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Using AI to Find New Antibiotics Still a Work in Progress

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Protein over a computer network

Each year, more than 2.8 million people in the United States develop bacterial infections that don’t respond to treatment and sometimes turn life-threatening [1]. Their infections are antibiotic-resistant, meaning the bacteria have changed in ways that allow them to withstand our current widely used arsenal of antibiotics. It’s a serious and growing health-care problem here and around the world. To fight back, doctors desperately need new antibiotics, including novel classes of drugs that bacteria haven’t seen and developed ways to resist.

Developing new antibiotics, however, involves much time, research, and expense. It’s also fraught with false leads. That’s why some researchers have turned to harnessing the predictive power of artificial intelligence (AI) in hopes of selecting the most promising leads faster and with greater precision.

It’s a potentially paradigm-shifting development in drug discovery, and a recent NIH-funded study, published in the journal Molecular Systems Biology, demonstrates AI’s potential to streamline the process of selecting future antibiotics [2]. The results are also a bit sobering. They highlight the current limitations of one promising AI approach, showing that further refinement will still be needed to maximize its predictive capabilities.

These findings come from the lab of James Collins, Massachusetts Institute of Technology (MIT), Cambridge, and his recently launched Antibiotics-AI Project. His audacious goal is to develop seven new classes of antibiotics to treat seven of the world’s deadliest bacterial pathogens in just seven years. What makes this project so bold is that only two new classes of antibiotics have reached the market in the last 50 years!

In the latest study, Collins and his team looked to an AI program called AlphaFold2 [3]. The name might ring a bell. AlphaFold’s AI-powered ability to predict protein structures was a finalist in Science Magazine’s 2020 Breakthrough of the Year. In fact, AlphaFold has been used already to predict the structures of more than 200 million proteins, or almost every known protein on the planet [4].

AlphaFold employs a deep learning approach that can predict most protein structures from their amino acid sequences about as well as more costly and time-consuming protein-mapping techniques.
In the deep learning models used to predict protein structure, computers are “trained” on existing data. As computers “learn” to understand complex relationships within the training material, they develop a model that can then be applied for making predictions of 3D protein structures from linear amino acid sequences without relying on new experiments in the lab.

Collins and his team hoped to combine AlphaFold with computer simulations commonly used in drug discovery as a way to predict interactions between essential bacterial proteins and antibacterial compounds. If it worked, researchers could then conduct virtual rapid screens of millions of new synthetic drug compounds targeting key bacterial proteins that existing antibiotics don’t. It would also enable the rapid development of antibiotics that work in novel ways, exactly what doctors need to treat antibiotic-resistant infections.

To test the strategy, Collins and his team focused first on the predicted structures of 296 essential proteins from the Escherichia coli bacterium as well as 218 antibacterial compounds. Their computer simulations then predicted how strongly any two molecules (essential protein and antibacterial) would bind together based on their shapes and physical properties.

It turned out that screening many antibacterial compounds against many potential targets in E. coli led to inaccurate predictions. For example, when comparing their computational predictions with actual interactions for 12 essential proteins measured in the lab, they found that their simulated model had about a 50:50 chance of being right. In other words, it couldn’t identify true interactions between drugs and proteins any better than random guessing.

They suspect one reason for their model’s poor performance is that the protein structures used to train the computer are fixed, not flexible and shifting physical configurations as happens in real life. To improve their success rate, they ran their predictions through additional machine-learning models that had been trained on data to help them “learn” how proteins and other molecules reconfigure themselves and interact. While this souped-up model got somewhat better results, the researchers report that they still aren’t good enough to identify promising new drugs and their protein targets.

What now? In future studies, the Collins lab will continue to incorporate and train the computers on even more biochemical and biophysical data to help with the predictive process. That’s why this study should be interpreted as an interim progress report on an area of science that will only get better with time.

But it’s also a sobering reminder that the quest to find new classes of antibiotics won’t be easy—even when aided by powerful AI approaches. We certainly aren’t there yet, but I’m confident that we will get there to give doctors new therapeutic weapons and turn back the rise in antibiotic-resistant infections.

References:

[1] 2019 Antibiotic resistance threats report. Centers for Disease Control and Prevention.

[2] Benchmarking AlphaFold-enabled molecular docking predictions for antibiotic discovery. Wong F, Krishnan A, Zheng EJ, Stark H, Manson AL, Earl AM, Jaakkola T, Collins JJ. Molecular Systems Biology. 2022 Sept 6. 18: e11081.

[3] Highly accurate protein structure prediction with AlphaFold. Jumper J, Evans R, Pritzel A, Kavukcuoglu K, Kohli P, Hassabis D., et al. Nature. 2021 Aug;596(7873):583-589.

[4] ‘The entire protein universe’: AI predicts shape of nearly every known protein. Callaway E. Nature. 2022 Aug;608(7921):15-16.

Links:

Antimicrobial (Drug) Resistance (National Institute of Allergy and Infectious Diseases/NIH)

Collins Lab (Massachusetts Institute of Technology, Cambridge)

The Antibiotics-AI Project, The Audacious Project (TED)

AlphaFold (Deep Mind, London, United Kingdom)

NIH Support: National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences


Unraveling the Role of the Skin Microbiome in Health and Disease

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broad areas of yellow with dots of magenta and green
Caption: Healthy human skin cells (yellow) are home to bacteria (bright pink), fungi (light blue), and other microorganisms. Credit: Alex Valm, University at Albany, NY

Human skin is home to diverse ecosystems including bacteria, viruses, and fungi. These microbial communities comprise hundreds of species and are collectively known as the skin microbiome. The skin microbiome is thought to play a vital role in fending off disease-causing microorganisms (pathogens), boosting barrier protection, and aiding immune defenses.

Maintaining a balanced skin microbiome involves a complex and dynamic interplay among microorganisms, immune cells, skin cells, and other factors. In general, bacteria far outnumber viral, fungal, or other microbial species on the skin. Bacterial communities, which are strongly influenced by conditions such as skin moisture, temperature, and pH, vary widely across the body. For example, facial cheek skin hosts mostly Cutibacterium along with a bit of the skin fungus Malassezia. The heel is colonized by different types of bacteria including Staphylococcus and Corynebacteria.

In some diseases, such as acne and eczema, the skin microbiome is altered. Typically, this means an increase in pathogenic microorganisms and a decrease in beneficial ones. An altered skin microbiome can also be associated with inflammation, severe disease symptoms, and changes in the human immune system.

Heidi H. Kong is working to understand the role of the skin microbiome in health and disease. She is a senior investigator in the Intramural Research Program at NIH’s National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) and an adjunct investigator at NIH’s National Cancer Institute (NCI).

More than a decade ago, Kong and Julie A. Segre, an intramural researcher at NIH’s National Human Genome Research Institute, analyzed the microbial makeup of healthy individuals. Kong swabbed the skin of these healthy volunteers in 20 different sites, from the forehead to the toenail. The study revealed that the surface of the human body provides various environmental niches, depending on whether the skin is moist, dry, or sebaceous (oily). Different bacterial species predominate in each niche. Kong and Segre were particularly interested in body areas that have predilections for disease. For example, psoriasis is often found on the outside of elbows and knees, and the back of the scalp.

Earlier this year, Kong and Segre published another broad analysis of the human skin microbiome [1] in collaboration with scientists at the European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), United Kingdom. This new catalog, called the Skin Microbial Genome Collection, is thought to identify about 85 percent of the microorganisms present on healthy skin from 19 body sites. It documents more than 600 bacterial species—including 174 that were discovered during the study—as well as more than 6,900 viruses and some fungi, including three newly discovered species.

Kong’s work has provided compelling evidence that the human immune system plays a role in shaping the skin microbiome. In 2018, she, Segre, and colleagues from the intramural programs of NCI and NIH’s National Institute of Allergy and Infectious Diseases analyzed skin from eight different sites on 27 people with a rare primary immunodeficiency disease known as DOCK8 deficiency [2].

People with the condition have recurrent infections in the skin, sinuses, and airways, and are susceptible to different cancers. Kong and colleagues found that the skin of people with DOCK8 deficiency contains significantly more DNA viruses (90 percent of the skin microbiome on average) than people without the condition (6 or 7 percent of the skin microbiome).

Other researchers are hoping to leverage features of the microbiome to develop targeted therapies for skin diseases. Richard L. Gallo, a NIAMS grantee at the University of California, San Diego, is currently focused on acne and eczema (also called atopic dermatitis). Acne is associated with certain strains of Cutibacterium acnes (C. acnes, formerly called Propionibacterium acnes or P. acnes). Eczema is often associated with Staphylococcus aureus (S. aureus).

Severe cases of acne and eczema are commonly treated with broad-spectrum antibiotics, which wipe out most of the bacteria, including beneficial species. The goal of microbiome-targeted therapy is to kill only the disease-associated bacteria and avoid increasing the risk that some strains will develop antibiotic resistance.

In 2020, Gallo and colleagues identified a strain of Staphylococcus capitis from healthy human skin (S. capitis E12) that selectively inhibits the growth of C. acnes without negatively impacting other bacteria or human skin cells [3]. S. capitis E12 produces four different toxins that act together to target C. acnes. The research team created an extract of the four toxins and tested it using animal models. In most cases, the extract was more potent at killing C. acnes—including acne-associated strains—than several commonly prescribed antibiotics (erythromycin, tetracycline, and clindamycin). And, unlike antibiotics, the extract does not appear to promote drug-resistance, at least for the 20 generations observed by the researchers.

Eczema is a chronic, relapsing disease characterized by skin that is dry, itchy, inflamed, and prone to infection, including by pathogens such as S. aureus and herpes virus. Although the cause of eczema is unknown, the condition is associated with human genetic mutations, disruption of the skin’s barrier, inflammation-triggering allergens, and imbalances in the skin microbiome.

In 2017, Gallo’s research team discovered that, in healthy human skin, certain strains of Staphylococcus hominis and Staphylococcus epidermis produce potent antimicrobial molecules known as lantibiotics [4]. These beneficial strains are far less common on the skin of people with eczema. The lantibiotics work synergistically with LL-37, an antimicrobial molecule produced by the human immune system, to selectively kill S. aureus, including methicillin-resistant strains (MRSA).

Gallo and his colleagues then examined the safety and therapeutic potential of these beneficial strains isolated from the human skin microbiome. In animal tests, strains of S. hominis and S. epidermis that produce lantibiotics killed S. aureus and blocked production of its toxin.

Gallo’s group has now expanded their work to early studies in humans. In 2021, two independent phase 1 clinical trials [5,6] conducted by Gallo and his colleagues investigated the effects of these strains on people with eczema. These double-blind, placebo-controlled trials involved one-week of topical application of beneficial bacteria to the forearm of adults with S. aureus-positive eczema. The results demonstrated that the treatment was safe, showed a significant decrease in S. aureus, and improved eczema symptoms in most patients. This is encouraging news for those hoping to develop microbiome-targeted therapy for inflammatory skin diseases.

As research on the skin microbiome advances on different fronts, it will provide deeper insight into the multi-faceted microbial communities that are so critical to health and disease. One day, we may even be able to harness the microbiome as a source of therapeutics to alleviate inflammation, promote wound healing, or suppress certain skin cancers.

References:

[1] Integrating cultivation and metagenomics for a multi-kingdom view of skin microbiome diversity and functions. Saheb Kashaf S, Proctor DM, Deming C, Saary P, Hölzer M; NISC Comparative Sequencing Program, Taylor ME, Kong HH, Segre JA, Almeida A, Finn RD. Nat Microbiol. 2022 Jan;7(1):169-179.

[2] Expanded skin virome in DOCK8-deficient patients. Tirosh O, Conlan S, Deming C, Lee-Lin SQ, Huang X; NISC Comparative Sequencing Program, Su HC, Freeman AF, Segre JA, Kong HH. Nat Med. 2018 Dec;24(12):1815-1821.

[3] Identification of a human skin commensal bacterium that selectively kills Cutibacterium acnes. O’Neill AM, Nakatsuji T, Hayachi A, Williams MR, Mills RH, Gonzalez DJ, Gallo RL. J Invest Dermatol. 2020 Aug;140(8):1619-1628.e2.

[4] Antimicrobials from human skin commensal bacteria protect against Staphylococcus aureus and are deficient in atopic dermatitis. Nakatsuji T, Chen TH, Narala S, Chun KA, Two AM, Yun T, Shafiq F, Kotol PF, Bouslimani A, Melnik AV, Latif H, Kim JN, Lockhart A, Artis K, David G, Taylor P, Streib J, Dorrestein PC, Grier A, Gill SR, Zengler K, Hata TR, Leung DY, Gallo RL. Sci Transl Med. 2017 Feb 22;9(378):eaah4680.

[5] Development of a human skin commensal microbe for bacteriotherapy of atopic dermatitis and use in a phase 1 randomized clinical trial. Nakatsuji T, Hata TR, Tong Y, Cheng JY, Shafiq F, Butcher AM, Salem SS, Brinton SL, Rudman Spergel AK, Johnson K, Jepson B, Calatroni A, David G, Ramirez-Gama M, Taylor P, Leung DYM, Gallo RL. Nat Med. 2021 Apr;27(4):700-709.

[6] Use of autologous bacteriotherapy to treat Staphylococcus aureus in patients with atopic dermatitis: A randomized double-blind clinical trial. Nakatsuji T, Gallo RL, Shafiq F, Tong Y, Chun K, Butcher AM, Cheng JY, Hata TR. JAMA Dermatol. 2021 Jun 16;157(8):978-82.

Links:

Acne (National Institute of Arthritis and Musculoskeletal and Skin Diseases/NIH)

Atopic Dermatitis (NIAMS)

Cutaneous Microbiome and Inflammation Laboratory, Heidi Kong (NIAMS)

Julie Segre (National Human Genome Research Institute/NIH)

Gallo Lab (University of California, San Diego)

[Note: Acting NIH Director Lawrence Tabak has asked the heads of NIH’s Institutes and Centers (ICs) to contribute occasional guest posts to the blog to highlight some of the cool science that they support and conduct. This is the fifth in the series of NIH IC guest posts that will run until a new permanent NIH director is in place.]


Building a Better Bacterial Trap for Sepsis

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NETs
Credit: Kandace Gollomp, MD, The Children’s Hospital of Philadelphia, PA

Spiders spin webs to catch insects for dinner. It turns out certain human immune cells, called neutrophils, do something similar to trap bacteria in people who develop sepsis, an uncontrolled, systemic infection that poses a major challenge in hospitals.

When activated to catch sepsis-causing bacteria or other pathogens, neutrophils rupture and spew sticky, spider-like webs made of DNA and antibacterial proteins. Here in red you see one of these so-called neutrophil extracellular traps (NETs) that’s ensnared Staphylococcus aureus (green), a type of bacteria known for causing a range of illnesses from skin infections to pneumonia.

Yet this image, which comes from Kandace Gollomp and Mortimer Poncz at The Children’s Hospital of Philadelphia, is much more than a fascinating picture. It demonstrates a potentially promising new way to treat sepsis.

The researchers’ strategy involves adding a protein called platelet factor 4 (PF4), which is released by clot-forming blood platelets, to the NETs. PF4 readily binds to NETs and enhances their capture of bacteria. A modified antibody (white), which is a little hard to see, coats the PF4-bound NET above. This antibody makes the NETs even better at catching and holding onto bacteria. Other immune cells then come in to engulf and clean up the mess.

Until recently, most discussions about NETs assumed they were causing trouble, and therefore revolved around how to prevent or get rid of them while treating sepsis. But such strategies faced a major obstacle. By the time most people are diagnosed with sepsis, large swaths of these NETs have already been spun. In fact, destroying them might do more harm than good by releasing entrapped bacteria and other toxins into the bloodstream.

In a recent study published in the journal Blood, Gollomp’s team proposed flipping the script [1]. Rather than prevent or destroy NETs, why not modify them to work even better to fight sepsis? Their idea: Make NETs even stickier to catch more bacteria. This would lower the number of bacteria and help people recover from sepsis.

Gollomp recalled something lab member Anna Kowalska had noted earlier in unrelated mouse studies. She’d observed that high levels of PF4 were protective in mice with sepsis. Gollomp and her colleagues wondered if the PF4 might also be used to reinforce NETs. Sure enough, Gollomp’s studies showed that PF4 will bind to NETs, causing them to condense and resist break down.

Subsequent studies in mice and with human NETs cast in a synthetic blood vessel suggest that this approach might work. Treatment with PF4 greatly increased the number of bacteria captured by NETs. It also kept NETs intact and holding tightly onto their toxic contents. As a result, mice with sepsis fared better.

Of course, mice are not humans. More study is needed to see if the same strategy can help people with sepsis. For example, it will be important to determine if modified NETs are difficult for the human body to clear. Also, Gollomp thinks this approach might be explored for treating other types of bacterial infections.

Still, the group’s initial findings come as encouraging news for hospital staff and administrators. If all goes well, a future treatment based on this intriguing strategy may one day help to reduce the 270,000 sepsis-related deaths in the U.S. and its estimated more than $24 billion annual price tag for our nation’s hospitals [2, 3].

References:

[1] Fc-modified HIT-like monoclonal antibody as a novel treatment for sepsis. Gollomp K, Sarkar A, Harikumar S, Seeholzer SH, Arepally GM, Hudock K, Rauova L, Kowalska MA, Poncz M. Blood. 2020 Mar 5;135(10):743-754.

[2] Sepsis, Data & Reports, Centers for Disease Control and Prevention, Feb. 14, 2020.

[3] National inpatient hospital costs: The most expensive conditions by payer, 2013: Statistical Brief #204. Torio CM, Moore BJ. Healthcare Cost and Utilization Project (HCUP) Statistical Briefs. Agency for Healthcare Research and Quality (US); 2016 May.

Links:

Sepsis (National Institute of General Medical Sciences/NIH)

Kandace Gollomp (The Children’s Hospital of Philadelphia, PA)

Mortimer Poncz (The Children’s Hospital of Philadelphia, PA)

NIH Support: National Heart, Lung, and Blood Institute


How Mucus Tames Microbes

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Scanning EM of mucus
Credit: Katharina Ribbeck, Massachusetts Institute of Technology, Cambridge

Most of us think of mucus as little more than slimy and somewhat yucky stuff that’s easily ignored until you come down with a cold like the one I just had. But, when it comes to our health, there’s much more to mucus than you might think.

Mucus covers the moist surfaces of the human body, including the eyes, nostrils, lungs, and gastrointestinal tract. In fact, the average person makes more than a liter of mucus each day! It houses trillions of microbes and serves as a first line of defense against the subset of those microorganisms that cause infections. For these reasons, NIH-funded researchers, led by Katharina Ribbeck, Massachusetts Institute of Technology, Cambridge, are out to gain a greater understanding of the biology of healthy mucus—and then possibly use that knowledge to develop new therapeutics.

Ribbeck’s team used a scanning electron microscope to take the image of mucus you see above. You’ll notice right away that mucus doesn’t look like simple slime at all. In fact, if you could zoom into this complex web, you’d discover it’s made up of mucin proteins and glycans, which are sugar molecules that resemble bottle brushes.

Ribbeck and her colleagues recently discovered that the glycans in healthy mucus play a long-overlooked role in “taming” bacteria that might make us ill [1]. This work builds on their previous findings that mucus interferes with bacterial behavior, preventing these bugs from attaching to surfaces and communicating with each other [2].

In their new study, published in Nature Microbiology, Ribbeck, lead author Kelsey Wheeler, and their colleagues studied mucus and its interactions with Pseudomonas aeruginosa. This bacterium is a common cause of serious lung infections in people with cystic fibrosis or compromised immune systems.

The researchers found that in the presence of glycans, P. aeruginosa was rendered less harmful and infectious. The bacteria also produced fewer toxins. The findings show that it isn’t just that microbes get trapped in a tangled web within mucus, but rather that glycans have a special ability to moderate the bugs’ behavior. The researchers also have evidence of similar interactions between mucus and other microorganisms, such as those responsible for yeast infections.

The new study highlights an intriguing strategy to tame, rather than kill, bacteria to manage infections. In fact, Ribbeck views mucus and its glycans as a therapeutic gold mine. She hopes to apply what she’s learned to develop artificial mucus as an anti-microbial therapeutic for use inside and outside the body. Not bad for a substance that you might have thought was nothing more than slimy stuff.

References:

[1] Mucin glycans attenuate the virulence of Pseudomonas aeruginosa in infection. Wheeler KM, Cárcamo-Oyarce G, Turner BS, Dellos-Nolan S, Co JY, Lehoux S, Cummings RD, Wozniak DJ, Ribbeck K. Nat Microbiol. 2019 Oct 14.

[2] Mucins trigger dispersal of Pseudomonas aeruginosa biofilms. Co JY, Cárcamo-Oyarce, Billings N, Wheeler KM, Grindy SC, Holten-Andersen N, Ribbeck K. NPJ Biofilms Microbiomes. 2018 Oct 10;4:23.

Links:

Cystic Fibrosis (National Heart, Lung, and Blood Institute/NIH)

Video: Chemistry in Action—Katharina Ribbeck (YouTube)

Katharina Ribbeck (Massachusetts Institute of Technology, Cambridge)

NIH Support: National Institute of Biomedical Imaging and Bioengineering; National Institute of Environmental Health Sciences; National Institute of General Medical Sciences; National Institute of Allergy and Infectious Diseases


Teaming Magnetic Bacteria with Nanoparticles for Better Drug Delivery

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Nanoparticles hold great promise for delivering next-generation therapeutics, including those based on CRISPR gene editing tools. The challenge is how to guide these tiny particles through the bloodstream and into the right target tissues. Now, scientists are enlisting some surprising partners in this quest: magnetic bacteria!

First a bit of background. Discovered in the 1960s during studies of bog sediments, “magnetotactic” bacteria contain magnetic, iron-rich particles that enable them to orient themselves to the Earth’s magnetic fields. To explore the potential of these microbes for targeted delivery of nanoparticles, the NIH-funded researchers devised the ingenious system you see in this fluorescence microscopy video. This system features a model blood vessel filled with a liquid that contains both fluorescently-tagged nanoparticles (red) and large swarms of a type of magnetic bacteria called Magnetospirillum magneticum (not visible).

At the touch of a button that rotates external magnetic fields, researchers can wirelessly control the direction in which the bacteria move through the liquid—up, down, left, right, and even “freestyle.” And—get this—the flow created by the synchronized swimming of all these bacteria pushes along any nearby nanoparticles in the same direction, even without any physical contact between the two. In fact, the researchers have found that this bacteria-guided system delivers nanoparticles into target model tissues three times faster than a similar system lacking such bacteria.

How did anyone ever dream this up? Most previous attempts to get nanoparticle-based therapies into diseased tissues have relied on simple diffusion or molecular targeting methods. Because those approaches are not always ideal, NIH-funded researchers Sangeeta Bhatia, Massachusetts Institute of Technology, Cambridge, MA, and Simone Schürle, formerly of MIT and now ETH Zurich, asked themselves: Could magnetic forces be used to propel nanoparticles through the bloodstream?

As a graduate student at ETH Zurich, Schürle had worked to develop and study tiny magnetic robots, each about the size of a cell. Those microbots, called artificial bacterial flagella (ABF), were designed to replicate the movements of bacteria, relying on miniature flagellum-like propellers to move them along in corkscrew-like fashion.

In a study published recently in Science Advances, the researchers found that the miniature robots worked as hoped in tests within a model blood vessel [1]. Using magnets to propel a single microbot, the researchers found that 200-nanometer-sized polystyrene balls penetrated twice as far into a model tissue as they did without the aid of the magnet-driven forces.

At the same time, others in the Bhatia lab were developing bacteria that could be used to deliver cancer-fighting drugs. Schürle and Bhatia wished they could direct those microbial swarms using magnets as they could with the microbots. That’s when they learned about the potential of M. magneticum and developed the experimental system demonstrated in the video above.

The researchers’ next step will be to test their magnetic approach to drug delivery in a mouse model. Ultimately, they think their innovative strategy holds promise for delivering nanoparticles carrying a wide range of therapeutic payloads right to a tumor, infection, or other diseased tissue. It’s yet another example of how basic research combined with outside-the-box thinking can lead to surprisingly creative solutions with real potential to improve human health.

References:

[1] Synthetic and living micropropellers for convection-enhanced nanoparticle transport. Schürle S, Soleimany AP, Yeh T, Anand GM, Häberli M, Fleming HE, Mirkhani N, Qiu F, Hauert S, Wang X, Nelson BJ, Bhatia SN. Sci Adv. 2019 Apr 26;5(4):eaav4803.

Links:

VIDEO: Synthetic and Living Micropropellers Stir Up Nanoparticles for Enhanced Drug Transport Powered by Magnetism

Nanotechnology (NIH)

What are genome editing and CRISPR-Cas9? (National Library of Medicine/NIH)

Sangeeta Bhatia (Massachusetts Institute of Technology, Cambridge, MA)

Simone Schürle-Finke (ETH Zurich, Switzerland)

NIH Support: National Cancer Institute; National Institute of General Medical Sciences


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