.
Spatially resolved transcriptomic techniques allow the characterization of spatial organization of cells in tissues, which revolutionize the studies of tissue function and disease pathology. New strategies for detecting spatial gene expression patterns are emerging, and spatially resolved transcriptomic data are accumulating rapidly. However, it is not convenient for biologists to exploit these data due to the diversity of strategies and complexity in data analysis. Here, we present SpatialDB, the first manually curated database for spatially resolved transcriptomic techniques and datasets. The current version of SpatialDB contains 24 datasets (305 sub-datasets) from 5 species generated by 8 spatially resolved transcriptomic techniques. SpatialDB provides a user-friendly web interface for visualization and comparison of spatially resolved transcriptomic data. To further explore these data, SpatialDB also provides spatially variable genes and their functional enrichment annotation. SpatialDB offers a repository for research community to investigate the spatial cellular structure of tissues, and may bring new insights into understanding the cellular microenvironment in disease. SpatialDB is freely available at https://www.spatialomics.org/SpatialDB.
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