Quantitative Proteomic Profiling of <i>Cryptococcus neoformans</i>
Bibliographic record
Abstract
Cryptococcus neoformans is an opportunistic human fungal pathogen commonly associated with infection in immunocompromised individuals (e.g., patients with HIV/AIDS). Important virulence determinants include the production of a polysaccharide capsule, melanin, and extracellular enzymes, as well as the ability to grow at 37°C. C. neoformans controls a plethora of host defense and evasion mechanisms to survive during infection and to proliferate within the host, causing meningoencephalitis and death. Traditionally, characterization of C. neoformans under different environmental conditions and stresses has relied on genetic and phenotypic analyses, as well as biochemical assays. However, advances in mass spectrometry instrumentation, sample preparation protocols, and bioinformatic tools and databases promote comprehensive profiling of fungal cellular processes, secretion or protein release into the extracellular environment, and vesicle contents. Moreover, proteomics provides insight into regulatory mechanisms influencing signal transduction cascades and protein complexes or networks through profiling of post-translational modifications and protein-protein interactions. Given the medical impact of C. neoformans infections and the recent emergence of antifungal-resistant strains, defining proteins produced in response to unique environments provides an opportunity to uncover antivirulence strategies and alternative therapeutic options to combat infection. Here, we describe culturing and sample preparation of C. neoformans and outline protocols for comprehensively profiling changes in protein abundance within the cellular proteome and secretome. © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: Growth and sample preparation of Cryptococcus neoformans Basic Protocol 2: Protein extraction from supernatant Basic Protocol 3: Protein extraction from cell pellet Basic Protocol 4: Proteomic profiling and bioinformatics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".