Identifying compartment-specific non-HLA targets after renal transplantation by integrating transcriptome and “antibodyome” measures
Identifying compartment-specific non-HLA targets after renal transplantation by integrating transcriptome and “antibodyome” measures is a research paper published in Proceedings of the National Academy of Sciences (2009). On theSindex it has a DataRank of 3.8. It has been cited 102 times, with 73 citing works in its 1-hop citation network.
Abstract
We have conducted an integrative genomics analysis of serological responses to non-HLA targets after renal transplantation, with the aim of identifying the tissue specificity and types of immunogenic non-HLA antigenic targets after transplantation. Posttransplant antibody responses were measured by paired comparative analysis of pretransplant and posttransplant serum samples from 18 pediatric renal transplant recipients, measured against 5,056 unique protein targets on the ProtoArray platform. The specificity of antibody responses were measured against gene expression levels specific to the kidney, and 2 other randomly selected organs (heart and pancreas), by integrated genomics, employing the mapping of transcription and ProtoArray platform measures, using AILUN. The likelihood of posttransplant non-HLA targets being recognized preferentially in any of 7 microdissected kidney compartments was also examined. In addition to HLA targets, non-HLA immune responses, including anti-MICA antibodies, were detected against kidney compartment-specific antigens, with highest posttransplant recognition for renal pelvis and cortex specific antigens. The compartment specificity of selected antibodies was confirmed by IHC. In conclusion, this study provides an immunogenic and anatomic roadmap of the most likely non-HLA antigens that can generate serological responses after renal transplantation. Correlation of the most significant non-HLA antibody responses with transplant health and dysfunction are currently underway.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
DataRank Breakdown
Base Score Contribution
0.695
From this paper's citation signal
Citation Network Contribution
3.1
From 63 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- Significance analysis of microarrays applied to the ionizing radiation responseProceedings of the National Academy of Sciences200110,665 citationsDataRank 1.4
- AILUN: reannotating gene expression data automaticallyNature Methods2007172 citationsDataRank 8.7
- Protein microarrays identify antibodies to protein kinase Cζ that are associated with a greater risk of allograft loss in pediatric renal transplant recipientsKidney International200960 citationsDataRank 2.1
- Evaluation and integration of 49 genome-wide experiments and the prediction of previously unknown obesity-related genesBioinformatics200755 citationsDataRank 1.8
- Transplantomics and Biomarkers in Organ Transplantation: A Report From the First International ConferenceTransplantation201135 citationsDataRank 1.5
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 18% comes from its base citations and 82% from the citation network (63 citing papers contributed measurable signal).
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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