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Superpowers of Hibernating Animals Could Lie Hidden in Human DNA

by Dr. Michael Lee – Health Editor
written by Dr. Michael Lee – Health Editor

Hibernation Secrets Could Reverse Disease

Unlocking dormant genetic abilities may hold keys to treating neurodegeneration and diabetes.

Researchers are exploring the remarkable resilience of hibernating animals, like ground squirrels and bats, hoping to uncover genetic mechanisms that could combat human diseases such as diabetes and neurodegenerative conditions.

Gene Regulation Sparks New Hope

A critical gene cluster, known as the FTO locus, plays a significant role in hibernation, allowing animals to efficiently manage weight gain and fat utilization. Intriguingly, humans possess these same genes, which are strongly linked to obesity. However, hibernators appear to utilize the FTO locus and its surrounding DNA in novel ways.

University of Utah scientists have pinpointed specific DNA regions in hibernators that regulate the activity of nearby genes, effectively fine-tuning their metabolic functions. These regulatory regions, distinct from genes themselves, act like an orchestra conductor, influencing the expression of numerous genes.

“This means that mutating a single hibernator-specific region has wide-ranging effects extending far beyond the FTO locus. When you knock out one of these elements — this one tiny, seemingly insignificant DNA region — the activity of hundreds of genes changes. It’s pretty amazing.”

—Dr. Susan Steinwand, Lead Researcher

When these unique regulatory regions were altered in mice, the animals exhibited significant shifts in weight, metabolism, and their ability to recover body temperature after simulated hibernation. This suggests that subtle genetic modifications can unlock profound metabolic flexibility.

A Blueprint for Human Health

The groundbreaking research, published in two separate studies in the journal *Science*, suggests that humans may already possess the genetic blueprint for hibernation-like adaptations. The challenge lies in identifying and controlling the specific “switches” that govern these dormant abilities.

Studying the evolution of hibernators like hedgehogs, bats, ground squirrels, and lemurs could reveal the secrets of their remarkable resilience. Image credit: Chrissy Richards.

Scientists identified these crucial genetic regions by looking for DNA sequences that have evolved rapidly in hibernating mammals compared to other species. They also analyzed genes that act as central coordinators of metabolic changes, particularly those triggered by fasting in mice, which mimic some aspects of hibernation.

By understanding how hibernators manage to avoid muscle atrophy, stay healthy through dramatic weight fluctuations, and achieve remarkable longevity, researchers believe they can develop strategies to confer similar resilience to humans. This could involve targeting specific genetic control mechanisms to combat age-related diseases and metabolic disorders.

“If we could regulate our genes a bit more like hibernators, maybe we could overcome type 2 diabetes the same way that a hibernator returns from hibernation back to a normal metabolic state,” stated Dr. Elliott Ferris, a lead author on one of the studies.

The potential implications are vast, offering a novel pathway to enhance human health by learning from the naturally preserved genetic code that grants hibernators their extraordinary metabolic powers.

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Health

CoVerage outperforms rivals in early detection of COVID mutations

by Dr. Michael Lee – Health Editor
written by Dr. Michael Lee – Health Editor

AI Platform Spots COVID Variants Weeks Early

New System Offers Crucial Head Start Against Future Pandemics

A groundbreaking AI-powered platform, named CoVerage, promises to revolutionize pandemic preparedness by identifying and characterizing concerning SARS-CoV-2 variants significantly in advance of official recognition. This innovation could provide health officials with a vital window of opportunity to respond to emerging threats.

Predicting the Next Threat

Researchers at the Helmholtz Centre for Infection Research and the German Center for Infection Research have unveiled CoVerage, a web-based system designed to meticulously analyze SARS-CoV-2 genomic sequence data. The platform leverages a sophisticated array of statistical and bioinformatic tools to pinpoint potentially more transmissible or immune-evasive viral strains.

CoVerage aims to outperform existing methods in the early detection of COVID-19 mutations.

The system continuously sifts through millions of sequences from the GISAID database, identifying strains with significantly higher mutation rates in the spike protein compared to the monthly average. These strains are then visualized on heatmaps, illustrating the timing and geographical hotspots of significant viral changes.

Outpacing Variants of Concern

In rigorous testing, CoVerage demonstrated remarkable efficacy, correctly identifying known SARS-CoV-2 Variants of Concern (VOCs), including Omicron, an average of 79 days before their official designation by the World Health Organization (WHO). This predictive power stems from a unique method that scores amino acid changes based on their potential for immune escape, validated against experimental neutralization data.

The platform’s heatmaps clearly delineate variants by their level of concern, progressing from those merely under monitoring to Variants of Interest (VOIs) and, most prominently, VOCs. This tiered visualization aids in rapid risk assessment and strategic response planning.

A New Era of Genomic Surveillance

CoVerage introduces three novel analytical methods. One identifies potential VOIs with increased transmissibility. Another tracks amino acid changes in the spike protein to detect those conferring a selective advantage. The third method quantifies antigenic alterations using a unidirectional immune escape matrix, offering a comprehensive assessment of a variant’s evolutionary trajectory.

The system achieves a notable 88% accuracy in identifying WHO-designated VOIs and VOCs, with a 79% precision and 72% recall rate. Crucially, no VOCs were missed, with most under-detected lineages being of lower public health significance.

The effectiveness of CoVerage is directly linked to the quality and extent of global genomic surveillance programs. Reduced surveillance in any country could diminish its predictive capabilities. While platforms like NextStrain and CoVariants also monitor SARS-CoV-2 mutations, CoVerage stands out by continuously scoring all circulating variants for potential advantage and antigenic change in real-time, while also offering benchmarking against experimental antigenicity data.

This integrated approach, combining GISAID data with links to external resources and providing reproducible, open-access analytics, establishes CoVerage as an indispensable tool for robust viral genomic surveillance. The platform’s development marks a significant stride in proactively managing infectious disease threats.

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Health

AI Reveals Six New Alzheimer’s Risk Genes Using Machine Learning

by Dr. Michael Lee – Health Editor
written by Dr. Michael Lee – Health Editor

Here’s a breakdown of the provided text, focusing on the key findings and their implications:

Key Findings Regarding AD Status Prediction:

Comparable Accuracy: All machine learning (ML) models predicted Alzheimer’s Disease (AD) status with similar accuracy.
 Model Correlations:
Gradient Boosting Machine (GBM) and Neural Networks (NN) were strongly correlated.
 GBM and Multi-Dimensional Reduction (MDR) with 1-day data were also strongly correlated. Polygenic Risk Score (PRS) was weakly correlated with NNs but strongly linked to GBMs.
 Predicting Differentiating Cases: GBM and PRS were better at identifying cases that were distinct from controls.
Reproducibility: Predictions were validated through random data splits, indicating high reproducibility.
 Sex Representation:
Females were overrepresented among predicted cases, aligning with the majority female dataset.
 GBM was an exception, showing similar proportions of males and females in both cases and controls.
Model Stability: predictions remained consistent across different cohorts and repeated random splits, suggesting robustness and lack of overfitting.

Comparison with Genome-Wide Association Studies (GWAS):

Overlap: Out of 130 previously reported AD-associated genes (86 loci),ML algorithms identified 19.
APOE Identification: All ML models identified the APOE gene. Two models identified seven loci.
 Impact of APOE Exclusion: Removing the APOE region from the training data led to the identification of more known AD risk genes, but with reduced accuracy.
Training Data replication: When using only the current data, ML models identified every SNP detected by GWAS in the training set.
 ML-Identified SNP Location & Function:
High-priority SNPs identified by ML were more concentrated in microglial and astrocytic regions.
 These SNPs were involved in AD-related pathways, including:
Regulation of beta-amyloid protein.
 Changes in protein concentrations like Ly6h (linked to neurotransmission and AD severity). glycosylation abnormalities related to AD tau protein processing.
 SNP Importance Ranking Differences: The methods ML models use to rank SNP importance (e.g., SHAP values, permutation p-values, network weights) differ from conventional GWAS meaning, highlighting fundamental differences in feature selection.

Importance of the Study:

ML Efficacy: The study demonstrates that ML can predict AD-linked genetic variants as effectively as traditional genome-wide methods, especially with large datasets. GWAS Heterogeneity: The moderate accuracy of GWAS meta-analyses might be due to the heterogeneity of included studies.More homogeneous samples tend to yield higher odds ratios.
Context-Specific Effects: Some SNPs identified by ML might only show effects in specific cohorts or conditions, which might not be detectable in large, heterogeneous external datasets. This explains why not all ML-identified SNPs could be replicated externally.
 Novel Findings: Despite replication challenges, the novel SNPs identified by ML affected biologically plausible pathways. Further research is needed to understand how to best identify vital SNPs across different methods.

conclusions:

ML’s Predictive Power: ML methods can achieve predictive performance comparable to classical genetic epidemiology approaches.
 Finding of New Loci: ML can identify new AD-associated loci that traditional GWAS might miss.
reproducibility and Bias Minimization: The study’s reproducible approach helps minimize bias.
 Promise and Limitations: The work highlights the potential of ML in AD genetics but also emphasizes the need for careful interpretation, replication, and methodological refinement.
* Future Directions: This study paves the way for developing and validating ML models to complement conventional methods in AD genetic research.

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Health

Genetic Tests Predict Obesity Risk, Raising Fairness Concerns

by Dr. Michael Lee – Health Editor
written by Dr. Michael Lee – Health Editor

Here’s a breakdown of the provided text, focusing on the key findings and conclusions regarding Polygenic Risk Scores (PGS) for BMI and obesity:

Key Findings:

PGS Performance Varies by Ancestry:
 PGS performance was generally intermediate between ancestry-matched and multi-ancestry PGSs, except for East Asian-like and European-like ancestry.
The PGS showed the highest prediction accuracy (explained variance of 17.6%) in participants of European (EUR) ancestry from the UK Biobank (UKBB).
 Performance was significantly lower in populations with African-like ancestry (6.3% explained variance), African American populations (5.1%), and the GPC-UGR population from rural southwestern Uganda (2.2%).

Demographic Factors within EUR Ancestry (UKBB):
 Explained variance was slightly higher in males than females.
Explained variance was higher in younger participants compared to older age groups.
 The PGS was better at differentiating between participants with and without obesity within the EUR population.

PGS and Obesity Severity/Progression:
 The Area Under the Receiver Operating Characteristic Curve (AUC) for the PGS increased with the severity of obesity. In children,a higher PGS (≥10th percentile) was associated with faster BMI increase compared to those with a lower PGS.
 The added value of PGS for predicting BMI was greatest at a vrey young age (up to age five), before measured BMI becomes a strong predictor. In older children,measured BMI is more informative.
Children’s higher mean PGS is a predictor of future obesity risk.
 PGS in the first few years after birth was a more reliable predictor of BMI in early adulthood.
PGS was more predictive of BMI than other body composition traits like body fat percentage or waist-to-hip ratio.

PGS and Weight Management Interventions:
Individuals with a higher PGS showed greater weight loss in the first year of an intervention (ILI) but were also more likely to regain weight after the first year, highlighting the need for ongoing support.

Crucial Caveats and Future Potential:
A higher genetic risk (PGS) does not mean obesity is inevitable.
 Individuals with higher PGS may be more responsive to environmental and lifestyle changes. Preventative strategies can be effective.
 Crucially, implementation of PGS-based tools must account for population differences to avoid worsening health inequities, especially for underrepresented groups like those of African ancestry.
Future potential exists for PGS to guide lifestyle interventions and new weight loss drug therapies.

Conclusions:

BMI PGSs have potential as a tool for predicting adult obesity throughout life, especially in early life.
This tool can identify individuals at high risk of obesity, allowing for timely and effective preventive strategies.
 Though,the use of PGS in clinical or public health practice requires careful attention to population differences and ethical considerations of genetic risk prediction.

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Health

New approach doubles chemotherapy effectiveness by targeting cancer cell memory

by Dr. Michael Lee – Health Editor
written by Dr. Michael Lee – Health Editor

Scientists Disrupt Cancer’s Defense Mechanism, Boosting Chemo Efficacy

Novel Approach Targets Cellular Adaptability, Not Just Tumors

A groundbreaking strategy from Northwestern University engineers has demonstrated the potential to significantly enhance chemotherapy’s power by preventing cancer cells from adapting and resisting treatment, a key factor in treatment failure.

Unlocking Chemo’s Full Potential

Researchers have discovered a way to neutralize cancer’s remarkable ability to evolve and withstand therapies. This new method focuses on the internal scaffolding of cancer cells, known as chromatin, which governs gene expression and cellular responses to stress. By subtly altering chromatin’s physical structure, scientists believe they can disarm cancer’s adaptive superpowers, making existing chemotherapy drugs far more effective.

This innovative approach achieved near-complete eradication of cancer in laboratory cell cultures. In animal trials involving human ovarian cancer models, the combination therapy dramatically improved the outcomes compared to chemotherapy alone.

“Cancer cells are great adapters. They can adapt to almost anything that’s thrown at them… We did not set out to directly kill cancer cells. We wanted to take away their superpower—removing their inherent abilities to adapt, to change and to evade.”

—Vadim Backman, Lead Study Author and Professor at Northwestern University

The findings, published in the Proceedings of the National Academy of Sciences, suggest that rather than developing new, potent drugs, the focus can shift to optimizing the delivery and efficacy of current treatments by inhibiting cancer’s resilience. This could lead to less toxic treatment regimens and improved patient quality of life.

Chromatin: The Key to Cancer’s Tenacity

Cancer’s persistent survival, even when faced with aggressive treatments, has long puzzled scientists. While genetic mutations play a role, they don’t fully explain the rapid adaptive responses observed. Researchers led by **Vadim Backman** identified the intricate organization of DNA and proteins within the cell nucleus, known as chromatin, as the critical factor. This complex structure not only controls gene activity but also acts as a cellular memory system, enabling cells to adapt to changing environments and stresses.

When this chromatin architecture becomes disordered, cancer cells gain increased “plasticity,” allowing them to quickly develop resistance to therapies like chemotherapy. The precise 3D arrangement of chromatin stores “transcriptional memory,” guiding cellular function and adaptation.

Reprogramming Cell Memory to Enhance Treatment

The Northwestern team developed a computational model to analyze how chromatin packing influences cancer cell survival. Their research pinpointed celecoxib, an existing anti-inflammatory medication, as a compound capable of modulating chromatin structure and reducing cellular plasticity. By combining celecoxib with standard chemotherapy agents, the researchers observed a significant increase in cancer cell death.

In preclinical trials, the combination of paclitaxel (a common chemotherapy) with celecoxib in mice with ovarian cancer significantly slowed tumor growth compared to paclitaxel alone. “It doubled the efficacy,” **Backman** stated, highlighting the substantial benefit of this combined approach.

The ability to make chemotherapy more effective could also allow for lower, more tolerable doses, potentially alleviating the severe side effects that often lead patients to discontinue treatment. For instance, in 2023, the American Cancer Society reported that over 1.9 million new cancer cases were diagnosed in the U.S. alone, underscoring the immense need for more effective and less burdensome treatments (American Cancer Society).

Future Applications Beyond Cancer

While initially focused on cancer, **Backman** believes that modulating chromatin conformation could offer therapeutic benefits for a range of complex diseases, including neurodegenerative conditions and heart disease. Many debilitating illnesses may stem from cells losing their correct functional “memories,” leading to loss of function or disease development. Restoring these cellular memories through chromatin reprogramming could offer a pathway to recovery.

“In many diseases, cells forget what they should be doing,” **Backman** explained. “Many impactful diseases of the 21st century are, to a large extent, related to cell memory.” He likens the computational processes within each cell’s chromatin to an early computer, emphasizing its capacity for storing and potentially altering cellular memory. This discovery could unlock a new era of “source code” manipulation for treating a variety of human ailments.

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Health

New study identifies four distinct autism subtypes with unique genetic signatures

by Dr. Michael Lee – Health Editor
written by Dr. Michael Lee – Health Editor

Autism Subtypes Unveiled Through Data Analysis

Largest autism study identifies distinct groups, linking traits to genetic factors for tailored support.

A new study is changing how we understand autism by identifying distinct subtypes. By linking shared traits to specific genetic variations, scientists are paving the way for personalized support and earlier interventions for individuals with autism.

Study Highlights Four Autism Subtypes

Researchers at the Flatiron Institute’s Center for Computational Biology (CCB) and their collaborators utilized data from SPARK, the largest autism study to date, to analyze phenotypic and genotypic data from over 5,000 participants with autism, ages 4–18. The study, published in Nature Genetics, successfully identified four distinct groups based on shared traits and linked them to biological processes associated with specific genetic variants.

According to the CDC, about 1 in 36 children has been identified with autism spectrum disorder (ASD) (CDC, 2023).

“A clinically grounded, data-driven subtyping of autism would really help kids get the support they need early on,” says study co-lead author Natalie Sauerwald, a CCB associate research scientist. She added that knowing a person’s subtype can help caregivers access appropriate resources, especially regarding co-occurring conditions like ADHD or anxiety.

Person-Centered Approach Key to Discovery

The study employed a “person-centered” approach, focusing on the full spectrum of traits an individual might exhibit rather than a single trait like IQ. Olga Troyanskaya, senior research scientist and deputy director for genomics at CCB, noted that this strategy was crucial for discovering clinically relevant autism classes and deciphering the underlying biology.

“Our study takes a ‘person-centered’ approach, in which we focus on the full spectrum of traits that an individual might exhibit rather than just one trait, like IQ. This approach was key to our discovery of these clinically relevant autism classes and to deciphering the biology that underlies them.”

—Olga Troyanskaya, senior research scientist and deputy director for genomics at CCB

Kelsey Martin, executive vice president of autism and neuroscience at the Simons Foundation, emphasized the power of leveraging machine learning approaches to analyze the extensive phenotypic and genotypic data available in SPARK.

SPARK: A Landmark Autism Research Initiative

SPARK, supported by the Simons Foundation Autism Research Initiative (SFARI), aims to improve the lives of people with autism by identifying its causes and supporting research that informs more effective therapies, treatments, services, and support. To date, SPARK has engaged over 150,000 individuals with autism and more than 200,000 of their family members.

“I think [SPARK] is the only cohort that has this combination of extensive phenotypic data as well as genetic data,” said Sauerwald.

Identifying Distinct Classes

The researchers classified SPARK participants into four main groups:

  • Social and Behavioral Challenges: Individuals in this group, around 37% of participants, have co-occurring traits such as ADHD, anxiety disorders, depression, and mood dysregulation, alongside restricted or repetitive behaviors and communication challenges. They tend to hit developmental milestones at the same rate as their neurotypical peers.
  • Mixed ASD with Developmental Delay: Representing approximately 19% of the participants, this group experiences developmental delays but typically does not have the same issues with anxiety, depression, mood dysregulation, or disruptive behaviors.
  • Moderate Challenges: This group, comprising roughly 34% of participants, shows challenges similar to the Social and Behavioral group but to a lesser degree and without developmental delays.
  • Broadly Affected: The smallest group, accounting for about 10% of participants, is characterized by widespread challenges, including restricted and repetitive behaviors, social communication difficulties, developmental delays, mood dysregulation, anxiety, and depression.

Troyanskaya notes that these classes aren’t definitive but a significant starting point, stating, “This doesn’t mean that there’s necessarily only four classes. I think what this demonstrates is that there are at least four classes. But having the four, which are clinically and biologically relevant, is significant.”

Genetic Pathways Unveiled

Studying the genetics within each class revealed that genetic variants affected biological processes in very distinct ways, with little overlap in the impacted pathways between the classes. Each autism subtype had its own biological signature.

“There was little to no overlap in the impacted pathways between the classes,” says Litman. “And what was even more interesting is that while the impacted pathways – things like neuronal action potentials or chromatin organization – were all previously implicated in autism, each one was largely associated with a different class.”

The team also discovered that the timing of gene activation differed by class. Impacted genes in the Social and Behavioral Challenges class were mostly active after birth, while those in the ASD with Developmental Delays class were primarily active prenatally.

Future Research Directions

Researchers aim to incorporate more data types, including the non-coding portion of the genome, to further refine their understanding of autism subtypes. Sauerwald notes, “The more data, the more discovery. We know there’s a lot of contribution from the non-coding genome in autism, but we haven’t been able to study it yet in the context of these classes. So a big next step is going to be adding in this other 98 percent.”

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