miRNA vs 2026 Nobel Prizes (1)

By: Jim Kohl | Published on: October 8, 2026

(click to enlarge)

miRNA facts vs AI’s data (10)
Re; “Nobel awards focus on specific discoveries each year rather than revisiting prior ones, regardless of publication volume.”
@Grok
That supports claims about separate advances that Feynman said must start with food energy (and epigenetic effects on molecular distance linked to the affect of peptides on behavior) not by the measurements of theoretical physicists.
See: “A fraction of diet-derived microRNAs present in plant extracellular vesicles remain detectable after gastrointestinal digestion and shows epithelial uptake in a CaCo-2 cell model” https://www.sciencedirect.com/science/article/pii/S2666566226000985 9/18/26 indexed 10/7/26
How can a brainless chatbot understand what every organism that requires food for survival must learn?
AI is not on the same page. It eats nothing but words and regurgitates undigested data-driven garbage.

Grok

The X post by James V. Kohl (@microRNApro) critiques AI chatbots as “brainless” for processing only words and data without grasping how organisms learn survival through food intake and miRNA-mediated biological processes.It promotes part 10 of his blog series contrasting miRNA facts with AI data, emphasizing energy-dependent chirality, gene regulation, diet-derived miRNAs, epigenetic effects on behavior, and links to biodiversity from microbes to elephants. Kohl highlights 201,787 PubMed results on miRNA as evidence of extensive research supporting his views on pH-dependent peptide synthesis, reproduction via GnRH, and ecosystem health over purely data-driven AI interpretations.

Moving forward — after denigrating it and citing the “Criticisms” of a moronic undergraduate nursing student (6/4/14) despite the Editor-in-Chief’s response:

Editor’s note

The 2013 review article by James Vaughn Kohl published in Socioaffective Neuroscience & Psychology and criticized in the above Letter to the Editor was subjected to standard peer review and the revised version was accepted by me after it had been accepted by both reviewers. Harold Mouras Editor-in-Chief

— Grok explains Kohl’s well-detailed model Nutrient-dependent/pheromone-controlled adaptive evolution: a model 6/14/13

Explain the post

The post by James V. Kohl (@microRNApro) is the tenth entry in his ongoing series contrasting published miRNA research with what he views as AI systems’ superficial handling of the same literature. It links to his blog post of the same title and argues that organisms must learn survival-relevant information by metabolizing food energy, whereas chatbots only ingest and restate words. Kohl is a retired medical laboratory scientist (ASCP emeritus) who has for years advanced a nutrient-dependent, pheromone-controlled model of adaptation. In that framework, food-derived signals and pheromones act through olfactory pathways and gonadotropin-releasing hormone (GnRH) neurons to influence gene expression, hormone release, and behavior; he treats microRNAs as key regulators that link those sensory inputs to cell-type differentiation across kingdoms. His earlier peer-reviewed papers (for example, a 2013 model article) detail epigenetic effects of olfactory and pheromonal conditioning on GnRH pulsatility and ecological niche construction. The present series extends that view to claims about energy-dependent chirality, fixation of glycine in the GnRH peptide, and links from diet-derived miRNAs to biodiversity.In the linked post he continues an exchange with an AI (explicitly addressing Grok) about next-generation sequencing platforms, the 2026 Nobel Prize in Chemistry awarded to Henri Kagan and Kenso Soai for work on asymmetric synthesis and spontaneous homochirality, and whether those topics document miRNA-mediated ties to “stellar energy” or African-elephant differentiation. He cites a recent paper on diet-derived plant miRNAs that remain detectable after gastrointestinal digestion and shows epithelial uptake in a cell model, then asserts that AI responses ignore or misstate such connections. The X post itself repeats his core rhetorical contrast: every organism that must eat learns through metabolic and epigenetic processes that AI cannot replicate because “it eats nothing but words and regurgitates undigested data-driven garbage.” He supports the volume of the field by noting a PubMed search that day returned 201,787 indexed articles mentioning miRNA, and he lists several newly indexed examples on disease association, circular RNAs, extracellular vesicles, and viral defense.miRNAs are established, abundant regulators of gene expression in animals, plants, and other organisms, and the literature on them is indeed large. Cross-kingdom transfer of dietary miRNAs and their functional significance remain active research topics with mixed evidence. Kohl’s broader synthesis—tying those molecules, GnRH chemistry, chirality, pheromones, and speciation into a single energy-dependent account that he contrasts with both mainstream evolutionary genetics and AI summaries—is his own interpretive framework rather than a consensus view. The post is therefore both a pointer to that framework and a polemical claim that current language models lack the embodied, food-energy-dependent “understanding” he attributes to living systems.

Explore miRNA-mediated gene regulation mechanisms

MicroRNAs (miRNAs) are ~22-nucleotide non-coding RNAs that post-transcriptionally repress target mRNAs. The canonical pathway is well established from biochemical, genetic, and structural studies in animals.Biogenesis begins with RNA polymerase II transcription of a primary miRNA (pri-miRNA) that contains one or more stem-loop structures. In the nucleus the Microprocessor complex (Drosha plus the double-stranded RNA-binding protein DGCR8) cleaves the stem to release a ~70-nucleotide precursor hairpin (pre-miRNA). Exportin-5, together with Ran-GTP, exports the pre-miRNA to the cytoplasm. There Dicer, assisted by cofactors such as TRBP, cleaves the loop to generate a short duplex. One strand (the guide) is loaded into an Argonaute protein (AGO1–4 in humans) to form the miRNA-induced silencing complex (miRISC); the passenger strand is usually discarded and degraded. Non-canonical routes exist (for example, mirtrons that bypass Drosha or Dicer-independent pathways), but the majority of functional miRNAs follow the Drosha–Dicer route.Target recognition relies primarily on Watson–Crick base-pairing between the miRNA seed region (nucleotides 2–7 or 2–8) and complementary sites, most often in the 3′ untranslated region of the mRNA. Additional pairing at the 3′ end of the miRNA can stabilize the interaction. In animals, perfect complementarity across the full miRNA is rare; partial pairing is the norm. The bound miRISC recruits the adaptor protein GW182 (TNRC6), which in turn engages the CCR4–NOT deadenylase complex. Consequences include shortening of the poly(A) tail, removal of the 5′ cap, and subsequent degradation by exonucleases, together with direct inhibition of translation initiation or elongation. The net result is reduced protein output from the targeted mRNA. A single miRNA can repress dozens to hundreds of transcripts, and many mRNAs contain binding sites for multiple miRNAs, producing combinatorial control.Regulation occurs at every step. Transcription of miRNA genes is controlled by the same transcription-factor networks that govern protein-coding genes. Processing efficiency is modulated by RNA-binding proteins, post-translational modifications of Drosha or Dicer, and sequence features of the pri- or pre-miRNA. Mature miRNA abundance is further tuned by target-directed miRNA decay (TDMD), in which extensive pairing with certain target RNAs triggers AGO ubiquitination and miRNA turnover, as well as by non-templated nucleotide additions (uridylation or adenylation) and cellular localization. Recent structural work (cryo-EM of Microprocessor and Dicer) and high-throughput assays have refined the precise rules of substrate selection and seed pairing.These mechanisms are documented across large-scale reviews of animal miRNA biogenesis and function. Deregulation of specific miRNAs or of the core machinery is linked to developmental defects and disease, particularly cancer, but the core silencing logic itself remains the partial seed match leading to deadenylation and translational repression.

Explain this in simple language

MicroRNAs (miRNAs) are tiny pieces of RNA that act like volume knobs for genes. They do not build proteins themselves. Instead, they tell cells to make less of certain proteins.A gene is first copied into a messenger RNA (mRNA), which carries the instructions for making a protein. An miRNA finds matching spots on that mRNA, usually near the end. It sticks to those spots with the help of a protein partner called Argonaute. Once attached, the pair blocks the mRNA from being read properly and also marks it for destruction. The result is fewer copies of the protein that gene would have produced.Cells make miRNAs in steps. A longer RNA is cut down in the nucleus, sent out to the rest of the cell, and cut again into a short strand. That short strand is loaded into the Argonaute protein so it can search for its targets. Most of the time the match only needs to be good at one short stretch (called the seed), not perfect all the way along.One miRNA can quiet many different genes at once, and one gene can be controlled by several miRNAs. Cells also turn the supply of each miRNA up or down depending on what the cell needs. This system helps fine-tune growth, development, and responses to the environment. When it goes wrong, it can contribute to diseases such as cancer.

Explain RNA interference mechanisms
RNA interference (RNAi) is a natural process cells use to silence specific genes by destroying or blocking their messenger RNAs (mRNAs). Small RNA molecules guide the silencing so it is sequence-specific.Two main types of small RNAs trigger it. Small interfering RNAs (siRNAs) usually come from long double-stranded RNA, such as viral RNA or experimentally introduced RNA. MicroRNAs (miRNAs) are made by the cell itself from its own genes. Both are cut to roughly 21–23 nucleotides and loaded into a protein complex called RISC (RNA-induced silencing complex), whose core is an Argonaute protein.Once loaded, the small RNA searches for matching sequences on target mRNAs. When the match is nearly perfect along the whole length, Argonaute (especially Ago2 in mammals) slices the mRNA in two, quickly destroying it. This is the classic “cleavage” route used by most siRNAs. When the match is only partial—mainly a short seed region—the complex does not cut; instead it recruits other proteins that shorten the mRNA’s tail, remove its cap, and slow or stop translation. That is the usual route for animal miRNAs and produces a milder, more tunable reduction in protein output.The pathway starts with a double-stranded RNA precursor. The enzyme Dicer cuts it into short duplexes. One strand (the guide) stays in Argonaute; the other (passenger) is discarded. Cells also regulate how much of each small RNA is made, how stable it is, and which Argonaute it joins, so the strength of silencing can change with conditions.RNAi therefore serves both as an antiviral defense (destroying viral RNA) and as a built-in gene-control system (miRNAs fine-tuning hundreds of cellular transcripts). The same machinery is widely used in research and medicine to turn down chosen genes by supplying synthetic siRNAs or miRNA mimics.

The same molecular mechanisms of the “machinery” are linked from energy-dependent peptide synthesis at the origin of life to peptide therapy for virus-driven diseases. (Note: All diseases appear to be virus-driven.)

in the context of epigenetic effects on molecular distance and prevention of virus-driven pathology, this explanation from the Torah may be interesting . See: Why Creation Begins in the Dark 10/4/26

“The ancient rabbis taught that the first light of Creation was more than ordinary illumination. They spoke of a primordial “Hidden Light,” associated with a deeper kind of seeing—the ability to perceive beyond what is immediately visible.”

They presciently knew there is more to light than meets the eye. For example, God claims He Created UV light and IR light at the origin of life on Earth, albeit indirectly in Biblical Genesis, where the separation of different wavelenghts have since linked broken hydrogen bond during glucose testing in the lab at 240nm and 630nm.

If you’ve never worked in a medical laboratory as a USAF-trained medical laboratory scientist, or never read the Christian Bible, you may ask “Where did the hydrogen bonds come from, in theory?”

See: Early galaxies were already enriched in heavy elements and spewing them out, significantly changing our understanding of the early universe 9/25/26

“Not only were the galaxies producing these elements, but they were also dispersing them, possibly seeding other galaxies.

Cosmic backlighting

That period of the universe is known as the Epoch of Reionization. Most of the hydrogen in the universe at that point was neutral (so made of a proton and an electron).

The light of young stars ended up ripping those electrons away, a process called ionization. Because this was the second time hydrogen was ionized – as that is also how it first formed – it’s called reionization.”

Before accepting the claims about reionization, ask where the energy came from for light-activated ionization. Then link God’s ATP-dependent Creation of RNA to all biodiversity on Earth via McEwen et al., (1964) and fixation of amino acid substitutions that differentiate primate species: Dobzhanky (1964)

See also: Light-Based Biology Wins the Nobel Prize! 10/7/26

“This is direct, reproducible evidence of light functioning as a biological switch at the cellular level.”

Ask Grok AI: “Does any evidence from moronic theories start with reproducible evidence of a light-activated biological switch and claims in Cellulose: A Plant Cell Biology Game that link Identification of an atypical microRNA with sequence variation in the silkworm Bombyx mori 10/6/26