The scalable precision medicine open knowledge engine (SPOKE): a massive knowledge graph of biomedical information.

MOTIVATION: Knowledge graphs (KGs) are being adopted in industry, commerce and academia. Biomedical KG presents a challenge due to the complexity, size and heterogeneity of the underlying information. RESULTS: In this work, we present the Scalable Precision Medicine Open Knowledge Engine (SPOKE), a biomedical KG connecting millions of concepts via semantically meaningful relationships. SPOKE contains 27 million nodes of 21 different types and 53 million edges of 55 types downloaded from 41 databases. The graph is built on the framework of 11 ontologies that maintain its structure, enable mappings and facilitate navigation. SPOKE is built weekly by python scripts which download each resource, check for integrity and completeness, and then create a 'parent table' of nodes and edges. Graph queries are translated by a REST API and users can submit searches directly via an API or a graphical user interface. Conclusions/Significance: SPOKE enables the integration of seemingly disparate information to support precision medicine efforts. AVAILABILITY AND IMPLEMENTATION: The SPOKE neighborhood explorer is available at https://spoke.rbvi.ucsf.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A Novel Multi-Biomarker Assay for Non-Invasive Quantitative Monitoring of Kidney Injury.

The current standard of care measures for kidney function, proteinuria, and serum creatinine (SCr) are poor predictors of early-stage kidney disease. Measures that can detect chronic kidney disease in its earlier stages are needed to enable therapeutic intervention and reduce adverse outcomes of chronic kidney disease. We have developed the Kidney Injury Test (KIT) and a novel KIT Score based on the composite measurement and validation of multiple biomarkers across a unique set of 397 urine samples. The test is performed on urine samples that require no processing at the site of collection and without target sequencing or amplification. We sought to verify that the pre-defined KIT test, KIT Score, and clinical thresholds correlate with established chronic kidney disease (CKD) and may provide predictive information on early kidney injury status above and beyond proteinuria and renal function measurements alone. Statistical analyses across six DNA, protein, and metabolite markers were performed on a subset of residual spot urine samples with CKD that met assay performance quality controls from patients attending the clinical labs at the University of California, San Francisco (UCSF) as part of an ongoing IRB-approved prospective study. Inclusion criteria included selection of patients with confirmed CKD and normal healthy controls; exclusion criteria included incomplete or missing information for sample classification, logistical delays in transport/processing of urine samples or low sample volume, and acute kidney injury. Multivariate logistic regression of kidney injury status and likelihood ratio statistics were used to assess the contribution of the KIT Score for prediction of kidney injury status and stage of CKD as well as assess the potential contribution of the KIT Score for detection of early-stage CKD above and beyond traditional measures of renal function. Urine samples were processed by a proprietary immunoprobe for measuring cell-free DNA (cfDNA), methylated cfDNA, clusterin, CXCL10, total protein, and creatinine. The KIT Score and stratified KIT Score Risk Group (high versus low) had a sensitivity and specificity for detection of kidney injury status (healthy or CKD) of 97.3% (95% CI: 94.6-99.3%) and 94.1% (95% CI: 82.3-100%). In addition, in patients with normal renal function (estimated glomerular filtration rate (eGFR) ≥ 90), the KIT Score clearly identifies those with predisposing risk factors for CKD, which could not be detected by eGFR or proteinuria (p < 0.001). The KIT Score uncovers a burden of kidney injury that may yet be incompletely recognized, opening the door for earlier detection, intervention and preservation of renal function.

Generation and Characterization of Alloantigen-Specific Regulatory T Cells For Clinical Transplant Tolerance.

Donor-specific CD4+CD127-CD25+FOXP3+ regulatory T cells (AgTregs) have the potential to induce clinical transplant tolerance; however, their expansion ex vivo remains challenging. We optimized a novel expansion protocol to stimulate donor-specific Tregs using soluble 4-trimer CD40 ligand (CD40L)-activated donor B cells that expressed mature antigen-presenting cell markers. This avoided the use of CD40L-expressing stimulator cells that might otherwise result in potential cellular contamination. Purified allogeneic "recipient" CD4+CD25+ Tregs were stimulated on days 0 and 7 with expanded "donor" B cells in the presence of IL-2, TGFß and sirolimus (SRL). Tregs were further amplified by polyclonal stimulation with anti-CD3/CD28 beads on day 14 without SRL, and harvested on day 21, with extrapolated fold expansion into the thousands. The expanded AgTregs maintained expression of classical Treg markers including demethylation of the Treg-specific demethylated region (CNS2) and also displayed constricted TcR repertoire. We observed AgTregs more potently inhibited MLR than polyclonally expanded Tregs and generated new Tregs in autologous responder cells (a measure of infectious tolerance). Thus, an optimized and more clinically applicable protocol for the expansion of donor-specific Tregs has been developed.

Protein biomarkers in renal transplantation.

INTRODUCTION: The standard of care for monitoring renal transplant patients include transplant biopsy and serum creatinine measurements. These are invasive procedures and are late and nonspecific markers of injury. Proteomics and proteins can provide biomarkers for monitoring pathology and become a useful tool in detection and treatment of conditions that occur after transplant. Areas covered: A survey of 273 studies in the biomarker detection field which included proteomics relating to renal pathology and normal controls was done. Analysis of this data showed pathways and biomarkers specific to different pathologies such as: AR, CR, IRI, TI, VR, DGF, AABMR, ACMR, immunosuppressant toxicity, and infection. It also revealed biomarkers proposed for better detection of these pathologies and the strength (sensitivity and specificity) of such biomarkers. Finally, the field of proteomics in renal transplant in terms of its methodologies, current challenges in clinical translation, and possible solutions are also discussed. Expert commentary: An analysis of biomarkers of acute and chronic rejection revealed acute rejection may be a more inflammatory process. A comparison of proteomic and protein based (n = 183), transcriptomic (n = 1), and genomic (n = 4) studies revealed that proteomic and protein based approaches may offer a clearer picture of inflammation in acute rejection.