Scientists at Stanford University have found a way to induce cell death in cancer cells with a method that could be effective in around 50% of cancers. In a paper, “Rewiring cancer drivers to activate apoptosis,” published in Nature, the team describes a new class of molecules called transcriptional/epigenetic CIPs (TCIPs) that can activate apoptosis with the help of cancer growth gene expressions within the cancer cells.
The researchers designed small molecules that bind specific transcriptional suppressors to transcription activators. The most potent molecule created, TCIP1, works by linking small molecules that bind BCL6 to those that bind transcriptional activators BRD4.
One of the components that makes cancer cells cancerous is that they ignore signals from surrounding healthy tissues to stop growing and to initiate apoptosis or cell death. The apoptosis pathways still exist but are actively blocked in certain types of cancer where the transcription factor B cell lymphoma 6 (BCL6) binds to the promoters of apoptosis genes and suppresses their expression through epigenetic mechanisms.
The origin and early evolution of life is generally studied under two different paradigms: bottom up and top down. Prebiotic chemistry and early Earth geochemistry allow researchers to explore possible origin of life scenarios. But for these “bottom–up” approaches, even successful experiments only amount to a proof of principle. On the other hand, “top–down” research on early evolutionary history is able to provide a historical account about ancient organisms, but is unable to investigate stages that occurred during and just after the origin of life. Here, we consider ancient electron transport chains (ETCs) as a potential bridge between early evolutionary history and a protocellular stage that preceded it. Current phylogenetic evidence suggests that ancestors of several extant ETC components were present at least as late as the last universal common ancestor of life. In addition, recent experiments have shown that some aspects of modern ETCs can be replicated by minerals, protocells, or organic cofactors in the absence of biological proteins. Here, we discuss the diversity of ETCs and other forms of chemiosmotic energy conservation, describe current work on the early evolution of membrane bioenergetics, and advocate for several lines of research to enhance this understanding by pairing top–down and bottom–up approaches.
In this “Ask Me Anything” (AMA) episode, Peter delves into the realm of genetics, unraveling its connection to disease and emphasizing the value of understanding one’s genetic risks. He elucidates essential background knowledge on genetics before delving into the myriad reasons why individuals might consider genetic testing. Peter differentiates scenarios where genetic testing provides genuine insights from those where it may not be as useful. From there, Peter explores a comprehensive comparison of commercial direct-to-consumer genetic tests, providing insights on interpreting results and identifying the standout options for gaining insights into personal health.
In this sneak peek, we discuss: 00:00 — Intro. 02:09 — Defining the term “genetics” and why it’s important. 04:03 — What is DNA, and how does it impact our biology and traits? 07:13 — How are genetics passed down from parent to child? 11:44 — How much do genes vary across individuals? 16:22 — Which traits are determined by genetics versus experience or environmental factors? 22:30 — Reasons for genetic testing.
In the full episode, we also discuss: –What exactly is being measured by a genetic test?; –Testing for monogenic disorders; –Understanding polygenic risk; –Is genetic testing more important for someone who doesn’t know their family history?; –What does it mean to be positive for a particular variant?; –What does it mean to be negative for a particular variant?; –How does someone get genetic testing through their healthcare provider, and how are these tests performed?; –The financial cost of various genetic tests; –Could having a risk allele for a disease result in an increase in one’s insurance premium?; –Other risks associated with genetic testing; –How do commercial, direct-to-consumer genetic tests compare to the information one might receive from clinical genetic testing?; –Are certain direct-to-consumer tests better than others?; –How long until whole genome sequencing becomes genuinely useful?; –How useful are personalized dietary recommendations based on genetics?; –Final thoughts and advice regarding genetic testing; and. –More.
——- About:
The Peter Attia Drive is a deep-dive podcast focusing on maximizing longevity, and all that goes into that from physical to cognitive to emotional health. With over 70 million episodes downloaded, it features topics including exercise, nutritional biochemistry, cardiovascular disease, Alzheimer’s disease, cancer, mental health, and much more.
Since the release of ChatGPT, huge amounts of attention and funding have been directed toward chatbots. These A.I. systems are trained on copious amounts of human-generated data and designed to predict the next word in a given sentence. They are hilarious and eerie and at times dangerous.
But what if, instead of building A.I. systems that mimic humans, we built those systems to solve some of the most vexing problems facing humanity?
In 2020, Google DeepMind unveiled AlphaFold, an A.I. system that uses deep learning to solve one of the most important challenges in all of biology: the so-called protein-folding problem. The ability to predict the shape of proteins is essential for addressing numerous scientific challenges, from vaccine and drug development to curing genetic diseases. But in the 50-plus years since the protein-folding problem had been discovered, scientists had made frustratingly little progress.
Enter AlphaFold. By 2022, the system had identified 200 million protein shapes, nearly all the proteins known to humans. And DeepMind is also building similar systems to accelerate efforts at nuclear fusion and has spun off Isomorphic Labs, a company developing A.I. tools for drug discovery.
Demis Hassabis is the chief executive of Google DeepMind and the leading architect behind AlphaFold. So I asked him on the show to talk me through how AlphaFold actually works, the kinds of problems similar systems could solve and what an alternative pathway for A.I. development could look like.
In October 2020, Argentina approved the world’s first genetically engineered wheat for cultivation and consumption. Production expanded dramatically in 2021, and will continue to expand in 2022, after Argentina received regulatory approval in late 2021 for exports to Brazil, a major consumer of Argentina’s wheat.
The lessons from Argentina’s experience are important as other countries decide whether they want to follow suit. Argentina’s genetically engineered, drought-tolerant wheat — named HB4 — could have large environmental benefits, but other countries’ choices will determine their scale.
Argentina is increasingly struggling with drought and saw an opportunity for HB4 wheat to help stabilize production and revenue. Yields have been steadily decreasing since 2017, partially due to drought, with the 2020/21 season yields the second-lowest in ten years. Yields in the 2021/22 season bounced back thanks to sufficient rainfall at critical times. HB4 wheat, genetically engineered to be drought resistant, can help protect against such variability by maintaining high yields even under drought conditions. HB4’s drought resistance gene comes from sunflowers, so it qualifies as transgenic — containing genes from a different species — and therefore as bioengineered, genetically modified, or a GMO.
While human beings have many living ancestors — bonobos, chimps, gorillas — our absence of fur immediately marks us as something different.
And though our big brains and bipedal posture have taken us to outer space, the reason our species transitioned to a mostly hairless body remains somewhat of a mystery.
Only a few other mammals share our genetic preference for sleek bodies, including rhinos, whales, elephants, and — everyone’s favorite — the naked mole rat.
Researchers led by a team at UT Southwestern Medical Center have identified cellular and molecular features of the brain that set modern humans apart from their closest primate relatives and ancient human ancestors. The findings, published in Nature, offer new insights into human brain evolution.
“Most evolutionary studies on the human brain have focused on neurons because this cell type was thought to be responsible for our intelligence and enhanced cognitive abilities. This study gives us a renewed appreciation for other cells involved in brain function and the role they have played both in advancing cognition and our susceptibility to a number of cognitive diseases,” said study leader Genevieve Konopka, Ph.D., Professor of Neuroscience and a member of the Peter O’Donnell Jr. Brain Institute at UT Southwestern.
Since ancient times, people have been curious about what gives humans abilities that other animals don’t have, such as speech and language, Dr. Konopka explained. A range of previous studies have sought to answer this question by examining brain anatomy or performing genetic or molecular studies on whole brains or sections, experiments that provide a view of thousands of cells at a time.
TALK ABSTRACT Life was solving problems in metabolic, genetic, physiological, and anatomical spaces long before brains and nervous systems appeared. In this talk, I will describe remarkable capabilities of cell groups as they create, repair, and remodel complex anatomies. Anatomical homeostasis reveals that groups of cells are collective intelligences; their cognitive medium is the same as that of the human mind: electrical signals propagating in cell networks. I will explain non-neural bioelectricity and the tools we use to track the basal cognition of cells and tissues and control their function for applications in regenerative medicine. I will conclude with a discussion of our framework based on evolutionary scaling of intelligence by pivoting conserved mechanisms that allow agents, whether designed or evolved, to navigate complex problem spaces.
Though almost every cell in your body contains a copy of each of your genes, only a small fraction of these genes will be expressed, or turned on. These activations are controlled by specialized snippets of DNA called enhancers, which act like skillful on-off switches. This selective activation allows cells to adopt specific functions in the body, determining whether they become—for example—heart cells, muscle cells, or brain cells.
However, these enhancers don’t always turn on the right genes at the right time, contributing to the development of genetic diseases like cancer and diabetes. A team of Johns Hopkins biomedical engineers has developed a machine-learning model that can predict which enhancers play a role in normal development and disease—an innovation that could someday power the development of enhancer-targeted therapies to treat diseases by turning genes on and off at will. The study results appeared in Nature Genetics.
“We’ve known that enhancers control transitions between cell types for a long time, but what is exciting about this work is that mathematical modeling is showing us how they might be controlled,” said study leader Michael Beer, a professor of biomedical engineering and genetic medicine at Johns Hopkins University.
