Time-course transcriptomic analysis of Petunia ×hybrida leaves under water deficit stress using RNA sequencing

Water deficit limits plant growth and development, resulting in quality loss of horticultural crops. However, there is limited information on gene regulation and signaling pathways related to water deficit stress response at multiple time points. The objective of this research was to investigate global gene expression patterns under water deficit stress to provide an insight into how petunia (Petunia ×hybrida 'Mitchell Diploid') responded in the process of stress. Nine-week-old petunias were irrigated daily or placed under water stress by withholding water. Stressed plants reduced stomatal conductance after five days of water deficit, indicating they perceived stress and initiated stress response mechanisms. To analyze transcriptomic changes at the early stage of water deficit, leaf tissue samples were collected 1, 3, and 5 days after water was withheld for RNA sequencing. Under water deficit stress, 154, 3611, and 980 genes were upregulated and 41, 2806, and 253 genes were downregulated on day 1, 3, and 5, respectively. Gene Ontology analysis revealed that redox homeostasis processes through sulfur and glutathione metabolism pathways, and hormone signal transduction, especially abscisic acid and ethylene, were enriched under water deficit stress. Thirty-four transcription factor families were identified, including members of AP2/ERF, NAC, MYB-related, C2H2, and bZIP families, and TFs in AP2/ERF family was the most abundant in petunia. Interestingly, only one member of GRFs was upregulated on day 1, while most of TFs were differentially expressed on day 3 and/or 5. The transcriptome data from this research will provide valuable molecular resources for understanding the early stages of water stress-responsive networks as well as engineering petunia with enhanced water stress tolerance.

Raman chemometric urinalysis (Rametrix) as a screen for bladder cancer

Bladder cancer (BCA) is relatively common and potentially recurrent/progressive disease. It is also costly to detect, treat, and control. Definitive diagnosis is made by examination of urine sediment, imaging, direct visualization (cystoscopy), and invasive biopsy of suspect bladder lesions. There are currently no widely-used BCA-specific biomarker urine screening tests for early BCA or for following patients during/after therapy. Urine metabolomic screening for biomarkers is costly and generally unavailable for clinical use. In response, we developed Raman spectroscopy-based chemometric urinalysis (Rametrix™) as a direct liquid urine screening method for detecting complex molecular signatures in urine associated with BCA and other genitourinary tract pathologies. In particular, the Rametrix^TM screen used principal components (PCs) of urine Raman spectra to build discriminant analysis models that indicate the presence/absence of disease. The number of PCs included was varied, and all models were cross-validated by leave-one-out analysis. In Study 1 reported here, we tested the Rametrix™ screen using urine specimens from 56 consented patients from a urology clinic. This proof-of-concept study contained 17 urine specimens with active BCA (BCA-positive), 32 urine specimens from patients with other genitourinary tract pathologies, seven specimens from healthy patients, and the urinalysis control Surine^TM. Using a model built with 22 PCs, BCA was detected with 80.4% accuracy, 82.4% sensitivity, 79.5% specificity, 63.6% positive predictive value (PPV), and 91.2% negative predictive value (NPV). Based on the number of PCs included, we found the Rametrix^TM screen could be fine-tuned for either high sensitivity or specificity. In other studies reported here, Rametrix^TM was also able to differentiate between urine specimens from patients with BCA and other genitourinary pathologies and those obtained from patients with end-stage kidney disease (ESKD). While larger studies are needed to improve Rametrix^TM models and demonstrate clinical relevance, this study demonstrates the ability of the Rametrix^TM screen to differentiate urine of BCA-positive patients. Molecular signature variances in the urine metabolome of BCA patients included changes in: phosphatidylinositol, nucleic acids, protein (particularly collagen), aromatic amino acids, and carotenoids.

Spectral characteristics of urine from patients with end-stage kidney disease analyzed using Raman Chemometric Urinalysis (Rametrix)

Raman Chemometric Urinalysis (Rametrix^TM) was used to discern differences in Raman spectra from (i) 362 urine specimens from patients receiving peritoneal dialysis (PD) therapy for end-stage kidney disease (ESKD), (ii) 395 spent dialysate specimens from those PD therapies, and (iii) 235 urine specimens from healthy human volunteers. Rametrix^TM analysis includes spectral processing (e.g., truncation, baselining, and vector normalization); principal component analysis (PCA); statistical analyses (ANOVA and pairwise comparisons); discriminant analysis of principal components (DAPC); and testing DAPC models using a leave-one-out build/test validation procedure. Results showed distinct and statistically significant differences between the three types of specimens mentioned above. Further, when introducing “unknown” specimens, Rametrix^TM was able to identify the type of specimen (as PD patient urine or spent dialysate) with better than 98% accuracy, sensitivity, and specificity. Rametrix^TM was able to identify “unknown” urine specimens as from PD patients or healthy human volunteers with better than 96% accuracy (with better than 97% sensitivity and 94% specificity). This demonstrates that an entire Raman spectrum of a urine or spent dialysate specimen can be used to determine its identity or the presence of ESKD by the donor.