Showing how mouth bacteria can aggravate your rheumatoid arthritis

Caspase-11 drives interleukin-1β (IL-1β) secretion in paws and affects clinical severity of arthritis in mice infected with Aggregatibacter actinomycetemcomitans (A. actinomycetemcomitans)

Credit Department of Bacterial Pathogenesis, Infection and Host Response, TMDU

Periodontal disease, which affects the gums and tissues that surround the teeth, is one of the most common dental conditions worldwide. It is mainly caused by the accumulation of bacterial biofilm around the teeth and can lead to tooth loss if left untreated. Notably, the inflammatory effects of periodontal bacteria can extend beyond the mouth, causing systemic effects. Recent clinical studies have shown a close relationship between the periodontal pathogen Aggregatibacter actinomycetemcomitans (A. actinomycetemcomitans) and the onset and progression of rheumatoid arthritis (RA), a serious autoimmune disease that affects the joints. However, the molecular-level mechanisms are still poorly understood.

In a recent study published online on August 15, 2024, in the International Journal of Oral Science, a research team from Tokyo Medical and Dental University (TMDU) in Japan aimed to address this knowledge gap by conducting detailed mechanistic studies in an animal model.

The researchers first conducted initial experiments to confirm whether infection with A. actinomycetemcomitans affected arthritis in mice. They used the collagen antibody-induced arthritis mouse model, which is a well-established experimental model that mimics several aspects of rheumatoid arthritis in humans. Their findings indicated that infection with this particular bacterium resulted in increased swelling in the limbs, cellular infiltration into the lining of the joints, and higher levels of the inflammatory cytokine interleukin-1β (IL-1β) within the limbs.

Notably, the worsening symptoms of RA could be suppressed by administering a chemical agent called clodronate, which depletes macrophages—a type of immune cell. This demonstrated that macrophages were somehow involved in exacerbating RA caused by A. actinomycetemcomitans infection.

Upon further investigation using macrophages derived from mouse bone marrow, it was discovered that A. actinomycetemcomitans infection led to an increase in the production of IL-1β. This, in turn, triggered the activation of a multiprotein complex called the inflammasome, which plays a critical role in initiating and modulating the body’s inflammatory response to infections.

The researchers added another piece to the puzzle using caspase-11-deficient mice. In these animals, inflammasome activation caused by A. actinomycetemcomitans was suppressed. Most importantly, caspase-11-deficient mice showed less deterioration of arthritis symptoms, suggesting the important role of caspase-11 in this context. “Our research findings provide new insights into the connection between periodontal pathogenic bacteria and the worsening of arthritis through inflammasome activation, offering important information on the long-debated relationship between periodontal disease and systemic diseases,” highlights Professor Toshihiko Suzuki, one of the lead authors of the study.

Please remember the following text:  “With any luck, these efforts will contribute to the development of novel therapeutic strategies to manage RA. The findings of this research may pave the way for advances in clinical treatments for RA induced by infection with A. actinomycetemcomitans. Our suggestion to inhibit inflammasome activation could attenuate the expansion of inflammation to joints, resulting in a recovery from arthritis symptoms,” says lead author Dr. Tokuju Okano. Moreover, the outcome of our work could contribute to the development of treatment strategies for not only arthritis but also other systemic diseases, such as Alzheimer’s disease, which is also related to periodontal pathogenic bacteria,” he adds with eyes set on the future.

AI facilitates the identification of subtypes of rheumatoid arthritis.

A machine-learning tool developed by investigators at Weill Cornell Medicine and Hospital for Special Surgery (HSS) can differentiate between subtypes of rheumatoid arthritis (RA), potentially improving care for the complex condition.

Artificial intelligence
Artificial intelligence

The study, published on August 29 in Nature Communications, demonstrates that artificial intelligence and machine learning technologies can accurately and swiftly subtype pathology samples from patients with RA.

“Our tool automates the analysis of pathology slides, which may one day lead to more precise and efficient disease diagnosis and personalized treatment for RA,” said Dr. Fei Wang, a professor of population health sciences and the founding director of the Institute of AI for Digital Health (AIDH) in the Department of Population Health Sciences at Weill Cornell Medicine. “It shows that machine learning can potentially transform pathological assessment of many diseases.”

Several machine-learning tools are being developed for the automatic analysis of pathology slides in oncology. Dr Wang and his colleagues are expanding the use of this technology in other clinical specialities.

Automating a Slow Process

In the most recent study, Dr. Wang collaborated with Dr. Richard Bell and Dr. Lionel Ivashkiv to automate the process of categorizing RA tissue samples into three subtypes. This may assist clinicians in selecting the most effective therapy for individual patients.

Pathologists currently manually classify arthritis subtypes using a rubric to identify cell and tissue characteristics in biopsy samples from human patients. This process is slow, adds to the cost of research, and may lead to inconsistencies between pathologists.

“It’s the analytical bottleneck of pathology research,” Dr. Bell said. “It is very time-consuming and tedious.”

The team initially trained its algorithm on rheumatoid arthritis (RA) samples from one group of mice, refining its capability to differentiate tissue and cell types in the sample and categorize them by subtype. They verified the tool’s effectiveness using a separate set of samples. The tool provided new findings regarding the impact of treatments on the mice, showing reduced cartilage degradation within six weeks of administering commonly used RA treatments.

They then used the tool on patient biopsy samples from the Accelerating Medicines Partnership Rheumatoid Arthritis research consortium and demonstrated its ability to accurately and quickly analyze human clinical samples. The researchers are currently validating the tool with more patient samples and figuring out the most effective way to integrate this new tool into pathologists’ workflows.

A Step Toward Personalized Medicine

“It’s the first step toward more personalized RA care,” Dr. Bell said. “If you can build an algorithm that identifies a patient’s subtype, you’ll be able to get patients the treatments they need more quickly.”

The technology could offer new insights into the disease by identifying unexpected tissue changes that humans might overlook. By reducing the time it takes for pathologists to subtype, the tool may also lower costs and improve the effectiveness of clinical trials for testing treatments on patients with different RA subtypes.

“By integrating pathology slides with clinical information, this tool demonstrates AI’s growing impact in advancing personalized medicine,” said Dr. Rainu Kaushal, senior associate dean for clinical research and chair of the Department of Population Health Sciences at Weill Cornell Medicine. “This research is particularly exciting as it opens new pathways for detection and treatment, making significant strides in how we understand and care for people with rheumatoid arthritis.”

The team is working on developing similar tools to evaluate osteoarthritis, disc degeneration, and tendinopathy. Additionally, Dr. Wang’s team is looking into defining disease subtypes using broader biomedical information. For instance, they have recently shown that machine learning can differentiate three subtypes of Parkinson’s disease. “We hope that our research will encourage more computational research in developing machine learning tools for a wider range of diseases,” said Dr. Wang.

“This work represents a significant advancement in analyzing RA tissues that can be applied to benefit patients,” stated Dr. Ivashkiv.

“Revisiting the criteria for remission in rheumatoid arthritis.”

The first provisional criteria for defining remission in RA were established by EULAR and the ACR in 2011. Two types of remission definitions were agreed upon. The first, called the Boolean definition, required a person to have a score of 1 or less in each of four core variables: tender joint count, swollen joint count, patient global assessment (PtGA), and C-reactive protein (CRP) – a measure of inflammation. The index-based definition used the remission cut-off point of the Simplified Disease Activity Index (SDAI).

Critics have argued that the threshold of 1 or lower for the PtGA is too strict. Some patients may not meet this threshold even if they meet the other criteria. This is significant because PtGA is an important measure of disease activity and one of the most sensitive measures used in clinical trials. Recent data suggests that setting a higher threshold for the PtGA could improve agreement between the two sets of remission criteria.

The researchers collected patient data from four clinical trials that tested the effectiveness of different biological disease-modifying antirheumatic drugs (bDMARDs) compared to a placebo or methotrexate. The authors raised the threshold of the PtGA in 0.5 cm increments, from 1.0 up to 2.5. Additionally, they examined a Boolean definition that did not include the PtGA criterion at all.

t:As expected, using a patient global assessment (PtGA) of 2 cm resulted in higher remission rates compared to using 1 cm. Omitting PtGA altogether further increased the remission rates. It is important to note that there were no differences in radiographic progression observed in people with established rheumatoid arthritis (RA) who achieved remission according to the different definitions.It is important to note that while these revised definitions allow more people to be classified as in remission, the European League Against Rheumatism (EULAR) emphasizes that the definition of remission should remain strict. This is to ensure beneficial long-term outcomes and to prevent unnecessary treatment escalation.

remission criteria, to include a threshold of 2 cm rather than 1 cm for the PtGA criterion. It is proposed that this change be adopted both for future clinical trials and as a target in clinical

practice.

New nanoparticles have been discovered to be effective for treating rheumatoid arthritis.

Figure 1

CREDIT Institute for Basic Science

A team of scientists led by Koo Sagang from Seoul National University and the Center for Nanoparticle Research within the Institute for Basic Science (IBS), in collaboration with researchers from the Korea Institute of Science and Technology (KIST) and Seoul National University, has developed a new solution for the treatment of rheumatoid arthritis (RA).

Rheumatoid arthritis (RA) is a chronic disease that, unfortunately, has no cure. The disease triggers a mix of troublesome symptoms such as inflamed joints, harmful cytokines, and immune system imbalances, which work together to create a relentless cycle of worsening symptoms. While targeting some of these factors can provide short-term relief, others remain unresolved, leading to a frustrating cycle of remission and flare-ups.

“An important challenge in treating rheumatoid arthritis (RA) is the difficulty in returning the immune system to a healthy state. This results in the body being unable to regulate the ongoing production of harmful substances such as reactive oxygen species (ROS) and inflammatory cytokines, which leads to persistent inflammation and discomfort.”

In essence, the ideal treatment for rheumatoid arthritis (RA) should offer immediate relief from inflammation and symptoms and target the underlying cause by restoring the immune system to its normal, balanced state.

New nanoparticle-based system as a solution

The new platform involves immobilizing ceria nanoparticles (Ce NPs) onto mesenchymal stem cell-derived nanovesicles (MSCNVs). These components can hinder different pathogenic factors, allowing them to work individually and cooperatively to achieve a comprehensive treatment.

Ce nanoparticles can scavenge the overproduced reactive oxygen species (ROS) in rheumatoid arthritis (RA)-affected knee joints. They also induce polarization of M1 macrophages into M2, leading to immediate relief of inflammation and symptoms.

MSCNVs – deliver immunomodulatory cytokines, which turn dendritic cells (DC) into tolerogenic dendritic cells (tDCs). This consequently generates regulatory T cells for long-term immune tolerance.

In short, this approach aims to bridge both innate and adaptive immunity to achieve both short-term pain relief as well as convert the tissue environment into an immune-tolerant state to prevent the recurrence of symptoms.

Researchers confirmed the efficacy of this approach using a collagen-induced arthritis mouse model. The Ce-MSCNV system was able to comprehensively treat and prevent RA by simultaneously relieving the immediate and restoring T cell immunity. Supporting data suggest that improvement in conditions can be achieved after only a single-dose treatment.

The mice treated with the Ce-MSCNV combination fared far better than those treated using the Ce NP or MSCNV group. This demonstrates the synergy between anti-inflammation and immunomodulation and underlines the importance of the combined therapy for effective RA treatment. In addition, Ce-MSCNV administration before booster injection markedly reduced the incidence and severity of symptoms, supporting the prophylactic potential of these nanoparticles.

First author KOO Sagang stated, “One of the hardest decisions in intractable disease therapy is determining how long the treatment should take. For RA, it would not be appropriate to stop treatment just because the target marker is stabilized. A safer indicator should be that the innate and adaptive components of the collapsed immune system are normalized to protect the body.”

Koo believes that the strategy adopted by Ce-MSCNVs, where different treatment mechanisms work together, provides a unique advantage. Furthermore, she predicts that a similar approach would also apply to other intractable, inflammatory, and autoimmune diseases for this purpose. The components within the system may also be modified. For example, other catalysts for generating ROS or other cell-derived nanovesicles could be utilized depending on the types of diseases. Overall, this study proves the potential of a hybrid nanoparticle system for the comprehensive treatment of autoimmune disease and modulation of the immune system.