Researchers use the mouse as a model organism to study the structure and function of skeletal muscle, neuromuscular diseases and aging processes in muscle. The scientists are aware of their responsibility in the use of animals and have committed themselves at the University of Basel to rigorously implement the so-called 3R principles — Replacement, Reduction, Refinement — in animal-assisted research and animal husbandry.

The new method developed by Professor Markus Rüegg’s research group at the Biozentrum, University of Basel, is a further step towards reducing the number of laboratory animals. This method also opens new ways to investigate several genes simultaneously or even entire signaling pathways in muscle fibers quickly, cost-effectively and efficiently. The results of the study have now been published in Nature Communications.

The difficulty of studying genes in muscle fibers

Studying gene function in muscle is challenging. On the one hand, muscle fibers are very large and very fragile when isolated. On the other hand, in humans, they are up to half a meter long and contain thousands of nuclei. In order to change and study gene function in muscle fibers, all of the muscle fiber nuclei must be changed, which is difficult to achieve.

For some years now, scientists have been using the CRISPR/Cas9 method to study gene function. This method uses a virus to introduce the so-called Cas9 protein and a specifically designed guide RNA into the organism and thus into the nuclei. The Cas9 protein cuts the genomic DNA at the site recognized by the guide RNA. This combination of Cas9 protein and guide RNA allows altering gene function in the cell.

The CRISPR-Cas9 method can be split up

However, to ensure that the virus only alters the gene expression of muscle fibers and not those of other organs at the same time, the research team combined the CRISPR/Cas9 method with another method: First, the researchers succeeded in breeding mice with the Cas9 protein already present in their muscle fibers — but only there. They then introduced the desired guide RNA into the organism with a so-called adeno-associated virus, which infects muscle.

This combination causes the guide RNA in the muscle fibers to encounter the Cas9 protein, changing the genetic material as desired. “The method enables us to ensure that only the muscle fibers actually change their genetic material,” explains first author Marco Thürkauf.

Fewer laboratory animals and more efficient results

Since the adeno-associated virus can also transport several guide RNAs simultaneously, the team can now use the method to investigate several genes simultaneously or even entire signaling pathways. Furthermore, the method significantly reduces the number of experimental animals required.

“All animals used are suitable for studying genes and do not have to be bred over years. This makes it possible to study muscle fibers as well as neuromuscular diseases without using a large number of mice,” says Marco Thürkauf.

Other research groups have also already signaled their interest. “We already have several interested groups in our research community that would like to use our method,” says Markus Rüegg. “This is a great gain for both muscle research per se as well as for our goal of reducing animal experiments.”

Now, a new paper in Nature illustrates a Harvard quantum computing platform’s potential to solve the longstanding problem known as quantum error correction.

Leading the Harvard team is quantum optics expert Mikhail Lukin, the Joshua and Beth Friedman University Professor in physics and co-director of the Harvard Quantum Initiative. The work reported in Nature was a collaboration among Harvard, MIT, and Boston-based QuEra Computing. Also involved was the group of Markus Greiner, the George Vasmer Leverett Professor of Physics.

An effort spanning the last several years, the Harvard platform is built on an array of very cold, laser-trapped rubidium atoms. Each atom acts as a bit — or a “qubit” as it’s called in the quantum world — which can perform extremely fast calculations.

The team’s chief innovation is configuring their “neutral atom array” to be able to dynamically change its layout by moving and connecting atoms — this is called “entangling” in physics parlance — mid-computation. Operations that entangle pairs of atoms, called two-qubit logic gates, are units of computing power.

Running a complicated algorithm on a quantum computer requires many gates. However, these gate operations are notoriously error-prone, and a buildup of errors renders the algorithm useless.

In the new paper, the team reports near-flawless performance of its two-qubit entangling gates with extremely low error rates. For the first time, they demonstrated the ability to entangle atoms with error rates below 0.5 percent. In terms of operation quality, this puts their technology’s performance on par with other leading types of quantum computing platforms, like superconducting qubits and trapped-ion qubits.

However, Harvard’s approach has major advantages over these competitors due to its large system sizes, efficient qubit control, and ability to dynamically reconfigure the layout of atoms.

“We’ve established that this platform has low enough physical errors that you can actually envision large-scale, error-corrected devices based on neutral atoms,” said first author Simon Evered, a Harvard Griffin Graduate School of Arts and Sciences student in Lukin’s group. “Our error rates are low enough now that if we were to group atoms together into logical qubits — where information is stored non-locally among the constituent atoms — these quantum error-corrected logical qubits could have even lower errors than the individual atoms.”

The Harvard team’s advances are reported in the same issue of Nature as other innovations led by former Harvard graduate student Jeff Thompson, now at Princeton University, and former Harvard postdoctoral fellow Manuel Endres, now at California Institute of Technology. Taken together, these advances lay the groundwork for quantum error-corrected algorithms and large-scale quantum computing. All of this means quantum computing on neutral atom arrays is showing the full breadth of its promise.

“These contributions open the door for very special opportunities in scalable quantum computing and a truly exciting time for this entire field ahead,” Lukin said.

Researchers from Boston University Chobanian & Avedisian School of Medicine now suggest studying persons after they sustain knee trauma such as anterior cruciate ligament tears (ACL).

“Given the repeated, expensive and discouraging past failures in the development of effective treatments for OA, a new approach is needed that focuses research into effective treatment on those with early disease,” said corresponding author David T. Felson, MD, MPH, professor of medicine and epidemiology at the School of Medicine and Boston University School of Public Health.

While most patients recover after sustaining a major joint injury like an ACL tear, a few experience persistent pain and develop OA. Felson suggests that sufficient numbers of such patients exist and could be identified in advance to form a high-risk group in which treatments to prevent disease could be tested.

Current options for treatments that reduce joint pain such as nonsteroidal anti-inflammatory drugs (NSAIDs) are successful in some patients but their use is limited by their toxicity . Exercise or weight loss are effective but long-term adherence is poor. Rates of total knee replacement surgeries are rising rapidly suggesting that nonsurgical treatments have not successfully alleviated patients’ pain and disability.

BU and Cleveland Clinic researchers reviewed the data from the MOON (Multicenter Orthopaedic Outcomes Network) cohort, a group of 2,340 persons undergoing ACL reconstructions (ACLR) after traumatic tears. The MOON investigators reported that 26% of the ACL reconstruction patients who responded had at least moderate knee pain on daily activities, especially stair climbing and walking. They also found that 16.6% had Knee Injury and Osteoarthritis Outcome Score (KOOS) pain scores of less than80 (0-100 scale where 100 is no pain) suggesting that mild to moderate pain is not rare after ACLR.

By using the MOON risk factors — incorporating pain and structural changes in all joint tissues, especially cartilage loss — to select persons at high risk of later pain, they could assemble a cohort at high risk of substantial post ACLR pain. “This approach offers the opportunity to prevent disease and is especially valuable in targeting young adults who, after a knee injury, may have significant joint pain and disability for many years before they become eligible for joint replacement,” he adds.

These findings appear online in the Annals of the Rheumatic Diseases.

Funding for this study was provided by the Arthritis Foundation and by the national Institutes of Health (NIH P30 AR072571).