Rheumatoid arthritis (RA) is a complex, chronic inflammatory disease that is thought to affect about one percent of the world’s population. RA happens when a person’s own antibodies attack joint tissue, causing painful swelling, stiffness, and redness. Some research has suggested that there is a link between RA and gum disease.
Gum disease is estimated to affect up to 47 percent of adults, and in the disorder, oral microbes can move to the blood after the gums start to bleed. An increase in disease activity has been observed in RA patients who also have gum disease. Gum disease has been shown to be more common in RA patients who carry a certain type of antibodies, called anti-citrullinated protein antibodies (ACPAs), though ACPAs are often found in the blood of individuals with RA. The presence of ACPAs can often predate the diagnosis of RA by a few years.
A new study investigated the connections between these observations. In this work, the researchers collected blood samples from a small group of ten people with RA, five with and five without gum disease. These samples were collected every week for one year, and the investigators assessed the expression of both human and bacterial genes in those samples.
Certain types of inflammatory immune cells carried gene expression signatures that were associated with the autoimmune flares of arthritis patients who also had periodontal disease, as well as the presence of certain oral bacteria in the blood.
Many of these oral bacteria were chemically altered by deimination; they were citrullinated. Citrullination can change the structure and function of proteins. Although citrullination can be a part of the normal function of tissues, high levels of citrullination have been linked to inflammation.
Citrullination can also create targets for ACPAs; when the normal, unconverted forms of the oral bacteria were incubated with ACPAs, the antibodies did not react, but when the citrullinated oral bacteria were exposed to ACPAs, there was a reaction. ACPAs appear to be bound to oral microbes in RA patients.
The study noted that the immune response to oral microbes could be influencing RA flares, that oral microbes can trigger a specific antibody reaction in patients with both RA and gum disease, and that RA flares cause varying immune signatures, which could reflect different flare triggers.
It could be that gum disease repeatedly causes the immune system to respond, and as the immune system keeps reacting and repeatedly increasing inflammation, RA may eventually begin to emerge. More work will be needed, however, to fully understand whether gum disease is playing a causative role in the development of RA.
Even after decades of research, the cause of the most common form of dementia, Alzheimer’s disease, is unclear. Evidence that pointed to plaques and tangles of disordered proteins called amyloid beta and tau has been called into question since therapeutics that aim at the protein clumps have not been particularly effective at relieving disease. In recent years, scientists have also found evidence that viral infections may be to blame for some serious long-term diseases; multiple sclerosis, for example, seems to only develop in people who have been infected with Epstein-Barr virus (EBV). The pandemic coronavirus SARS-CoV-2 also appears to have neurological impacts in some people.
Researchers have now used the power of biobanks that contain health data from hundreds of thousands of people to look for links between viral infections like influenza and EBV and neurodegenerative diseases including Alzheimer’s disease, amyotrophic lateral sclerosis (ALS), generalized dementia, and Parkinson’s disease. Data from FinnGen on over 300,000 people was used to look for associations that were then assessed in the nearly half a million people in the UK Biobank for confirmation. An additional cohort of almost 100,000 people who had not had any neurodegenerative disorder was used as a control group. COVID-19 hospitalizations were not included in this study. The findings have been reported in Neuron.
The research showed that there is a connection between viral infections and neurodegeneration, which is particularly significant for viruses that are able to cross the blood brain barrier and infect the central nervous system, perhaps unsurprisingly. The study authors suggested that these so-called neurotropic viruses may be causing neurodegeneration because of neuroinflammation.
A first pass of the data suggested there were 45 significant associations between viral infections and neurodegenerative disease. After querying the UK Biobank, the researchers refined it to 22 associations.
The top candidate was generalized dementia, which was linked to infections with six different viruses: all influenza, flu and pneumonia, viral pneumonia, viral encephalitis, viral warts, and other viral diseases seem to raise the risk of generalized dementia. The research also showed that anyone who had viral encephalitis was 20 times more likely or more to receive an Alzheimer’s diagnosis compared to people who have not had viral encephalitis.
Severe flu was connected to a greater likelihood of different types of neurodegeneration. Influenza and pneumonia exposures were also linked to an increased risk of all neurodegenerative disorders except multiple sclerosis.
Senior study author Michael Nalls, Ph.D., the NIH Center for Alzheimer’s Related Dementias (CARD) Advanced Analytics Expert Group leader, also noted that the viral infections that are being considered in this study were quite severe and led to hospitalizations; they were not mild cases of the common cold.
“Nevertheless, the fact that commonly used vaccines reduce the risk or severity of many of the viral illnesses observed in this study raises the possibility that the risks of neurodegenerative disorders might also be mitigated,” said Nalls.
“Our results support the idea that viral infections and related inflammation in the nervous system may be common and possibly avoidable risk factors for these types of disorders,” added study co-author Andrew B. Singleton, Ph.D., the director of CARD.
The human organism is a complex ecosystem of coexisting microbiomes, including those in the gut, the skin, and the vagina in females. These play a crucial role in health and disease. However, a great deal remains to be learned about them.
A new paper recently published online in Trends in Microbiology journal reviews the systems biology approach to explore the vaginal microbiome (VMB), helping to understand its composition and function and the mechanisms by which it interacts with the host.
The VMB is vital in female fertility, and disruptions can be associated with pregnancy disorders, gynecologic diseases such as pelvic inflammatory disease (PID), and an array of infections involving the female genitourinary and reproductive tract. In addition, the VMB may be instrumental in affecting drug efficacy in women.
However, the VMB is little understood beyond a vague idea that a preponderance of Lactobacillus is associated with a ‘good’ state with a homogeneous community structure. Conversely, an undesirable state of the VMB exists when more diverse species are identified in greater abundance.
This latter suboptimal state is often linked to bacterial vaginosis (BV), found in one in three women during their reproductive period, which can have severe consequences on their fertility. As such, research in this area is required to understand the directionality and magnitude of such associations.
The problem
While many studies have been performed in this area, it is difficult to understand what an optimal VMB looks like because of the complex interactions between microbes and other host factors. This means that the healthy VMB can differ considerably from woman to woman and at different points in the same individual’s life cycle.
Such changes occur within days, which contrasts with the much slower shift seen with the gut, skin, and oral microbiomes, which may change over months or even years. Unfortunately, this makes cross-sectional data quite non-representative when it comes to studying the association of VMB composition, function, and disease – and thus makes most of this data less useful than it could be.
Again, the human VMB differs significantly from that of animals, as well as from culture-based models. In the former, even non-human primates fail to show the characteristic conditions of the human vagina, including the acidic pH and Lactobacillus dominance.
In the latter, some microbes are incredibly resistant to culture in vitro, while various culture conditions are used in different laboratories, depending on the media. This could make the growth environment quite different from that of the human cervix and vagina, invalidating the results of such experiments.
As such, clinical samples from which vaginal microflora are cultured, identified, and quantified form the primary source of information about the human VMB. This information is colored by experimental and host variables, which require sophisticated statistical adaptations to achieve a valid conclusion.
“While relevant to all microbiome sites, [this] is particularly applicable to the VMB because of its lack of experimental models that allow for interrogation of vaginal microbiota under controlled conditions.”
The solution
Such an impasse can be solved with a systems biology approach, where quantitative analyses are used to extract the important factors affecting the behavior and function of a microbial community. As such, “Leveraging systems biology techniques applied to other microbiomes, as well as developing novel techniques and applying these methods to the VMB, will have a significant impact on improving women’s health.”
The use of systems biology can overcome the challenges of such complex and multiple external and internal interactive networks. Furthermore, multiple approaches can be used, depending on the type of information available and the aim of the study.
Thus, statistical or data-driven methods are ideal when high-throughput data are abundant in a relatively new field of study. This can help suggest what microbial profiles are linked to disease or health. Since little is known so far about the VMB, data-driven models have predominated so far.
Conversely, based on hypotheses, mechanistic methods are better when much is already known about a system, or at least the fundamental data is available, and the need is to understand the mechanisms of cause-effect associations underlying biological function. In addition, they help to set the ranges within which microbial composition and interactions can occur in normal and abnormal situations.
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Some mechanistic methods include mass-action kinetic or population dynamics models (based on differential equations), genome-scale metabolic models (GEMs), and agent-based models (ABMs).
What has been achieved?
The systems biology approach has already helped to identify and categorize community state types (CSTs) associated with health, disease, or transitions between the two. First defined by microbial abundance, they incorporated patient demographic and health data to form hierarchical clustering groups. In addition, other methods like nearest centroid classification have been developed to overcome the inherent variation in the dataset with the former approach.
CST groupings help simplify VMB composition and thus suggest associations with community composition and function. But this is at the cost of overlooking community-specific factors specific to different taxa.
Multi-omics approaches could be integrated with systems biology strategies to identify associations with different types of community and specific metabolomics, transcriptomics, and metagenomics profiles, for instance. In addition, random forest models and other advanced machine learning models are being pressed into service to help distinguish VMBs with a predominance of different microbes, such as L. crispatus vs. L. iners or Bifidobacteriaceae.
Interestingly, neural network models have shown the superiority of metabolomics in describing the cervicovaginal environment accurately compared to either VMB composition or immunoproteomics. The integrated use of these strategies could help pick out the important drivers of VMB states in health and disease.
Especially important could be the insights obtained regarding sexually transmitted infection (STI) risk with an increased abundance of ‘bad’ microbes. For instance, an increase in L. iners seems to be associated with a higher risk for STIs, while L. gasseri is associated with health. Conversely, Gardnerella vaginalis and Prevotella species are linked to Chlamydia infection.
Mechanistic models include the technique called MIMOSA (Model-based Integration of Metabolite Observations and Species Abundances) that uses metabolic network modeling to understand community function via its gene content. This helped identify Prevotella species and Atopobium vaginae as key modulators of the VMB, using a calculated community-based metabolite potential (CMP) score. The CMP shows the turnover of each metabolite by any given community.
Similarly, genome-scale network reconstructions (GENREs) could help understand the role of fastidious microbes in the VMB. Ordinary differential equation (ODE)-based models are being used to examine how drugs can affect the VMB and the ecology of this system, showing how the composition fluctuates following exposure to different factors.
What lies in the future?
A multitude of studies has focused on the gut microbiome, with almost $150 million being poured into developing and standardizing new tools for its exploration. VMB researchers may be able to use these to serve their aims. This includes BURRITO, a web tool that helps visualize a microbiome community by relative abundance. This could be extended to examine VMB metagenomics, showing how patient symptoms relate to the CSTs.
Supervised machine learning approaches to understand the VMB better include Data Integration Analysis for Biomarker Discovery using Latent cOmponents (DIABLO), where omics datasets are integrated by correlation, and Sparse regularized generalized canonical correlation analysis (SRGCCA), used in Crohn’s disease.
To overcome the limitations imposed by the lack of knowledge about the functional classification of the VMB, unsupervised learning strategies may be useful, such as multi-omic factor analysis (MOFA).
Many ODE models can also be used based on the Generalized Lotka–Volterra (gLV) models. These include web-gLV, Microbial dynamical systems inference engine for microbiome time-series analysis (MDSINE), and the learning interactions from microbial time series (LIMITS) method, as well as newer adaptations like the compositional Lotka–Volterra (cLV) and the ‘Biomass Estimation and Model Inference with an Expectation Maximization’ algorithm (BEEM), that are not dependent on the culturability of the community or on the availability of extensive longitudinal datasets.
Newer methods include algorithms like Constant yield expectation framework (conYE) and MMinte, that simulate conditions for community metabolism and growth based on dense interactions between the species. Such ingenious adaptations and approaches could help understand the factors that shape the dynamic VMB in health and disease in different populations.
Lyme disease is caused by a bacterium called Borrelia burgdorferi, which is transmitted by tick bites. The incidence of Lyme disease has been increasing steadily, in part because diagnosis is improving, but also because the range of ticks that spread the disease is expanding. There is still a lot we don’t know about Lyme disease, including how to treat it. But diagnosis may get easier soon, now that researchers have identified 35 genes that are more active in people with a long-term form of the disease; the genes could be useful in Lyme disease diagnostics. These genes may also aid in the development of therapeutics for Lyme. The findings have been outlined in Cell Reports Medicine.
While about 30,000 people are diagnosed with Lyme disease in the United States every year, the true number could be as high as 476,000, according to the Centers for Disease Control and Prevention. People who are diagnosed can be treated with antibiotics, but about 20 percent of patients will go on to develop long-term complications that can affect the neurological system, cause arthritis, or heart problems.
There is a major need for better tests for Lyme disease, and this work is the first to look for transcriptional changes associated with long-term cases.
“We wanted to understand whether there is a specific immune response that can be detected in the blood of patients with long-term Lyme disease to develop better diagnostics for this debilitating disease,” said senior study author Avi Ma’ayan, Ph.D., a Professor and Director of the Mount Sinai Center for Bioinformatics at Icahn Mount Sinai.
In this study, the researchers sequenced the RNA in blood samples from 152 patients with post-treatment Lyme disease symptoms, 72 patient with acute Lyme disease, and 44 uninfected individuals. That RNA can provide a snapshot of how active the patient’s genes were (in the blood) at the time the blood was drawn. The researchers were particularly interested in genes related to the immune response.
This revealed that there was a unique inflammatory pattern in the post-treatment Lyme disease patients compared to those with acute Lyme disease. Within that group of inflammatory genes, there were some that were expressed in a way that is different from other patterns caused by infections with other pathogens. Machine learning was used to create a set of mRNA biomarkers that can differentiate between healthy people, those with post-treatment Lyme disease, or acute Lyme disease. This highly expressed subset of genes has been found before, and could be used to diagnose Lyme disease, as well as which stage it is.
“A diagnostic for Lyme disease may not be a panacea but could represent meaningful progress toward a more reliable diagnosis and, as a result, potentially better management of this disease,” said Dr. Ma’ayan.
The investigators want to repeat the study with other samples, develop a diagnostic tool, and test it with patient samples. They are also interested in using machine learning to find ways to diagnose other challenging disorders.
A vaccine for Lyme disease is also being tested, as outlined in the video.
Shingles, or herpes zoster (HZ), is caused by the varicella zoster virus, which is the same virus that causes chicken pox. Varicella zoster virus may infect people as children, then it can hide away in ganglionic neurons. If the virus reactivates later in life, it can cause a rash and excruciating pain. Scientists have found that shingles can raise the risk of stroke, particularly for people younger than 40. There is a vaccine for shingles, but it has not yet been approved for use in people under age 50.
Now researchers have found that tiny sacs in cells called exosomes, which move cargo like proteins and genetic sequences around cells, can link stroke and shingles. The findings have been reported in The Journal of Infectious Diseases.
While most people are familiar with the painful rash that shingles can cause, many may not know that the risk of a stroke increases in the year after infection, noted corresponding study author Andrew Bubak, Ph.D., an assistant research professor at the University of Colorado School of Medicine. “Importantly, the rash is often completely healed and individuals feel normal but nonetheless are walking around with this significant elevation in stroke risk.”
People who get a shingles facial rash face the highest risk of stroke, which may be related to its proximity to the brain.
The investigators wanted to known if exosomes may be involved, because these sacs can carry dangerous cargo that may cause thrombosis and inflammation far from the site of where an infection is actually occurring, Bubak explained. “That could ultimately lead to a stroke in patients.”
In this study, the researchers obtained plasma from thirteen shingles patients and ten healthy individuals. The plasma samples were collected when the patients had an infection, as well as three months later for some patients. Exosomes were isolated from the samples and analyzed.
The scientists identified blood clot-causing prothrombotic exosomes in the shingles patients. The follow-up samples taken three months later also contained proinflammatory exosomes that could increase the risk of stroke.
Bubak suggested that in some shingles patients, the virus does not become dormant again, or there are persistently circulating exosomes that prolong a prothrombotic state, even after therapy has concluded and the shingles rash is gone. A combination therapy that includes antiviral drugs, and antiplatelet and anti-inflammatory medications may be helpful in those cases, Bubak added.
However, more work will be needed to affirm the conclusions of this small study. If that happens, “this could change clinical practice,” said Bubak.
Physicians are often unaware that shingles and stroke are linked, even though it is important and easy to prevent. There is a vaccine for shingles, and when someone gets it, they should be treated with antiplatelet agents, Bubak noted.
Scientists have developed a gel that can block the receptor for a molecule called succinate, which is a normal byproduct of metabolism. By blocking its receptor, the gel can lower inflammation levels in the mouth, and alter the community of oral bacteria living there. The work, which used a mouse model and human cells in culture, is the first step on the path to a simple, at-home treatment for gum disease. The work has been published in Cell Reports.
Periodontitis, also known as periodontal or gum disease, is one of the most common inflammatory disorders, and is thought to affect almost half of all people older than 30. There are three hallmarks of gum disease: inflammation, an imbalance or dysbiosis in the oral microbiome, and degradation of the support structures and bones underlying teeth. When gum disease is left untreated, it can cause gums to become painful and bleed. Gum disease patients might have trouble chewing, and may eventually start to lose teeth. Serious gum problems can also interfere with other aspects of health.
Right now, there is not a gum disease treatment that can reduce inflammation, while simultaneously limiting disruption to the oral microbiome and preventing the loss of bone, noted co-first study author Yuqi Guo, an associate research scientist at NYU Dentistry. “There is an urgent public health need for more targeted and effective treatments for this common disease.”
Previous research has indicated that a molecule that is generated during cellular metabolism, called succinate, is associated with gum disease. Higher succinate levels also correspond to higher levels of inflammation. Abnormally high succinate levels were found in human dental plaque. This team has also shown that excess succinate activates its receptor and triggers bone loss. Thus, succinate seemed like a good target to aim for when developing treatments for excessive inflammation, and potentially, gum disease or bone loss.
The researchers engineered mice that lacked the succinate receptor. When gum disease was modeled in these succinate receptor-depleted mice, there was less dysbiosis in their oral microbiomes, and lower levels of inflammation compared to normal mice with gum disease. When mice were exposed directly to succinate, gum disease got worse in normal mice, while the mice without succinate receptors did not experience inflammation, dysbiosis, or bone loss.
Now that the researchers had evidence that high succinate levels can lead to gum disease, and blocking the receptor can relieve it, they formulated a gel containing a succinate receptor-blocking compound.
In a cell culture model, the compound lowered inflammation and other biochemical mechanisms leading to bone loss. When a mouse model of gum disease was treated with the gel, the animals had less inflammation and bone loss compared to mice that did not get the treatment, and within only a few days. There was also a shift in the oral microbiome of the mice. Bacteria called Bacteroidetes, which are dominant during gum disease, were reduced after gel treatment.
However, the investigators determined that the gel was not acting as an antibiotic and was not influencing bacterial growth directly. “This suggests that the gel changes the community of bacteria through regulating inflammation,” suggested co-senior study author Deepak Saxena, a professor at NYU Dentistry.
The researchers want to eventually create gels or oral strips that can be used at home by gum disease patients or those at risk, and a formulation that releases more slowly that can be applied to patches of gum disease.
