Toward discovering a novel family of peptides targeting neuroinflammatory states of brain microglia and astrocytes
Bibliographic record
Abstract
Microglia are immune-derived cells critical to the development and healthy function of the brain and spinal cord, yet are implicated in the active pathology of many neuropsychiatric disorders. A range of functional phenotypes associated with the healthy brain or disease states has been suggested from in vivo work and were modeled in vitro as surveying, reactive, and primed sub-types of primary rat microglia and mixed microglia/astrocytes. It was hypothesized that the biomolecular profile of these cells undergoes a phenotypical change as well, and these functional phenotypes were explored for potential novel peptide binders using a custom 7 amino acid-presenting M13 phage library (SX7) to identify unique peptides that bind differentially to these respective cell types. Surveying glia were untreated, reactive were induced with a lipopolysaccharide treatment, recovery was modeled with a potent anti-inflammatory treatment dexamethasone, and priming was determined by subsequently challenging the cells with interferon gamma. Microglial function was profiled by determining the secretion of cytokines and nitric oxide, and expression of inducible nitric oxide synthase. After incubation with the SX7 phage library, populations of SX7-positive microglia and/or astrocytes were collected using fluorescence-activated cell sorting, SX7 phage was amplified in Escherichia coli culture, and phage DNA was sequenced via next-generation sequencing. Binding validation was done with synthesized peptides via in-cell westerns. Fifty-eight unique peptides were discovered, and their potential functions were assessed using a basic local alignment search tool. Peptides potentially originated from proteins ranging in function from a variety of supportive glial roles, including synapse support and pruning, to inflammatory incitement including cytokine and interleukin activation, and potential regulation in neurodegenerative and neuropsychiatric disorders.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".