genomicspark

[Peter J Park @peter_j_park]

On biorxiv this week: "Integrated map of somatic mosaicism across human tissues in 25 individuals" from the Somatic Mosaicism across Human Tissues Network (https://t.co/xISaapWwl7)

[Eric Topol @EricTopol]

We recently learned that blood monocytes replace brain microglia immune cells beginning around age 50. Today, it's blood T cells infiltrate the brain to promote inflammation and Alzheimer's disease in the mouse model @NatureNeuro https://t.co/i7X9L63W8h

[Nature Methods @naturemethods]

Spatial-ATAC-Hi-C enables profiling chromatin accessibility and spatial organization, as shown in mouse and human brain samples.

https://t.co/SUmtBKnTsI

[Eric Topol @EricTopol]

Futuristic.
An engineered probiotic with an ingested glucose sensor (GIFT) worked in a sense-and-respond way to treat Type 2 diabetes in monkeys! @Nature
https://t.co/prPiZXsDFh

[Anirban Maitra @Aiims1742]

The Human Cancer Models Initiative (HCMI) paper is basically like a TCGA for diverse cancers, including rare cancers, but using organoids.
https://t.co/tnnZNZO1Ae
The profiling data can be queried through this interactive site (HCMI Explorer Suite):
https://t.co/V9Hanpdw7J

[Nature Methods @naturemethods]

SecAct: a framework leveraging transcriptomic data to infer secreted protein signaling activities.

https://t.co/JS2qYABWUq

[antisense. @razoralign]

ENCODE 4: The Encyclopedia of DNA Elements https://t.co/Tcnl7Q4Y5w

[Agingdoc🩺Dr David Barzilai🔔MD PhD MS MBA DipABLM @agingdoc1]

Mitochondrial drivers of stem cell aging and inflammaging

https://t.co/C3H5qXMb4V

[Prof Steve Horvath @prof_horvath]

New in Nature Aging: an open, searchable atlas of how DNA methylation changes with age across 17 human tissues: over 15,000 methylomes.
Three things stood out to me:
1) Aging isn't uniform drift. The methylome shows conserved directional change, rising person-to-person variability, AND rising disorder (entropy), three analysis approaches most studies don't measure together.
2) A cell-adhesion hub, PCDHGA1, recurs across organs, while an NAD+ module stands out as one of the few modifiable nodes among otherwise fragile aging networks. Co-methylation modules were built with WGCNA (per tissue).
3) Best of all, the atlas and summary data are open: explore it yourself: https://t.co/5OnhwxSuBn
Jacques M & Eynon N (2026), Meta-analysis of DNA methylation aging signatures in 17 human tissues. https://t.co/HsEyPtnoDo

[Aging Science News @AgingBiology]

StackAge: an ensemble-based clock for precise quantification of biological age using multi-omics data
https://t.co/GwDGC4vpde

[Agingdoc🩺Dr David Barzilai🔔MD PhD MS MBA DipABLM @agingdoc1]

Mammalian aging involves genome-wide splicing degeneration leading to functional decline

“….we quantified the degree of splicing degeneration. Its level increased with age but was alleviated following calorie restriction or rapamycin treatment, indicating that it can serve as a new molecular hallmark of aging.”

@gladyshev_lab

https://t.co/PzF1OtnFjT

[Ming "Tommy" Tang @tangming2005]

TRUST4: immune repertoire reconstruction from bulk and single-cell RNA-seq data https://t.co/vB9K3RhjSM

[Nature Methods @naturemethods]

HiCFoundation: a foundation model trained on Hi-C data for comprehensive 3D genome and epigenomics analysis.

https://t.co/PFqHNWukVZ

[오창명 @changmyung1981]

MASLD/MASH is not just “fatty liver.”
It is a systemic metabolic disease where adipose tissue dysfunction, insulin resistance, glucagon imbalance, gut–liver signaling, and mitochondrial stress converge into inflammation and fibrosis.
Key mechanisms:

🧈 Adipose tissue failure → hepatic lipid overload
Dysfunctional adipose tissue loses its lipid-buffering capacity, increasing NEFA and glycerol delivery to the liver. This fuels steatosis, lipotoxicity, insulin resistance, and inflammatory signaling.

🔥 Metabolic stress → metaflammation
Excess NEFAs, DAGs, ceramides, glucose, fructose, and amino acids overload hepatocyte metabolism. Mitochondrial dysfunction, ER stress, ROS, apoptosis, and DAMP release activate Kupffer cells and recruited macrophages.
🧬 Fibrosis is the key outcome driver
Chronic hepatocyte injury and macrophage cytokines activate hepatic stellate cells, leading to extracellular matrix deposition and progressive fibrosis. Fibrosis stage—not steatosis alone—is the major predictor of clinical outcome.

⚖️ Hormonal imbalance matters
Hyperinsulinaemia promotes DNL and may directly activate HSC profibrotic signaling. Hyperglucagonaemia and selective glucagon resistance disturb glucose, lipid, and amino acid metabolism. Impaired incretin signaling further worsens metabolic flexibility.
🦠 Gut–liver–adipose axis amplifies injury
Dysbiosis, increased permeability, LPS, altered bile acids, and microbial metabolites activate TLR/NLRP3/NF-κB pathways, reinforcing hepatic inflammation and fibrogenesis.

💊 Therapeutic map
Mechanism-based targets include:
• THR-β agonists: reduce lipotoxicity and fibrosis
• GLP-1/GIP/glucagon-based agents: reduce weight, steatosis, and metabolic stress
• PPAR agonists: improve adipose insulin sensitivity and inflammatory/fibrotic signaling
• FGF19/FGF21 analogues: coordinate liver–adipose–CNS metabolic remodeling
Take-home: MASLD progression is driven by multi-organ metabolic crosstalk. Precision therapy will likely require matching patients to dominant disease drivers: adipose insulin resistance, hyperinsulinaemia, glucagon dysregulation, mitochondrial stress, gut-derived inflammation, or fibrogenic HSC activation.

[오창명 @changmyung1981]

🧬 Can microplastics rewire liver biology at single-cell resolution?

A new Science Advances study used spatial transcriptomics, Raman spectroscopy, and O-PTIR imaging to map how polyethylene (PE) microplastics remodel the liver microenvironment.

Key findings:

🔹 Chronic PE exposure induced hepatotoxicity and worsened MASH-like pathology in mice.

🔹 Bulk RNA-seq revealed major disruption of lipid metabolism pathways and activation of nuclear receptor programs, including increased Ppara expression.

🔹 Xenium spatial transcriptomics uncovered inflammatory hotspots with reduced cellular diversity, loss of Kupffer-cell abundance, and expansion of midlobular hepatocyte populations.

🔹 Spatial analysis identified Anxa2 as a PE-responsive gene specifically enriched in high-inflammation pericentral hepatocytes.

🔹 Mechanistically, PPARα directly bound Anxa2 enhancer/promoter regions, and pharmacologic activation of PPARα increased Anxa2 expression, while PPARα inhibition or knockdown abolished PE-induced Anxa2 upregulation.

🔹 Raman and O-PTIR imaging confirmed polyethylene particle localization within liver tissue and demonstrated proximity to Kupffer-cell–rich inflammatory niches.

Why it matters:

Most microplastic liver studies have focused on polystyrene. This work instead investigates polyethylene—the most abundant microplastic detected in humans—and provides one of the first spatially resolved maps linking environmental microplastic exposure to hepatic transcriptional remodeling.

The emerging model:

PE accumulation
→ inflammatory niche formation
→ PPARα activation
→ ANXA2 induction
→ hepatocyte stress adaptation / tissue remodeling
Beyond toxicology, the study highlights how environmental exposures reshape tissue architecture and cell-cell communication in metabolic disease.
Paper:
Jung et al. Science Advances (2026)
“Spatial transcriptome mapping identifies Ppara-Anxa2 cross-talk in microplastic-induced hepatotoxicity”
#Microplastics #SpatialTranscriptomics #LiverDisease #MASH #NAFLD #PPARalpha #ANXA2 #SingleCell #ScienceAdvances #EnvironmentalHealth

[오창명 @changmyung1981]

Aging-associated inflammation ("inflammaging") is a major driver of tissue dysfunction, frailty, and chronic disease. But what if a previously overlooked nucleic acid structure is helping fuel it?

A new study in Nature Aging identifies a mechanistic link between R-loops, cellular senescence, and inflammaging.

Key findings:

🔹 Senescent cells accumulate cytoplasmic R-loops derived from the nucleus.

🔹 These R-loops localize to cytoplasmic chromatin fragments (CCFs) and activate the cGAS–STING innate immune pathway, driving the senescence-associated secretory phenotype (SASP).

🔹 The study identifies a previously unrecognized transport mechanism:

Nuclear R-loop → DDX1 → XPO1 export → CCF localization → cGAS activation → SASP → inflammaging

DDX1 acts as an R-loop-binding export adaptor, while XPO1 mediates nuclear export.

🔹 Cytoplasmic R-loops were enriched for alpha-satellite repeat sequences, linking centromeric instability and repetitive-element biology to age-associated inflammation.

🔹 Pharmacologic inhibition of XPO1 using KPT-330 (selinexor):

• Reduced cytoplasmic R-loops
• Suppressed cGAS-STING signaling
• Lowered SASP factors
• Reduced liver inflammation and fibrosis
• Decreased circulating TNF and IL-6
• Improved body composition
• Extended lifespan in aged mice
Conceptually, this work introduces R-loop trafficking as a new aging mechanism.
Rather than acting solely as nuclear genome-instability intermediates, R-loops can become exported inflammatory signals that couple DNA damage responses to innate immunity.
The study positions the:
DDX1–XPO1–R-loop–cGAS axis
as a potentially druggable pathway for suppressing inflammaging and age-related functional decline.
Nature Aging (2026)
Hao et al.
Nuclear export of R-loop by the DDX1 and XPO1 complex promotes senescence-associated secretory phenotype and inflammaging

#Aging #Inflammaging #Senescence #SASP #RLoops #cGAS #STING #DDX1 #XPO1 #Selinexor #Longevity #NatureAging

[Henning Willers, MD, FASTRO @HenningWillers]

🔥Genomics of aging in @Nature !!🔥

https://t.co/gNhZiRo5GP

[Daisy Ding @daisyding_]

To solve aging, we first need to measure it. Excited to share our study in @NatureMedicine! Different cell types age at different rates within our body. From a tube of blood, we track aging across 40+ cell types, from immune cells to neurons, revealing signatures that forecast disease risk and resilience. @wysscoray 🧵1/9

[Agingdoc🩺Dr David Barzilai🔔MD PhD MS MBA DipABLM @agingdoc1]

Review article: Improving mitochondrial function: Current therapeutic perspectives in neurodegenerative diseases

https://t.co/7slH1tA0nX

[Anirban Maitra @Aiims1742]

Very nice resource paper in @Cancer_Cell that enables correlation of cell type specific expression data with survival in #PancreaticCancer.
https://t.co/9qvooPYaup
Takes TCGA type datasets based on bulk RNA to the next level. Interactive web tool (ctPANDA) allows facile queries.