Echidnas, the only mammals that lay eggs, have an unusual reproductive system that includes a pseudo-pouch where their young, called puggles, grow and develop during lactation. Researchers from the University of Adelaide have made a fascinating discovery about the microbiome in these pseudo-pouches, which changes significantly while the mother is nursing her young.

The study, published in FEMS Microbiology Ecology, reveals that the microbial communities in echidna pseudo-pouches undergo dramatic changes during lactation, creating an environment that’s conducive to the health and well-being of their puggles. This is particularly important since puggles hatch at a very early developmental stage, lacking a functional immune system.

“We know that the reproductive microbiome is crucial for infant health in many species, including humans,” says Isabella Wilson, lead researcher on the study. “However, little was known about how it functions in egg-laying monotremes like echidnas.”

One of the key findings of this research is that during lactation, the pseudo-pouch microbial communities show significant differences in composition compared to samples taken outside of breeding season or during courtship and mating. This suggests that the echidna pseudo-pouch environment changes during lactation to accommodate young that lack a functional adaptive immune system.

The way puggles suckle may contribute to this shift in microbes. Unlike other species, echidnas don’t have nipples; instead, their young rub their beaks against a part of the pseudo-pouch called the milk patch, causing milk to come out of the skin, similar to a sweat or oil gland.

Compounds within the milk and from the skin probably contribute to the changes seen in the pseudo-pouch microbiota during lactation. This study highlights the importance of understanding these unique reproductive dynamics for conservation efforts and breeding programs for echidnas.

The research also sheds light on previous findings that showed big differences in the gut microbiome between echidnas in zoos and those in the wild. Surprisingly, no major difference was found in the pseudo-pouch microbiota between zoo-managed and wild animals. This suggests that the milk, rather than external environmental factors like captivity, is what primarily shapes the bacterial landscape of the pseudo-pouch.

For conservation efforts and breeding programs, it’s essential to learn more about the bacteria found in echidna pseudo-pouches and how they affect echidna health. This knowledge will help ensure the well-being of these unique animals and their young, ultimately contributing to the preservation of this fascinating species.

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Harnessing Sunlight: A Breakthrough in Carbon Capture Technology

Scientists at Cornell University have developed a groundbreaking method to capture and release carbon dioxide using an energy source that’s abundant, clean, and free: sunlight. This innovative approach mimics the way plants store carbon, making it a game-changer in the fight against global warming.

The research team, led by Phillip Milner, associate professor of chemistry and chemical biology, has created a light-powered system that can separate carbon dioxide from industrial sources. They’ve used sunlight to make a stable enol molecule reactive enough to “grab” the carbon, and then driven an additional reaction to release the carbon dioxide for storage or reuse.

This is the first light-powered separation system for both carbon capture and release, and it has significant implications for reducing costs and net emissions in current methods of carbon capture. The team tested their system using flue samples from Cornell’s Combined Heat and Power Building, and it was successful in isolating carbon dioxide, even with trace contaminants present.

Milner is excited about the potential to remove carbon dioxide from air, which he believes is the most practical application. “Imagine going into the desert, you put up these panels that are sucking carbon dioxide out of the air and turning it into pure high-pressure carbon dioxide,” he said. This could then be put in a pipeline or converted into something on-site.

The research team is also exploring how this light-powered system could be applied to other gases, as separation drives 15% of global energy use. “There’s a lot of opportunity to reduce energy consumption by using light to drive these separations instead of electricity,” Milner said.

The study was supported by the National Science Foundation, the U.S. Department of Energy, the Carbontech Development Initiative, and Cornell Atkinson. This breakthrough has the potential to revolutionize carbon capture technology and make it more efficient, effective, and sustainable.

The world of newborn genetic screening has come a long way since the launch of the BabySeq Project over a decade ago. Today, more than 30 international initiatives are exploring the expansion of this critical public health tool using genomic sequencing (NBSeq). However, a recent study by researchers from Mass General Brigham highlights the substantial variability in gene selection among these programs. In a paper published in Genetics in Medicine, the researchers offer a data-driven approach to prioritizing genes for public health consideration.

“It’s essential that we be thoughtful about which genes and conditions are included in genomic newborn screening programs,” said co-senior author Nina Gold, MD, director of Prenatal Medical Genetics and Metabolism at Massachusetts General Hospital (MGH), a founding member of the Mass General Brigham healthcare system. “By leveraging machine learning, we can provide a tool that helps policymakers and clinicians make more informed choices, ultimately improving the impact of genomic screening programs.”

The researchers introduced a machine learning model that brings structure and consistency to the selection of genes for NBSeq programs. This is the first publication from the International Consortium of Newborn Sequencing (ICoNS), founded in 2021 by senior author Robert C. Green, MD, MPH, director of the Genomes2People Research Program at Mass General Brigham, and David Bick, MD, PhD, of Genomics England in the United Kingdom.

The study analyzed 4,390 genes included across 27 NBSeq programs, identifying key factors influencing gene inclusion. While the number of genes analyzed by each program ranged from 134 to 4,299, only 74 genes (1.7%) were consistently included in over 80% of programs. The strongest predictors of gene inclusion were whether the condition is on the U.S. Recommended Uniform Screening Panel, has robust natural history data, and if there is strong evidence of treatment efficacy.

Using these insights, the team developed a machine learning model incorporating 13 predictors, achieving high accuracy in predicting gene selection across programs. The model provides a ranked list of genes that can adapt to new evidence and regional needs, enabling more consistent and informed decision-making in NBSeq initiatives worldwide.

“This research represents a significant step toward harmonizing NBSeq programs and ensuring that gene selection reflects the latest scientific evidence and public health priorities,” said Green.