Urine Exosomal MicroRNA Profiling in the Family Investigation of Nephropathy and Diabetes (FIND)
Bibliographic record
Abstract
About 1 in 3 adults with either type 1 or type 2 diabetes (T2D) has some degree of renal injury or diabetic nephropathy (DN). There is a need to develop more sensitive and specific means of detecting and gauging DN progression so that therapies can be started as early as possible. We hypothesized that UE microRNAs (miRs), small, non-coding 21-25 nucleotide species, a relatively stable readout of renal metabolic health, could be utilized in this fashion. From the NIH, NIDDK Central Repository, we obtained 299 urine samples (300 ml) from African American subjects previously enrolled in the FIND study. The FIND study was initially designed to elucidate genetic determinants of DN in related patient populations using both T2D and non-diabetic control (CTRL), male (M) and female (F) subjects. Our samples included: 20 CTRL-M, 46 CTRL-F, 43 T2D-M, and 90 T2D-F). Other deidentified demographic and clinical data was obtained from the NIH. We measured urine albumin and creatinine in the samples and calculated ratios (mg albumin/g creatinine). Patients were categorized as normal (N, < 30, 120 subjects), microalbuminuric (MIC, 30-300, 33 subjects), or macroalbuminuric (MAC, >300, 46 subjects). Urine exosomes (UE) were isolated from each urine using the Total Exosome Isolation (from urine) kit (Thermofisher), which allows for precipitation of water-insoluble exosomes via centrifugation at medium speed (10,000g) for 1 hour. Small RNA was isolated followed by cDNA synthesis using commercially-available kits (Qiagen). Previously, we found miR-451a to be more highly expressed in UE from human subjects with chronic kidney disease, than in control subjects. We also found this miR to be a potential predictive biomarker for albuminuria in diabetic rats. Therefore, first we used conventional qRT-PCR to evaluate UE levels of miR-451a, as well as, the anti-sense product, miR-451b. MiR-127 was used as the internal standard, as it has been reported not to change with T2D. In MAC subjects, we found that the UE median miR-451a level increased with a fold change (FC) of 3.5 (relative to N). Similarly, miR-451b showed a 3.6-fold increase. FC in the MIC groups were 2.9 and 2.5 for miR-451a and b, respectively (relative to N). Next we conducted an unbiased screen of commonly excreted miRs in these subjects using a miR Array (miRCURY LNA miRNA Focus PCR panel, Qiagen) configured with 88 targets highly expressed in UE. We screened 19 subjects representing a range of conditions, i.e., albuminuric and normal, diabetic and non-diabetic, male and female. We found positive signals in 5 or more subjects for 16 miRs. Of those, 12, i.e., let-7b-5p, miR-16-5p, miR-23b-3p, miR-26a-5p, miR-27a-3p, miR-30a-5p, miR-30b-5p, miR-30c-5p, miR30e-5p, miR-141-3p, miR-203a, and miR-204-5p were expressed at mean levels 2-fold higher in MAC versus N subjects. Furthermore, 13 of these miRs were also over 2-fold higher when subjects were divided as T2D versus CTRL, i.e., the above list plus miR-200c-3p and miR-30d-5p, but not let-7b-5p in this case. When subjects were not divided by groups, miR-598-5p had the highest positive correlation with urinary albumin excretion, R = 0.53 and p = 0.035 (for slope ≠ 0). Using miRNet (McGill University), a freely available miR analysis software tool, we mined for common targets of miR-451(a & b) and miR-598. One putative target of both miR-451b and miR-598 was vascular endothelial growth factor A (VEGFA), which has been shown to play a role in DN. In sum, these miRs represent candidate targets to undergo additional scrutiny. We conclude UE miRs may hold clinical value in the non-invasive assessment of DN severity and predicting progression. NIH/NINDS U01-DK103225 (Feldman, PI); Ecelbarger Pilot. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".