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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".