Rosa Rubicondior: Unintelligent Design - The Irreducibly Complex Cause Of Alzheimers

Clues to Alzheimer’s disease may be hiding in our ‘junk’ DNA

Researchers from the University of New South Wales (UNSW), Sydney, Australia, have identified DNA switches that help control how astrocytes work. These are brain cells that support neurons and are known to play a role in Alzheimer’s disease. They have just published their findings in Nature Neuroscience.

Coming soon after researchers at Aarhus University in Denmark discovered a design defect in astrocytes that contributes to the development of Alzheimer’s, this represents a double embarrassment for those creationists who understand its implications.

Firstly, there is the embarrassment that the cause of Alzheimer’s is indistinguishable from Michael J. Behe’s favourite ‘proof’ of intelligent design — irreducible complexity — in that all the elements must be present for Alzheimer’s to occur.

Secondly, there is the discovery by the Australian team of which triggers ‘switch on’ which genes that affect the astrocytes implicated in Alzheimer’s. These switches are embedded in the 98% of the human genome that is non-coding, or so-called ‘junk’ DNA. Since they can be separated from the genes they regulate by thousands of base pairs, it has been notoriously difficult to identify which switches control which genes. Now, using CRISPR, the team have identified around 150 of these regulatory elements.

The existence of this non-coding DNA has long been an embarrassment for creationists, who have been unable to explain why an intelligent designer would produce so much DNA that does not contain the roughly 20,000 genes that actually code for proteins. Why such prolific waste, adding massively to the risk of errors that can result in cancer?

The creationist response has been to conflate the terms ‘non-coding’ and ‘non-functional’, and then proclaim this ‘functional DNA’ as intelligently designed — reducing, but by no means eliminating, the amount of ‘junk’ they still have to explain away. Of course, ‘non-coding’ does not mean ‘not transcribed’, only that the RNA does not code for a functional protein. However, this non-coding but functional DNA does play a role in gene expression, in that the resulting RNA can act as controls or ‘switches’ that turn genes on and off.

So, creationists — having triumphantly waved ‘functional, non-coding DNA’ as evidence for intelligent design after all — are now presented with the fact that it is part of the ‘irreducible’ cause of Alzheimer’s, and probably the cause of many other diseases with a genetic basis.

What is currently known about the causes of Alzheimer’s disease?

Examples of roles of astrocytes in normal condition and in AD.

Monterey, Michael D.; et al (2021)

Alzheimer’s disease is a progressive neurodegenerative disorder and the most common cause of dementia. It does not have a single cause; instead, it arises from the interaction of genetic, molecular, cellular, and environmental factors over many years or decades.

Amyloid-β plaques

One of the earliest and best-known features of Alzheimer’s is the accumulation of amyloid-β protein outside neurons, forming sticky plaques. These result from abnormal processing of the amyloid precursor protein (APP). While amyloid accumulation alone does not fully explain the disease, it is thought to trigger downstream pathological processes.

Tau tangles

Inside neurons, the protein tau can become abnormally phosphorylated and form neurofibrillary tangles. These disrupt the cell’s internal transport system and are closely correlated with neuronal dysfunction and cognitive decline. Tau pathology spreads through the brain in a predictable pattern as the disease progresses.

Synapse and neuron loss

Long before widespread neuron death occurs, Alzheimer’s causes loss of synapses, the connections between neurons. This synaptic failure is the strongest anatomical correlate of memory loss and cognitive impairment.

Neuroinflammation and glial cells

Non-neuronal brain cells — especially astrocytes and microglia — play a central role. Chronic activation of these cells leads to neuroinflammation, which can exacerbate amyloid and tau pathology, impair synaptic function, and accelerate neuronal damage. Recent research highlights failures in astrocyte regulation as a contributing factor.

Genetic risk factors

Early-onset Alzheimer’s (rare, often before age 60) can be caused by inherited mutations in a small number of genes involved in amyloid processing.
Late-onset Alzheimer’s (the vast majority of cases) is influenced by many genetic variants, the most significant being APOE-ε4, which increases risk but does not determine outcome.

Non-coding DNA and gene regulation

Most genetic risk for Alzheimer’s lies not in protein-coding genes, but in non-coding regions of the genome. These regions regulate when, where, and how strongly genes are expressed. Disruption of these regulatory “switches” can alter brain cell behaviour, immune responses, and vulnerability to degeneration.

Vascular and metabolic factors

Conditions such as hypertension, diabetes, obesity, and atherosclerosis increase Alzheimer’s risk. Reduced cerebral blood flow and impaired glucose metabolism appear to make the brain more vulnerable to degenerative processes.

Age and cumulative damage

Age is by far the greatest risk factor. Alzheimer’s reflects long-term accumulation of molecular damage, declining cellular repair mechanisms, and reduced resilience of neural networks.

In summary

Alzheimer’s disease is best understood not as a single defect, but as a systems failure involving protein misfolding, gene regulation, immune dysfunction, and cellular breakdown. Each component is necessary, but none is sufficient on its own — a fact that has important implications for both treatment and claims of “perfect” biological design.

The work of the UNSW research team is summarised in a UNSW news release.

Clues to Alzheimer’s disease may be hiding in our ‘junk’ DNA
UNSW scientists have uncovered the hidden switches in DNA, revealing new insights into Alzheimer’s disease.
When most of us think of DNA, we have a vague idea it’s made up of genes that give us our physical features, our behavioural quirks, and keep our cells and organs running.

But only a tiny percentage of our DNA – around 2% – contains our 20,000-odd genes. The remaining 98% – long known as the non-coding genome, or so-called ‘junk’ DNA – includes many of the switches that control when and how strongly genes are expressed.

Now researchers from UNSW Sydney have identified the DNA switches that help control how astrocytes work – these are brain cells that support neurons, and are known to play a role in Alzheimer’s disease.

In research published today in Nature Neuroscience, researchers from UNSW’s School of Biotechnology & Biomolecular Sciences described how they tested nearly 1000 potential switches – strings of DNA known as enhancers – in human astrocytes grown in the lab. Enhancers can be located very far away from the gene they control, sometimes hundreds of thousands of DNA letters away – making them difficult to study.

The team used CRISPRi, a tool that lets you turn off small sections of DNA without cutting it, combined with single-cell RNA sequencing, which measures gene expression in individual cells. This approach allowed them to test the function of nearly 1000 enhancers at once.
We used CRISPRi to turn off potential enhancers in the astrocytes to see whether it changed gene expression, and if it did, then we knew we’d found a functional enhancer and could then figure out which gene – or genes – it controls. That’s what happened for about 150 of the potential enhancers we tested. And strikingly, a large fraction of these functional enhancers controlled genes implicated in Alzheimer’s disease.

Dr Nicole F. O. Green, co-lead author.
School of Biotechnology and Biomolecular Sciences
University of New South Wales
Sydney, New South Wales, Australia.

Going from 1000 candidates to 150 real switches dramatically narrows where scientists need to look in the non-coding genome to find clues to the genetics of Alzheimer’s disease.

These findings suggest that similar studies in other brain cell types are needed to highlight the functional enhancers in the vast space of non-coding DNA.

Dr Nicole F. O. Green.

Reading between the lines

Professor Irina Voineagu, who oversaw the study, says the results give researchers a catalogue of DNA regions that can help interpret the results of other genetic studies as well.

When researchers look for genetic changes that explain diseases like hypertension, diabetes and also psychiatric and neurodegenerative disorders like Alzheimer's disease – we often end up with changes not within genes so much, but in-between.

Professor Irina Voineagu, corresponding author.
School of Biotechnology and Biomolecular Sciences
University of New South Wales
Sydney, New South Wales, Australia.

Those “in-between” regions are the enhancers her team has now tested directly in human astrocytes – revealing which ones genuinely control important brain genes.

We’re not talking about therapies yet. But you can’t develop them unless you first understand the wiring diagram. That’s what this gives us — a deeper view into the circuitry of gene control in astrocytes.

Professor Irina Voineagu.

From gene switches to AI

Testing nearly a thousand enhancers in the lab was painstaking work. And it is the first time a CRISPRi screen of enhancers of this scale has been done in brain cells. But with the groundwork now done, the data can be used to train computer tools to predict which potential enhancers are true switches, potentially saving years of experimental time.
This dataset can help computational biologists test how good their prediction models are at predicting enhancer function.

Professor Irina Voineagu.

In fact, Google’s DeepMind team is already using the dataset to benchmark their recent deep learning model called AlphaGenome, she adds.

Potential tools for gene therapy

Because specific enhancers are only active in specific cell types, targeting them could allow precise control of gene expression in astrocytes without affecting neurons or other brain cells.
While this is not close to being used in the clinic yet – and much work remains before these findings could lead to treatments – there is a clear precedent. The first gene editing drug approved for a blood disease – sickle cell anaemia – targets a cell-type specific enhancer.

Professor Irina Voineagu.

This is something we want to look at more deeply: finding out which enhancers we can use to turn genes on or off in a single brain cell type, and in a very controlled way.

Dr Nicole F. O. Green.

Publication:
Green, N.F.O., Sutton, G.J., Pérez-Burillo, J. et al.
CRISPRi screening in cultured human astrocytes uncovers distal enhancers controlling genes dysregulated in Alzheimer’s disease. Nat Neurosci (2025). https://doi.org/10.1038/s41593-025-02154-3

Abstract
Genetic variants associated with complex traits often lie in distal enhancers. While candidate enhancers have been mapped genome wide, their functional state and gene targets in specific cell types remain unclear. Here we present AstroREG, a resource of enhancer–gene interactions in human primary astrocytes, generated by combining CRISPR inhibition (CRISPRi), single-cell RNA-seq and machine learning. By functionally testing nearly 1,000 PsychENCODE enhancers, we identified more than 150 regulatory interactions, revealing enhancers that control key astrocyte functions and genes implicated in Alzheimer’s disease. The CRISPRi screen also provided valuable ground-truth data from a primary cell type for training and benchmarking prediction models of enhancer activity. We thus developed EGrf, a random forest (RF) model trained on these data, and applied it genome wide to predict regulatory interactions with high specificity. Together, our data provide a comprehensive functional map of enhancer-mediated regulation in a key glial cell type, shedding light on brain function and disease.

Main
CRISPR screening with gene expression readout enables the examination of enhancer function within its native genomic locus^1,2,3,4,5. Large-scale screens using this technology to study hundreds to thousands of enhancers have been performed primarily in K562 cells^1,2,3,6, while smaller-scale experiments have been conducted in T cells⁷, induced pluripotent stem cells (iPSC)-derived neurons⁸ and as a massively parallel reporter assay (MPRA) validation approach^9,10.

Here we set out to uncover transcriptional regulatory circuitry in human primary astrocytes, the most abundant type of glia in the human brain, having key roles in modulating neuronal function and plasticity^11,12.

We carried out a CRISPR inhibition (CRISPRi) screen of 979 PsychENCODE¹³ distal candidate enhancers, identifying 158 regulatory interactions in human primary astrocytes (Fig. 1a). These data uncovered a regulatory network that controls astrocyte-specific gene expression and identified distal enhancers that control the expression of genes dysregulated in Alzheimer’s disease (AD). We further used the screen data to train a random forest model (EGrf) that achieved higher prediction accuracy for enhancer–gene regulatory interactions than existing prediction models (ABC4, ENCODE rE2G¹⁴, TAP-seq RF¹⁵). Applying EGrf genome wide to intergenic open chromatin regions, we predicted more than 1,300 regulatory interactions with high specificity. The AstroREG resource (Fig. 1a) combines enhancer–gene regulatory interactions identified through CRISPRi screening and EGrf prediction.

Fig. 1: CRISPRi screen of intergenic enhancers in astrocytes.

a, Study overview. The selection pipeline (see Results for details) led to a set of 979 candidate enhancer regions. Candidate regions were tested in a CRISPRi screen with scRNA-seq readout, which identified 158 functional enhancer–gene interactions. Functional enhancers and their target genes were extensively characterized and used to construct an enhancer–gene regulatory network controlling astrocyte-specific gene expression. Furthermore, the CRISPRi results were used to train an RF model, which was then applied to predict enhancer–gene regulatory interactions genome wide. The AstroREG resource includes the experimentally tested and predicted enhancer–gene interactions. Panel a was created with BioRender.com. b, PCA plot of RNA-seq data from NHAs and brain samples immunopanned with cell-type-specific antibodies from ref. ¹⁶. All samples in this study were from healthy donors except peritumor astrocytes and sclerotic astrocytes; the cortex sample did not undergo immunopanning. The PCA plot was generated based on the intersample Spearman correlation matrix. c, Top, barplot of cell type and developmental stage annotation of NHA CRISPRi screen scRNA-seq data (n = 47,577 cells). Reference-based annotation of single cells was performed using SingleR, with reference snRNA-seq from the human brain maturation atlas¹⁷ (Methods). Cells annotated as not assigned did not reach the minimal identity threshold for any cell type. Bottom, UMAP plots of CRISPRi screen scRNA-seq data with cells colored by SingleR cell-type annotation (left), developmental stage annotation (middle) and cell-cycle stage annotation (right); the latter was obtained using the CellCycle Scoring Seurat function. The identity of C1 is characterized in Supplementary Note 1. d, Overlap of candidate peaks with ENCODE-annotated²³ genomic regions. Distal enhancers, P = 4 × 10⁻; proximal enhancers, P = 4 × 10⁻⁴⁴; promoters, P = 2 × 10⁻²⁷. Two-sided Fisher’s exact test. e, Upset plot displaying the overlap between the selected candidate enhancer set and four datasets used in the selection pipeline—two ATAC–seq datasets of NeuN− cells from adult human brain^18,19, a dataset of iPSC-derived astrocytes²¹ and a dataset of astrocytes immunopanned with anti-HepaCAM or FACS-sorted with anti-GFAP from hCS maintained for up to 595 days in culture²⁰. f, Heatmap of candidate enhancer chromatin accessibility based on pseudobulked snATAC–seq data across cell types and developmental stages from ref. ¹⁷. Rows display enhancers and are sorted by the enhancer’s module membership in the co-accessibility network (Methods). Cell types—astrocytes (astro), excitatory neurons (exc), inhibitory neurons (inh), microglia (micro) and oligodendrocytes and OPCs (oligo). Developmental stages—fetal (gestational age of 22–24 weeks), neonatal (28 days), infancy (86 days–1 year), childhood (1.5–8 years), adolescence (10–16 years) and adulthood (20–40 years). g, Co-accessibility network module significance. Cell type and stage—a linear model was used to assess the variation of module eigengene values across all cell types and developmental stages. P values were extracted using ANOVA. Astrocyte—a two-sided Wilcoxon rank-sum test was performed for a two-group comparison of module eigengene values between astrocytes and all other cell types. Astrocyte stage—a Spearman correlation coefficient was used to assess the effect of developmental stage within the astrocyte samples. PCA, principal component analysis; OPCs, oligodendrocyte precursor cells; hCS, human cortical spheroids; C0, cluster 0; C1, cluster 1; ANOVA, analysis of variance.

Source data

Taken together, this picture of Alzheimer’s disease is profoundly awkward for creationism. The condition does not arise from a single failure, but from the interaction of many individually necessary components — proteins, regulatory DNA, glial cells, immune responses, and ageing processes — all functioning exactly as designed, yet collectively producing catastrophic breakdown. This is not design gone wrong; it is design that fails because it works as intended.

The role of non-coding regulatory DNA is especially damaging to creationist claims. What was once dismissed as “junk” is now known to be essential to gene regulation, yet it also introduces layers of fragility and error-prone complexity. These regulatory switches do not merely permit disease when damaged; in Alzheimer’s they actively participate in the pathological process. A system in which normal gene control mechanisms are an indispensable part of degenerative disease is indistinguishable from an evolved system shaped by historical constraints, trade-offs, and accumulated compromises.

For those creationists who understand the science, this leaves no refuge in slogans like “irreducible complexity” or “functional information”. Alzheimer’s is irreducibly complex in exactly the wrong way: remove any element and the disease does not occur, yet include them all and the system self-destructs. That is not evidence of foresight or optimisation, but of **blind tinkering**, the hallmark of evolutionary history.

For those creationists who insist that irreducible complexity is evidenc of intelligent desgign, the conclusion is inescapable - the designer of Alzheimers is malevolent.

In short, Alzheimer’s disease exemplifies what biology looks like when it has not been intelligently designed, but incrementally patched together over deep time. If creationists genuinely understood how this disease arises — and why it cannot be simplified without eliminating normal brain function — it would represent not just an embarrassment for their superstition, but a decisive refutation of it.

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