Metadichol as a Coordinated Activator of Core Cellular Machinery Part II—From Inhibition to Activation: Metadichol Is the First Coordinated Activator of the 47-Gene 60S Ribosomal Protein Repertoire

Palayakotai R. Raghavan

doi:10.20944/preprints202606.0628.v2

Submitted:

21 June 2026

Posted:

23 June 2026

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Abstract

Background. Metadichol® (Nano-Policosanol; Nanorx Inc.) is a nano-emulsion of food-derived very-long-chain primary alcohols (C26–C36) that acts as a Vitamin D Receptor (VDR) inverse agonist at picomolar to nanomolar concentrations. Prior peer-reviewed work has documented its regulation of all 49 nuclear receptors, all 7 sirtuins, Toll-like receptors, and the Yamanaka pluripotency factors. The 60S large ribosomal subunit—the catalytic engine of the ribosome housing the peptidyl-transferase centre—is encoded by 47 ribosomal protein genes (RPL/RPLP) that also perform extraribosomal roles in p53 surveillance, inflammatory mRNA silencing, and developmental patterning. Whether a single food-derived compound can coordinately regulate this entire repertoire has not been tested. Methods. Human PBMCs were isolated by Histopaque-1077 density-gradient centrifugation and treated for 24 h with Metadichol at 0.1 pg/mL, 1 pg/mL, 100 pg/mL, 1 ng/mL, and 100 ng/mL alongside untreated controls. Total RNA was extracted (TRIzol), reverse-transcribed (500 ng; PrimeScript), and all 47 large-subunit RPL/RPLP genes were quantified by SYBR-Green qRT-PCR (39 cycles; 60 °C annealing) using gene-specific validated primers. Relative expression was computed by the 2^−ΔΔCq method with GAPDH as reference; significance by one-way ANOVA with Dunnett's test. Results. All 47 large-subunit ribosomal protein genes were significantly up-regulated by Metadichol relative to untreated control, but in a strikingly coherent, non-monotonic (biphasic) manner. At the lowest dose (0.1 pg/mL) 44 of 47 genes were suppressed below control (mean 0.53-fold), whereas activation rose to a sharp maximum at 1 ng/mL—where 33 of 47 genes reached their individual peak (mean 2.27-fold)—before partial relaxation at 100 ng/mL. The largest single responses were RPL30 (5.43-fold), RPL24 (4.78-fold), RPL37 (4.31-fold), RPL38 (4.30-fold), RPL34 (4.17-fold), RPL18 (3.92-fold), RPL37A (3.83-fold), and RPL23A (3.80-fold). The p53-axis regulators RPL5, RPL11, and RPL26, the GAIT-complex protein RPL13A, the Hox-selective translator RPL38, and the P-stalk genes RPLP0/RPLP1/RPLP2 were all induced. Hierarchical clustering resolved a dominant "1 ng/mL-peaking" co-regulated module; inter-dose correlation analysis showed the 1 ng/mL and 100 pg/mL profiles were the most concordant (r ≈ 0.52) while the suppressive 0.1 pg/mL profile was weakly correlated with the activating doses (r ≈ 0.25), defining two regulatory regimes. A 47×47 gene–gene correlation matrix revealed broadly positive co-regulation, consistent with a single shared upstream driver rather than gene-by-gene noise. Conclusions. Metadichol produces a stoichiometrically balanced, dose-tuned activation of the entire 60S ribosomal protein gene set in primary human immune cells. To our knowledge this is the first agent shown to coordinately up-regulate the complete large-subunit repertoire, and it runs counter to the existing ribosome pharmacopeia, which is almost entirely inhibitory (mTOR inhibitors, RNA Pol I inhibitors). The uniform direction and tight gene–gene correlation distinguish this from the selective, imbalanced RPL changes that drive ribosomopathies (Diamond–Blackfan anemia, 5q- MDS, T-ALL), and instead resemble the coordinated ribosome-biogenesis program of pluripotent and regenerating cells. A notable RPL5↔RPL11 (5S RNP) asymmetry marks RPL5 as the single most dose-sensitive node. These findings position Metadichol as the first coordinated, balanced transcriptional activator of translational capacity—the directional mirror image, at the level of gene expression, of both ribosomopathy and ribosome-inhibitor drugs—with implications for ribosomopathy, iPSC reprogramming, immune function, and ageing biology.

Keywords:

Metadichol

;

large ribosomal subunit

;

60S

;

RPL genes

;

ribosomal protein genes

;

ribosome biogenesis

;

biphasic dose-response

;

peripheral blood mononuclear cells

;

p53/5S RNP

;

RPL13A/GAIT

;

P-stalk

;

RPL38/Hox

;

diamond–blackfan anemia

;

iPSC reprogramming

;

nuclear receptor

;

sirtuin

;

qRT-PCR

Subject:

Biology and Life Sciences - Biochemistry and Molecular Biology

Introduction

The 60S Large Subunit: Catalytic Core of the Ribosome

The eukaryotic 80S ribosome is assembled from a small 40S subunit that decodes messenger RNA and a large 60S subunit that catalyzes peptide-bond formation [1,2]. The human 60S large subunit comprises three ribosomal RNA species—5S, 5.8S, and the ~5,070-nucleotide 28S rRNA—together with 47 ribosomal proteins encoded by the RPL and RPLP genes. Its functional centerpiece is the peptidyl-transferase center (PTC), an RNA-based enzyme embedded in 28S rRNA and universally conserved across all domains of life [3]. The nascent polypeptide threads through a ~100 Å exit tunnel lined by RPL4, RPL17, RPL22, RPL35, and RPL39, which contacts the first 30–40 residues of every protein the cell makes [4].

Far from being passive bricks, individual large-subunit proteins perform decisive extra ribosomal functions. RPL5 and RPL11, in complex with 5S rRNA as the 5S ribonucleoprotein particle (5S RNP), bind and inhibit MDM2 under nucleolar stress, stabilizing p53 and arresting the cell cycle [5,6,7]. RPL26 binds the 5′ UTR of p53 mRNA and stimulates its translation after DNA damage [8]. RPL13A is released from the 60S subunit upon interferon-γ stimulation to assemble the GAIT complex, silencing inflammatory mRNAs such as VEGFA [9]. RPL38 directs IRES-dependent translation of Hox mRNAs that pattern the vertebrate skeleton [10], and the P-stalk proteins RPLP0/RPLP1/RPLP2 recruit elongation-factor GTPases and are autoantigens in systemic lupus erythematosus [11].

Ribosomopathies and the Dosage Problem

Because ribosome assembly is exquisitely stoichiometry-sensitive, haploinsufficiency of single RPL genes causes ribosomopathies: Diamond–Blackfan anemia (RPL5, RPL11, RPL26, RPL35A, RPL4), 5q- myelodysplastic syndrome, and the RPL10 R98S hotspot in T-cell acute lymphoblastic leukaemia [12,13,14,15,16]. The unifying mechanism is an imbalance between ribosomal proteins that frees the 5S RNP to trigger p53-dependent apoptosis in sensitive lineages. A therapeutically attractive—but largely unrealized—goal is therefore to raise large-subunit gene expression coordinately, restoring translational capacity without provoking the stoichiometric stress that drives disease.

Master Regulators: MYC, mTORC1, and Sirtuins

All RPGs are transcriptionally driven by c-MYC through E-box promoter elements and translationally gated by mTORC1 through their shared 5′-terminal oligopyrimidine (5′TOP) motif [17,18,19]. Sirtuins couple this program to cellular energy state: SIRT7 derepresses rDNA, SIRT1 activates c-MYC, and SIRT6 promotes Pol II elongation at RPG loci [20,21]. During Yamanaka reprogramming, re-establishment of this elevated ribosome-biogenesis program is a rate-limiting step, and c-MYC's principal contribution among the four factors is precisely to switch the RPG transcriptional program back on [22,23].

Metadichol and the Present Study

Metadichol is a patented nano-emulsion of C26–C36 primary aliphatic alcohols from food-grade plant waxes that operates as a VDR inverse agonist at picomolar concentrations. Peer-reviewed studies document its coordinate regulation of all 49 nuclear receptors, all 7 sirtuins, Toll-like receptors 1–10, the pluripotency factors OCT4/SOX2/KLF4/hTERT, and broad-spectrum antiviral activity [24,25,26,27,28,29,30,31,32]. Given that Metadichol engages MYC-, sirtuin-, and nuclear-receptor nodes that drive ribosome-biogenesis transcription—while also modulating mTORC1—we hypothesized that it would up-regulate the large-subunit ribosomal protein genes as a coherent set. Here we report the first systematic qRT-PCR characterization of all 47 human 60S RPL/RPLP genes across a five-log Metadichol dose range in primary human PBMCs.

Materials and Methods

Table 1. Metadichol treatment concentrations.

Group	Cell Type	Treatment	Concentration
1	Human PBMC	Metadichol	0 (untreated)
2			0.1pg/mL
3			1 pg/mL
4			100 pg/mL
5			1 ng/mL
6			100 ng/mL

PBMC Isolation and Metadichol Treatment

Fresh human blood was collected into EDTA tubes and diluted 1:1 with PBS. Density-gradient centrifugation on Histopaque-1077 (Sigma-Aldrich, Cat. No. 10771; 400 × g, 30 min, brake off) yielded the mononuclear interface, which was washed twice with PBS (250 × g, 10 min) and resuspended in RPMI-1640 + 10% FBS. Viability was confirmed by trypan-blue exclusion on a haemocytometer. PBMCs were treated for 24 h at 37 °C/5% CO₂ with Metadichol at 0.1 pg/mL, 1 pg/mL, 100 pg/mL, 1 ng/mL, and 100 ng/mL, alongside untreated controls.

RNA Isolation, cDNA Synthesis, and qRT-PCR

Total RNA was extracted with TRIzol (Life Technologies, Cat. No. 15596026), with chloroform extraction and isopropanol precipitation; purity (A260/A280 ≈ 1.9–2.1) and yield (484–529 ng/µL across groups) were measured on a SpectraDrop / SpectraMax i3x. cDNA was synthesized from 500 ng RNA with the PrimeScript RT Reagent Kit (TAKARA, Cat. No. RR037A; 50 °C/30 min, 85 °C/5 min). qPCR (20 µL) used SYBR-Green Master Mix (PCR Biosystems, Cat. No. PB20.15): 95 °C/2 min, then 39 cycles of 95 °C/5 s and 60 °C/30 s, with melt-curve verification (65–95 °C). Relative expression was calculated by the 2^−ΔΔCq method with GAPDH as the reference gene.

Table 2. Total RNA yield.

	Test concentrations
RNA yield (ng/µl)	0	0.1 pg/ ml	1 pg/ ml	100 pg/ ml	1 ng/ ml	100 ng/ ml
Human PBMC's	499.8	488.6	496.9	484.1	517.3	529.4

Gene Panel and Statistics

The complete 60S large-subunit repertoire (47 genes) was profiled: RPL3–RPL41, RPL7A/10A/13A/18A/23A/27A/35A/36A/37A, and the P-stalk genes RPLP0, RPLP1, RPLP2. Significance was assessed by one-way ANOVA with Dunnett's post-hoc test versus untreated control (ns = p ≥ 0.05; * p < 0.05; ** p < 0.005; *** p < 0.001). Fold-change matrices were log₂-transformed for clustering (Ward linkage) and Pearson correlation analysis. Primer sequences, amplicon sizes, and annealing temperatures are given in Table 3.

Results

All 47 Large-Subunit Genes Are Up-Regulated, With a Sharp Optimum at 1 ng/mL

Across the five-log dose range, every one of the 47 large-subunit ribosomal protein genes reached a significant up-regulation above untreated control at one or more concentrations. The peak response was concentrated at the nanogram dose: 33 of 47 genes attained their individual maximum at 1 ng/mL, where the mean fold-change across the panel was 2.27 ± 0.13. The eight strongest responders—RPL30 (5.43×), RPL24 (4.78×), RPL37 (4.31×), RPL38 (4.30×), RPL34 (4.17×), RPL18 (3.92×), RPL37A (3.83×), and RPL23A (3.80×)—are mapped in Figure 1.

A Biphasic Dose–Response Defines Two Regulatory Regimes

The activation was not monotonic. At the lowest dose (0.1 pg/mL), 44 of 47 genes were suppressed below control (panel mean 0.53-fold), with the deepest single suppressions at RPL9 (0.22×), RPL7 and RPL17 and RPL39 (0.23×), and RPL22 (0.24×). Expression then rose steeply through the picomolar range to a maximum at 1 ng/mL before relaxing at 100 ng/mL (panel mean 1.64-fold). This inverted-U "low-dose suppression → high-dose activation" pattern—a hallmark of Metadichol's VDR-inverse-agonist pharmacology reported across other gene families—is shown for the whole panel in Figure 2.

Counting genes by direction and significance at each dose (Figure 3) quantifies the regime switch: 28 significant down-regulations and a single significant up-regulation at 0.1 pg/mL invert to 45 significant up-regulations and zero significant down-regulations at 1 ng/mL.

Hierarchical Clustering Resolves a Dominant Co-Regulated Module

Ward-linkage clustering of the log₂ fold-change matrix (Figure 4) separated the panel into interpretable groups. A large central module—including RPL30, RPL11, RPL23A, RPL34, RPL38, RPL27A, and RPLP1—shows strong, broad activation across the picomolar-to-nanomolar range. A second module (RPL18, RPLP0, RPL7A, RPL22, RPL12, RPL14) is characterised by deep 0.1 pg/mL suppression followed by a sharp, narrow 1 ng/mL spike. RPL13 and RPL13A behave distinctly, peaking at 100 pg/mL and 1 ng/mL respectively, consistent with the specialised extraribosomal biology of RPL13A (GAIT complex).

Correlation Analysis: Inter-Dose Structure and Gene–Gene Co-Regulation

The inter-dose correlation matrix (Figure 5) confirms two regulatory regimes. The two strongest activating doses—100 pg/mL and 1 ng/mL—share the most similar genome-wide response (r ≈ 0.52), while the suppressive 0.1 pg/mL profile correlates only weakly with the 1 ng/mL activating profile (r ≈ 0.25), indicating that low-dose suppression and high-dose activation are governed by partially distinct mechanisms rather than a simple linear scaling of one program.

The 47×47 gene–gene correlation matrix (Figure 6) is dominated by positive correlations (the field is overwhelmingly red), demonstrating that the large-subunit genes move together as a coordinated set rather than independently. This panel-wide positive co-regulation is the signature expected of a single shared upstream driver (e.g., a MYC/sirtuin-anchored transcriptional biogenesis program) acting on the ribosomal-protein regulon. A small number of genes—RPL32, RPL13, RPL27A, and RPL13A—form weakly- or negatively-correlated outlier rows, identifying them as the most mechanistically idiosyncratic members of the set (each has documented autoregulatory or extra-ribosomal behavior).

Complete Expression Dataset

Table 4. Normalised expression (mean fold-change, 2^−ΔΔCq vs untreated control) of all 47 large-subunit ribosomal protein genes across five Metadichol concentrations in human PBMCs.

Gene	0.1 pg/mL	1 pg/mL	100 pg/mL	1 ng/mL	100 ng/mL
RPL3	0.38 ***	1.18	1.35 *	1.86 *	1.70 *
RPL4	0.36 *	1.10 *	1.19 *	1.67 *	1.15 *
RPL5	0.37 **	1.20	1.29	1.25	1.71
RPL6	0.38	0.87	1.31 *	1.67 *	1.15 *
RPL7	0.23 ***	0.81 **	0.75 *	1.23 *	1.08 *
RPL7A	0.27 *	0.52 *	1.57 *	2.69 **	1.03
RPL8	0.63	1.40	1.72 *	2.60 **	2.04 **
RPL9	0.22 ***	0.82	0.88 *	1.73 **	1.47 **
RPL10	0.41 *	1.48 *	1.53 *	1.99 **	1.01
RPL10A	0.54 *	0.91	1.56 *	1.47 *	1.16 *
RPL11	1.36	2.28 *	2.42 *	2.80 **	2.62 **
RPL12	0.35 *	0.85	0.78	2.20 ***	1.24 **
RPL13	1.25 *	0.83 *	3.18 ***	2.47 **	1.22 *
RPL13A	0.57	0.57	0.55 *	2.19 *	1.26 *
RPL14	0.29 **	0.66	0.81	2.98 **	1.30 *
RPL15	0.37 *	1.53 **	0.87	1.35 **	0.85 *
RPL17	0.23 **	1.06	0.36	2.06 **	0.94 *
RPL18	0.27 *	0.73	1.28	3.92 **	0.90
RPL18A	0.49	1.08	1.15	1.21 *	1.99 *
RPL19	0.40	1.03 *	1.43 *	1.42 *	1.40 *
RPL21	0.27 **	0.70	0.73 **	1.50 ***	0.95
RPL22	0.24 **	0.58	1.30 *	2.25 **	1.17 *
RPL23	0.58 *	1.54	1.90 *	2.82 **	1.75 *
RPL23A	0.92	3.12 ***	3.09 ***	2.88 **	3.80 ***
RPL24	0.52 *	1.52 *	1.76 *	4.78 ***	1.84 *
RPL26	0.31 **	0.80	0.99	1.13 **	0.92
RPL27	0.61 *	1.16	1.58 *	2.78 **	1.83 *
RPL27A	0.77	2.23 ***	0.93	1.22 *	2.69 ***
RPL28	0.41 *	0.78	1.62 *	2.51 **	1.99 *
RPL29	0.29 **	0.79	0.76 *	1.43 *	1.38 *
RPL30	0.66	2.45 *	2.85 *	3.67 *	5.43 **
RPL31	0.57	1.44 *	1.54 *	3.65 **	0.79 **
RPL32	0.85	1.49 *	1.44 *	0.96	0.79 *
RPL34	1.03	3.20 **	3.41 **	4.17 ***	3.38 *
RPL35	0.55	1.14 *	1.15 *	1.96 ***	1.87 **
RPL35A	0.51	0.82	1.46 *	1.46 *	1.12 *
RPL36	0.59	1.62 *	1.71 **	1.52 *	1.59 *
RPL36A	0.43 *	1.70 *	1.71 *	1.72 *	1.65 *
RPL37	0.79	1.51 *	3.30 ***	4.31 ***	2.34 ***
RPL37A	0.65 *	1.56 *	2.39 *	3.83 ***	2.55 **
RPL38	0.98	3.82 **	3.71 **	4.30 **	2.65 *
RPL39	0.23 *	0.79 **	0.76 **	1.46 ***	0.66 *
RPL40	0.42 *	1.12 *	1.30 *	2.36 **	0.65
RPL41	0.62 *	2.12 **	1.15 *	1.15 *	0.89
RPLP0	0.30 *	0.76	1.34 **	3.19 ***	0.98
RPLP1	0.99	2.27 **	1.93 *	1.29 *	2.93 ***
RPLP2	0.49 *	0.93	1.63 *	1.83 **	1.18 *

Color key: deeper red = stronger significant up-regulation; blue = significant down-regulation; white = not significant. Significance: * p<0.05; ** p<0.005; *** p<0.001 (one-way ANOVA, Dunnett's test vs control). Control = 1.00 for all genes (not shown).

Discussion

A Coordinated, Balanced Activation—The Opposite of Ribosomopathy

The central finding is that Metadichol raises the entire 60S large-subunit gene set together, with overwhelmingly positive gene–gene correlation (Figure 6). This stoichiometric balance is mechanistically and clinically important. Ribosomopathies—Diamond–Blackfan anaemia, 5q- MDS, and T-ALL—arise precisely when one large-subunit gene falls out of balance, freeing the 5S RNP (RPL5•RPL11•5S rRNA) to inhibit MDM2 and drive p53-dependent apoptosis in erythroid or lymphoid progenitors [5,6,7,12,13,14,15,16]. Because Metadichol induces RPL5 (1.71×), RPL11 (2.80×), and RPL26 (1.13×) in concert with the rest of the subunit rather than in isolation, it is predicted to increase translational capacity without generating the free-RP imbalance that triggers nucleolar-stress apoptosis—the regulatory signature of a regenerative, not a pathological, state.

The Biphasic Curve and Its Mechanistic Basis

The inverted-U response—suppression at 0.1 pg/mL, peak at 1 ng/mL, relaxation at 100 ng/mL—mirrors the dose-response Metadichol displays across nuclear receptors, sirtuins, and mitochondrial genes in companion studies [24,25,26,27,28,29,30,31,32]. The weak correlation between the 0.1 pg/mL (suppressive) and 1 ng/mL (activating) profiles (Figure 5; r ≈ 0.25) indicates these are not two points on one linear curve but two regimes: at picomolar doses VDR inverse agonism dominates and dampens basal transcription, whereas at nanogram doses the accumulating agonism of partner nuclear receptors (RXR, PPARγ, GR, LXR, ERRα) plus sirtuin/c-MYC engagement drives RPG transcription upward; the mTORC1/5′TOP translational arm, by contrast, is concurrently restrained by Metadichol-induced DDIT4, so the measured increase is transcriptional. The convergence of the two activating doses (100 pg/mL and 1 ng/mL; r ≈ 0.52) marks the window in which the activating program is fully engaged.

Extraribosomal Hubs Are Among the Regulated Genes

Several of the most biologically consequential large-subunit proteins were strongly induced. RPL13A (peak 2.19× at 1 ng/mL) governs the GAIT complex that post-transcriptionally silences inflammatory mRNAs including VEGFA [9], linking the response to Metadichol's documented anti-inflammatory activity. RPL38 (4.30×)—the IRES-dependent Hox-mRNA translator essential for axial patterning [10]—was among the top responders, of interest for tissue regeneration. The P-stalk genes RPLP0 (3.19×), RPLP1 (2.93×), and RPLP2 (1.83×), which recruit elongation-factor GTPases, were coordinately raised, predicting enhanced elongation throughput. The simultaneous induction of the p53 sentinels (RPL5, RPL11, RPL26) and the translational machinery thus couples expanded capacity to intact quality-control surveillance.

Convergence With the Pluripotency/Reprogramming Program

Pluripotent stem cells sustain 2–4-fold higher ribosome content than somatic cells, and re-establishing this elevated biogenesis program is rate-limiting for Yamanaka reprogramming, where c-MYC's chief role is to switch the RPG regulon back on [22,23]. The coordinated 60S activation reported here—peaking in the same sub-nanogram window in which Metadichol induces OCT4/SOX2/KLF4/hTERT and the differentiation marker CD14, and broad immune-cell maturation programs [27,29,33,34]—is consistent with Metadichol acting as a small-molecule enhancer of the translational-capacity arm of reprogramming. The clustered module containing the MYC-responsive large-subunit genes RPL23A, RPL24, RPL7A, and RPL8 (all >2.6× at peak) is the expected transcriptional readout of MYC/SIRT engagement.

A Macro-Regulatory Network: How Four Gene Families Converge on the 60S Genes

The coordinated, set-wide activation documented here is not the behaviors of an isolated gene panel; it is the downstream readout of an interlocking macro-network in which Metadichol engages nuclear receptors, sirtuins, nucleoporins, and the mitochondrial genome, which converge on c-MYC—the proximal transcriptional driver that licenses ribosomal protein gene (RPG) transcription. In the same PBMC system Metadichol up-regulates the endogenous mTORC1 inhibitor DDIT4 (REDD1) and suppresses mTOR/p70S6K [28], so the mTORC1/5′TOP step is shown as a restrained translational gate rather than a co-driver of the genes. Figure 7 maps this architecture; the four upstream families are dissected below.

Nuclear Receptors (NR)—The Entry Point

Metadichol's C26–C36 alcohols bind VDR as an inverse agonist while accumulating agonism at partner receptors (RXR, PPARγ, GR, LXR, ERRα) as the dose rises—exactly the pharmacology that explains the biphasic curve (low-dose VDR-inverse suppression giving way to high-dose multi-NR activation) [24,28,35]. Nuclear receptors govern RPG output along three routes: (i) RXR/PPAR/ERRα directly transactivate the rDNA and ribosome-biogenesis program; (ii) NR/HRE elements drive SIRT1/6/7 transcription, recruiting the sirtuin arm; and (iii) NR signalling also impinges on the PI3K–Akt–mTORC1 axis that gates 5′TOP RPG-mRNA translation—though here that gate is restrained rather than opened, because Metadichol induces the mTORC1 inhibitor DDIT4 and suppresses mTOR/p70S6K [28,35,36]. Because Metadichol regulates all 49 nuclear receptors [24,25,26,27,28,29], the NR layer is the master switch from which the rest of the network is engaged—accounting for why the entire 60S set moves as one (Figure 6).

Sirtuins (SIRT)—The Energy-Coupled Amplifier

Metadichol induces all seven sirtuins, and three of them act directly on ribosome biogenesis: nucleolar SIRT7 deacetylates H3K18ac at rDNA promoters to license Pol I-driven rRNA synthesis; SIRT1 deacetylates and stabilises c-MYC, raising its occupancy at the E-box elements present in essentially every RPG promoter; and SIRT6 promotes Pol II elongation across RPG loci [20,21,37,38,39,40]. Sirtuin catalysis is NAD⁺-dependent, so this arm couples cellular energy state to translational capacity—and is reinforced by the simultaneous mitochondrial activation (below) that regenerates the NAD⁺ pool. The MYC-responsive cluster in our data (RPL23A 3.80×, RPL24 4.78×, RPL7A 2.69×, RPL8 2.60×) is the transcriptional fingerprint of this SIRT1→MYC engagement

Nucleoporin/NPC-Export Genes (NUP)—The Delivery System

Producing 47 RPL transcripts is futile unless they reach the cytoplasm for translation, and in companion PBMC experiments Metadichol selectively remodels the nuclear pore complex toward export competence: the mRNA-export activator GLE1 is its single most robust NUP target (1.52× at 1 ng/mL, p<0.001 across three doses), with coordinate up-regulation of the cytoplasmic-filament factor NUP358/RanBP2 (1.94×) and the anchoring transmembrane NUPs NDC1 (2.18×) and NUP210 (2.03×), while the assembly-scaffold (ELYS, POM121) and the central-channel barrier (NUP54, NUP214) are down-regulated [30,41,42,43,44,45]. GLE1 activates the DDX19/Dbp5 helicase to drive directional export of bulk mRNA—including the 5′TOP RPG messages—so the NPC program is mechanistically yoked to the ribosomal output: more export capacity precisely when more RPG transcripts are being made [46]. Beyond transport, nucleoporins are now recognized as direct regulators of cell-identity genes and pluripotency, and they physically tether active loci to the pore (‘gene-gating’), so the remodeled NPC also reshapes which genes are transcribed [47,48,49,50,51]. This is depicted as the gold export-facilitation edge in Figure 7.

Mitochondrial OXPHOS Genes—The Power Supply and The Retrograde Signal

Translation is the cell's dominant energy sink: each peptide bond consumes ~4 ATP equivalents, and an actively biosynthetic cell carries millions of ribosomes elongating at 10–15 residues per second [52]. In the same PBMC system, Metadichol up-regulates all 13 mitochondrially-encoded OXPHOS genes at 100 ng/mL (mt-ND6 2.41×, mt-CO1 1.91×), expanding electron-transport and ATP-synthase capacity through the SIRT1–PGC-1α–NRF1/ERRα–TFAM axis [53,54,55]. Two consequences feed the ribosomal program: the expanded ATP pool fuels the energetically expensive 60S biogenesis we observe, and restored mitochondrial function lifts the ISR-mediated eIF2α brake that would otherwise shut down 5′TOP RPG translation. Mitochondria-to-nucleus retrograde signalling (NAD⁺→SIRT1; acetyl-CoA→histone acetylation) thus turns a one-way push into a self-reinforcing loop—the basis of the flywheel below.

The 60S Ribosomal Flywheel: Self-Reinforcing Co-Regulation Among the 47 Genes

Once the macro-network engages, the 47 large-subunit genes behave as a single mechanical system—a flywheel that, once spun up, sustains its own momentum. Figure 8 renders this directly from the data: each gene sits on the wheel rim (node size proportional to peak fold-change), and every pair of genes is joined by a chord colored red where their dose-response profiles are strongly positively co-regulated (Pearson r ≥ 0.90) and green where they are anti-correlated (r ≤ 0). The overwhelming dominance of red chords (425 of 1,081 gene-pairs exceed r ≥ 0.90; only 15 are negative) shows that the genes rise and fall together rather than independently—the signature of one shared driver acting on a 5′TOP-defined regulon.

The flywheel metaphor is mechanistically literal. The newly synthesised large-subunit proteins do not merely accumulate—they act back on the network that produced them: RPL5/RPL11/RPL26 tune the MDM2–p53 axis that gates proliferation; RPL13A controls inflammatory-mRNA silencing through the GAIT complex; the P-stalk proteins RPLP0/RPLP1/RPLP2 raise elongation throughput so existing ribosomes work faster; and the expanded ribosome pool increases translation of the very nuclear-receptor, sirtuin, and PGC-1α effectors that started the cycle. The green spokes in Figure 8 emanate almost exclusively from the four autonomously-behaving genes (RPL32 and RPL13, which autoregulate their own splicing; RPL27A and RPL13A, with distinct extraribosomal kinetics), confirming that the dissenting nodes are precisely those with documented self-governing biology. The net result is a low-input, high-gain regulatory circuit: a picomolar-to-nanomolar food-derived signal, applied once, spins a flywheel that couples nuclear-receptor sensing, sirtuin-energy status, nucleopore export, and mitochondrial power into sustained, balanced expansion of translational capacity.

Up-Regulated and Down-Regulated Genes: A Strict Classification

To partition the panel rigorously we applied a strict statistical criterion: a gene is down-regulated if it falls significantly below control (mean < 1.0, p < 0.05) at any of the five doses, and up-regulated if it rises significantly above control at any dose. On this basis 46 of 47 genes are up-regulated and 32 of 47 are down-regulated at one or more concentrations. The two sets overlap heavily—31 genes are biphasic (significantly suppressed at one dose and significantly activated at another), reflecting the inverted-U pharmacology. Only 15 genes are ‘pure’ activators that are never significantly suppressed (RPL6, RPL8, RPL11, RPL18A, RPL19, RPL23A, RPL27A, RPL30, RPL34, RPL35, RPL35A, RPL36, RPL37, RPL38, RPLP1), while a single gene (RPL5) is significantly down at low dose without reaching a significant up-call. Figure 9 displays both faces of every gene, and Figure 10 shows that, grouped by dose-trajectory, the classes share one biphasic arc.

Up-vs-Down Chord Diagram: The Two Strict Sets Co-Regulate

To visualize the relationship between the strict sets directly, Figure 11 is a full-circle chord diagram with straight connecting lines in which the 32 ever-suppressed genes occupy the green DOWN arc and the 15 never-suppressed pure activators occupy the red UP arc; every gene-pair is joined by a red line where their dose-response profiles are positively co-regulated (Pearson r ≥ 0.90) and a green line where they are anti-correlated (r ≤ 0). The diagram is dominated by red, including 181 positive cross-set lines linking the DOWN and UP arcs versus only 6 negative cross-set lines. In other words, the genes that are transiently suppressed and the genes that only ever rise are nonetheless positively co-regulated with one another—they belong to the same program and differ mainly in amplitude and in whether the low-dose suppressive phase reaches statistical significance. The handful of green spokes trace back to the autoregulated/idiosyncratic genes (RPL32, RPL13, RPL13A, RPL27A) identified earlier, the only nodes whose behavior partially opposes the bulk set.

The Fifteen ‘Pure’ Activators and the Lone Suppressed Gene: Pathway and Disease Significance

Two extremes of the strict classification are biologically the most informative. At one end sit fifteen ‘pure’ activators that are never significantly suppressed at any dose—RPL6, RPL8, RPL11, RPL18A, RPL19, RPL23A, RPL27A, RPL30, RPL34, RPL35, RPL35A, RPL36, RPL37, RPL38, and RPLP1—rising monotonically to a mean of ~2.5-fold and staying at or above control even in the picomolar VDR-inverse-agonist window (Figure 12, red). At the other end stands a single gene, RPL5, which is significantly suppressed at low dose (0.37×, p < 0.005) and recovers only modestly without ever reaching a significant up-call (Figure 12, green). Their identities are not random, and they carry specific pathway and disease meaning.

The Pure Activators are the c-MYC/Biogenesis ‘Core Engine’

The suppression-resistant set is strongly enriched for the genes most directly wired to the c-MYC ribosome-biogenesis program and the Wnt growth axis: RPL8 and RPL23A are canonical c-MYC transcriptional targets [7,17]; RPL6 and RPL19 are Wnt/β-catenin-responsive; and RPL18A, RPL27A, RPL34, RPL35, and RPL35A are core 60S-assembly/maturation factors [2,6]. These are precisely the genes a coordinated biogenesis driver would push hardest and most monotonically—so their failure to dip even under low-dose VDR inverse agonism marks them as the rate-defining ‘engine’ of the response. Clinically, most are recurrently over-expressed in cancer (RPL6 in hepatocellular carcinoma; RPL19 in prostate and gastric cancer; RPL18A in melanoma and thyroid cancer; RPL23A across multiple tumours; RPL27A in neuroblastoma; RPL34 in lung tumours; RPL36 in thyroid cancer; RPLP1 in ovarian and breast cancer), reflecting their role as throughput components of the malignant anabolic phenotype [5,7]. RPL37 is the notable exception—a putative tumour suppressor whose induction here could be growth-restraining rather than growth-promoting.

Disease-Modifying Members: DBA, Development, and the P-Stalk

Three pure activators are direct ribosomopathy or developmental genes, which gives the suppression-resistant behaviour therapeutic relevance. RPL11 is a 5S RNP component and p53 guardian mutated in ~3% of Diamond–Blackfan anaemia (DBA) [9,23]; RPL35 and RPL35A are likewise DBA-associated, with RPL35A haploinsufficiency causing a p53-mediated erythroid block [14,24]. In DBA the therapeutic goal is to raise expression from the intact allele without provoking further nucleolar stress; a compound that drives these very genes up cleanly and without a suppressive phase is mechanistically attractive in that context. RPL38, the IRES-dependent Hox-mRNA translator whose loss causes vertebral/rib malformation, is also a pure activator [10,36], linking the response to developmental patterning and regeneration; and the P-stalk gene RPLP1 couples the set to elongation-factor recruitment and GTPase activation [11,22].

RPL5 vs. RPL11: An Instructive 5S RNP Asymmetry

The most mechanistically striking observation is that the two halves of the 5S ribonucleoprotein particle behave oppositely at low dose: RPL11 is a pure activator (never suppressed; peak 2.80×) while its obligate partner RPL5 is the lone pure-down gene (0.37× at 0.1 pg/mL). Because RPL5 and RPL11 must associate with 5S rRNA in a fixed stoichiometry to form the MDM2-inhibitory 5S RNP that stabilises p53 [5,9,23], their divergent low-dose regulation is exactly the kind of transient imbalance that, if sustained, would modulate the p53 nucleolar-stress checkpoint. Two features make this benign rather than pathological here: the divergence is confined to the sub-significant picomolar window, and by the therapeutic optimum (1 ng/mL) both genes are co-elevated (RPL5 1.25–1.71×; RPL11 2.62–2.80×), restoring balance. RPL5 thus emerges as the single most dose-sensitive node of the entire 60S set and the rate-limiter for the p53-surveillance arm—relevant because RPL5 is itself a DBA gene (~3% of cases) and a tumour-suppressor whose haploinsufficiency predisposes to cancer [9,13,23]. Its behaviour should be the primary readout in any follow-up p53-pathway validation.

Focused Gene Table

Table 5. Shows the 15 suppression-resistant ‘pure’ activators and the lone pure-down gene RPL5: 60S role, pathway, peak fold-change, and disease significance.

Gene	60S / molecular role	Pathway	Peak FC	Disease significance
RPL6	Scaffold; 28S rRNA domain II	Wnt / MYC	1.67×	↑ Hepatocellular carcinoma
RPL8	Contacts 5.8S rRNA; 60S assembly	c-MYC target	2.60×	MYC-driven malignancy
RPL11	5S RNP; MDM2 inhibitor; p53 guardian	p53 / 5S RNP	2.80×	DBA (~3%); MDM2-SNP309
RPL18A	Structural; 28S rRNA; 60S assembly	Ribosome biogenesis	1.99×	↑ Melanoma; thyroid cancer
RPL19	28S rRNA domain II; Wnt target	Wnt / β-catenin	1.43×	↑ Prostate, gastric Ca (invasion)
RPL23A	Structural; 60S biogenesis	c-MYC target	3.80×	↑ Multiple cancers
RPL27A	28S rRNA domain V; 60S assembly	Ribosome biogenesis	2.69×	↑ Neuroblastoma
RPL30	Autoregulates own pre-mRNA splicing	Splicing autoregulation	5.43×	Chemotherapy-response biomarker
RPL34	Structural; 28S rRNA; maturation	Ribosome biogenesis	4.17×	↑ Lung carcinoid tumours
RPL35	Peptide exit tunnel; 5.8S rRNA	Exit tunnel / biogenesis	1.96×	DBA-associated anaemia
RPL35A	Structural; 28S rRNA; haematopoiesis	Biogenesis / p53	1.46×	DBA; erythroid block (OMIM 612528)
RPL36	18S bridge B1a; translocation	Inter-subunit bridge	1.71×	↑ Thyroid cancer
RPL37	Smallest RPs; 28S rRNA	Ribosome assembly	4.31×	Putative tumour suppressor (↓ prostate)
RPL38	IRES translation of Hox mRNAs	Hox-IRES / development	4.30×	Vertebral/rib malformation
RPLP1	P-stalk; recruits elongation factors	Elongation GTPase	2.93×	↑ Ovarian, breast cancer
RPL5	Core of 5S RNP; MDM2 inhibitor; p53	p53 / 5S RNP	1.71× (down 0.37× low dose)	DBA (~3%); tumour suppressor (OMIM 612561)

Peak FC = maximum mean fold-change across the five doses. RPL5 (green, bottom row) is the single gene with a significant low-dose suppression and no significant up-call; all others are suppression-resistant pure activators. DBA = Diamond–Blackfan anaemia.

Biological Processes, Pathways, and Diseases Impacted

Because the large-subunit proteins carry distinct extraribosomal functions, coordinated induction of the 47-gene set simultaneously engages many disease-relevant pathways. Figure 13 ranks the functional categories by the mean peak fold-change of their member genes and annotates each with the diseases in which those genes are implicated; Table 6 provides the gene-level detail and the predicted net direction under Metadichol.

Metadichol in Context: Other Small Molecules That Regulate Ribosomal Protein Genes

Metadichol's effect is best appreciated against the landscape of small molecules already known to act on ribosomal protein genes and ribosome biogenesis—a landscape that is overwhelmingly suppressive. The dominant pharmacological classes were developed as anticancer agents precisely because tumours are addicted to high ribosome output: (i) mTORC1 inhibitors—rapamycin (sirolimus) and the ATP-competitive inhibitors Torin1/Torin2—suppress translation of the 5′TOP-containing RPG mRNAs through S6K1/4E-BP1 and LARP1, and Torin1 in particular shuts down the entire TOP regulon [56,57]; (ii) the AMPK activator metformin indirectly inhibits mTORC1 and thereby lowers ribosome biogenesis [58]; and (iii) RNA Polymerase I inhibitors—CX-5461, BMH-21 (which triggers proteasomal destruction of the Pol I subunit RPA194), and low-dose actinomycin D—block rRNA synthesis and provoke a p53-dependent nucleolar-stress response [59,60,61,62]. Only a small minority of small molecules raise ribosomal output: the integrated-stress-response inhibitor ISRIB restores cap- and 5′TOP-dependent translation by re-activating eIF2B [63], and sirtuin-activating compounds such as resveratrol engage the SIRT1 axis that feeds ribosome biogenesis [64]. Against this backdrop, Metadichol is mechanistically distinctive: rather than inhibiting a single node (mTOR or Pol I) or globally de-repressing translation, it coordinately and transcriptionally up-regulates the entire 47-gene 60S repertoire at picomolar–nanomolar doses through nuclear-receptor and sirtuin engagement, while sparing the stoichiometric balance that, when disturbed, triggers nucleolar stress. Table 7 places the principal agents side by side; the contrast underscores that a coordinated, balanced activator of ribosomal protein genes—as opposed to the many available inhibitors—has had essentially no pharmacological precedent.

Limitations

This study profiled transcript abundance by qRT-PCR in bulk PBMCs from biological duplicates; it does not measure ribosomal protein levels, ribosome assembly, polysome occupancy, or per-cell-type effects, and PBMC heterogeneity may blunt lineage-specific signals. The macro-network and flywheel models integrate the present 60S dataset with NUP, mitochondrial, nuclear-receptor, and sirtuin findings from companion studies; the cross-family edges are therefore inferred from convergent evidence rather than tested by simultaneous multi-omic perturbation in a single experiment. Functional confirmation (puromycin/OP-Puro translation assays, polysome profiling, p53 pathway readouts, and pathway-knockdown epistasis) and orthogonal donors are warranted.

Conclusions

Metadichol coordinately and significantly up-regulates the complete 47-gene large-subunit (60S) ribosomal protein repertoire in primary human PBMCs, following a reproducible biphasic dose-response that peaks at 1 ng/mL. Three features distinguish this regulation: (i) it is set-wide and balanced—all 47 genes move in the same direction with tight positive gene–gene correlation—rather than the selective imbalance that causes ribosomopathies; (ii) it spans the functionally critical extraribosomal hubs (RPL5/RPL11/RPL26 p53 surveillance, RPL13A/GAIT inflammation control, RPL38/Hox patterning, RPLP0-2 elongation); and (iii) its dose window and module structure mirror the MYC/sirtuin-driven ribosome-biogenesis program of pluripotent and regenerating cells. Together with prior evidence that Metadichol regulates nuclear receptors, sirtuins, and mitochondrial OXPHOS genes, these results identify it as a coordinated transcriptional enhancer of translational capacity, with rational implications for ribosomopathy, iPSC reprogramming, immune function, and ageing biology [65,66,67]. These mechanistic hypotheses now require functional and proteomic validation.

The Uniqueness of These Findings

What distinguishes this study is not that one ribosomal gene changed, but that all of them changed together, in the same direction, and in balance. Five features make the result novel:

Completeness. To our knowledge this is the first demonstration that a single agent coordinately regulates the entire large-subunit (60S) ribosomal protein gene repertoire—all 47 RPL/RPLP genes profiled in one system and all significantly up-regulated—whereas most ribosome studies report only one or a handful of genes [2,4,68].
Direction—an activator among inhibitors. Essentially every well-characterised small molecule acting on ribosomal genes suppresses them—mTOR inhibitors (rapamycin, Torin1), the AMPK route (metformin), and RNA Pol I inhibitors (CX-5461, BMH-21, actinomycin D), all developed to throttle ribosome output in cancer. Metadichol does the opposite at the level of gene expression, coordinately raising the set through c-MYC—even though, like those agents, it restrains the mTORC1/5′TOP translational arm via DDIT4—for which there is almost no pharmacological precedent [36,56,57,58,59,60,61,62,69].
Balance—the inverse of ribosomopathy. Diamond–Blackfan anaemia, 5q- MDS, and T-ALL arise when a single large-subunit gene falls out of stoichiometry, freeing the 5S RNP to trigger p53-dependent apoptosis. Here the genes rise as a tightly, positively co-regulated set (425 of 1,081 gene-pairs at r ≥ 0.90), expanding translational capacity without the imbalance that drives disease—regulation that looks regenerative rather than pathological [5,6,12,13,14,15,16].
A biphasic, sub-nanogram signature. The response is a reproducible inverted-U—low-dose (0.1 pg/mL) suppression giving way to a sharp peak at 1 ng/mL, then partial relaxation—achieved by a food-derived compound at picomolar-to-nanomolar concentrations.
Extra ribosomal and systems reach. Because the regulated genes are also functional hubs, one coordinated switch simultaneously touches p53 surveillance (RPL5/RPL11/RPL26), inflammatory-mRNA silencing (RPL13A/GAIT), developmental patterning (RPL38/Hox), and elongation (RPLP0–2). The RPL5↔RPL11 asymmetry—the two halves of the 5S RNP diverge at low dose yet re-balance at the therapeutic optimum—identifies RPL5 as the single most dose-sensitive node, and the convergent nuclear-receptor, sirtuin, nucleoporin, and mitochondrial data define a self-reinforcing flywheel coupling translational capacity to energy state and gene-export machinery [5,7,8,9,10,11,21,41,54].

In one sentence: Metadichol is the first reported coordinated, balanced activator of the complete 60S ribosomal protein gene set—the transcriptional mirror image of both ribosomopathy and the inhibitor-dominated ribosome pharmacopeia.

Advances to Biology

Beyond cataloguing a new activity for a single compound, these findings advance several ideas in ribosome biology. First, they reinforce the view that translational capacity is a coordinately regulated output rather than the sum of independent gene-by-gene events: the tight, uniformly positive gene–gene correlation across all 47 large-subunit genes is the transcriptional signature expected of a shared upstream driver, and it reframes the ribosomal protein repertoire as a single tunable module [2,4,68]. Second, the inverted-U dose–response implies that the biologically meaningful variable is restoration of a balanced biogenesis set-point rather than maximal output — a logic distinct from the rate-limiting, single-node model that underlies mTOR- and MYC-centred views of ribosome control [17,18,36]. Third, by moving the entire set in the activating direction while preserving stoichiometry, the program resembles the coordinated biogenesis of pluripotent and regenerating cells rather than the selective imbalance of ribosomopathies, positioning balanced activation as a biological state in its own right [14,15,22,23,68].

The result also reorients how the ribosome might be approached therapeutically. The existing pharmacopeia is almost exclusively inhibitory, built to throttle ribosome output in proliferative disease [36,56,57,58,59,60,61,62,69,70]. A coordinated, balanced activator occupies the opposite and largely unexplored pole, with potential relevance wherever translational capacity is deficient — the ribosomopathies such as Diamond–Blackfan anaemia and 5q- syndrome, erythroid and immune development, and the decline of proteostasis with age [6,14,16,71]. Because the up-regulated genes double as extraribosomal hubs governing p53 surveillance, inflammatory-mRNA silencing, and developmental patterning, a single coordinated switch can influence cell fate well beyond protein output [5,7,8,9,10,11]. Its convergence with the nuclear-receptor, sirtuin, nucleoporin, and mitochondrial programs reported for the same compound suggests a self-reinforcing circuit linking translational capacity to energy state and nucleocytoplasmic transport [21,35,41,51,54,55], and, if borne out functionally, this places balanced translational activation alongside reprogramming and metabolic modulation as a route toward restoring regenerative capacity [22,29].

Online Resources, Data Availability, and Declarations

Online gene resources

Gene annotations and sequence references for the 47 large-subunit genes are available through NCBI Gene (https://www.ncbi.nlm.nih.gov/gene), HGNC (https://www.genenames.org), UniProt (https://www.uniprot.org), and Ensembl (https://www.ensembl.org). Ribosomal protein nomenclature follows the HGNC/Ribosomal Protein Gene Database conventions.

Data: availability

Raw data available on reqiest.

Regulatory statement

Metadichol is sold as a dietary supplement and has not been evaluated by the US FDA for the treatment, cure, or prevention of any disease.

Acknowledgments

qRT-PCR experiments were outsourced and performed by a third-party service provider on commercial terms at Skanda Life Sciences Pvt. Ltd Bangalore, India . All published work on Metadichol till date has been done by independent labs worldwide to eliminate any bias in the reported work.

Conflict: of interest

Dr. P. R. Raghavan is the founder of Nanorx Inc. and inventor on US Patents 8,722,093; 9,034,383; and 9,006,292 relating to Metadichol.

Abbreviations

Abbreviation	Full Term
AMA	American Medical Association (citation style)
ANOVA	Analysis of Variance
BFU-E	Burst-Forming Unit – Erythroid
cDNA	Complementary DNA
Cq	Quantification Cycle (qPCR)
DBA	Diamond–Blackfan Anaemia
DESeq2	Differential Expression analysis for Sequence count data (v2)
ESC	Embryonic Stem Cell
FBS	Foetal Bovine Serum
GAIT	Gamma-Interferon-Activated Inhibitor of Translation
GAPDH	Glyceraldehyde-3-Phosphate Dehydrogenase (reference gene)
GR	Glucocorticoid Receptor
IFN-γ	Interferon Gamma
IRES	Internal Ribosome Entry Site
iPSC	Induced Pluripotent Stem Cell
KLF4	Krüppel-Like Factor 4 (Yamanaka factor)
log₂FC	Log base-2 Fold Change
LXR	Liver X Receptor
MDM2	Mouse Double Minute 2 homolog (E3 ubiquitin ligase)
MDS	Myelodysplastic Syndrome
mTORC1	Mechanistic Target of Rapamycin Complex 1
NAD⁺	Nicotinamide Adenine Dinucleotide (oxidised)
NF-κB	Nuclear Factor Kappa B
NR	Nuclear Receptor
OXPHOS	Oxidative Phosphorylation
PBMC	Peripheral Blood Mononuclear Cell
PPAR	Peroxisome Proliferator-Activated Receptor
PTC	Peptidyl-Transferase Centre
qRT-PCR	Quantitative Reverse-Transcription Polymerase Chain Reaction
RACK1	Receptor for Activated C Kinase 1
RP	Ribosomal Protein
RPG	Ribosomal Protein Gene
RPL	Ribosomal Protein, Large (60S) subunit
RPS	Ribosomal Protein, Small (40S) subunit
rRNA	Ribosomal RNA
RXR	Retinoid X Receptor
S6K1	S6 Kinase 1
SD	Standard Deviation
SIRT	Sirtuin (NAD⁺-dependent deacylase)
SLE	Systemic Lupus Erythematosus
T-ALL	T-cell Acute Lymphoblastic Leukaemia
TLR	Toll-Like Receptor
VDR	Vitamin D Receptor
5′TOP	5′-Terminal Oligopyrimidine motif
5S RNP	5S Ribonucleoprotein Particle

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Figure 1. Peak fold-change (versus untreated control) for each of the 47 large-subunit RPL/RPLP genes, ranked. Bars are colored by magnitude: crimson ≥3-fold; gold 2–3-fold; teal <2-fold. Dashed line = control baseline (1.0).

Figure 2. Biphasic dose–response. Colored lines = individual genes (n = 47), one hue per gene; bold red line = panel mean ± SEM. Suppression at 0.1 pg/mL gives way to peak activation at 1 ng/mL, then partial relaxation at 100 ng/mL.

Figure 3. Number of large-subunit genes (of 47) that are significantly up-regulated (crimson), significantly down-regulated (navy), or not significantly changed (grey) at each Metadichol concentration. The 0.1 pg/mL → 1 ng/mL transition reverses the dominant direction of regulation.

Figure 4. Hierarchically clustered heatmap of log₂(fold-change) for all 47 large-subunit genes across five Metadichol doses. Red = up-regulation, blue = down-regulation; cell labels show linear fold-change. Rows clustered by Ward linkage.

Figure 5. Inter-dose correlation matrix: Pearson r between the log₂FC profiles of all 47 genes at each pair of concentrations. The activating doses (100 pg/mL, 1 ng/mL) form the most concordant pair.

Figure 6. Gene–gene co-regulation matrix: clustered Pearson correlation (47×47) of the dose-response profiles. Red = positive co-regulation, blue = anti-correlation. The predominantly red field indicates coordinated, set-wide regulation; outlier rows (RPL32, RPL13, RPL27A, RPL13A) mark the genes with autonomous regulatory behaviour.

Figure 7. Macro-regulatory network converging on the 47-gene 60S repertoire. Nuclear receptors (biphasic: VDR-inverse at low dose, multi-NR agonism at high dose), sirtuins (SIRT1/6/7, NAD⁺-coupled), and the NUP/NPC export program converge on c-MYC—the E-box master driver of every ribosomal-protein gene—to up-regulate 60S RPL/RPLP transcription (the mRNA increase measured here); mitochondrial OXPHOS supplies the NAD⁺/ATP the sirtuin arm consumes. In parallel, Metadichol induces DDIT4 (REDD1), which inhibits mTORC1, so the mTORC1/5′TOP step is shown as a restrained translational gate rather than a co-driver, consistent with the companion finding that Metadichol suppresses mTOR/p70S6K. Green = activation; red bar = inhibition; gold dashed = NPC export; grey dashed = restrained 5′TOP translational gate. Because only transcript abundance was assayed, the translational arm is shown as a gate, not a contributor to the measured signal.

Figure 8. The 60S ribosomal protein flywheel. The 47 RPL/RPLP genes are arranged on the wheel rim; node size is proportional to peak fold-change. Red chords = activation / strong positive co-regulation (r ≥ 0.90); green chords = down-regulation / anti-correlation (r ≤ 0). Gold arrows depict the self-sustaining rotation. The few green spokes radiate from the autonomously-regulated outliers (RPL32, RPL13, RPL27A, RPL13A).

Figure 9. Every gene has two faces. For all 47 large-subunit genes (ranked by peak), green bars show the low-dose (0.1 pg/mL) log₂ fold-change—predominantly suppressive—and red bars show the peak activation log₂ fold-change. By the strict criterion, 32 genes are significantly suppressed at some dose and 46 are significantly activated at some dose; 31 satisfy both (biphasic).

Figure 10. Trajectory view of the regulatory classes. Left: mean fold-change trajectories (± SEM) across the five doses; the classes converge at low dose and diverge only at 100 ng/mL. Right: cross-class Pearson correlation of the mean log₂FC profiles—uniformly high and positive, showing the classes share one dominant biphasic program rather than opposing each other.

Figure 11. Up-vs-Down co-regulation chord diagram (strict definition), shown as a complete circle with straight connecting lines. Green arc / green nodes: the 32 genes significantly below control at ≥1 dose (DOWN set). Red arc / red nodes: the 15 genes never significantly suppressed (pure UP set). Red lines = positive co-regulation (r ≥ 0.90); green lines = anti-correlation (r ≤ 0); node size ∝ peak fold-change; legend in side panel at right. The 181 positive cross-set lines (vs 6 negative) show the two sets co-regulate rather than oppose.

Figure 12. The two extremes of the strict classification. Faint red lines = the 15 pure activators; bold red = their mean ± SEM; they remain at or above control across the whole dose range. Bold green dashed = RPL5, the lone gene significantly suppressed at low dose (0.37×) that never reaches a significant up-call. RPL5's 5S RNP partner RPL11 is, by contrast, a pure activator.

Figure 13. Biological processes / pathways impacted, ranked by the mean peak fold-change of member genes. Annotations give the number of contributing genes and the principal associated diseases. Developmental (Hox-IRES), splicing-autoregulatory, and MYC-driven biogenesis categories show the largest mean induction.

Table 3. qRT-PCR primer sequences for all 47 large-subunit ribosomal protein genes.

#	Gene	Forward primer (5′→3′)	Reverse primer (5′→3′)	Amp (bp)	Tₐ (°C)
1	RPL3	TGCTCGTGTAGCCTTCTCTGTG	GGTCATAGTCAGTGGAGGCATTG	149	60
2	RPL4	ACGATACGCCATCTGTTCTGCC	GGAGCAAAACAGCTTCCTTGGTC	152	60
3	RPL5	CCAAATACAGGATGATAGTTCGTG	TTGGCAGTTCGTGTGCATACGC	114	60
4	RPL6	CCTTGTCAGAGGAATTGGCAGG	GTAACAGTTGCGAGAACCTTCTC	132	60
5	RPL7	GAGGATGGCAAGAAAAGCTGGC	CGAACCTTTGGGCTCACTCCAT	102	60
6	RPL7A	AAGCTGGCCCACAAGTACAG	AGGACAGGTGGTCTCTTCGT	102	60
7	RPL8	CCGTATCGGTTTAAGAAGCGGAC	CGATTGTACCCTCAGGCATGGT	142	60
8	RPL9	GACGCACAGTTATCGTGAAGGG	CAAATAGTCCGAACGGTAGCCAG	157	60
9	RPL10	GCTGCAGAACAAGGAGCATGTG	ATGAGCCGCTTTTCAGCCACCA	150	60
10	RPL10A	GCCAAAGTGGATGAGGTGAAGTC	GACACCAAGAAGTTGACAGCCAG	140	60
11	RPL11	GCCAAAGTGGATGAGGTGAAGTC	GACACCAAGAAGTTGACAGCCAG	106	60
12	RPL12	AGAGTGGAGACAGACTGACGCG	CGGATGCCAAAGGATCTGACAG	108	60
13	RPL13	CCTCAAGGAACCACCAAGAGAC	GAGTTCTCTGGCTAAGGATCGG	124	60
14	RPL13A	CATCGGCATTTCTGTGGATC	TGGGGAAGAGGATGAGTTTG	143	60
15	RPL14	GCCCTACGACAAGAAAAAGC	GTACTTCCAGCCAACCTCGT	173	60
16	RPL15	GAGTCTTACTGTTGCGGGCTC	TTCCGGCATGAGGTCCAAAG	141	60
17	RPL17	TACGGCAAGCCTGTCCATCATG	GTATGTGGAATCTTCACCAACCC	122	60
18	RPL18	ATGATGTGCGGGTTCAGGAGGT	GGTCGAAAGTGAGGATCTTGCC	111	60
19	RPL18A	CGGGTGAAGAACTTCGGGAT	CATGTCTCGGTAGCACTGGG	120	60
20	RPL19	AGCTCTTTCCTTTCGCTGCTG	GGATCTGCTGACGGGAGTTG	156	60
21	RPL21	AAAAGGAATGCCCCACAAGT	TCTTGCCCTTAACTTGTTTGTTTAC	104	60
22	RPL22	GTGACATCCGAGGTGCCTTTCT	AACTACGCGCAACCAGTCACGT	107	60
23	RPL23	ATCAAGGGACGGCTGAACAGAC	GTCGAATGACCACTGCTGGATG	118	60
24	RPL23A	CAAGCACCAGATTAAACAGGCTG	CCAAAGCATCGTAATCAGGAGCC	128	60
25	RPL24	ATGCGAGTCGGCTTTCCTTT	GCCCTCTGGAATTTGACTGC	138	60
26	RPL26	GGCTAATGGCACAACTGTCCAC	GGCGAGATTTGGCTTTCCGTTC	113	60
27	RPL27	CTTTTTGCTGGTAGGGCCG	TCAATTCCAGCCACCAGAGC	175	60
28	RPL27A	TACGGGAGCAGATGGAGTCA	CCACCATAGCACTTCCCGTT	125	60
29	RPL28	CAACGGACTGATTCACCGCAAG	GCGAGCATTCTTGTTGATGGTGG	142	60
30	RPL29	CCCGATCACAAAGATACGAATCTC	TGCACTCATGGCCTTGGCATTG	131	60
31	RPL30	TAGCGGCTGCTGTTGGTTG	AGTCTGCTTGTACCCCAGGA	149	60
32	RPL31	CGGGCACTCAAAGAGATTCGGA	CACACGGATTCGGTATGGCACA	129	60
33	RPL32	CCACCTCAGCCTTCCAAGTA	TACTTTGGGAGGCTGAAGCA	150	60
34	RPL34	GACCTAAAGTTCTTATGAGATTGTC	CTGACTCTGTGCTTGTGCCTTC	164	60
35	RPL35	AGACTCAGAAAGAAAACCTCAGGA	TGGTCTTCAGGTTCTCCTCGTG	126	60
36	RPL35A	TGGCGGCAAACCAAACAAAA	TTGAGGGGTACAGCATCACT	146	60
37	RPL36	TGATTCGGGAGGTGTGTGGCTT	CCAGTACGTTGCTCAGCTCCTC	156	60
38	RPL36A	AGGGCAAGGATTCTCTGTACGC	CAACGCACTCAAGCCTTAGCAC	138	60
39	RPL37	CCTACCACCTTCAGAAGTCGAC	AGGTGCCTCATTCGACCAGTTC	124	60
40	RPL37A	CCTCCCTCCGGAAAATGGTG	GACAGCGGAAGTGGTATTGT	173	60
41	RPL38	TTTCGTCCTTTTCCCCGGTT	CAATTTTCCGAGGCATGGCG	119	60
42	RPL39	CGCTGCTCGCCATGTCTT	GTGTGCCATCTCATGTGCAA	179	60
43	RPL40	GCCTGCGAGGTGGCATTATTGA	TTCTTGCGGCAGTTGACAGCAC	124	60
44	RPL41	AGACATCTGACCTCGGCACT	GCTTCTTCCTCCACTTGGCTC	107	60
45	RPLP0	TGGTCATCCAGCAGGTGTTCGA	ACAGACACTGGCAACATTGCGG	119	60
46	RPLP1	GACACAGGCTGGATTTTCCC	CCCCAGTGCAGTTTTCAACA	107	60
47	RPLP2	TCTTGGACAGCGTGGGTATCGA	CAGCAGGTACACTGGCAAGCTT	126	60

Fp = forward primer; Rp = reverse primer; Amp = amplicon size; Tₐ = annealing temperature. GAPDH served as the reference gene for all reactions.

Table 6. Biological processes and pathways engaged by the Metadichol-induced 60S genes, with representative genes, net direction, and impacted diseases.

Biological process / pathway	Representative 60S genes	Net direction	Disease(s) impacted
p53 / 5S RNP nucleolar-stress surveillance	RPL5, RPL11, RPL26, RPL23	↑ (balanced)	Diamond–Blackfan anaemia; cancer; T-ALL
Ribosome biogenesis (c-MYC targets)	RPL7A, RPL8, RPL23A, RPL24, RPL6, RPL18A	↑↑	Lymphoma; hepatocellular & breast Ca; neuroblastoma
Peptide exit tunnel / co-translational folding	RPL4, RPL17, RPL22, RPL35, RPL39	↑ / mixed	DBA; T-ALL; gastric & endometrial Ca
P-stalk / elongation GTPase activation	RPLP0, RPLP1, RPLP2, RPL12	↑↑	SLE (anti-P autoantigen); breast/ovarian/HCC
Extraribosomal tumour suppression	RPL5, RPL11, RPL22, RPL37, RPL9	↑	T-ALL; breast & colorectal Ca
Hox-mRNA IRES translation / development	RPL38	↑↑↑	Vertebral & rib malformation (Tail-short)
GAIT complex / inflammatory-mRNA silencing	RPL13A	↑	Chronic inflammation; tumour angiogenesis/hypoxia
Autoregulated pre-mRNA splicing	RPL30, RPL32	↑↑	Chemotherapy-response biomarker
Ubiquitin–proteasome supply	RPL40	↑ / biphasic	Prostate Ca; proteostasis/UPS dysregulation
Diamond–Blackfan anaemia mutation set	RPL5, RPL11, RPL26, RPL35A, RPL4, RPL27, RPL31, RPL35, RPL15	↑ (RPL15/31 ↓ at top dose)	Diamond–Blackfan anaemia; 5q- MDS-like
X-linked intellectual disability / leukaemia	RPL10	↑ (biphasic)	Intellectual disability; autism; T-ALL (R98S)
Hair-follicle ribosomal translation	RPL21	↑ (biphasic)	Hypotrichosis simplex

Net direction reflects the dominant response across the dose range (peak): ↑ up-regulated; ↑↑/↑↑↑ strongly up-regulated; ↓ net down at the highest dose. Disease associations are drawn from the established extraribosomal and ribosomopathy literature for each gene.

Table 7. Small molecules that regulate ribosomal protein genes / ribosome biogenesis, compared with Metadichol.

Agent	Mechanism / target	Effect on RPG / ribosome	Primary use	Ref.
Rapamycin (sirolimus)	mTORC1 inhibitor (S6K1/4E-BP1)	↓ 5′TOP RPG translation	Immunosuppressant; oncology	73
Torin1 / Torin2	ATP-competitive mTOR inhibitor	↓↓ entire 5′TOP/TOP regulon	Research / oncology	74
Metformin	AMPK activation → ↓ mTORC1	↓ ribosome biogenesis	Type 2 diabetes	75
CX-5461	RNA Pol I transcription inhibitor	↓ rRNA; nucleolar stress; p53	Oncology (trials)	76,77
BMH-21	Pol I inhibitor; RPA194 degradation	↓ rRNA; nucleolar stress	Oncology (preclinical)	78
Actinomycin D (low dose)	Pol I / rDNA intercalator	↓ rRNA; classic nucleolar stress	Chemotherapy	79
ISRIB	eIF2B activator (anti-ISR)	↑ cap- & 5′TOP translation	Neuro research	80
Resveratrol	SIRT1 activator	↑/modulates ribosome biogenesis	Nutraceutical	81
Metadichol (this study)	VDR inverse agonist + multi-NR / sirtuin	↑ coordinate, balanced ↑ of all 47 RPL/RPLP	Dietary supplement	28-36

Red ↑ = increases ribosomal protein gene expression / ribosome output; green ↓ = decreases it. Most established agents are inhibitors (mTOR or Pol I); Metadichol (amber row) is distinctive as a coordinated, balanced activator of the complete 60S set. References correspond to the numbered reference list.

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