Known collectively as the human microbiome, these tiny entities influence an astonishing range of activities, from metabolism to behavior and play a central role in health and disease. Some 39 trillion non-human microbes flourish on and within us, in a ceaseless, interdependent bustle. Together, they make up over half of the human body’s cells, though they may possess 500 times as many genes as are found in human cells. Identifying and making sense of this microbial mélange has been a central challenge for researchers.

In a new study, Qiyun Zhu and his colleagues describe a new method for probing the microbiome in unprecedented detail. The technique provides greater simplicity and ease of use compared with existing approaches. Using the new technique, the researchers demonstrate an improved ability to pinpoint biologically relevant characteristics, including a subject’s age and sex based on microbiome samples.

The innovative research holds the promise of rapidly advancing investigations into the mysteries of the microbiome. With such knowledge, researchers hope to better understand how these microbes collectively act to safeguard human health and how their dysfunction can lead to a broad range of diseases. In time, drugs and other therapies may even be tailor-made based on a patient’s microbiomic profile.

Professor Zhu is a researcher in the Biodesign Center for Fundamental and Applied Microbiology and ASU’s School of Life Sciences. The research team includes collaborators from the University of California, San Diego, including co-corresponding author Rob Knight, Zhu’s former mentor.

The group’s research results appear in the current issue of the journal mSystems.

Tools of the trade

Two powerful technologies have been used to help researchers unlock the diversity and complexity of the microbiome, by sequencing the microbial DNA present in a sample. These are known as 16S and metagenomic sequencing. The technique described in the current study draws on the strengths of both methods to create a new way of processing data from the microbiome.

“We borrow some of the wisdom that developed from 16S RNA sequencing and apply it to metagenomics,” Zhu says. Unlike other sequencing methods, including 16S, metagenomics allows researchers to sequence all the DNA information present in a microbiome sample. But the new study shows that the metagenomic approach has room for improvement. “The way people currently analyze metagenomic data is limited, because whole genome data has to first be translated into taxonomy.”

The new technique, known as Operational Genomic Units (OGU) does away with the laborious and sometimes misleading practice of assigning taxonomic categories like genus and species to the multitude of microbes present in a sample. Instead, the method uses individual genomes as the basic units for statistical analysis and simply attempts to align sequences present in a sample to sequences found in existing genomic databases.

By doing this, researchers can get much more fine-grained resolution, which is particularly useful when microbes are present that are closely related in DNA sequence. This is true because most taxonomic classifications are based on sequence similarity. If two sequences differ by less than a certain threshold, they fall into the same taxonomic category, however the OGU approach can help researchers tell them apart.

Further, the method overcomes errors in taxonomy that persist as relics from the pre-sequencing epoch, when different species were defined by their morphology rather than from DNA sequence data.

In addition to improvements in resolution and simplicity, OGU can help researchers analyze data using what are known as phylogenetic trees. As the name implies, these are branching structures that can describe the degree of relatedness between organisms, based on their sequence similarity. Just as two distantly related species like worms and antelope will appear on more distant branches of a phylogenetic tree, so will more distantly related bacteria and other constituents of the microbiome.

Innovations in sequencing

The most widely used technique for probing the microbiome, known as 16S ribosomal RNA sequencing or just 16S, relies on a simple idea. All bacteria have a 16S gene, which is essential to the machinery bacteria need to initiate protein synthesis. The bacterial 16S gene, measuring 1500 base pairs in length, consists of distinct regions. Some of these regions change very little between different bacteria and over evolutionary timeframes, while others are highly variable.

Researchers realized that the conserved and variable regions of the 16S gene allow it to act as a molecular clock, keeping track of bacteria that are more closely or more distantly related, based on their sequence similarity. Thus, the 8 conserved and 9 variable regions of 16S can be used to fingerprint bacteria.

To do this, a microbiome sample is first collected. This could be a fecal sample, to evaluate the gut microbiome, or a sample from the skin or from the mouth. Each body site is home to a different bacterial menagerie.

Next, PCR technology is used to amplify portions of the 16S gene. By sequencing highly conserved regions, a broad swath of bacteria can be identified, while sequencing of variable regions helps narrow the identity of particular bacteria.

Although 16S is an inexpensive and well-developed method, it has limitations. The technique can only give a general idea of the kinds of bacteria present, with limited resolution. In general, 16S is only accurate to the genus level of identification.

Enter metagenomic sequencing. This technique sequences the full genomes of all microbes present in a microbiome sample, (not just bacteria, as with 16S). Metagenomics allows researchers to sequence thousands of organisms in parallel, providing accurate, species-level resolution. The greater resolution however does come with costs. Metagenomic data is far richer and more computationally challenging to analyze than 16S data and more expensive in time and money to process.

A new path for metagenomics

The OGU technique streamlines metagenomic sequencing, while providing even greater resolution. The approach classifies microbes in a sample strictly according to their alignment with a reference database — no taxonomic assignment required. The approach enables researchers to evaluate the degree of species diversity present in a sample.

Compared with 16S and standard metagenomic sequencing, the new approach is superior in ferreting out biologically relevant information. Using the classic Human Microbiome Project dataset of 210 metagenomes sampled from seven body sites of male and female human subjects, the study demonstrates better correlation between body site and host sex.

Next, 6,430 stool samples collected through a random sampling of the Finnish population were analyzed, using both 16S and metagenomic sequencing. The samples belong to a large, randomly sampled cohort of the Finnish population, known as FINRISK. The aim was to predict the age of sampled individuals, based on gut microbial composition. Again, the OGU method outperformed 16S and conventional metagenomic analysis, providing more accurate predictions.

New research drawing on still larger datasets will further enhance the resolution of the new technique and expand the descriptive power of taxonomy-independent analysis.

Similarly, across the globe — in 19 Eastern European countries, 25 Muslim countries and in Israel — low belief in evolution was linked to higher biases within a person’s group, prejudicial attitudes toward people in different groups and less support for conflict resolution.

The findings supported the hypothesis of lead author Stylianos Syropoulos, a Ph.D. candidate in the War and Peace Labof senior author Bernhard Leidner, associate professor of social psychology. They collaborated with co-first author Uri Lifshin at Reichman University in Israel and co-authors Jeff Greenberg and Dylan Horner at the University of Arizona in Tucson. The researchers theorized that belief in evolution would tend to increase people’s identification with all humanity, due to the common ancestry, and would lead to less prejudicial attitudes.

“People who perceive themselves as more similar to animals are also people who tend to have more pro-social or positive attitudes toward outgroup members or people from stigmatized and marginalized backgrounds,” Syropoulos explains. “In this investigation, we were interested in examining whether belief in evolution would also act in a similar way, because it would reinforce this belief that we are more similar to animals.”

In eight studies involving different areas of the world, the researchers analyzed data from the American General Social Survey (GSS), the Pew Research Center and three online crowdsourced samples. In testing their hypothesis about the associations of different levels of belief in evolution, they accounted for education, political ideology, religiosity, cultural identity and scientific knowledge.

“We found the same results each time, which is basically that believing in evolution relates to less prejudice, regardless of the group you’re in, and controlling for all of these alternative explanations,” Syropoulos says.

For example, religious beliefs, like political ideology, were measured separately from a belief or disbelief in evolution, the researchers note. “Regardless of whether one considers religion an important part of their life, belief in evolution relates to less prejudice independently from belief, or lack thereof, in God or any particular religion,” Syropoulos says.

Leidner adds, “This whole effect and pattern seems to be present in all major political systems. It’s very much a human phenomenon, no matter where you are in the world.”

The researchers note that Darwin’s 19^th century theory of evolution has been cited to perpetrate racism, prejudice and homophobia, in part through the phrase, “survival of the fittest,” used to describe the process of natural selection.

“There have been theoretical accounts that predict the opposite of what we found, so it was exciting for us to show that this actually is not the case, that the opposite is true and that belief in evolution seems to have pretty positive effects,” Leidner says.

The U.S.-based study involved data from 1993, 1994, 2000, 2006, 2008, 2010, 2012, 2014, 2016 and 2018 — the years the GSS surveyed Americans about their beliefs in evolution, as well as measures of attitudes toward immigrants, Blacks, affirmative action, LGBTQ people and other social matters.

The data analysis showed unfailingly “that the disbelief in human evolution is the driving factor and most consistent predictor of prejudice in comparison to other relevant constructs,” the paper states.

In the Israel-based study, people with a higher belief in evolution were more likely to support peace among Palestinians, Arabs and Jews. In the study involving countries in the Islamic world, belief in evolution was associated with less prejudice toward Christians and Jews. And in the study based in Eastern Europe, where Orthodox Christians are the majority, a belief in evolution was linked with less prejudice toward gypsies, Jews and Muslims.

Syropoulos posits that a belief in evolution may expand people’s “moral circle,” leading to a sense that “we have more in common than things that are different.”

The findings also suggest that “teaching evolution seems to have side effects that might make for a better or more harmonious society,” Leidner adds.

The next step, the researchers say, is to investigate how evolution is taught in the classroom and work toward developing models to study and strengthen the positive effects.

“These findings reveal a core principle of the nervous system, flexibility in the kinds of calculations the brain makes to represent aspects of the sensory world,” said Krishnan Padmanabhan, Ph.D., an associate professor of Neuroscience and senior author of the study recently published in Cell Reports. “Our work provides scientists with new tools to quantify and interpret the patterns of activity of the brain.”

Researchers developed a model to simulate the workings of the early olfactory system — the network the brain relies on for smelling. Employing computer simulations, they found a specific set of connections, called centrifugal fibers, which carry impulses from other parts of the central nervous system to the early sensory regions of the brain, played a critical role. These centrifugal fibers act as a switch, toggling between different strategies to efficiently represent smells. When the centrifugal fibers were in one state, the cells in the piriform cortex — where the perception of an odor forms — relied on the pattern of activity within a given instant in time. When the centrifugal fibers were in the other state, the cells in the piriform cortex improved both the accuracy and the speed with which cells detected and classified the smell by relying on the patterns of brain activity across time.

These processes suggest the brain has multiple responses to representing a smell. In one strategy, the brain uses a snapshot, like a painting or a photograph, at a given moment to capture the essential features of the odor. In the other strategy, the brain keeps track of the evolving patterns. It is attuned to which cells turn on and off and when — like a symphony.

The mathematical models the researchers developed highlight the critical feature of the nervous system — not only diversity in terms of the components that make up the brain but also how these components work together to help the brain experience the world of smell. “These mathematical models reveal critical aspects of how the olfactory system in the brain might work and could help build brain-inspired artificial computing systems,” Padmanabhan said. “Computational approaches inspired by the circuits of the brain such as this have the potential to improve the safety of self-driving cars, or help computer vision algorithms more accurately identify and classify objects in an image.”

Additional authors include Zhen Chen of the University of Rochester. The research was funded by the National Institutes of Health, the National Science Foundation, the Cystinosis Research Foundation, and the Del Monte Institute for Neuroscience at the University of Rochester Pilot Program.

Story Source:

Materials provided by University of Rochester Medical Center. Original written by Kelsie Smith Hayduk. Note: Content may be edited for style and length.

The study, published Friday in JAMA Pediatrics, is the first large-scale research effort to compare the health outcomes of coronavirus infection from Omicron to Delta in children 4 and younger — the age group not yet able to be vaccinated.

The findings show that the Omicron variant is 6-8 times more infectious than the Delta variant. The severe clinical outcomes ranged from a 16% lower risk for emergency room visits to 85% less risk for mechanical ventilation. And about 1.8% of children infected with Omicron were hospitalized, compared to 3.3% with Delta.

The Case Western Reserve-led team analyzed the electronic health records of more than 651,640 children in the United States who had medical encounter with healthcare organizations between 9/2021-1/2022-including more than 22,772 children infected with Omicron in late December and late January — to more than 66,000 children infected when Delta was prevalent in the fall. The study also compared the records of more than 10,000 children immediately before the detection of Omicron in the U.S., but when Delta was still predominant.

Children younger than 5 are not yet eligible for COVID-19 vaccines and have a low rate of previous SARS-CoV-2 infections, which also limits their pre-existing immunity.

The team examined clinical health outcomes for pediatric patients during a 14-day window following SARS-CoV-2 infection. Among the factors they reviewed were: emergency room visits, hospitalizations, ICU admissions and mechanical ventilation use.

Further demographic data analysis found that children infected with Omicron were on average younger-1.5 years of age versus 1.7 years-and had fewer comorbidities.

“The major conclusion to our research was that many more children were infected with Omicron when compared to Delta, but the children who are infected are not impacted as severely as were children infected with the Delta variant,” said Pamela Davis, the Arline H. and Curtis F. Garvin Research Professor at the Case Western Reserve School of Medicine. “However, because there are so many more children infected, our hospitals were affected over the winter months by an influx of young children.”

“We saw the number of hospitalizations within this age group skyrocket in January of this year because the infection rate of Omicron is about 10 to 15 times compared to that of the Delta variant,” said Rong Xu, professor of biomedical infomatics and director of the Center for AI in Drug Discovery at the School of Medicine. “Omicron is less severe than Delta, however, the reduction of the severity range in clinical outcomes is only 16 to 85%. Furthermore, since so many un-vaccinated children were infected, the long-term effects of COVID-19 infections on the brain, heart, immune systems and other organs of children remains unknown and worrisome. “

The CDC recommends those age 5 and older receive a COVID-19 vaccine, and fully vaccinated people 12 and older receive a booster shot. According to updated guidance by the CDC, Americans no longer need to mask indoors in counties with low or medium “Covid-19 Community Level.”

Story Source:

Materials provided by Case Western Reserve University. Note: Content may be edited for style and length.