CDC: Ebola outbreak in Central Africa could reach 20,000 cases without strong countermeasures

NEW YORK — The Ebola outbreak in Central Africa could grow to 20,000 cases or more, depending on how quickly infected people are isolated to slow the spread, according to a new analysis by U.S. health officials.

The Centers for Disease Control and Prevention published a range of scenarios generated by computer models Friday, spanning from 10,000 cases to more than 20,000. If accurate, a worst-case scenario could approach the worst Ebola outbreak in history, the West Africa epidemic in 2014-2016 — which resulted in more than 28,000 reported cases and more than 11,000 deaths.

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STAT+: Newer GLP-1s, pushback on research cuts, and a protest 

You’re reading the web edition of STAT’s ADA in 30 Seconds newsletter from the American Diabetes Association’s annual conference. Sign up here

This is Elizabeth Cooney saying hello from New Orleans, where the weather is warm, the conference center is cool, and debates can be fiery. Welcome to the first of three ADA in 30 newsletters, in which my colleague Elaine Chen and I curate some of the news and analyses circulating here near the banks of the mighty Mississippi.

First up, thoughts from Rick Woychik, a senior adviser to NIH chief Jay Bhattacharya who subbed for him as keynote speaker, plus some background from our STAT colleague Anil Oza. Then, what the weekend will bring.

If you are here too, come say Hi, or reach me at elizabeth.cooney@statnews.com.

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Evaluating an Abbreviated Internet-Delivered Stress Recovery Intervention for Health Care Workers: Pre-Post Feasibility Study of Outcomes, Usability, and Acceptability

<strong>Background:</strong> Health care workers face numerous occupational stressors that place them at heightened risk for burnout and poor mental health. Internet-delivered interventions have shown promise in reducing stress and related symptoms, yet adherence is often low, and users do not complete programs. Abbreviated interventions may help address engagement barriers such as high workload, limited time, and varying user preferences. There is a need to evaluate brief, accessible formats of internet-delivered programs for this population. <strong>Objective:</strong> This study aimed to examine the initial outcomes, usability, and acceptability of a 4-week abbreviated internet-delivered stress recovery intervention for health care workers. Specifically, it evaluated changes in stress recovery, perceived stress, depression and anxiety symptoms, and psychological well-being. The study also sought to understand participants’ experiences with the brief format to determine whether it meets their needs. <strong>Methods:</strong> This single-arm pre-post study examined a 4-week abbreviated version of the online guided cognitive behavioral therapy-based stress recovery program FOREST among self-enrolled health care workers recruited through professional networks (N=52; mean age 39.31, SD 11.31 years; 49/52, 94.2% women). Outcomes included stress recovery (the Recovery Experience Questionnaire), perceived stress (the Perceived Stress Scale-4), depression and anxiety symptoms (the Patient Health Questionnaire-4), psychological well-being (the World Health Organization Well-being Index), and usability and acceptability ratings. <strong>Results:</strong> We found that after the abbreviated version of the FOREST intervention participants showed moderate improvements in stress recovery (<i>d</i>=0.54, 95% CI 0.25-0.83); reductions in stress (<i>d</i>=–0.43, 95% CI –0.72 to –0.14), anxiety and depression symptoms (<i>d</i>=–0.51, 95% CI –0.80 to –0.22); and increase in psychological well-being (<i>d</i>=0.39, 95% CI 0.08-0.70). The majority (37/52, 71.2%) accessed all 6 modules. Users reported high satisfaction with the abbreviated program. <strong>Conclusions:</strong> While preliminary and limited by the pre-post design, these findings indicate that abbreviated internet-based stress recovery programs are a promising and practical tool for supporting the mental health of health care workers. Future research should examine the long-term effects, compare the abbreviated and standard versions, and explore implementation in routine practice. <strong>Trial Registration:</strong>

STAT+: What stripping civil service protections for thousands of federal workers will mean for HHS

Thousands of Health and Human Services Department staff who shape policy, including on public health, federal health insurance programs, and health data privacy, have had their employment status changed to a designation that makes it easier for them to be fired, and thus makes them more vulnerable to political pressure from the White House. 

The reclassification of roughly 8,000 employees across the federal government, outlined in an executive order President Trump issued late Wednesday, also impacts some National Institutes of Health workers who oversee grant funding.

On the whole, health policy experts said, the shift toward a more politicized workforce is part of a broader goal of the Trump administration to shift power away from Congress and toward the executive branch. The policy, known as “Schedule F,” dates back to Trump’s first administration and would create a new class of federal employees that are not political appointees but could be fired at will.

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Senior NIH official pushes MAHA strategy to skeptical ADA audience

NEW ORLEANS — A senior adviser to the leader of the National Institutes of Health opened his speech to a national gathering of diabetes researchers with a full-throated endorsement of the Make America Healthy Again movement. Then, during the fireside chat that followed, he withstood sustained cheers for criticism of deep funding cuts to the nation’s biomedical research enterprise that he was asked to explain.

“I could have written the MAHA agenda,” Richard Woychik, who works closely with NIH Director Jay Bhattacharya, said Friday, recalling when he first learned last October of the policy embraced by Health and Human Services Secretary Robert F. Kennedy Jr. 

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How Germinal Centers Generate Antibodies Through Noisy Rounds of Mutation and Selection

A study tracking thousands of B cells across more than 100 germinal centers (GCs) in mice has revealed how the system consistently produces highly effective antibodies. The findings overturn longstanding ideas about how germinal centers function, revealing that they are far more selective than once thought, and challenge the idea that antibody improvement is driven mainly by rare growth “bursts” among the most successful B cells. The discovery could have implications for immune cell evolution, and ultimately guide the design of vaccines against rapidly mutating pathogens like influenza. It could also lead to new ways of studying evolution itself.

“The traditional, mechanistic view of germinal centers is to think of them as selection machines sorting out the best antibodies,” said research lead Gabriel D. Victora, PhD, head of the Laboratory of Lymphocyte Dynamics at The Rockefeller University. “But when you look very, very closely, you see a process that’s almost essentially random—a little bit better than a coin toss—which repeats many times until the immune system arrives at the right answer consistently. That’s much more akin to how evolution operates than the way a machine does.”

Victora and colleagues reported on their findings in Cell, in a paper titled “Replaying germinal center evolution on a quantified affinity landscape.”

Inside germinal centers, B cells rapidly mutate and compete to produce antibodies that bind successively better to pathogens. “Darwinian evolution of immunoglobulin genes within germinal centers (GCs) underlies the progressive increase in antibody affinity following antigen exposure,” the authors wrote. That puts B cells under intense pressure to optimize a single trait: binding affinity, or how well an antibody recognizes its target.

But how they accomplish that feat has very much remained an open question, the team noted. “Whereas the cellular mechanics of how competition between B cells increases affinity are well established, the evolutionary dynamics of this process are less clear.” Because weak and strong B cells often coexist side by side in the germinal center, scientists have long wondered whether the immune system temporarily preserves weaker cells in case they later acquire useful mutations. The phenomenon of clonal bursts, in which the descendants of a single B cell rapidly take over an entire germinal center, are also poorly understood.

The authors explained that GC B cells evolve by rapidly mutating only two Ig genes, which are the heavy chain (Igh) and light chain (either Igk or Igl). Victora’s team engineered mice in which all competing B cells began with the same antibody sequences, allowing them to replay a single evolutionary process across more than 100 germinal centers at once. “… we established a system in which GCs are composed entirely of B cells carrying the same pre-rearranged Igh and Igk genes, ensuring identical starting specificity and affinity,” they explained. Victora added, “We simplified it to the bare bones, and asked how repeatable is the exact sequence of mutations that leads to stronger antibodies.”

Once each of the B cells was primed with the exact same unmutated antibody sequence, the team triggered germinal center formation through immunization. They then tracked the resulting sprint toward immune efficiency with multiphoton microscopy and laser-based photoactivation, and sequenced thousands of individual B cells across 119 germinal centers.

With this data, the team managed to construct a detailed family tree that mapped how different lineages of B cells had developed. They also built a mutational dictionary, using deep mutational scanning (DMS), a technique that links almost every possible amino-acid change to antibody performance. This advance allowed the team to determine how mutations affected binding strength and structural stability simply by reading a cell’s DNA sequence.

“DMS was the big technical advance here,” says first author Ashni Vora, PhD, a graduate fellow in the lab. “With it we could determine the affinities of thousands of cells just by looking at their sequence, without having to produce an antibody.”

The researchers compare the resulting picture to a casino game. Watching a single B cell evolve inside a germinal center looked almost random, with some cells rapidly expanding, others disappearing, and even promising mutations failing as if random chance ruled the day. Some germinal centers were overtaken by clonal bursts while others contained many competing lineages with no clear winner. The differences had little to do with affinity or merit. “We find that, even in this simplified setting, GC selection yields widely divergent tree topologies, ranging from clonal-burst-type structures to multi-pronged GCs where multiple line ages evolve in parallel,” they noted.

But the team discovered that the germinal center game is rigged. In a casino, the house always wins not because of the odds on any individual game, but because a slight statistical bias is built into the system and repeated thousands of times. Germinal centers appear to operate similarly. Each round of cellular competition is only slightly biased toward cells carrying beneficial mutations, and random chance means that there is often little correlation between affinity and success. But by repeating that same noisy, almost random process over and over across many germinal centers, the immune system ultimately produces stronger antibodies.

“If you see someone get a jackpot, you might wonder how the casino makes money,” Victora says. “The answer is that the casino puts in a little bit of bias, so that you win and you lose, but on average, you lose more than you win. If there are just one or two people playing, the casino might lose money due to random chance. But if there are a thousand people playing, it’s going to average out and the house wins. That’s essentially how germinal centers work.”

The researchers also found that the immune system favors mutations that are easiest for its cellular machinery to generate, rather than the mutations that would produce the strongest antibodies. And by tracking B cell lineages over time, they also showed that germinal centers are far more selective than previously thought, rapidly eliminating inferior B cells. “By combining phylogenetic reconstructions with a fitness landscape inferred from populations sampled over time, we show that both the apparent permissiveness of GCs to low-affinity lineages and the apparent early plateau in affinity maturation are best explained by survivorship biases that distort the histories of lineages present at sampling,” the investigators wrote in summary.

Taken together, the findings overturn several longstanding ideas about how germinal centers function and may provide new tools for vaccine developers hoping to steer antibody evolution against influenza and HIV. “What was once theoretical speculation about what must happen in the germinal center, we are now showing in action—the real thing,” Victora says.

At the same time, this work also illustrates how germinal centers could become a powerful model for studying evolution more broadly. Scientists have long relied on bacteria grown in the lab over many generations to plumb the depths of evolutionary biology and determine how much of evolution is driven by random chance. In clarifying the rules governing germinal centers, the researchers revealed why the immune system could offer a potentially more tractable experimental avenue: Unlike bacterial evolution, which centers around adapting to many possible survival strategies, B cells are all aiming for the same target. “I see this as an opening salvo in a longer effort to understand evolution by using the immune system as a model,” Victora added.

The post How Germinal Centers Generate Antibodies Through Noisy Rounds of Mutation and Selection appeared first on GEN – Genetic Engineering and Biotechnology News.

Using Social Media to Maximize the Research Impact of Surgeons: Exploratory Linguistic Analysis

Background: Surgeons work in a progressive field where communicating research is vital to advancing health care and enabling meaningful interactions among clinicians. It also contributes to societal impact, increases access to information, and reduces misinformation. Additionally, there can be barriers to accessing papers. Social media enhances research impact through sharing scholarly work and improving its translation into clinical practice, but little is known about how to design specific posts to maximize research impact through language. Objective: The purpose of this study was to determine the linguistic cues that optimize research impact among surgeons through Twitter (subsequently rebranded X). Additionally, this research combines the linguistic features of the posts and article access to determine their unique contributions. Methods: An exploratory linguistic analysis of 84 posts extracted from Twitter was conducted, which shared scholarly activity by 17 of the most-followed surgeons. The linguistic cues were measured on a continuous scale, computed from the percentage of each linguistic cue used in the text, and reported as mean (SD). Regression analysis and analysis of covariance were conducted to determine which cues influenced research impact and to estimate the potential association with study accessibility (open vs restricted access). Results: Analyzed tweets were highly analytic (mean 94.77, SD 9.00), moderate in clout (mean 42.69, SD 19.84), low in tone (mean 20.06, SD 33.91), suggesting negative tone use, and low in authenticity (mean 19.52, SD 24.50). Results suggest that a high use of formal language negatively impacts readership and citations. Analytical language was indirectly associated with readership (β=−0.296, 95% CI −423.57 to −59.95; =.01) and citations (β=−0.524, 95% CI −0.442 to −0.187; <.001). Linguistic clout had a positive association with readership (β=0.260, 95% CI 8.58-186.91; =.03), and tone in tweets had a negative association with readership (β=−0.317, 95% CI −138.52 to −5.39; =.04). Negative language tone was found to increase the impact of research. With respect to linguistic cues and study accessibility, the results also suggest that the number of citations was impacted by readership (=4.11, 95% CI 2.459E-06 to 0.003; =.047) and analytic linguistic cues (=18.77, 95% CI −0.402 to −0.149; <.001) used in the post, but the association of open (mean 3.04, SE 1.062) versus restricted access (mean 1.83, SE 0.716) was not statistically significant (=0.877, 95% CI 0.405-3.266; =.352). Conclusions: This research is the first to explore article accessibility and linguistic cues used in creating posts that share research on social media to determine their influence on research impact, making this study both innovative and unique relative to existing studies in the surgery field. Through language, the medical field can expand its impact and encourage dialogue between scientists and the public, thereby increasing scientific and societal contributions while reducing the negative effects of limited article access.

Twin Prime Editing Enables Rapid Trait Stacking in Crops

Researchers working to advance genome engineering in crops face many challenges, including simultaneously introducing diverse genome edits. Although a major goal of modern crop breeding is to efficiently combine multiple desirable traits by “stacking” the favorable alleles that contribute to those traits in a single crop variety, current strategies are time-consuming and inefficient.

Now, a team led by Caixia Gao, PhD, professor at the Institute of Genetics and Developmental Biology of the Chinese Academy of Sciences, has developed a genome engineering platform that allows multiple trait stacking in crops by combining gene knockout, precise sequence editing, and chromosome engineering within a single framework. The advance is “a twin prime editing-based knockout (TKO) system that installs stop codon clusters (SCCs) for precise translational termination with minimal in-frame mutations.” TKO achieved knockout efficiencies of up to 70.5%, 58.6% and 75.1% in rice, maize, and wheat protoplasts, respectively.

This work was published in Nature Biotechnology in the article, “Multiplexed, precise genome engineering in monocots with twin prime editing systems.

The researchers first developed a precise and efficient gene knockout tool called twin prime editing (twinPE)-mediated gene knockout (TKO), which precisely inserts a small fragment containing a stop codon cluster at the target site. TKO achieves predictable gene disruption through precise installation of stop codons, avoiding in-frame indels caused by insertions or deletions in multiples of three nucleotides, which are often seen in Cas9 systems.

In protoplasts, TKO demonstrated efficient knockout capabilities in monocot crops such as rice, wheat, and maize. In regenerated T0 rice plants, the average efficiency for single gene knockout reached 96.8%.

To eliminate cross-editing between different loci and to achieve precise, safe multiplex gene knockout, the researchers developed 10 orthogonal TKO systems, enabling efficient simultaneous knockout of up to 10 genes. Unlike Cas9-mediated multiplex editing, which can lose effectiveness because in-frame mutations accumulate across multiple targets, the orthogonal TKO systems maintain high knockout efficiency even when multiple genes or homologous gene copies are edited simultaneously.

Building on TKO, the researchers then developed two integrated genome engineering platforms, TRIM1 and TRIM2—forming a unified platform known as TRIM.

TRIM1 combines TKO with prime editing-based sequence modification, enabling simultaneous gene knockout, base substitution, insertion, deletion, duplication, and inversion within a single editing framework. In regenerated T0 rice plants, TRIM1 achieved simultaneous knockout of one gene together with homozygous precise editing of three additional targets with an efficiency of 22.8%.

TRIM2 incorporates a prime editor–Cre recombinase fusion protein and enables kilobase-scale DNA insertion, replacement, deletion, inversion, and chromosomal translocation through recombinase-assisted genome engineering.

Unlike existing genome editing tools that typically perform only a limited number of sequence modifications, TRIM integrates gene knockout, small-scale precise sequence editing, and large-scale chromosome engineering into a single platform. This “all-in-one” platform provides a powerful way to rapidly stack multiple favorable alleles, thus enhancing precision breeding of complex traits in monocot crops.

The post Twin Prime Editing Enables Rapid Trait Stacking in Crops appeared first on GEN – Genetic Engineering and Biotechnology News.

Blood-Based Risk Score Could Enable Earlier Lung Cancer Prevention

Researchers have identified a 14-protein blood signature capable of predicting lung cancer risk more than five years before diagnosis, potentially opening the door to a new era of precision cancer prevention.

The study, published in Cell by investigators at The Francis Crick Institute and University College London (UCL), combines large-scale human population data, mechanistic laboratory studies, and clinical trial analyses to demonstrate that a blood-based inflammatory signature can identify individuals at elevated risk of lung cancer and may pinpoint those most likely to benefit from preventive treatment.

The findings move beyond traditional risk models based on age and smoking history and offer what researchers describe as a potential equivalent of cholesterol testing for lung cancer prevention.

Moving beyond smoking-based risk assessment

Current lung cancer screening programs primarily target older individuals with a history of smoking. While smoking remains the leading risk factor, many lung cancers arise in people who would not qualify for screening under existing criteria, including never-smokers and individuals exposed to environmental pollutants.

The research team sought to develop a biologically informed method of identifying risk by focusing on inflammation, which has emerged as a critical driver of tumor development.

Previous work from the group demonstrated that air pollution can promote lung cancer by triggering inflammatory responses that awaken dormant cells carrying cancer-causing mutations. The new study aimed to determine whether this inflammatory state could be detected in the blood before cancer becomes clinically apparent.

Machine learning identifies a 14-protein signature

Using plasma protein measurements from more than 48,000 participants in the UK Biobank, researchers applied machine learning approaches to identify blood proteins associated with future lung cancer diagnoses.

The algorithm incorporated conventional risk factors such as age, smoking status, prior lung disease, and plasma protein profiles. Analysis revealed a panel of 14 circulating proteins that consistently predicted lung cancer risk within five years of diagnosis.

The signature was validated across eight independent international datasets and remained predictive across diverse populations, including a cohort composed entirely of non-smokers.

Individuals who later developed lung cancer consistently exhibited elevated levels of the signature years before their diagnosis.

“This is a proof of concept that, one day, we could use this signature to offer preventive treatment to people at risk of lung cancer,” said Tej Pandya, clinical PhD student at UCL and visiting scientist at The Francis Crick Institute.

Detecting an inflammatory state before cancer emerges

One of the study’s most significant findings is that the signature appears to reflect a pre-cancerous inflammatory environment rather than the presence of an undetected tumor.

The researchers found evidence that the protein profile originates from changes within the lung microenvironment before malignant transformation occurs.

Further analyses showed that the same signature was elevated in individuals who later developed chronic obstructive pulmonary disease (COPD) or idiopathic pulmonary fibrosis, suggesting it may identify a broader inflammatory state that predisposes individuals to multiple lung diseases.

Studies in mouse models provided additional support for this hypothesis. Exposure to air pollution increased both the protein signature and the abundance of a cellular state known as KAC cells—adaptive cells that emerge during tissue injury but can become malignant when cancer-driving mutations are present.

Mutant cells arising from several distinct lung cell populations converged on this same KAC state during the earliest stages of cancer development.

Linking air pollution, inflammation, and cancer

The findings build on earlier research implicating the inflammatory cytokine interleukin-1 beta (IL-1β) as a critical mediator of pollution-driven lung cancer.

The investigators demonstrated that air pollution exposure increased IL-1β signaling, elevated components of the 14-protein signature, and expanded KAC cell populations.

Blocking IL-1β in mice reduced KAC cell numbers and slowed early tumor formation, providing experimental evidence that inflammatory signaling contributes directly to cancer initiation.

These observations suggest that the blood signature may serve not only as a risk marker but also as a biological indicator of an underlying process that can be therapeutically targeted.

Revisiting a major clinical trial

To determine whether the signature could identify patients most likely to benefit from preventive intervention, the researchers revisited data from the landmark CANTOS trial.

The trial originally evaluated the IL-1β inhibitor canakinumab for cardiovascular disease prevention and unexpectedly reported reduced lung cancer incidence as an exploratory outcome. However, the overall cancer-prevention benefit appeared too modest to justify widespread use of the drug for this purpose.

The new analysis tells a different story.

Researchers examined data from 4,651 CANTOS participants and found that individuals with elevated levels of the 14-protein signature experienced the greatest benefit from canakinumab treatment. In this high-risk subgroup, lung cancer incidence was nearly cut in half.

By restricting treatment to those identified by the biomarker signature, the number needed to treat to prevent one lung cancer case fell to 55, a figure comparable to widely accepted cardiovascular prevention strategies such as statin therapy.

Toward precision cancer prevention

The work represents a shift in how researchers think about cancer prevention.

Rather than treating large populations indiscriminately, the study suggests that molecular biomarkers could identify individuals in a reversible pre-disease state and guide targeted interventions before cancer develops.

“Drugs like statins have transformed the prevention of cardiovascular disease, used to treat individuals with a high low-density lipoprotein (LDL),” said Charlie Swanton, FRCP, PhD, clinical research director at The Francis Crick Institute and professor of cancer at UCL.

“But we don’t yet have an LDL-like marker of risk or a statin for lung cancer.”

Swanton added that identifying an inflammatory state before tumor formation provides a potential “window of opportunity” in which preventive treatment could be most effective.

Implications beyond lung cancer

The investigators note that the inflammatory signature may reflect a broader biological phenomenon associated with aging and chronic disease.

Because the signature was also associated with future COPD and pulmonary fibrosis, it may represent a shared pre-symptomatic inflammatory state that precedes multiple age-related lung disorders.

If validated in prospective studies, the approach could ultimately support routine blood-based risk assessment and targeted prevention strategies not only for lung cancer but potentially for other inflammation-driven diseases.

For now, the findings provide one of the strongest demonstrations yet that cancer risk can be detected years before diagnosis and that those biological signals may be actionable.

The challenge ahead will be determining whether identifying high-risk individuals and intervening early can translate into measurable reductions in lung cancer incidence, a question future prospective prevention trials will seek to answer.

The post Blood-Based Risk Score Could Enable Earlier Lung Cancer Prevention appeared first on Inside Precision Medicine.

Microglial State Shift May Determine Whether Alzheimer’s Disease Pathology Leads to Dementia

Researchers from the VIB-KU Leuven Center for Neuroscience, the UK Dementia Research Institute, and Muna Therapeutics have discovered a biological transition that occurs in Alzheimer’s disease (AD) that may influence whether the accumulation of amyloid-β plaques and tau pathology progresses to dementia. Using human brain tissue from octogenarians with and without dementia as well as cognitively healthy centenarians, the team found that a shift in the behavior of microglia occurs at a critical point between amyloid-driven inflammation and tau-associated neurodegeneration. The research, published in Nature Medicine, point to these microglial transitions as a potential new target for treating the disease.

“This has been an exciting journey with many partners,” said co-senior author Bart De Strooper, MD, PhD, a professor at VIB-KU Leuven Center for Neuroscience, Belgium. “The study, entirely based on human donor material, provides insight into one type of resilience mechanism in the progression of AD to dementia.”

Alzheimer’s disease affects more than 55 million people worldwide, but prior research has not been able to fully explain why some people with AD remain cognitively healthy despite having significant amounts of amyloid plaques and tau tangles in their brains. Current models of AD progression have assumed a linear path from amyloid accumulation to tau pathology, neurodegeneration, and cognitive decline. However, observations of cognitively intact older adults with substantial pathology have indicated there may be other biological factors that influence dementia development.

“AD is not an inevitable outcome of pathology but a dynamic process shaped by how brain cells respond to amyloid-β (Aβ) and tau,” the researchers wrote, adding that “Clinical symptoms may thus arise only when these compensatory mechanisms fail, crossing inflection points that shift the brain from adaptation to degeneration.”

Previous studies using single-cell and spatial transcriptomics had shown that microglia, astrocytes, oligodendrocytes, and neurons undergo stage-specific changes in response to amyloid accumulation. Those findings, combined with observations that some centenarians maintain cognition despite extensive pathology, suggested that immune-cell responses might influence disease outcomes and were the underpinnings of the current research.

To further understand these mechanisms, the investigators examined 24 well-characterized octogenarian brains and 20 brains from cognitively intact centenarians enrolled in the Dutch 100-plus Study. Using spatial transcriptomics, single-nucleus RNA sequencing, and in situ hybridization to tissue from the superior frontal gyrus, the team was able to analyze gene activity at single-cell resolution while preserving the spatial relationships between cells and pathological features in the brain.

Their analysis identified six distinct tissue domains representing a continuum of Alzheimer’s disease progression. Within this “spatial pathological” continuum of AD the researchers found a “key inflection point marked by a shift from Aβ-associated inflammatory changes to tau-associated cellular programs.”

This transition coincided with a significant change in microglial behavior. Early in the disease process, microglia adopted inflammatory states associated with amyloid plaques. Later, they shifted into antigen-presenting states linked to emerging tau pathology. The researchers described these as early and late plaque-induced gene, or PIG, programs.

Data from the study indicated that resilience to disease progression involved different microglial responses in the different groups studied. Octogenarians who accumulated amyloid plaques but remained free of dementia mounted the early inflammatory microglial response but did not progress to the later antigen-presenting state. This compared with cognitively intact centenarians in which the later microglial program was activated, but this activation occurred without the corresponding buildup of tau pathology typically associated with neurodegeneration.

“Resilient individuals showed distinct pathological patterns: octogenarians without dementia lacked late PIGs, whereas centenarians showed late PIG activation that was uncoupled from tau accumulation,” the researchers wrote.

According to the investigators, these findings indicate that resilience to development of dementia in AD is not simply a matter of avoiding pathology, but may depend on how the brain regulates the cellular consequences of AD pathology.

The research also revealed clues about how microglia might be targeted therapeutically. The researchers suggest that preserving beneficial early microglial functions involved in amyloid clearance and synaptic maintenance while preventing chronic antigen-presenting activation associated with tau pathology could help slow disease progression. Potential targets include pathways involving TREM2, CSF1R, and molecules associated with microglial state transitions.

“These findings open new opportunities to target microglial states—especially pathways such as TREM2—and extend resilience rather than simply focusing on plaque removal,” said co-senior author Niels Plath, PhD, chief scientific officer of Muna Therapeutics. “We are excited to continue this journey and understand the causal role of microglial transitions leading to the identification of novel therapeutic approaches to delay or prevent disease progression.”

Continued research will look to determine the causal mechanisms that drive these microglial state transitions and identifying the genetic, immune, or aging-related factors that allow some individuals to remain resilient despite significant Alzheimer’s pathology.

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