Many human diseases can differ between males and females in their prevalence, manifestation, severity or age of onset. Examples include Lupus, where more than 80% of patients are females; Alzheimer’s disease, where females have higher incidence and tend to suffer quicker cognitive decline; and COVID-19 infections that are frequently more severe in males.

These sex differences may have a genetic basis that is attributable to the sex chromosomes. The X chromosome — one of the two sex chromosomes — is known to play an important role in human development and disease. New research led by Penn State College of Medicine reveals for the first time that sex-biased diseases can be attributable to genes that escape X chromosome inactivation (XCI), a process that ensures that females do not overexpress genes on their X-chromosomes.

The team developed a genetic tool that can identify these XCI escape genes, and it may also help in determining whether a female will develop a sex-biased disease and if the disease will become progressively worse over time. The tool may even be useful in understanding the sex differences in immune responses to COVID-19, as the disease is thought to produce more severe symptoms and higher mortality in men than in women.

“The X chromosome plays an important role in human development and disease, yet the X chromosome is frequently ignored in human genetic studies because of bioinformatics challenges in the analysis of the data,” said Laura Carrel, associate professor of biochemistry and molecular biology, Penn State College of Medicine. “Our new method gets around these challenges and allows us to identify XCI escape genes and assess their role in sex-biased diseases. With further research and fine-tuning, we think it could serve as a predictive tool in these disorders and could lead to the identification of new disease treatments and interventions.”

The human genome is organized into 23 pairs of chromosomes, one pair of which is the sex chromosomes. This pair comprises two X chromosomes for females and one X and one Y chromosome for males. Early in embryonic development in females, one of the two X chromosomes is randomly inactivated to ensure that, like in males, only one functional copy of the X chromosome — either the one inherited from the female’s mother or the one inherited from her father — occurs in each cell.

“In females, about 30% of the genes on the X chromosome escape this inactivation — or XCI — leaving them with two functional copies of those genes,” said Carrel. “The question is, does having two copies of those genes make a female more susceptible to traits, such as lupus, that show a sex bias?”

To answer this question, a critical first step is to identify the XCI escape genes. Yet, conducting a chromosome-wide analysis is difficult due to the random nature of XCI in early development, as XCI affects the X chromosome that a female inherits from one parent in some cells, but the other X in other cells.

In their study, which published on Aug. 23 in the journal Genome Research, the researchers developed a novel statistical model, called XCIR (X-Chromosome Inactivation for RNA-seq), that can identify XCI escape genes using bulk RNA-sequencing data, a type of genetic data. The method separately evaluates how much a gene is expressed from each X chromosome. A gene is deemed to escape XCI if the ratio of its expression from the two X chromosomes differs significantly from genes that are known to be X inactivated. The method outperforms other approaches because it can more effectively handle the errors arising from next-generation sequencing technologies and the complex biology of XCI.

“Our method — available in an intuitive, well-documented and freely available software — is more powerful than alternative approaches and is computationally efficient to handle large population-scale datasets,” said Dajiang Liu, associate professor of public health sciences and biochemistry and molecular biology, Penn State College of Medicine.

The team applied its method to a dataset including nearly half a million people, and identified hundreds of traits, including male- or female-biased diseases such as lupus, that may be influenced by these genes that escape XCI. As shown by others, the escape genes also contribute to Alzheimer’s disease and response to COVID-19 infections as well.

“We have developed the methodology needed to establish XCI status for population-sized datasets,” said Liu. “This work highlights the increased importance of XCI escape genes to female-biased diseases and may one day be used to accurately predict disease. Importantly, a better understanding of the sex chromosomes will be an important step in resolving health disparities between the sexes.”

Other Penn State authors on the paper include Renan Sauteraud, a recent doctoral graduate in biostatistics; Jill M. Stahl and Jesica James, former graduate students in genetics and biomedical sciences; Marisa Englebright, research technologist; and Fang Chen, postdoctoral researcher in biostatistics. Xiaowei Zhan, assistant professor of population and data sciences, at the University of Texas Southwestern Medical Center also is an author.

This research was supported by the National Institutes of Health, the Lupus Research Alliance and the Pennsylvania Department of Health.

Overview of the study CREDIT Yoshihiko Tomofuji et al.

Systemic lupus erythematosis (SLE) is an autoimmune disease in which the immune system targets tissues of the body, causing widespread inflammation and affecting multiple organs such as the kidney and the brain. The gut “microbiome”—all the micro-organisms that live in the human gut—is known to be altered in SLE patients. Now, a research team at Osaka University has comprehensively profiled the associations between the gut microbiome and SLE.

Healthy human intestines contain billions of micro-organisms that are essential for the normal functioning of the gut. They protect against pathogens, assist in the metabolism of food, and are also able to affect the immune response. Various diseases have been linked to disturbances of the gut bacteria leading to imbalance, or “dysbiosis,” including autoimmune conditions.

The team isolated DNA from the intestinal microbiome using fecal samples, and then used a next-generation sequencing machine to carry out metagenome shotgun sequencing. This is a technique that allows the sequencing of all genes present in a complex sample by fragmenting all the genomes, sequencing the short fragments, and then reassembling the sequences. By collecting samples from both SLE patients and healthy comparisons, they could comprehensively assess the relationship between the gut microbiome and SLE. “We were able to show distinct changes to the gut microbiome in SLE patients,” says lead author Yoshihiko Tomofuji, “as we found that two species of Streptococcus bacteria, Streptococcus anginosus and Streptococcus intermedius, were significantly increased in the gut microbiome of patients with SLE.”

The gut microbiome can affect the wider functions of the body by altering the population of small molecules, or metabolites, circulating in the liquid part of the blood, called the plasma. The team therefore went on to integrate the gut microbiome data with data on the entire population of plasma metabolites, known as the plasma metabolome.

“Our analysis revealed interactions between the microbiome and the host that were mediated by the metabolome,” explains senior author Yukinori Okada. “A particular molecule known as acylcarnitine had a positive correlation with the SLE-associated bacterium.”

Acylcarnitine is known to induce inflammation and could therefore potentially act as a trigger for the overactivation of the immune system seen in SLE.

This research reveals for the first time the specific microbial landscape of SLE patients, contributing to our understanding of the relationship between the gut microbiome and SLE and providing useful resources for future research.

New web-based clinical tool gives physicians streamlined prediction model for lupus nephritis treatment response. The tool needs further validation before it can be used for treatment decision-making, but it can indicate the likelihood that a patient will not respond well to treatment by the 1-year point. CREDIT Oates laboratory, MUSC, with funding from the NIH and the VA

While systemic lupus erythematosus (SLE) manifests in many ways and affects all organ systems, the complication of lupus nephritis is particularly devastating. This inflammatory kidney disease occurs in about half of patients with SLE, and nearly half of lupus nephritis patients will develop chronic and potentially end-stage kidney disease that requires dialysis or kidney transplant.

Many factors contribute to lupus nephritis, but most patients have lesions with inflammatory cells that invade the capillaries in the kidney, blocking these vessels and leading to kidney dysfunction. This damage can cause irreversible scarring and fibrosis, which in turn may lead to other health problems, such as heart attacks, strokes, infections and even death.

The standard treatment for someone diagnosed with lupus nephritis is one of two immunosuppressive drugs given as a trial for 6 months. At that point, the patient’s disease is assessed to see if they are responding to treatment. If they are not, the FDA has approved several second-line drugs that can be used instead, but these are expensive and are usually reserved for known nonresponders.

The challenge for clinicians is deciding when to add second-line drugs and other resources to help patients whose nephritis is not responsive to the initial treatment, because irreversible kidney damage can occur during the trial period.

To help physicians with this complex decision, Jim Oates, a professor of medicine and Director of the Division of Rheumatology and Immunology at the Medical University of South Carolina, and his colleagues published a study in Lupus Science and Medicine, along with a companion podcast episode, to share a novel prediction tool created in their laboratory.

Because resources in rheumatology are scarce, be they expensive medicines, care coordinators or physician time, it is important to deploy those resources strategically.

“We want to find the patients who are most at risk for poor outcomes but whose outcomes can be reversed,” said Oates.

Because there is no single indicator that shows if a patient’s disease will respond to therapy, Oates and his colleagues developed the new tool by considering many variables and disease indicators. In a rheumatology clinic where physicians see lupus patients day in and day out, the specialists look at the whole picture to decide on treatment and how to monitor patients. The tool mirrors that holistic perspective and may provide a useful clinical aid for clinicians who do not encounter SLE and lupus nephritis as often.

The prediction tool is a web-based application with a simple interface that allows health care providers to submit specific pathology and laboratory values and then see an output that shows the likelihood that a patient will not respond well to therapy over the course of 1 year.

The MUSC team created the tool by looking at kidney biopsy indicator values and other selected laboratory values and then using a variety of machine learning models to test which indicators were most useful for prediction. For testing purposes, the study used values from 83 MUSC Health lupus patients who had renal biopsy information and 1-year follow-up data available. These patients were representative of the population at that clinic and were mainly African American women. While the tool is certainly relevant to this population, more testing is needed to make sure that it applies to other populations as well.

The research team narrowed the testing results to seven key indicators that they used to develop the web-based tool. These are the International Society of Nephrology/Renal Pathology Society biopsy scores for activity, chronicity, interstitial fibrosis and interstitial inflammation as well as the urine protein-to-creatinine ratio, white blood cell count and hemoglobin level. Based on these values, the tool predicts the likelihood of a treatment nonresponse in the patient, with statistical diagrams and values included to guide the clinician.

Because of the small test population and the retrospective nature of the study, Oates cautions that clinicians should not make treatment decisions based on the results until the tool is further validated. But physicians may still find the tool useful in the meantime. For example, if the results indicate a high chance of treatment failure for some patients, they may choose to monitor these patients more frequently or engage a care coordinator or social worker to make sure that patients are taking the medications, can afford them, and can tolerate them.

“One of the things that just breaks my heart is when I prescribe something and a barrier to medication adherence pops up, unbeknownst to me,” said Oates. “When I discover that barrier months later, it is possible that irreversible damage has already occurred.”

Using the tool can help physicians understand which patients will benefit from extra assistance or more frequent monitoring. The ultimate goal for helping lupus nephritis patients is to preserve as much kidney function as possible. Knowing that a patient is not likely to respond to the initial therapy may mean lowering the threshold for when to change therapy, perhaps not waiting the full 6 months of a standard trial.

Now that the tool is established and validated with the initial data, Oates and his colleagues plan to expand the validation to different populations at both MUSC and other institutions. The laboratory is working on other prediction tools as well, but some of the biomarkers in those studies are not widely available or involve genetic markers that need further study.

Oates is gratified that even at this stage the new tool can begin to give immediate benefits. “I delved into making this tool because the variables are available and can make an impact now,” he said. “And I’ve been amazed by the high percentage of lupus patients who agreed to be part of this long-term study to help their fellow patients. They really make this work possible.”