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December 23, 2024

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This Week's Headlines

1) HOT TAKE: The week of Sequence-mas

2) Our skin has it's own distinct immune system

3) The epigenome isn't just some biological afterthought

4) Is the genetic material protein or DNA? A blender decided.

5) Weekly Reading List


The week of Sequence-mas

'Twas the week of Sequence-mas, when all through the industry,
Not a creature was stirring, not even a BALB/c;
The instruments were all running with the utmost of care,
Since we all know Illumina customer service won’t be there;

With fragments nestled all snug in the flowcells,
Visions of nucleotides danced in their nanowells;
And the techs in their labcoats, their eyes all bloodshot,
Staring at clusters and Q-scores and the intensity plot.

When out of the lab arose such a clatter,
I sprang from my desk to see what was the matter.
Away to the bench I flew in a dash,
Hoping no sequencers had been lost in a crash.

The LEDs were casting the eeriest of glows,
Giving a green luster to all of the shadows.
When what to my wondering eyes should appear,
But a flashing red light—oh no! Not this year!

With a silent prayer and an acknowledgement so quick,
I crossed my fingers, hoping it would do the trick.
“You won’t win this time,” I yelled with a grin!
The system responded, and hummed with a din. 

As cycles repeated, I smiled with glee,
For the flowcell was flawless, as perfect as could be.
No clogging, no bubbles, no failures this run,
The sequencing concerto, a symphony had spun.

More rapid than reagents, the analysis came,
As I whistled and muttered and called them by name:

"Now FASTQ! Now BAM! Now, VCF and Assembler!
On MiniMap! On PLINK! On, genome browser!
To the end of the reads! To the Telomere wall!
Now map away! Align away! Process it all!"

The reads streamed in, all bright and clean,
Each passing base with a glittering sheen.
And then, in a twinkling, I heard from the cloud,
The results had been processed—I laughed out loud.

As I uploaded the files and refreshed with great care,
The final data appeared with metrics to spare.
The variants were called, the sequences aligned,
The output so glorious, it blew my mind.

Exomes and genomes, bases complete,
A holiday miracle—the dataset was sweet.
With a sigh of relief, I strode out of sight,
"Happy Sequence-mas to all, and to all, a good night!"

###

We'll get back to the goss next week.

I hope you all have a happy holiday!


Our immune system plays a critical role in recognizing, fighting, and preventing infections, but now we’ve discovered that our skin has its own!

Our skin and mucosa are our first line of defense against infection with deadly pathogens.

But these surfaces and membranes aren’t sterile environments!

They’re teeming with biological activity and because they provide this “barrier” function, they’re constantly fending off infection from pathogenic microbiota.

And we call this a barrier, because that’s exactly what it is!

Our mucosa are like a moat around a castle that slows down any invaders (or drowns them if they can’t swim!)

And our skin is like the castle wall, helping to physically prevent big and small things from entering our bodies.

But that’s not where our skins’ defenses stop!

We’ve known for some time that the immune system is active in the skin and that it plays a vital role in managing the communities of microbes that live in and around our bodies.

This is important, because not all of the microbiota that we encounter are bad!

So our immune system has to play this tricky dance of letting the good guys in and keeping the bad guys out.

We now know that it does this using its own localized immune regulatory system!

In this week’s paper, researchers showed in mice that the skin has its own localized immune system that acts (mostly) independently.

In the figure above they a) topically associated (paint brush) a commensal (good) bacteria, S. epidermis with wild-type mice (WT), Bcl6Flox/cd4cre mice that can’t make B cells (make antibodies), µMT mice that can’t make T Helper cells (stimulate b cells to make antibodies), and Ltα-/- mice that lack secondary lymph nodes. Mice were then injected with S. epidermis 45 days later to see their immune response (a,b,c graphs). WT and mice lacking secondary lymphnodes mounted a strong response while mice who couldn’t make antibodies did not! d) is a control showing IgG production was the result of TA and not infusion e) shows representative bacterial growth from the serum of infused mice and f) is an overview of how the skin immune system works!

In this scheme, Langerhan’s cells (LCs) see antigens that collect in the hair follicle. This activates t-helper cells (Treg to TFH) that form complexes with b cells to form a germinal center like structure (where b cells grow up to make and secrete antibodies). These antibodies are the secreted into the follicle to protect the skin and into systemic distribution to the rest of the body.

So, the skin isn’t just some simple barrier!

It is a major part of our lymphatic system helping to make, distribute, and protect the entire body against infection!

In a second paper, the team modified S. epidermidis to display a piece of the tetanus toxin to the skin’s immune system and showed that this could be used to vaccinate mice against tetanus infection.

The implication here (if it all works the same in humans) is pretty mind blowing because it means that needle free vaccines could be closer than many of us ever imagined!

###

Gribonika I, et al. 2024. Skin autonomous antibody production regulates host-microbiota interactions. Nature. DOI: 10.1038/s41586-024-08376-y


The Epigenome: What it can tell us and why we should care to look at it thoughtfully.

Epigenetics is the study of modifications that aren’t coded directly in the sequence of our DNA.

These modifications can include methylation of cytosine DNA bases (they’re still cytosines, though) and modifications to the histones that coil DNA up into chromosomes.

The major effect of these changes is that they result in the opening or closing of our DNA for reading by the cellular machinery that converts this code into proteins.

The epigenome differs from the genome in that these modifications can change frequently and are influenced by a variety of external environmental factors and cellular microenvironmental factors.

Some epigenetic modifications can even be inherited from your parents!

However, it’s early days.

And it’s safe to say that we have A LOT to learn about the epigenome.

There are still many questions about whether epigenetic modifications are drivers (causes) or passengers (effects).

But we do have a lot of evidence that epigenetic changes are important in key areas of human health and disease:

Cancer - Mutations are frequently observed in DNA and histone modifying enzymes, histones themselves, and other genes involved in changing the 3D structure of the genome. Under certain circumstances, it also seems that epigenetic changes can be major drivers of cancer progression in the absence of other obvious changes to DNA. All of these combined serve to turn on tumor promoting genes (oncogenes) and turn off tumor suppressors, ultimately leading to cancer!

Aging - Often referred to as the epigenetic clock, it has been observed that the amount of methylation in the genome changes as we age! These changes have been correlated with cellular dysfunction (loss of control, cell death, etc) and are exacerbated by obesity and low levels of physical activity.

Rare Diseases - Mutations in epigenetic regulatory genes can cause a number of rare diseases. These are often characterized as traditional single gene diseases, but they underscore how important it is to have a properly functioning epigenome. There is also quite a bit of research on the role of epigenetics in rare complex diseases like Schizophrenia and Autism Spectrum Disorder where it has been notoriously hard to pin down major genetic causes.

Disease Risk - Exposure to environmental factors such as drugs, chemicals, infections, or other stressors in the early stages of development or early years of life can cause lasting epigenetic changes that may result in disease later in life. These can include diabetes, obesity, cancer and neurological disorders. But one important new area of research is how behavioral stresses in adolescence can contribute to epigenetic changes that lead to the development of psychiatric disorders.

That said, there’s no doubt we should be excited about the future of epigenetics and all of the secrets we still have to learn from the epigenome!


Deoxyribonucleic acid (DNA) is the genetic material, but early on, most of science thought the genetic material was protein. That changed in 1952.

Ok, it didn’t change overnight, because scientists are pretty stubborn.

It actually wasn’t accepted until the 60’s, but, in 1951, Alfred Hershey and Martha Chase began a series of experiments that conclusively showed that DNA was the genetic material.

There was already good evidence that the genetic material wasn’t protein, but early molecular biologists couldn’t wrap their heads around how 4 bases of DNA could code for all of the diversity we see, especially when proteins are made up of long chains of 20 amino acids.

Surely, since 20 is greater than 4, you get more diversity with 20!

So the hypothesis was that DNA must be the scaffold that holds everything together.

Of course, we know now that’s absolutely not true, and it’s basically the opposite - DNA is the genetic material and protein serves a support function.

The work that synthesized all of these ideas together was completed by Hershey and Chase in what are now referred to as the Hershey-Chase Experiments.

They worked with bacterial viruses called bacteriophages.

These are very simple viruses that consist of a protein shell surrounding the DNA of the virus.

But before the 1950’s, no one knew what phages looked like or how they infected bacteria.

In 1951, Thomas F. Anderson, a pioneer of electron microscopy, showed that phages are like little moon landers and attach themselves to the outside of the bacteria but do not enter them.

This created a bit of a conundrum, how can protein be the genetic material if none of the phage protein gets inside?

Generating the figure ainvolved Hershey-Chase, a Waring blender and phages grown in the presence of radioactive food items.

Luckily, proteins do not contain Phosphorus (P) and DNA does not contain Sulfur (S), so radioactive P-32 and S-35 could be used to label each component of the bacteriophage individually.

Radioactive phages were then combined with bacteria and stuck in a blender.

Hershey and Chase discovered that this technique didn’t kill the bacteria, but it was perfect for shaking the empty phage husks from the bacteria.

This is documented in the figure which shows 90% viability of the infected bacteria but that 80% of the protein (S35) remained external while only 30% of the DNA (P32) does after blending.

This result supported the electron microscopy observation that the phages are basically just little protein covered DNA injectors!

Hershey and Chase followed up by showing that 30% of the DNA that makes it into baby phages after the blender experiment was radiolabeled while <1% of the protein was, essentially proving that DNA, and not protein, is the heritable genetic material.

###

Hershey, AD and Chase, M. 1952. Independent Functions of Viral Protein and Nucleic Acid in Growth of Bacteriophage. J Gen Physiol. DOI: 10.1085/jgp.36.1.39


Weekly Reading List

CRISPR genome-editing grows up: advanced therapies head for the clinic
Gene-editing technologies for cancer and blood disorders are maturing a little more than a year after the first CRISPR drug was approved.
Study Failure Spotlights Challenges for ctDNA Strategies in Breast Cancer Drug Trials
Researchers stopped a Phase III trial testing GlaxoSmithKline's PARP inhibitor Zejula (niraparib) as an adjuvant treatment of early-stage breast cancer due to low detection of circulating tumor DNA (ctDNA) in patients after their initial treatment, investigators said Friday at the San Antonio Breast Cancer Symposium.
Fierce Biotech’s Rotten Tomatoes of 2024
Biotech experienced a year of mixed fortunes in 2024. | Fierce Biotech recounts the biggest biotech blunders and one inspiring intervention in the 2024 edition of Rotten Tomatoes.
From early methods for DNA diagnostics to genomes and epigenomes at high resolution during four decades – a personal perspective | Upsala Journal of Medical Sciences
Structural basis of H3K36 trimethylation by SETD2 during chromatin transcription
During transcription, RNA polymerase II traverses through chromatin, and post-translational modifications including histone methylations mark regions of active transcription. Histone protein H3 lysine 36 trimethylation (H3K36me3), which is established by the histone methyltransferase SETD2, suppresses cryptic transcription, regulates splicing, and serves as a binding site for transcription elongation factors.
Metabolic rearrangement enables adaptation of microbial growth rate to temperature shifts - Nature Microbiology
Growth rate dynamics after temperature shifts can be explained by metabolic rearrangement due to temperature-sensitive enzyme activities in bacteria and yeast.
Keeping it in the family: using protein family templates to rescue low confidence AlphaFold2 models
High confidence structure prediction models have become available for nearly all protein sequences. More than 200 million AlphaFold2 models are now publicly available. We observe that there can be significant variability in the prediction confidence as judged by plDDT scores across a protein family. We have explored whether the predictions with lower plDDT in a family can be improved by the use of higher plDDT templates from the family as template structures in AlphaFold2.
The Causal Pivot: A Structural Approach to Genetic Heterogeneity and Variant Discovery in Complex Diseases
We present the Causal Pivot (CP) as a structural causal model (SCM) for analyzing genetic heterogeneity in complex diseases. The CP leverages one established causal factor to detect the contribution of a second suspected cause. Specifically, polygenic risk scores (PRS) serve as known causes, while rare variants (RV) or RV ensembles are evaluated as candidate causes. The CP incorporates outcome-induced association by conditioning on disease status. We derive a conditional maximum likelihood procedure for binary and quantitative traits and develop the Causal Pivot Likelihood Ratio Test (CP-LRT) to detect causal signals. Through simulations, we demonstrate the CP-LRT’s robust power and superior error control compared to alternatives. We apply the CP-LRT to UK Biobank (UKB) data, analyzing three exemplar diseases: hypercholesterolemia (HC, LDL-c ≥ 4.9 mmol/L; nc=24,656), breast cancer (BC, ICD10 C50; nc=12,479), and Parkinson’s disease (PD, ICD10 G20; nc=2,940). For PRS, we utilize UKB-derived values, and for RVs, we analyze ClinVar pathogenic/likely pathogenic variants and loss-of-function mutations in disease-relevant genes: LDLR for HC, BRCA1 for BC, and GBA for PD. Significant CP-LRT signals were detected for all three diseases. Cross-disease and synonymous variant analyses serve as controls. We further develop ancestry adjustment using matching and inverse probability weighting, and we extend the CP to examine oligogenic burden in the lysosomal storage pathway for PD. The CP reveals an approach to address heterogeneity and is an extensible method for inference and discovery in complex disease genetics. ### Competing Interest Statement The authors have declared no competing interest.
Bridging genomics’ greatest challenge: The diversity gap
Achieving diverse representation in biomedical data is critical for healthcare equity. Failure to do so perpetuates health disparities and exacerbates biases that may harm patients with underrepresented ancestral backgrounds. We present a quantitative assessment of representation in datasets used across human genomics, including genome-wide association studies (GWASs), pharmacogenomics, clinical trials, and direct-to-consumer (DTC) genetic testing. We suggest that relative proportions of ancestries represented in datasets, compared to the global census population, provide insufficient representation of global ancestral genetic diversity.
She took a DNA test for fun. Police used it to charge her grandmother with murder in a cold case
By Taylor Galgano(CNN) — It was the middle of Jenna Gerwatowski’s workday at the local flower shop in Newberry, Michigan, when she got a call from an unknown number.The now 23-year-old doesn’t u
Specificity, length, and luck: How genes are prioritized by rare and common variant association studies
Standard genome-wide association studies (GWAS) and rare variant burden tests are essential tools for identifying trait-relevant genes. Although these methods are conceptually similar, we show by analyzing association studies of 209 quantitative traits in the UK Biobank that they systematically prioritize different genes. This raises the question of how genes should ideally be prioritized. We propose two prioritization criteria: 1) trait importance — how much a gene quantitatively affects a trait; and 2) trait specificity — a gene’s importance for the trait under study relative to its importance across all traits. We find that GWAS prioritize genes near trait-specific variants , while burden tests prioritize trait-specific genes . Because non-coding variants can be context specific, GWAS can prioritize highly pleiotropic genes, while burden tests generally cannot. Both study designs are also affected by distinct trait-irrelevant factors, complicating their interpretation. Our results illustrate that burden tests and GWAS reveal different aspects of trait biology and suggest ways to improve their interpretation and usage. ### Competing Interest Statement The authors have declared no competing interest.
New insights into protein–protein interaction modulators in drug discovery and therapeutic advance - Signal Transduction and Targeted Therapy
Signal Transduction and Targeted Therapy - New insights into protein–protein interaction modulators in drug discovery and therapeutic advance
An amino acid-resolution interactome for motile cilia identifies the structure and function of ciliopathy protein complexes
Motile cilia are ancient, evolutionarily conserved organelles whose dysfunction underlies motile ciliopathies, a broad class of human diseases. Motile cilia contain a myriad of different proteins that assemble into an array of distinct machines, and understanding the interactions and functional hierarchies among them presents an important challenge. Here, we defined the protein interactome of motile axonemes using cross-linking mass spectrometry in Tetrahymena thermophila.
Gene-edited pig kidney transplanted into a third person, moving xenotransplants closer to trials
NYU Langone Health announced that, for the third time, a genetically modified pig kidney had been transplanted into a living patient
Infamous paper that popularized unproven COVID-19 treatment finally retracted
Study on hydroxychloroquine by Didier Raoult and colleagues gets pulled on ethical and scientific grounds
Roche transforms mass spectrometry diagnostics with launch of cobas® Mass Spec solution
Roche launches its cobas® Mass Spec solution, bringing mass spectrometry to the routine clinical labClinical mass spectrometry testing offers unparalleled sensitivity and specificity, providing…
The ternary complex of Mn2+, synthetic decapeptide DP1 (DEHGTAVMLK), and orthophosphate is a superb antioxidant
This study reveals the mechanism for protein radioprotection by the superb Mn-based MDP antioxidant containing Pi and decapeptide DP1, designed through considerations of Deinococcus radiodurans low molecular-weight Mn-antioxidants: The exceptionally active antioxidant in MDP is shown to be a Mn2+ (Pi/DP1) ternary complex, which, protects enzymes from radiation doses far exceeding the capabilities of Mn2+-Pi complexes alone.
LDT Final Rule Series: Part 3 – Legal Challenges
This year, we have seen several monumental events that already are, or potentially could be, pivotal to the future of the Laboratory Developed Test (“LDT”) industry – first, the issuance of the U.S.
Vertex ‘ends the year in pain’ as latest non-opioid drug data disappoint
Mixed results from a study focused on lower back pain left analysts wanting as well as confused about Vertex’s plans to forge ahead in the indication.
Clinical Decision Support Software Frequently Asked Questions (FAQs)
This graphic provides a visual overview of certain policies described in the Clinical Decision Support Software guidance.
CDC confirms first severe H5N1 case in US patient
JCI - Nicotinamide and pyridoxine stimulate muscle stem cell expansion and enhance regenerative capacity during aging
Pathway enrichment-based unsupervised learning identifies novel subtypes of cancer-associated fibroblasts in pancreatic ductal carcinoma
Existing single-cell clustering methods are based on gene expressions that are susceptible to dropout events in single-cell RNA sequencing (scRNA-seq) data. To overcome this limitation, we proposed a pathway-based clustering method for single cells (scPathClus). scPathClus first transforms single-cell gene expression matrix into pathway enrichment matrix and generates its latent feature matrix. Based on the latent feature matrix, scPathClus clusters single cells using the method of community detection. Applying scPathClus to PDAC scRNA-seq datasets, we identified two types of cancer-associated fibroblasts (CAFs), termed csCAFs and gapCAFs, which highly expressed complement system and gap junction-related pathways, respectively. Spatial transcriptome analysis revealed that gapCAFs and csCAFs are located at cancer and non-cancer regions, respectively. Pseudotime analysis suggest a potential differentiation trajectory from csCAFs to gapCAFs. Bulk transcriptome analysis showed that gapCAFs-enriched tumors are more endowed with tumor-promoting characteristics and worse clinical outcomes, while csCAFs-enriched tumors confront stronger antitumor immune responses. Compared to established CAF subtyping methods, this method displays better prognostic relevance. ### Competing Interest Statement The authors have declared no competing interest.

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