Netzel, Brian C.

Overexpression of 11b‐hydroxysteroid dehydrogenase type 1 (11b‐HSD1) in adipose tissue leads to obesity in mice. We hypothesized that endogenous 11b‐HSD1 played a significant functional role in adipogenesis. Stable transfection of 3T3‐L1 cells with small hairpin RNA (shRNA), compared to a nucleotide‐substituted control, silenced 11b‐HSD1 expression and substantially attenuated the accumulation of lipid droplets and the expression of adipogenesis marker genes induced by a mixture containing either corticosterone or dexamethasone. 11b‐HSD1 silencing significantly elevated the concentration of 11‐dehydrocorticosterone in the culture supernatant, but did not affect the levels of corticosterone or dexamethasone, measured by LC‐MS/MS. 11b‐HSD1 silencing significantly attenuated the translocation of glucocorticoid receptors to the nucleus and reduced the number of cells entering the S phase in response to glucocorticoids (13 ± 1% vs. 30 ± 1% in control, n=5, P<0.05), a critical event in the initiation of adipogenesis. 11b‐HSD1 shRNA delivered by lentiviral vectors after the induction of differentiation reduced 11b‐HSD1 expression, but did not affect the further progression of adipogenesis. Our results established a functional and stage‐dependent role of 11b‐HSD1 in the initiation of 3T3‐L1 adipogenesis, and provided new insights into the development of obesity and related diseases.

AbstractObjectiveDescribe the secretion and profile of adrenal steroids in patients with adrenal incidentalomas compared to control subjects.Design, Setting and ParticipantsA prospective study, 73 patients with adrenal incidentalomas, 21 bilateral and 52 unilateral and 34 matched controls in University Hospital.MethodsCollect fasting blood sample before and 60 min after ACTH test (250 µg IV). One week later, perform overnight 1 mg dexamethasone test. The following steroids were measured by liquid chromatography‐mass spectrometry (LC‐MS): pregnenolone, 17‐OH pregnenolone, 17‐OH progesterone, 11‐deoxycorticosterone, 11‐deoxyortisol, 21‐deoxycortisol, corticosterone, cortisol, androstenedione and aldosterone.ResultsMean baseline serum cortisol was higher in incidentalomas, bilateral 361 ± 124, (range 143–665) nmol/L,(p < .0001), unilateral 268 ± 89 3.2 (range 98–507) nmol/L (p < .019) compared to controls 207 ± 100 (range 72–502) nmol/L. ACTH stimulation showed significantly higher levels in bilateral and unilateral cases compared to controls. After dexamethasone, mean serum cortisol levels suppressed in bilaterals 89 ± 69 (range 30–3) nmol/L (p < .0001), 58 ± 52 (range 16–323) nmol/L in unilateral (p < .01) compared to 26 ± 9 (range 7–46) nmol/L in controls. Mean baseline serum corticosterone was higher in bilateral 9.3 ± 4.8 (range 2.4–18.4) nmol/L (p < .005) and unilateral 7.3 ± 5.7 (range 0.1–30.3) nmol/L (p < .01) compared to controls 4.2 ± 2.4 (range 1.1–10.2) nmol/L, after ACTH stimulation significantly increased to higher levels in bilateral (p < .0002) and unilateral cases (p < .044) compared to controls. After dexamethasone, mean levels were 2.5 ± 2.6 (range 0.5–12.5) nmol/L in bilateral (p < .0006), 1.5 ± 1.6 (range 0.3–9.3) nmol/L in unilateral (p < .09) and 0.75 ± 0.46 (range 0.1–2.1) nmol/L in controls. Mean baseline serum 11‐deoxycorticosterone (DOC) was higher in bilaterals 0.32 ± 0.23 (range 0.08–1.1) nmol/L (p < .03) compared to controls 0.15 ± 0.21 (range 0.08–1.1) nmol/L. ACTH stimulation increased levels to 3.27 ± 1.72 (range 0.5–7.4) nmol/L in bilateral cases compared to controls 1.369 ± 1.53 (range 0.1–7.1) nmol/L (p < .0001). Dexamethasone decreased levels to baseline (p ns). There were significant differences in serum 21‐deoxycortisol (p < .0002) and serum pregnenolone (p < .004) only after ACTH stimulation.ConclusionsThere is increased activity in several steroid biosynthesis pathways and higher steroid levels in bilateral compared to unilateral cases and evidence of hypercortisolism in 30% unilateral and 62% of bilateral incidentalomas.

Abstract Objectives A matrix assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS) method (Mass-Fix) as a replacement for gel-based immunofixation (IFE) has been recently described. To utilize Mass-Fix clinically, a validated automated method was required. Our aim was to automate the pre-analytical processing, improve positive specimen identification and ergonomics, reduce paper data storage and increase resource utilization without increasing turnaround time. Methods Serum samples were batched and loaded onto a liquid handler along with reagents and a barcoded sample plate. The pre-analytical steps included: (1) Plating immunopurification beads. (2) Adding 10 μl of serum. (3) Bead washing. (4) Eluting the immunoglobulins (Igs), and reducing to separate the heavy and light Ig chains. The resulting plate was transferred to a second low-volume liquid handler for MALDI plate spotting. MALDI-TOF mass spectra were collected. Integrated in-house developed software was utilized for sample tracking, driving data acquisition, data analysis, history tracking, and result reporting. A total of 1,029 residual serum samples were run using the automated system and results were compared to prior electrophoretic results. Results The automated Mass-Fix method was capable of meeting the validation requirements of concordance with IFE, limit of detection (LOD), sample stability and reproducibility with a low repeat rate. Automation and integrated software allowed a single user to process 320 samples in an 8 h shift. Software display facilitated identification of monoclonal proteins. Additionally, the process maintains positive specimen identification, reduces manual pipetting, allows for paper free tracking, and does not significantly impact turnaround time (TAT). Conclusions Mass-Fix is ready for implementation in a high-throughput clinical laboratory.

High-throughput shotgun proteomics is the mainstay of protein identification in biological samples. Efficient proteomic analysis requires streamlined and accurate workflows for protein identification. Database searching has been the most basic and reliable workflow for identifying the peptides and proteins that are present in the sample. This method derives peptides from a list of protein sequences and matches them against the experimental MS2 spectra. The resulting peptide spectrum matches are scored to quantify their goodness of fit. Spectral library searching has been recently developed as a fast, and viable, alternative to sequence database searching. This method attempts to identify the peptides by matching their corresponding experimental MS2 spectra to a library of curated MS2 peptide spectra. Each method has its own merit and application in the proteomics field. This chapter aims to highlight the foundations of peptide spectrum matching via protein sequence database and spectral library searching.