Our reader Aleksandra recently sent me
a complicated paper by Meilin Fernandez Garcia and colleagues from Yale.
The corresponding senior author is Professor Kristen Brennand, whose laboratory
is internationally recognised for using human induced pluripotent stem cells
(hiPSCs), CRISPR gene editing and neuronal models to study neuropsychiatric
disorders.
At least one autism parent is doing
something similar to help their child. Instead of engineering mutations into
generic cell lines to identify therapies that might benefit many patients, the
same approach is being applied to neurons derived from a single individual with
autism, allowing researchers to search for drugs that reverse that person's
unique cellular abnormalities. You do need money to this.
Diverse
risk genes have been identified for neurodevelopmental disorders (NDDs), but
how these genes converge on similar biological pathways in neurons, and thus
give rise to similar phenotypes, is unclear. Here we apply a pooled CRISPR
approach to successfully target 23 NDD loss-of-function genes with roles in
chromatin biology and examine convergent effects on gene expression across
human induced pluripotent stem cell-derived neural progenitor cells,
glutamatergic neurons and GABAergic neurons. Points of convergence vary between
these cell types, with the greatest number of convergent genes and strongest
convergent networks in mature glutamatergic neurons, where they broadly
represent synaptic, epigenetic and, unexpectedly, mitochondrial pathways. The
most convergent networks were observed between NDD genes with shared biological
annotations, clinical associations and co-expression patterns in human
post-mortem brain. Drugs that were predicted to reverse convergent
transcriptomic signatures and/or arousal and sensory processing behaviors
ameliorated behavioral phenotypes in zebrafish NDD gene mutants. These results
suggest that convergent effects of NDD risk genes could provide clinically
useful insights.
As I looked through this paper, I
experienced an unusual feeling. The methodology could hardly be more different
from the approach I have taken over the past decade, yet many of the
conclusions felt surprisingly familiar.
This blog has never had research
grants, laboratories, graduate students or sophisticated genomic platforms. It
has simply been the product of many years of reading the scientific literature,
following developments in genetics, pharmacology and physiology, and trying to
understand how apparently unrelated findings might fit together.
The Yale team approached the problem
from the opposite direction. Using CRISPR gene editing, single-cell
transcriptomics, machine learning and computational drug discovery, they asked
a simple question:
Do hundreds of
different neurodevelopmental disorder genes converge on a smaller number of
common downstream biological pathways that might be therapeutically targetable?
Their answer was yes.
For long-time readers of EpiphanyASD,
that conclusion will sound very familiar.
From hundreds of
autism genes to common biology
One of the recurring themes of this
blog has been that autism is unlikely to require hundreds of completely
different treatments simply because there are hundreds of different autism
genes.
Many genetic mutations disrupt the
same biological systems.
If those downstream pathways can be
identified and safely modulated, treatment may become considerably simpler than
developing a separate therapy for every mutation.
The Yale investigators reached this
conclusion using an extraordinarily sophisticated experimental pipeline.
They used CRISPR to knock out 23
neurodevelopmental disorder genes in human stem-cell-derived neurons and
performed single-cell RNA sequencing on more than 118,000 cells.
Machine-learning models were then used to extend these findings to more than
one hundred neurodevelopmental disorder genes.
Instead of focusing on what made each
mutation different, they searched for what they had in common.
The strongest convergence occurred in
mature glutamatergic neurons, where many mutations disrupted shared
transcriptional programmes involving synaptic function, chromatin regulation
and, interestingly, mitochondrial biology.
Using AI to
identify potential therapies
The next stage of the study was
particularly elegant.
Rather than beginning with drugs
already proposed for autism, the researchers searched the Connectivity Map
database for medicines that produced gene-expression changes opposite to those
caused by the convergent neurodevelopmental signatures.
In effect they asked:
Which existing
drugs make diseased neurons look genetically more like healthy neurons?
After computational ranking, the most
promising candidates were tested in zebrafish models.
Ten of the eleven drug–gene
combinations rescued at least one behavioural phenotype.
That does not demonstrate efficacy in
people with autism, but it does provide strong proof-of-principle for this
approach.
Looking beyond
the individual drugs
Being a practical sort of person, my
first instinct was to examine the supplementary data rather than simply read
the discussion section.
With the help of AI, this took only a
few minutes.
The Yale analysis compared
approximately 520 drug signatures from the Connectivity Map against the
convergent transcriptomic changes. Although this represents only a fraction of
all approved medicines, it is sufficient to identify the biological pathways
most strongly associated with transcriptomic reversal.
I was curious to see how these
pathways compared with those that have gradually emerged on EpiphanyASD over
more than ten years.
The comparison encouraged me.
| Biological Pathway | Yale Computational Hits | Epiphany ASD Approach | Interpretation |
|---|---|---|---|
| Mevalonate / Cholesterol | Pravastatin, Rosuvastatin | Atorvastatin | Independent identification of statin therapy, although different statins were selected. |
| Renin–Angiotensin System | Valsartan | Telmisartan | Different ARBs targeting the same signalling pathway. |
| Calcium Signalling | Diltiazem | Verapamil, Amlodipine | Different L-type calcium-channel blockers acting on related pathways. |
| Histamine / Remyelination | Clemastine | Clemastine | Complete agreement. |
| mTOR Signalling | Sirolimus | — | Pathway highlighted by Yale and EpiphanyASD. No safe drug. |
| Immune / Inflammatory Modulation | Corticosteroids | Pioglitazone, NAC | Different therapeutic strategies aimed at immune regulation. |
| Chloride Homeostasis | — | Bumetanide | Proposed by Ben-Ari and adopted by EpiphanyASD but not identified by the transcriptomic analysis. |
| Oxidative Stress / Mitochondrial Support | — | NAC, Alpha-lipoic acid, Taurine | Important focus of this blog but not highlighted by the Yale analysis. |
| Polyamine / NMDA Modulation | — | Agmatine | Outside the principal pathways identified by Yale. |
The important point is not that the same drugs were identified.
They were not.
Nor does this study validate any
treatment combination discussed on this blog.
The interesting observation is that two
completely independent approaches have converged on many of the same biological
pathways.
One approach relied on years of
reviewing genetics, pharmacology, physiology and clinical reports.
The other relied on CRISPR gene
editing, human neuronal models, single-cell transcriptomics, machine learning
and computational drug discovery.
When such different methodologies
begin pointing towards the same pathways, it increases confidence that those
pathways deserve serious attention.
Why this matters
One feature of the Yale study
particularly resonated with me.
The computational analysis did not
identify a single "autism drug."
Instead, it identified multiple
interacting biological systems. This mirrors another long-standing theme of
EpiphanyASD.
If autism involves disturbances in
several interconnected biological systems, then it seems unlikely that one drug
alone will restore normal function in many patients.
Instead, carefully chosen combinations
of therapies, each targeting a different aspect of the biology, may ultimately
prove more effective than monotherapy.
That remains a hypothesis requiring
proper clinical evaluation, but the Yale analysis certainly does not argue
against such an approach.
Where the
approaches differ
The Yale study identified sirolimus as
an interesting candidate, whereas this has not previously featured prominently
on this blog. Some readers have trialed it, but there are side effects, so it is not high on my list of practical therapies.
Conversely, therapies frequently
discussed here—including bumetanide, NAC, alpha-lipoic acid, taurine and
agmatine—did not emerge from the transcriptomic analysis.
That should not be overinterpreted.
The Connectivity Map used in this
study analysed approximately 520 compounds, representing only a subset of
approved medicines. Closely related drugs such as atorvastatin, telmisartan and
verapamil were either absent from the analysed library or did not emerge as
leading transcriptomic reversers.
Transcriptomic reversal is only one
method for identifying therapeutic candidates. In the case of a very complex
conditional like autism it can likely only solve part of the problem.
Transcriptomic mapping captures gene
expression reversal, whereas therapies like Bumetanide (NKCC1 chloride
transporter) or NAC (direct antioxidant/glutamate buffer) act primarily through
direct protein/ion channel activity, which might not generate a strong early
transcriptomic signature in cell culture models.
What proportion
of effective drugs would be missed by the Yale approach?
The 3-Bucket
Pharmacological Model
When evaluating how small-molecule
drugs work across all FDA-approved therapeutics—and specifically within central
nervous system (CNS) and neurodevelopmental disorders—drugs fall into three
distinct functional buckets based on their primary Mechanism of Action (MoA):
A transcriptomic-only screening
platform (scRNA-seq + Connectivity Map) is structurally blind to Buckets 2 and
3, meaning it may miss approximately 50% to 60% of effective CNS and
neurodevelopmental therapeutics.
Breakdown by
Bucket
Bucket 1: Primary
Transcriptomic & Genomic Modulators (about 40% of drugs)
- Mechanisms: Nuclear receptor agonists/antagonists,
chromatin remodelers, HDAC inhibitors, and upstream growth factor/kinase
cascades (e.g., mTOR signaling).
- Representative Therapies: Corticosteroids, Pioglitazone
(PPAR-gamma), Sirolimus (mTOR), Statins, and ARBs.
- Screening Sensitivity: HIGH. Because these compounds
work by intentionally altering mRNA transcription and gene expression
programs, scRNA-seq and CMap pick them up effectively. This is where the
Yale pipeline excels.
Bucket 2:
Electrophysiological & Membrane Modulators (about 40% of drugs)
- Mechanisms: Direct blockade or opening of ion
channels, cell-membrane transporters, and fast ligand-gated ion channels.
These alter intracellular ion concentrations and membrane voltage within
milliseconds to minutes.
- Representative Therapies: Bumetanide (NKCC1
chloride-transporter blocker), Calcium channel blockers (Diltiazem,
Verapamil), GABA-A allosteric modulators, and Sodium channel blockers.
- Screening Sensitivity: LOW (High Risk of Being Missed).
The primary therapeutic event is electrophysiological (shifting
excitation/inhibition balance or hyperpolarizing neurons). While long-term
cellular adaptation can produce secondary transcriptional noise,
the primary mechanism produces little to no acute transcriptomic signature
in cell culture.
Bucket 3: Direct
Metabolic & Biochemical Buffers (about 20% of drugs)
- Mechanisms: Direct stoichiometric chemical
reactions, free-radical scavenging, direct precursor supply for endogenous
enzymatic cycles, or polyamine/receptor modulation that occurs without
altering target gene expression.
- Representative Therapies: N-Acetylcysteine (NAC) (direct ROS
scavenger and glutathione precursor), Alpha-Lipoic Acid, Taurine, and
Agmatine (polyamine/NMDA pathway modulator).
- Screening Sensitivity: VERY LOW (Almost Entirely Missed).
These molecules correct cellular redox potential or metabolic fluxes
directly. Because they act via direct biochemistry rather than
transcriptional reprogramming, a gene expression screen will almost always
drop them.
Why the
"miss rate" is exceptionally high in Autism / NDDs
In fields like oncology, where the
goal is altering cell cycle or cell death pathways, Bucket 1 dominates (making
transcriptomic matching highly effective).
However, in Neurodevelopmental
Disorders (NDDs), core neurobiology relies heavily on:
1.
Intracellular
ionic gradients & E/I balance (Bucket 2)
2.
Mitochondrial
health, redox balance & oxidative stress (Bucket 3)
Because roughly 50-60% of potential
NDD interventions live in Buckets 2 and 3, relying solely on transcriptomic
reversal naturally drops more than half of the potential drugs.
To capture those therapies, systems
biology must complement transcriptomic screens with Microelectrode Arrays
(MEAs) for Bucket 2 and Functional Metabolomics/High-Content Imaging for Bucket
3.
Final thoughts
For me, this paper is encouraging for
two reasons.
First, it provides compelling evidence
that many genetically distinct neurodevelopmental disorders converge on a
relatively small number of shared downstream biological pathways.
Second, after more than a decade of
independent, unfunded research on EpiphanyASD, it is gratifying to see that a
large, well-funded research programme at one of the world's leading
universities has independently highlighted many of those same pathways and drugs.
That does not prove that any
particular treatment discussed on this blog is correct.
It does suggest that the central
idea—that autism should often be approached as a disorder of convergent
downstream biology rather than hundreds of isolated gene defects—is
increasingly supported by modern systems biology.
If future studies continue to point in
the same direction, the prospect of rational, pathway-based precision medicine
for autism becomes much more realistic.
One final thought. Looking back at my
original TRH hypothesis from more than a decade ago, today's technology finally
offers a practical way to test it. Rather than immediately embarking on a
clinical trial, a logical first step would be to investigate both the direct
TRH super-agonist taltirelin (Ceredist) and the indirect TRH-enhancing approach
using rifaximin in patient-derived induced pluripotent stem cells (hiPSCs),
differentiated into neurons.
The rifaximin story is particularly
intriguing because it appears to stimulate endogenous TRH production through
the gut–brain axis, rather than replacing TRH pharmacologically. This is
exactly the type of experimental platform now being used by groups such as
Professor Kristen Brennand's laboratory at Yale to identify drugs that reverse
disease-related transcriptomic abnormalities. If either taltirelin or rifaximin
were shown to normalize gene-expression patterns or neuronal function in
patient-derived neurons, it would provide a much stronger scientific rationale
for moving on to carefully designed clinical studies. It would also be
fascinating to compare the effects of the two approaches directly, asking
whether a TRH super-agonist and a gut-mediated increase in endogenous TRH
ultimately converge on the same downstream molecular pathways. If they did, it
would represent another example of two completely different therapeutic
strategies converging on the same underlying biology—a recurring theme
throughout this article
Epiphany:
TRH and Rifaximin – an alternative to intranasal TRH or oral
Taltirelin/Ceredist?
Epiphany:
The Peter Hypothesis of TRH-induced Behavioural Homeostasis in Autism
Taltirelin sits directly in Bucket 1
as a G Protein-Coupled Receptor agonist that triggers immediate nuclear gene
transcription (upregulating BDNF etc), whereas Rifaximin overlaps Buckets 1 and
3 by using gut-microbiome metabolic modulation to indirectly drive central TRH
expression via the gut–brain axis.
