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Cell-free extrachromosomal circular DNA (cf-eccDNA) has been proposed as a promising early biomarker for disease diagnosis, progression and drug response. Its established biomarker features are changes in the number and length distribution of cf-eccDNA. Another novel promising biomarker is a set of eccDNA excised from a panel of genes specific to a condition compared to a control. Deficiencies in two endonucleases that specifically target DNA, Dnase1 and Dnase1l3, are associated with systemic lupus erythematosus (SLE). To study the genic eccDNA profiles in the case of their deficiencies, we mapped sequenced eccDNA data from plasma, liver and buffy coat from Dnase1 and Dnase1l3 knockouts (KOs), and wild type controls in mouse. Next, we performed an eccDNA differential analysis between KO and control groups using our DifCir algorithm. We found a specific genic cf-eccDNA fingerprint of the Dnase1l3 group compared to the wild type controls involving 131 genes; 26% of them were associated with human chromosomal fragile sites (CFSs) and with a statistically significant enrichment of CFS-associated genes. We found six genes in common with the genic cf-eccDNA profile of SLE patients with DNASE1L3 deficiency, namely Rorb, Mvb12b, Osbpl10, Fto, Tnik and Arhgap10; all of them were specific and present in all human plasma samples, and none of them were associated with CFSs. A not so distinctive genic cf-eccDNA difference involving only seven genes was observed in the case of the Dnase1 group compared to the wild type. In tissue—liver and buffy coat—we did not detect the same genic eccDNA difference observed in the plasma samples. These results point to a specific role of a set of genic eccDNA in plasma from DNase KOs, as well as a relation with CFS genes, confirming the promise of the genic cf-eccDNA in studying diseases and the need for further research on the relationship between eccDNA and CFSs. Full article

To bring new extrachromosomal circular DNA (eccDNA) enrichment technologies closer to the clinic, specifically for screening, early diagnosis, and monitoring of diseases or lifestyle conditions, it is paramount to identify the differential pattern of the genic eccDNA signal between two states. Current studies using short-read sequenced purified eccDNA data are based on absolute numbers of unique eccDNAs per sample or per gene, length distributions, or standard methods for RNA-seq differential analysis. Previous analyses of RNA-seq data found significant transcriptomics difference between sedentary and active life style skeletal muscle (SkM) in young people but very few in old. The first attempt using circulomics data from SkM and blood of aged lifelong sedentary and physically active males found no difference at eccDNA level. To improve the capability of finding differences between circulomics data groups, we designed a computational method to identify Differentially Produced per Gene Circles (DPpGCs) from short-read sequenced purified eccDNA data based on the circular junction, split-read signal, of the eccDNA, and implemented it into a software tool DifCir in Matlab. We employed DifCir to find to the distinctive features of the influence of the physical activity or inactivity in the aged SkM that would have remained undetected by transcriptomics methods. We mapped the data from tissue from SkM and blood from two groups of aged lifelong sedentary and physically active males using Circle_finder and subsequent merging and filtering, to find the number and length distribution of the unique eccDNA. Next, we used DifCir to find up-DPpGCs in the SkM of the sedentary and active groups. We assessed the functional enrichment of the DPpGCs using Disease Gene Network and Gene Set Enrichment Analysis. To find genes that produce eccDNA in a group without comparison with another group, we introduced a method to find Common PpGCs (CPpGCs) and used it to find CPpGCs in the SkM of the sedentary and active group. Finally, we found the eccDNA that carries whole genes. We discovered that the eccDNA in the SkM of the sedentary group is not statistically different from that of physically active aged men in terms of number and length distribution of eccDNA. In contrast, with DifCir we found distinctive gene-associated eccDNA fingerprints. We identified statistically significant up-DPpGCs in the two groups, with the top up-DPpGCs shed by the genes AGBL4, RNF213, DNAH7, MED13, and WWTR1 in the sedentary group, and ZBTB7C, TBCD, ITPR2, and DDX11-AS1 in the active group. The up-DPpGCs in both groups carry mostly gene fragments rather than whole genes. Though the subtle transcriptomics difference, we found RYR1 to be both transcriptionally up-regulated and up-DPpGCs gene in sedentary SkM. DifCir emphasizes the high sensitivity of the circulome compared to the transcriptome to detect the molecular fingerprints of exercise in aged SkM. It allows efficient identification of gene hotspots that excise more eccDNA in a health state or disease compared to a control condition. Full article