Brain RNA profiling highlights multiple disease pathways in persons with HIV-associated neurocognitive disorder
Bibliographic record
Abstract
OBJECTIVE: To discover microRNA (miRNA)-RNA transcript interactions dysregulated in brains from persons with HIV-associated neurocognitive disorder (HAND), we investigated RNA expression using machine learning tools. DESIGN: Brain-derived host RNA transcript and miRNA expression was examined from persons with or without HAND using bioinformatics platforms. METHODS: By combining next generation sequencing, droplet digital (dd)PCR quantitation of HIV-1 genomes, with bioinformatics and statistical tools, we investigated differential RNA expression in frontal cortex from persons without HIV [HIV(-)], with HIV without brain disease [HIV(+)], with HAND, or HAND with encephalitis (HIVE). RESULTS: Expression levels for 147 transcripts and 43 miRNAs showed a minimum four-fold difference between clinical groups with a predominance of antiviral (type I interferon) signaling-related, neural cell maintenance-related, and neurodevelopmental disorder-related genes that was validated by gene ontology and molecular pathway inferences. Scale of signal-to-noise ratio (SSNR) and biweight midcorrelation (bicor) analyses identified 14 miRNAs and 45 RNA transcripts, which were highly correlated and differentially expressed ( P ≤ 0.05). Machine learning applications compared regression models predicated on HIV-1 DNA, or RNA viral quantities that disclosed miR-4683 and miR-154-5p were dominant variables associated with differential expression of host RNAs. These miRNAs were also associated with antiviral-related, cell maintenance-related, and neurodevelopmental disorder-related genes. CONCLUSION: Antiviral as well as neurodevelopmental disorder-related pathways in brain were associated with HAND, based on correlated RNA transcripts and miRNAs. Integrated molecular methods with machine learning offer insights into disease mechanisms, underpinning brain-related biotypes among persons with HIV that could direct clinical care.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".