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Abstract Background Individual assessment of CYP enzyme activities can be challenging. Recently, the potato alkaloid solanidine was suggested as a biomarker for CYP2D6 activity. Here, we aimed to characterize the sensitivity and specificity of solanidine as a CYP2D6 biomarker among Finnish volunteers with known CYP2D6 genotypes. Results Using non-targeted metabolomics analysis, we identified 9152 metabolite features in the fasting plasma samples of 356 healthy volunteers. Machine learning models suggested strong association between CYP2D6 genotype-based phenotype classes with a metabolite feature identified as solanidine. Plasma solanidine concentration was 1887% higher in genetically poor CYP2D6 metabolizers (gPM) (n = 9; 95% confidence interval 755%, 4515%; P = 1.88 × 10–11), 74% higher in intermediate CYP2D6 metabolizers (gIM) (n = 89; 27%, 138%; P = 6.40 × 10–4), and 35% lower in ultrarapid CYP2D6 metabolizers (gUM) (n = 20; 64%, − 17%; P = 0.151) than in genetically normal CYP2D6 metabolizers (gNM; n = 196). The solanidine metabolites m/z 444 and 430 to solanidine concentration ratios showed even stronger associations with CYP2D6 phenotypes. Furthermore, the areas under the receiver operating characteristic and precision–recall curves for these metabolic ratios showed equal or better performances for identifying the gPM, gIM, and gUM phenotype groups than the other metabolites, their ratios to solanidine, or solanidine alone. In vitro studies with human recombinant CYP enzymes showed that solanidine was metabolized mainly by CYP2D6, with a minor contribution from CYP3A4/5. In human liver microsomes, the CYP2D6 inhibitor paroxetine nearly completely (95%) inhibited the metabolism of solanidine. In a genome-wide association study, several variants near the CYP2D6 gene associated with plasma solanidine metabolite ratios. Conclusions These results are in line with earlier studies and further indicate that solanidine and its metabolites are sensitive and specific biomarkers for measuring CYP2D6 activity. Since potato consumption is common worldwide, this biomarker could be useful for evaluating CYP2D6-mediated drug–drug interactions and to improve prediction of CYP2D6 activity in addition to genotyping.

Abstract In search of novel breast cancer (BC) risk variants, we performed a whole-exome sequencing and variant analysis of 69 Finnish BC patients as well as analysed loss-of-function variants identified in DNA repair genes in the Finns from the Genome Aggregation Database. Additionally, we carried out a validation study of SERPINA3 c.918-1G>C, recently suggested for BC predisposition. We estimated the frequencies of 41 rare candidate variants in 38 genes by genotyping them in 2482–4101 BC patients and in 1273–3985 controls. We further evaluated all coding variants in the candidate genes in a dataset of 18,786 BC patients and 182,927 controls from FinnGen. None of the variants associated significantly with cancer risk in the primary BC series; however, in the FinnGen data, NTHL1 c.244C>T p.(Gln82Ter) associated with BC with a high risk for homozygous (OR = 44.7 [95% CI 6.90–290], P = 6.7 × 10–5) and a low risk for heterozygous women (OR = 1.39 [1.18–1.64], P = 7.8 × 10–5). Furthermore, the results suggested a high risk of colorectal, urinary tract, and basal-cell skin cancer for homozygous individuals, supporting NTHL1 as a recessive multi-tumour susceptibility gene. No significant association with BC risk was detected for SERPINA3 or any other evaluated gene.

Abstract We assessed the PREDICT v 2.2 for prognosis of breast cancer patients with pathogenic germline BRCA1 and BRCA2 variants, using follow-up data from 5453 BRCA1/2 carriers from the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA) and the Breast Cancer Association Consortium (BCAC). PREDICT for estrogen receptor (ER)-negative breast cancer had modest discrimination for BRCA1 carrier patients overall (Gönen & Heller unbiased concordance 0.65 in CIMBA, 0.64 in BCAC), but it distinguished clearly the high-mortality group from lower risk categories. In an analysis of low to high risk categories by PREDICT score percentiles, the observed mortality was consistently lower than the expected mortality, but the confidence intervals always included the calibration slope. Altogether, our results encourage the use of the PREDICT ER-negative model in management of breast cancer patients with germline BRCA1 variants. For the PREDICT ER-positive model, the discrimination was slightly lower in BRCA2 variant carriers (concordance 0.60 in CIMBA, 0.65 in BCAC). Especially, inclusion of the tumor grade distorted the prognostic estimates. The breast cancer mortality of BRCA2 carriers was underestimated at the low end of the PREDICT score distribution, whereas at the high end, the mortality was overestimated. These data suggest that BRCA2 status should also be taken into consideration with tumor characteristics, when estimating the prognosis of ER-positive breast cancer patients.

Abstract Background The use of glucocorticoids has given contradictory results for treating acute respiratory distress syndrome (ARDS). The use of intravenous Interferon beta (IFN β) for the treatment of ARDS was recently tested in a phase III ARDS trial (INTEREST), in which more than half of the patients simultaneously received glucocorticoids. Trial results showed deleterious effects of glucocorticoids when administered together with IFN β, and therefore, we aimed at finding the reason behind this. Methods We first sequenced the genes encoding the IFN α/β receptor of the patients, who participated in the INTEREST study (ClinicalTrials.gov Identifier: NCT02622724 , November 24, 2015) in which the patients were randomized to receive an intravenous injection of IFN β-1a (144 patients) or placebo (152 patients). Genetic background was analyzed against clinical outcome, concomitant medication, and pro-inflammatory cytokine levels. Thereafter, we tested the influence of the genetic background on IFN α/β receptor expression in lung organ cultures and whether, it has any effect on transcription factors STAT1 and STAT2 involved in IFN signaling. Results We found a novel disease association of a SNP rs9984273, which is situated in the interferon α/β receptor subunit 2 (IFNAR2) gene in an area corresponding to a binding motif of the glucocorticoid receptor (GR). The minor allele of SNP rs9984273 associates with higher IFNAR expression, more rapid decrease of IFN γ and interleukin-6 (IL-6) levels and better outcome in IFN β treated patients with ARDS, while the major allele associates with a poor outcome especially under concomitant IFN β and glucocorticoid treatment. Moreover, the minor allele of rs9984273 associates with a less severe form of coronavirus diseases (COVID-19) according to the COVID-19 Host Genetics Initiative database. Conclusions The distribution of this SNP within clinical study arms may explain the contradictory results of multiple ARDS studies and outcomes in COVID-19 concerning type I IFN signaling and glucocorticoids. Graphical abstract

The sequence contexts of genomic variants play important roles in understanding biological significances of variants and potential sequencing related variant calling issues. However, methods for assessing the diverse sequence contexts of genomic variants such as tandem repeats and unambiguous annotations have been limited. Herein, we describe the Variant Sequence Context Annotation Tool (VarSCAT) for annotating the sequence contexts of genomic variants, including breakpoint ambiguities, flanking bases of variants, wildtype/mutated DNA sequences, variant nomenclatures, distances between adjacent variants, tandem repeat regions, and custom annotation with user customizable options. Our analyses demonstrate that VarSCAT is more versatile and customizable than the currently available methods or strategies for annotating variants in short tandem repeat (STR) regions or insertions and deletions (indels) with breakpoint ambiguity. Variant sequence context annotations of high-confidence human variant sets with VarSCAT revealed that more than 75% of all human individual germline and clinically relevant indels have breakpoint ambiguities. Moreover, we illustrate that more than 80% of human individual germline small variants in STR regions are indels and that the sizes of these indels correlated with STR motif sizes. VarSCAT is available from https://github.com/elolab/VarSCAT.

T helper 17 (Th17) cells protect against fungal and bacterial infections and are implicated in autoimmunity. Several long intergenic noncoding RNAs (lincRNA) are induced during Th17 differentiation, however, their contribution to Th17 differentiation is poorly understood. We aimed to characterize the function of the lincRNA Myocardial Infarction Associated Transcript (MIAT) during early human Th17 cell differentiation. We found MIAT to be upregulated early after induction of human Th17 cell differentiation along with an increase in the chromatin accessibility at the gene locus. STAT3, a key regulator of Th17 differentiation, directly bound to the MIAT promoter and induced its expression during the early stages of Th17 cell differentiation. MIAT resides in the nucleus and regulates the expression of several key Th17 genes, including IL17A, IL17F, CCR6 and CXCL13, possibly by altering the chromatin accessibility of key loci, including IL17A locus. Further, MIAT regulates the expression of protein kinase C alpha (PKCα), an upstream regulator of IL17A. A reanalysis of published single-cell RNA-seq data showed that MIAT was expressed in T cells from the synovium of RA patients. Our results demonstrate that MIAT contributes to human Th17 differentiation by upregulating several genes implicated in Th17 differentiation. High MIAT expression in T cells of RA patient synovia suggests a possible role of MIAT in Th17 mediated autoimmune pathologies.

Abstract Background Detection of copy number variations (CNVs) from high-throughput next-generation whole-genome sequencing (WGS) data has become a widely used research method during the recent years. However, only a little is known about the applicability of the developed algorithms to ultra-low-coverage (0.0005–0.8×) data that is used in various research and clinical applications, such as digital karyotyping and single-cell CNV detection. Result Here, the performance of six popular read-depth based CNV detection algorithms (BIC-seq2, Canvas, CNVnator, FREEC, HMMcopy, and QDNAseq) was studied using ultra-low-coverage WGS data. Real-world array- and karyotyping kit-based validation were used as a benchmark in the evaluation. Additionally, ultra-low-coverage WGS data was simulated to investigate the ability of the algorithms to identify CNVs in the sex chromosomes and the theoretical minimum coverage at which these tools can accurately function. Our results suggest that while all the methods were able to detect large CNVs, many methods were susceptible to producing false positives when smaller CNVs ( 3 h) compared with FREEC (~ 3 min), which we considered the second-best method. Conclusions Our comparative analysis demonstrates that CNV detection from ultra-low-coverage WGS data can be a highly accurate method for the detection of large copy number variations when their length is in millions of base pairs. These findings facilitate applications that utilize ultra-low-coverage CNV detection.

Single-cell omics technologies are currently solving biological and medical problems that earlier have remained elusive, such as discovery of new cell types, cellular differentiation trajectories and communication networks across cells and tissues. Current advances especially in single-cell multi-omics hold high potential for breakthroughs by integration of multiple different omics layers. To pair with the recent biotechnological developments, many computational approaches to process and analyze single-cell multi-omics data have been proposed. In this review, we first introduce recent developments in single-cell multi-omics in general and then focus on the available data integration strategies. The integration approaches are divided into three categories: early, intermediate, and late data integration. For each category, we describe the underlying conceptual principles and main characteristics, as well as provide examples of currently available tools and how they have been applied to analyze single-cell multi-omics data. Finally, we explore the challenges and prospective future directions of single-cell multi-omics data integration, including examples of adopting multi-view analysis approaches used in other disciplines to single-cell multi-omics.

Insertions and deletions (indels) in human genomes are associated with a wide range of phenotypes, including various clinical disorders. High-throughput, next generation sequencing (NGS) technologies enable the detection of short genetic variants, such as single nucleotide variants (SNVs) and indels. However, the variant calling accuracy for indels remains considerably lower than for SNVs. Here we present a comparative study of the performance of variant calling tools for indel calling, evaluated with a wide repertoire of NGS datasets. While there is no single optimal tool to suit all circumstances, our results demonstrate that the choice of variant calling tool greatly impacts the precision and recall of indel calling. Furthermore, to reliably detect indels, it is essential to choose NGS technologies that offer a long read length and high coverage coupled with specific variant calling tools.

Rheumatoid arthritis (RA) is a complex autoimmune disease targeting synovial joints. Traditionally, RA is divided into seropositive (SP) and seronegative (SN) disease forms, the latter consisting of an array of unrelated diseases with joint involvement. Recently, we described a severe form of SN-RA that associates with characteristic joint destruction. Here, we sought biological characteristics to differentiate this rare but aggressive anti-citrullinated peptide antibody-negative destructive RA (CND-RA) from early seropositive (SP-RA) and seronegative rheumatoid arthritis (SN-RA). We also aimed to study cytotoxic CD8+ lymphocytes in autoimmune arthritis. CND-RA, SP-RA and SN-RA were compared to healthy controls to reveal differences in T-cell receptor beta (TCRβ) repertoire, cytokine levels and autoantibody repertoires. Whole-exome sequencing (WES) followed by single-cell RNA-sequencing (sc-RNA-seq) was performed to study somatic mutations in a clonally expanded CD8+ lymphocyte population in an index patient. A unique TCRβ signature was detected in CND-RA patients. In addition, CND-RA patients expressed higher levels of the bone destruction-associated TNFSF14 cytokine. Blood IgG repertoire from CND-RA patients recognized fewer endogenous proteins than SP-RA patients’ repertoires. Using WES, we detected a stable mutation profile in the clonally expanded CD8+ T-cell population characterized by cytotoxic gene expression signature discovered by sc-RNA-sequencing. Our results identify CND-RA as an independent RA subset and reveal a CND-RA specific TCR signature in the CD8+ lymphocytes. Improved classification of seronegative RA patients underlines the heterogeneity of RA and also, facilitates development of improved therapeutic options for the treatment resistant patients.

Long non-coding RNAs (lncRNAs) play crucial roles in human physiology, and have been found to be associated with various cancers. Transcribed ultraconserved regions (T-UCRs) are a subgroup of lncRNAs conserved in several species, and are often located in cancer-related regions. Breast cancer is the most common cancer in women worldwide and the leading cause of female cancer deaths. We investigated the association of genetic variants in lncRNA and T-UCR regions with breast cancer risk to uncover candidate loci for further analysis. Our focus was on low-penetrance variants that can be discovered in a large dataset. We selected 565 regions of lncRNAs and T-UCRs that are expressed in breast or breast cancer tissue, or show expression correlation to major breast cancer associated genes. We studied the association of single nucleotide polymorphisms (SNPs) in these regions with breast cancer risk in the 122970 case samples and 105974 controls of the Breast Cancer Association Consortium’s genome-wide data, and also by in silico functional analyses using Integrated Expression Quantitative trait and in silico prediction of GWAS targets (INQUISIT) and expression quantitative trait loci (eQTL) analysis. The eQTL analysis was carried out using the METABRIC dataset and analyses from GTEx and ncRNA eQTL databases. We found putative breast cancer risk variants (p < 1 × 10–5) targeting the lncRNA GABPB1-AS1 in INQUISIT and eQTL analysis. In addition, putative breast cancer risk associated SNPs (p < 1 × 10–5) in the region of two T-UCRs, uc.184 and uc.313, located in protein coding genes CPEB4 and TIAL1, respectively, targeted these genes in INQUISIT and in eQTL analysis. Other non-coding regions containing SNPs with the defined p-value and highly significant false discovery rate (FDR) for breast cancer risk association were discovered that may warrant further studies. These results suggest candidate lncRNA loci for further research on breast cancer risk and the molecular mechanisms.

Background: Methicillin resistant Staphylococcus aureus (MRSA) is a common pathogen which has increased both morbidity and mortality in hospitals and out-door patients. Objective: Aim of our study is to measure prevalence of MRSA in in-door patients and out- door patients, as well as its sensitivity against gentamycin, vancomycin and ciprofloxacin. Methods: A sample of 194 S. aureus was taken retrospectively from Pathology lab of a tertiary care hospital. MRSA was identified and further subjected against gentamycin, vancomycin and ciprofloxacin. Result: 65.9% of S. aureus isolates were MRSA. 22.7%, 35.7%, 99.1% of MRSA were susceptible to ciprofloxacin, gentamycin and vancomycin, respectively. 75.8% MRSA was from pus samples. 66% MRSA was from in-door patient department (IPD). Conclusion: MRSA was more prevalent in IPD. MRSA prevalence is escalating. Vancomycin is losing its effectiveness against MRSA as one resistant case was found. Inappropriate use and unprofessional prescriptions for antibiotics can be the main cause. Proper legislative action is required to keep in control the prescriptions for antibiotics. Keywords: Methicillin-Resistant Staphylococcus aureus, Bacterial drug resistance, Antimicrobial drugs

Genetic variance can influence breast cancer prognosis. Here, the authors conduct a meta-analysis to explore genetic variance linked to breast cancer in young women, identifying a prognostic association with germline variation of ADAMTSL1.

A region on chromosome 19p13 is associated with the risk of developing ovarian and breast cancer. Here, the authors genotyped SNPs in this region in thousands of breast and ovarian cancer patients and identified SNPs associated with three genes, which were analysed with functional studies.

Oestrogen negative breast cancer is associated with a poor prognosis. In this study, the authors perform a meta-analysis of 11 breast cancer genome-wide association studies and identify four new loci associated with oestrogen negative breast cancer risk. These findings may aid in stratifying patients in the clinic.

Population-based genome wide association studies have identified a locus at 9p22.2 associated with ovarian cancer risk, which also modifies ovarian cancer risk in BRCA1 and BRCA2 mutation carriers. We conducted fine-scale mapping at 9p22.2 to identify potential causal variants in BRCA1 and BRCA2 mutation carriers. Genotype data were available for 15,252 (2,462 ovarian cancer cases) BRCA1 and 8,211 (631 ovarian cancer cases) BRCA2 mutation carriers. Following genotype imputation, ovarian cancer associations were assessed for 4,873 and 5,020 SNPs in BRCA1 and BRCA 2 mutation carriers respectively, within a retrospective cohort analytical framework. In BRCA1 mutation carriers one set of eight correlated candidate causal variants for ovarian cancer risk modification was identified (top SNP rs10124837, HR: 0.73, 95%CI: 0.68 to 0.79, p-value 2× 10-16). These variants were located up to 20 kb upstream of BNC2. In BRCA2 mutation carriers one region, up to 45 kb upstream of BNC2, and containing 100 correlated SNPs was identified as candidate causal (top SNP rs62543585, HR: 0.69, 95%CI: 0.59 to 0.80, p-value 1.0 × 10-6). The candidate causal in BRCA1 mutation carriers did not include the strongest associated variant at this locus in the general population. In sum, we identified a set of candidate causal variants in a region that encompasses the BNC2 transcription start site. The ovarian cancer association at 9p22.2 may be mediated by different variants in BRCA1 mutation carriers and in the general population. Thus, potentially different mechanisms may underlie ovarian cancer risk for mutation carriers and the general population.

Common variation on 14q24.1, close to RAD51B, has been associated with breast cancer: rs999737 and rs2588809 with the risk of female breast cancer and rs1314913 with the risk of male breast cancer. The aim of this study was to investigate the role of RAD51B variants in breast cancer predisposition, particularly in the context of familial breast cancer in Finland. We sequenced the coding region of RAD51B in 168 Finnish breast cancer patients from the Helsinki region for identification of possible recurrent founder mutations. In addition, we studied the known rs999737, rs2588809, and rs1314913 SNPs and RAD51B haplotypes in 44,791 breast cancer cases and 43,583 controls from 40 studies participating in the Breast Cancer Association Consortium (BCAC) that were genotyped on a custom chip (iCOGS). We identified one putatively pathogenic missense mutation c.541C>T among the Finnish cancer patients and subsequently genotyped the mutation in additional breast cancer cases (n = 5259) and population controls (n = 3586) from Finland and Belarus. No significant association with breast cancer risk was seen in the meta-analysis of the Finnish datasets or in the large BCAC dataset. The association with previously identified risk variants rs999737, rs2588809, and rs1314913 was replicated among all breast cancer cases and also among familial cases in the BCAC dataset. The most significant association was observed for the haplotype carrying the risk-alleles of all the three SNPs both among all cases (odds ratio (OR): 1.15, 95% confidence interval (CI): 1.11-1.19, P = 8.88 x 10-16) and among familial cases (OR: 1.24, 95% CI: 1.16-1.32, P = 6.19 x 10-11), compared to the haplotype with the respective protective alleles. Our results suggest that loss-of-function mutations in RAD51B are rare, but common variation at the RAD51B region is significantly associated with familial breast cancer risk.

The Cancer Genetic Markers of Susceptibility genome-wide association study (GWAS) originally identified a single nucleotide polymorphism (SNP) rs11249433 at 1p11.2 associated with breast cancer risk. To fine-map this locus, we genotyped 92 SNPs in a 900kb region (120,505,799-121,481,132) flanking rs11249433 in 45,276 breast cancer cases and 48,998 controls of European, Asian and African ancestry from 50 studies in the Breast Cancer Association Consortium. Genotyping was done using iCOGS, a custom-built array. Due to the complicated nature of the region on chr1p11.2: 120,300,000-120,505,798, that lies near the centromere and contains seven duplicated genomic segments, we restricted analyses to 429 SNPs excluding the duplicated regions (42 genotyped and 387 imputed). Per-allelic associations with breast cancer risk were estimated using logistic regression models adjusting for study and ancestry-specific principal components. The strongest association observed was with the original identified index SNP rs11249433 (minor allele frequency (MAF) 0.402; per-allele odds ratio (OR) = 1.10, 95% confidence interval (CI) 1.08-1.13, P = 1.49 x 10-21). The association for rs11249433 was limited to ER-positive breast cancers (test for heterogeneity P≤8.41 x 10-5). Additional analyses by other tumor characteristics showed stronger associations with moderately/well differentiated tumors and tumors of lobular histology. Although no significant eQTL associations were observed, in silico analyses showed that rs11249433 was located in a region that is likely a weak enhancer/promoter. Fine-mapping analysis of the 1p11.2 breast cancer susceptibility locus confirms this region to be limited to risk to cancers that are ER-positive.

Genetic variations, such as single nucleotide polymorphisms (SNPs) in microRNAs (miRNA) or in the miRNA binding sites may affect the miRNA dependent gene expression regulation, which has been implicated in various cancers, including breast cancer, and may alter individual susceptibility to cancer. We investigated associations between miRNA related SNPs and breast cancer risk. First we evaluated 2,196 SNPs in a case-control study combining nine genome wide association studies (GWAS). Second, we further investigated 42 SNPs with suggestive evidence for association using 41,785 cases and 41,880 controls from 41 studies included in the Breast Cancer Association Consortium (BCAC). Combining the GWAS and BCAC data within a meta-analysis, we estimated main effects on breast cancer risk as well as risks for estrogen receptor (ER) and age defined subgroups. Five miRNA binding site SNPs associated significantly with breast cancer risk: rs1045494 (odds ratio (OR) 0.92; 95% confidence interval (CI): 0.88-0.96), rs1052532 (OR 0.97; 95% CI: 0.95-0.99), rs10719 (OR 0.97; 95% CI: 0.94-0.99), rs4687554 (OR 0.97; 95% CI: 0.95-0.99, and rs3134615 (OR 1.03; 95% CI: 1.01-1.05) located in the 3' UTR of CASP8, HDDC3, DROSHA, MUSTN1, and MYCL1, respectively. DROSHA belongs to miRNA machinery genes and has a central role in initial miRNA processing. The remaining genes are involved in different molecular functions, including apoptosis and gene expression regulation. Further studies are warranted to elucidate whether the miRNA binding site SNPs are the causative variants for the observed risk effects.

Genome wide association studies (GWAs) of breast cancer mortality have identified few potential associations. The concordance between these studies is unclear. In this study, we used a meta-analysis of two prognostic GWAs and a replication cohort to identify the strongest associations and to evaluate the loci suggested in previous studies. We attempt to identify those SNPs which could impact overall survival irrespective of the age of onset.To facilitate the meta-analysis and to refine the association signals, SNPs were imputed using data from the 1000 genomes project. Cox-proportional hazard models were used to estimate hazard ratios (HR) in 536 patients from the POSH cohort (Prospective study of Outcomes in Sporadic versus Hereditary breast cancer) and 805 patients from the HEBCS cohort (Helsinki Breast Cancer Study). These hazard ratios were combined using a Mantel-Haenszel fixed effects meta-analysis and a p-value threshold of 5×10(-8) was used to determine significance. Replication was performed in 1523 additional patients from the POSH study.Although no SNPs achieved genome wide significance, three SNPs have significant association in the replication cohort and combined p-values less than 5.6×10(-6). These SNPs are; rs421379 which is 556 kb upstream of ARRDC3 (HR = 1.49, 95% confidence interval (CI) = 1.27-1.75, P = 1.1×10(-6)), rs12358475 which is between ECHDC3 and PROSER2 (HR = 0.75, CI = 0.67-0.85, P = 1.8×10(-6)), and rs1728400 which is between LINC00917 and FOXF1.In a genome wide meta-analysis of two independent cohorts from UK and Finland, we identified potential associations at three distinct loci. Phenotypic heterogeneity and relatively small sample sizes may explain the lack of genome wide significant findings. However, the replication at three SNPs in the validation cohort shows promise for future studies in larger cohorts. We did not find strong evidence for concordance between the few associations highlighted by previous GWAs of breast cancer survival and this study.

1α,25-Dihydroxyvitamin D3 (1α,25(OH)2D3) had earlier been regarded as the only active hormone. The newly identified actions of 25-hydroxyvitamin D3 (25(OH)D3) and 24R,25-dihydroxyvitamin D3 (24R,25(OH)2D3) broadened the vitamin D3 endocrine system, however, the current data are fragmented and a systematic understanding is lacking. Here we performed the first systematic study of global gene expression to clarify their similarities and differences. Three metabolites at physiologically comparable levels were utilized to treat human and mouse fibroblasts prior to DNA microarray analyses. Human primary prostate stromal P29SN cells (hP29SN), which convert 25(OH)D3 into 1α,25(OH)2D3 by 1α-hydroxylase (encoded by the gene CYP27B1), displayed regulation of 164, 171, and 175 genes by treatment with 1α,25(OH)2D3, 25(OH)D3, and 24R,25(OH)2D3, respectively. Mouse primary Cyp27b1 knockout fibroblasts (mCyp27b1 (-/-)), which lack 1α-hydroxylation, displayed regulation of 619, 469, and 66 genes using the same respective treatments. The number of shared genes regulated by two metabolites is much lower in hP29SN than in mCyp27b1 (-/-). By using DAVID Functional Annotation Bioinformatics Microarray Analysis tools and Ingenuity Pathways Analysis, we identified the agonistic regulation of calcium homeostasis and bone remodeling between 1α,25(OH)2D3 and 25(OH)D3 and unique non-classical actions of each metabolite in physiological and pathological processes, including cell cycle, keratinocyte differentiation, amyotrophic lateral sclerosis signaling, gene transcription, immunomodulation, epigenetics, cell differentiation, and membrane protein expression. In conclusion, there are three distinct vitamin D3 hormones with clearly different biological activities. This study presents a new conceptual insight into the vitamin D3 endocrine system, which may guide the strategic use of vitamin D3 in disease prevention and treatment.