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This is just one example of research papers we can review to gain insights from in relation to RNA data.

You can test all your RNA (20,000 markers per person) and see how you compare to Herberg et al., findings below:

amaticahealth.com/me-cfs-long-...

Distinguishing Bacterial vs Viral Infection in Febrile Children This study describes the development and diagnostic accuracy of a host-response RNA signature for distinguishing bacterial from viral infection in febrile children younger than 5 years.

Full classification method comes from:

“Diagnostic Accuracy of a 2-Transcript Host RNA Signature for Discriminating Bacterial vs Viral Infection in Febrile Children”

Herberg et al., JAMA, 2016 jamanetwork.com/journals/jam...

Important note:

These are preliminary. They rely on a simplified 2-gene model and only reference ranges from 3 control. More genes, controls, clinical data, and lab results will strengthen the interpretation.

Study tracked children with active fevers; relevance to adults without fever is unknown

This method doesn’t require identifying the pathogen itself.

It’s based on how the immune system responds.

Summary:

C2 & C5 = viral

C3 & C4 = bacterial

C1 = unclear (possibly mild viral)

These groupings are based solely on gene expression patterns of IFI44L and FAM89A discussed in Herberg et al.

Interestingly, the person in C5 had improvements from COVID Monoclonal Antibodies (mAbs), consistent with persistent viral load.

🟡 C5: Viral

C5 has very high IFI44L and only moderate FAM89A.

This produces a strongly “viral” signal.

The interferon-driven IFI44L is strongly induced here.
→ Fits the viral subset.

🟢 C4: Bacterial

C4 also shows elevated FAM89A and minimal IFI44L.

Though less extreme than C3, the imbalance points toward a bacterial immune signature.

🟢 C3: Bacterial

C3 has high FAM89A and very low IFI44L.

This pattern matches the typical bacterial infection response.

🔵 C2: Viral

C2 shows elevated IFI44L with low FAM89A.

This matches the viral infection pattern seen in Herberg et al.’s work.

Fits into the viral infection group.

🟣 C1: Unclear (borderline viral)

C1 has modest expression of both genes. IFI44L is slightly higher than FAM89A.

→ Slight viral tilt, but not enough to say confidently.

Possibly an indeterminate or mild infection.

The key idea is this:

- High IFI44L, low FAM89A → viral infection

- High FAM89A, low IFI44L → bacterial infection

We applied this rule to each preliminary cluster to infer possible infection type.

amatica 20,000 results in one test - understand your disease while helping advance research

This classification uses a method from Herberg et al. (JAMA, 2016). They showed that expression levels of just two genes (RNA) can help separate viral from bacterial infections in children with fever.

The RNA are available to be tested here:
amaticahealth.com/me-cfs-long-...

🔬We identified five preliminary immune clusters (C1–C5) in our Long COVID & ME/CFS cohort using two RNA markers: IFI44L and FAM89A.

Research shows these genes can help distinguish bacterial from viral infections.

Let’s break down which clusters likely fall into each category.

All markers above can be tested through our full RNA seq panel - 20,000 results per participant:

amaticahealth.com/me-cfs-long-...

For Research Use Only - not for use in diagnostic procedures. Results are educational, not medical advice, and not validated to assess BBB function.

Here’s the key study on Long COVID, brain fog, and blood–brain barrier disruption using blood RNA profiling:

www.nature.com/articles/s41...

A carefully chosen gene panel (like the one above) helps researchers focus on meaningful BBB signals - even when they’re low in abundance.

As more studies are done, this approach may help monitor or predict brain conditions from a simple blood test.

This suggests a systemic response - possibly due to vascular stress or early BBB damage in Alzheimer’s.

Even in chronic diseases, blood gene expression can reflect changes in brain vascular health and BBB stability.

Study 4: Alzheimer’s & Vascular Genes

Researchers looked at VEGF-related gene expression in blood and brain. They found:

- Higher PGF and VEGFB in blood

- Lower expression in brain

This imbalance was linked to faster cognitive decline.

This study shows that blood RNA changes reflect the inflammatory response after stroke - especially the response that damages the BBB and allows immune cells into the brain.

The method used is directly applicable to tracking BBB injury over time.

Study 3: Intracerebral Hemorrhage (Stroke)

Researchers sequenced blood RNA at 24h and 72h after stroke.

At 72h, they found:

- Increased IL8, MMP9, NF-κB, ICAM1

All tied to immune-driven BBB damage.

Study 2: Repeated Blast Exposure

Military breachers (who experience repeated low-level blasts) showed changes in blood RNA:

- Immune genes like LILRB5, CD200

- A brain-specific gene CNTNAP2

This suggests chronic stress or damage to brain barriers.

They also showed that these patients’ blood cells stuck more strongly to lab-grown brain endothelial cells, and their serum caused inflammation in those cells.

The changes in blood RNA reflected inflammation and damage at the BBB.

Study 1: Long COVID + Brain Fog

Researchers sequenced immune cells from Long COVID patients with brain fog. They found:

Increased clotting and complement genes

Decreased adaptive immune genes

This matched signs of a leaky BBB on MRI.

Now let’s look at how this has been used in real research.

Studies have used blood RNA sequencing to detect changes in BBB-related genes in humans - both in acute injuries and chronic brain conditions.

Together, these genes give a picture of:

- Tightness of the BBB

- Inflammation affecting the barrier

- Signs of brain cell stress or breakdown

- Signals that help maintain or damage the barrier over time

- All visible in blood RNA - when sequenced deeply.

🧬 Signaling and BBB-Influencing Genes:

- ICAM1, VCAM1 - cause immune cells to stick to vessel walls

- MMP9, MMP2 - enzymes that break down the BBB

- VEGFA, ANGPT1/2 - control vessel tightness and leakiness

- IL1B, TNF, IL6, TGFB1, APOE - inflammation and repair signals

🧬 Astrocyte Genes (help maintain the BBB from the brain side):

GFAP, AQP4, S100B, ALDH1L1, SLC1A2, SLC1A3 - markers of astrocyte activity, damage, and glutamate balance

They may appear in blood when there’s stress, injury, or inflammation.

🧬 Pericyte Genes (support and stabilize vessels):

- PDGFRB, CSPG4, RGS5, NOTCH3 - involved in signaling and vessel stability

- ACTA2, DES - structural proteins found in pericytes and smooth muscle cells

These genes may appear in blood through vesicles or damage.

🧬 Endothelial Cell Genes (they form the BBB wall):

CLDN5, OCLN, TJP1, CDH5 - tight junction proteins that keep the barrier sealed

SLC2A1, ABCB1, MFSD2A - transporters that control what enters/exits

PECAM1, VWF - general endothelial health