Hexbyte Glen Cove Fingerprint patterns are linked to limb development genes

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This figure shows human fingerprint patterns are grouped into three types: arch, whorl and loop. Credit: Fudi Wang

In the most comprehensive analysis to date, researchers found that the shapes of fingerprints—whether they are circular, wavy, or winding—are influenced by the genes responsible for limb development instead of skin patterning. The study, presented January 6 in the journal Cell, could help scientists better understand the association between genes and phenotypical traits in humans.

“People may wonder why our team is working on fingerprints,” says Sijia Wang, a geneticist at the Shanghai Institute of Nutrition and Health, of Chinese Academy of Sciences, and co-senior author on the paper. “We started the work purely out of curiosity. But later it turns out fingerprint pattern is associated with genes for limb growth, which are critical for . This provides another classic example of pleiotropy, when multiple phenotypes are interrelated to each other and are affected by the same genes.”

While fingerprints are unique to individuals, they are generally categorized into three types: arch, loop, and whorl. These furrows and ridges begin to form on a fetus’ fingers and toes after the third month of pregnancy. Scientists have suspected that fingerprints have potentially evolved to help grab objects and sense their textures, but exactly how these patterns are formed remains unknown.

Wang and colleagues scanned the DNA of more than 23,000 people across and found that at least 43 regions on the genome are associated with . One of the most influential regions appeared to be regulating the expression of a gene called EVI1, which is known for its role in embryonic limb development.

To test their finding, the team modified the DNA of mice so their expression of EVI1 was turned down. They found that mice with downregulated EVI1 developed abnormal skin patterns on their digits compared with wild-type mice.

Analysis of human data revealed that fingerprint patterns are genetically correlated with finger length. For example, people with whorl-shaped fingerprints on both of their little fingers tend to have longer little fingers than those who do not, and this correlation is strongly linked to genes involved ini .

“We don’t know exactly how the genes shape fingerprint patterns, but it could be determined by the amount of strength from growth that’s put on an embryonic tissue called volar pads that plays an important role in the formation of different patterns of fingerprint,” says Jinxi Li, a geneticist at the Human Phenome Institute at Fudan University in Shanghai, and a co-first author on the paper. She explains that as a fetus’ hands grow, the palms and fingers would stretch and elongate. These forces could turn a whorl into a loop, for example.

Notably, previous research has suggested that EVI1 is linked to risk of leukemia, and some studies have observed that people with more whorl patterns are more susceptible to the disease,” Wang says.

“Many congenital genetic disorders are related to different dermatoglyphic patterns, such as fingerprints,” he says. For example, children with Down’s syndrome are more likely to have a single crease running across the palm of their hands. “Our study suggests that dermatoglyphic patterns are affected by crucial development , which provides a strong theoretical basis for this kind of pleiotropy.”

The research is part of the International Human Phenome Project led by Fudan University in Shanghai that aims to map how the human phenotypical traits are correlated with each other. Next, the team plans to conduct more research on how dermatoglyphic patterns are related to diseases and the underlying pleiotropic mechanism.



More information:
Sijia Wang, Limb development genes underlie variation in human fingerprint patterns, Cell (2022). DOI: 10.1016/j.cell.2021.12.008. www.cell.com/cell/fulltext/S0092-8674(21)01446-X

Journal information:
Cell



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Hexbyte Glen Cove Spatial patterns of gene transcripts captured across single cells of mouse embryo thumbnail

Hexbyte Glen Cove Spatial patterns of gene transcripts captured across single cells of mouse embryo

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A depiction of how sci-Space captures the personalities and localities of individual cells as they come together to form organs in a developing mouse embryo. Credit: Nigel Sussman

A new technique called sci-Space, combined with data from other technologies, could lead to four-dimensional atlases of gene expression across diverse cells during embryonic development of mammals.

Such atlases would map how the gene transcripts in reflect the passage of time, , , and location on the developing embryo. They would also help illuminate the spatial regulation of gene expression.

Mammalian embryonic development is a remarkable phenomenon: a fertilized egg divides repeatedly and turns, in a matter of weeks or months, into a complex organism capable of a myriad of physiological processes and composed of a variety of , tissues, organs, anatomical structures.

A better understanding of how mammals form before birth—particularly the prenatal spatial patterns of gene expression at a single-cell level during embryonic development—could advance biomedical and veterinary research on a variety of conditions. These range from inherited disorders to congenital malformations and developmental delays. Understanding how organs originate might also assist future regenerative medicine efforts.

An international team led by scientists at UW Medicine, Howard Hughes Medical Institute and the Brotman Baty Institute for Precision Medicine in Seattle demonstrated the proof-of-concept of their sci-Space technique in mouse embryos.

Their results are published in the July 2 edition of Science. The lead authors are Sanjay R. Srivatsan of the Department of Genome Sciences at the University of Washington School of Medicine, and Mary C. Regier of the UW Department of Bioengineering.

The senior authors are Jay Shendure, UW Medicine professor of genome sciences, and director of the Brotman Baty Institute, and an investigator at the Allan Discovery Center for Cell Lineage Tracing; Kelly R. Stevens, UW assistant professor of bioengineering; and Cole Trapnell, associate professor of genome sciences. Regier and Stevens are also investigators at the UW Medicine Institute for Stem Cell and Regenerative Medicine Research.

The researchers observed the orchestration of genes in 120,000 cell nuclei. All the body’s somatic cells contain the same DNA code. The researchers captured information on which genes were turned on or off in these nuclei as mouse embryos took shape. The scientists also investigated how cells’ locations in an embryo affected which genes were activated during development.

This technique builds on previous work in which these scientists and other groups developed ways of conducting whole-organism profiling of gene expression and DNA-code accessibility, in thousands of single cells, during embryonic development. They did so to track the emergence and trajectory of various cell types.

How cells are organized spatially—what physical positions they take as an embryo forms—is critical to normal development. Misplacements, disruptions, or cells not showing at the right time in the right spot can cause serious problems or even prenatal death.

However, gaining knowledge on spatial patterns of gene expression has been technically difficult. It has been unwieldy to assay gene transcripts of individual cells over wide swaths of the embryo. This limited the scientific understanding of how spatial organization influences gene expression and, consequently, why which cell types form where, or how neighboring groups of cells influence each other’s future roles.

The scientists on the present study had earlier developed a method to label cell nuclei, a technique they called sci-Plex. They then went on to index single-cell RNA sequencing, with a method called sci-RNA-sequencing.

Now, with sci-Space, by analyzing spatial coordinates and cell gene transcripts the scientists identified thousands of whose expression was anatomically patterned. For example, certain genetic profiles emerged in neurons in the brain and spinal cord and others in cardiac muscle cells in the heart.

The scientists also used spatial and gene profile information to annotate subtypes of cells. For example, while both blood vessel cells and heart muscle might both express the gene for a particular growth factor, only the heart muscle cells produced certain growth factor receptors.

The researchers also observed that cell types varied greatly in the extent of their spatial patterning of gene expression. For example, connective tissue progenitor cells showed a relatively large proportion of spatially restricted gene expression. This observation suggests that subtypes of these cells behave in a position-dependent manner throughout the body.

To measure the power of spatial position on a cell type’s gene transcript profile, the researchers also calculated the physical distance between cells and the angular distance of their gene expression profiles.

“For many cell types, as the between cells increased, so did the angular distance between their transcriptomes,” the researchers noted in their paper. However, they added that this trend varied considerably. It was most pronounced in certain brain and spinal cord cells.

The genetic transcript profiles of some other cell types were highly influenced by their position in the developing embryo. Among these are certain cartilage cells, which become part of the scaffolding for bones of the head and face.

The researchers also studied gene expression dynamics that took place as part of brain cell differentiation and migration during mouse embryonic development. The researchers examined how various brain cell trajectories were anatomically distributed. The researchers did so by using the Allen Institute’s Anatomical Reference Brain Atlas as a guide.

“Cells from each trajectory overwhelmingly occupied distinct brain regions,” the researchers noted. They also observed gradients of developmental maturity in different regions of the brain. These gradients revealed both known and new patterns of migration.

In the future, the researchers hope sci-Space will be further applied to serial sections that span the entire mouse embryo and that cover many points of time.



More information:
S.R. Srivatsan el al., “Embryo-scale, single-cell spatial transcriptomics,” Science (2021). science.sciencemag.org/lookup/ … 1126/science.abb9536

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Hexbyte Glen Cove Uncovering patterns in California's blazing wildfires thumbnail

Hexbyte Glen Cove Uncovering patterns in California’s blazing wildfires

Hexbyte Glen Cove

Brody Hessin, CC BY 4.0” data-thumb=”https://scx1.b-cdn.net/csz/news/tmb/2021/uncoveringpa.jpg”>

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The Apple Fire, seen here burning on 31 July north of Beaumont, Calif., was one of thousands of wildfires that burned across the state in 2020. Credit: Brody Hessin, CC BY 4.0

California’s 2020 wildfire season was unprecedented, the latest tragedy in a decades-long trend of increasing fire. Six of the 20 largest fires in state history burned during the calendar year. In August, a 14,000-strike “lightning siege” sparked 900 fires, and by the end of the year, roughly 17,200 square kilometers had burned across the state.

In California and elsewhere, the environmental context, including topography and vegetation, combines with climate to dictate fire probabilities at any given location. Humans play a role too. Past research shows, for example, that population density and distance to the wildland-urban interface help explain fire frequency.

Chen et al. took a closer look at the variables affecting fires in California, focusing on the Sierra Nevada, the state’s mountainous spine that runs more than 600 kilometers north to south. Using a fire database from state and federal natural resources agencies that spans more than 30 years, from 1984 to 2017, the researchers modeled fire probability in the Sierra Nevada.

The researchers developed a fire probability model with Maxent, a machine learning algorithm, across a 4-by-4-kilometer grid blanketing the mountain range. They evaluated three versions of the model: one considering only physical and climatic variables, one considering only like population density and human modification, and one integrating both natural and human variables.

By looking at each variable’s relative contribution to model performance, the authors found that the annual mean vapor pressure deficit was the most significant predictor of fire occurrence. (Vapor pressure deficit is the difference between the air’s water content and its saturation point.) This result supports the hypothesis that increasing aridity in the region, driven by human-caused , will increase California’s fire risk, the researchers noted.

Population density and fuel amount also play a large role in where fires erupt, according to the modeling. Less densely populated areas had a higher risk, as did more densely vegetated tracts. However, these trends did not hold across all elevations. For instance, population density affects low-elevation forests more than higher-elevation forests.

According to the authors, the results highlight factors shaping wildfires in California and provide region-specific guidance for forest management in the state, which could help limit risk in future years.



More information:
Bin Chen et al. Climate, Fuel, and Land Use Shaped the Spatial Pattern of Wildfire in California’s Sierra Nevada, Journal of Geophysical Research: Biogeosciences (2021). DOI: 10.1029/2020JG005786

This story is republished courtesy of Eos, hosted by the American Geophysical Union. Read the original story here.

Citation:
Uncovering patterns in California’s blazing wildfires (2021, March 2)
retrieved 3 March 2021
from https://phys.org/news/2021-03-uncovering-patterns-california-blazing-wildfires.html

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part may be reproduced without the written permission. The content is provided for information purposes only.

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