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Missing Y chromosome kept us apart from Neanderthals

The Y chromosome is a hindrance

Modern humans diverged from Neanderthals some 600,000 years ago – and a new study shows the Y chromosome might be what kept the two species separate.

It seems we were genetically incompatible with our ancient relatives – and male fetuses conceived through sex with Neanderthal males would have miscarried. We knew that some cross-breeding between us and Neanderthals happened more recently – around 100,000 to 60,000 years ago.

Neanderthal genes have been found in our genomes, on X chromosomes, and have been linked to traits such as skin colour, fertility and even depression and addiction. Now, an analysis of a Y chromosome from a 49,000-year-old male Neanderthal found in El Sidrón, Spain, suggests the chromosome has gone extinct seemingly without leaving any trace in modern humans.

This could simply be because it drifted out of the human gene pool or, as the new study suggests, it could be because genetic differences meant that hybrid offspring who had this chromosome were infertile – a genetic dead end.

Four gene mutations

Fernando Mendez of Stanford University, and his colleagues compared the Neanderthal Y chromosome with that of chimps, and ancient and modern humans.

They found mutations in four genes that could have prevented the passage of Y chromosome down the paternal line to the hybrid children.

“Some of these mutations could have played a role in the loss of Neanderthal Y chromosomes in human populations,” says Mendez.

For example, a mutation in one of the genes, KDM5D that plays a role in cancer suppression, has previously been linked to increased risk of miscarriages as it can elicit an immune response in pregnant mothers.

“That could be one reason why we don’t see Neanderthal Y chromosomes in modern human populations,” says Mark Pagel an evolutionary biologist at the University of Reading.

It could also be one factor keeping the two species as separate species.

The researchers also used the new DNA sequences to estimate the time when the most recent common ancestor of Neanderthal and modern human Y chromosomes existed. They came up with a figure of around 590,000 years ago, which agrees with other estimates for the split of the two groups.

 

Journal reference: The American Journal of Human Genetics, DOI: 10.1016/j.ajhg.2016.02.023

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How a Killer Parasite Evolved from Pond Scum

A genomic study of a corkscrew-shaped parasite living in the guts of insects shows how it originated from algae — just like another notorious killer, the malaria parasite. 
 
The transition to a parasitic lifestyle completely dependent on a host usually comes with genomic reduction — why keep genes that help you function and take care of yourself when you’re living off someone else at their expense? The most extreme of these transitions is from free-living algae making their own food (autotrophs) to obligate parasites. 
 
Helicosporidium parasiticum — which kills juvenile blackflies, caterpillars, beetles and mosquitoes — was first described about a century ago, but we still don’t know much about their origin. When a team led by Patrick Keeling from the University of British Columbia sequenced the genome of Helicosporidium, they found that the parasitic protist evolved from green algae. But surprisingly, the parasite kept most of its ancestral functions. Compared with the closely related green algae, Coccomyxa subellipsoidea and Chlorella variabilis, the parasite’s genome was hardly reduced at all.
 
There is, however, one major exception: It preserved virtually all of its genes except those needed for harvesting light and photosynthesis, which it doesn’t need as a parasite. “It’s as if photosynthesis has been surgically removed from its genome,” Keeling says in a news release
 
The researchers have previously shown that the malaria pathogen, Plasmodium, shares a common evolutionary lineage with the algae known for those toxic red tides. But unlike Helicosporidium, which lost nearly nothing, malaria reduced its genome dramatically and became dependent on its host for nutrients. “Both malaria and Helicosporidium started out as alga and ended up as intracellular parasites preying on animals, but they have done it in very different ways,” Keeling says.
 
By comparing how parasites evolve at the molecular level in these two distantly related lineages, the researchers hope to better understand their methods of infection. Maybe it could help control the population of pest-insect hosts.
 
The work was published in PLOS Genetics 


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Y Chromosome Is More Than a Sex Switch

Here to stay. The Y chromosome is small compared with the X, but is required to keep levels of some genes high enough for mammals to survive.

The small, stumpy Y chromosome—possessed by male mammals but not females, and often shrugged off as doing little more than determining the sex of a developing fetus—may impact human biology in a big way. Two independent studies have concluded that the sex chromosome, which shrank millions of years ago, retains the handful of genes that it does not by chance, but because they are key to our survival. The findings may also explain differences in disease susceptibility between men and women.

“The old textbook description says that once maleness is determined by a few Y chromosome genes and you have gonads, all other sex differences stem from there,” says geneticist Andrew Clark of Cornell University, who was not involved in either study. “These papers open up the door to a much richer and more complex way to think about the Y chromosome.”

The sex chromosomes of mammals have evolved over millions of years, originating from two identical chromosomes. Now, males possess one X and one Y chromosome and females have two Xs. The presence or absence of the Y chromosome is what determines sex—the Y chromosome contains several genes key to testes formation. But while the X chromosome has remained large throughout evolution, with about 2000 genes, the Y chromosome lost most of its genetic material early in its evolution; it now retains less than 100 of those original genes. That’s led some scientists to hypothesize that the chromosome is largely indispensable and could shrink away entirely.

To determine which Y chromosome genes are shared across species, Daniel Winston Bellott, a biologist at the Whitehead Institute for Biomedical Research in Cambridge, Massachusetts, and colleagues compared the Y chromosomes of eight mammals, including humans, chimpanzees, monkeys, mice, rats, bulls, and opossums. The overlap, they found, wasn’t just in those genes known to determine the sex of an embryo. Eighteen diverse genes stood out as being highly similar between the species. The genes had broad functions including controlling the expression of genes in many other areas of the genome. The fact that all the species have retained these genes, despite massive changes to the overall Y chromosome, hints that they’re vital to mammalian survival.

“The thing that really came home to us was that these ancestral Y chromosome genes—these real survivors of millions of years of evolution—are regulators of lots of different processes,” Bellott says.

Bellott and his colleagues looked closer at the properties of the ancestral Y chromosome genes and found that the majority of them were dosage-dependent—that is, they required two copies of the gene to function. (For many genes on the sex chromosomes, only one copy is needed; in females, the copy on the second X chromosome is turned off and in males, the gene is missing altogether.) But with these genes, the female has one on each X chromosome and the male has a copy on both the X and Y chromosomes. Thus, despite the disappearance of nearby genes, these genes have persisted on the Y chromosome, the team reports online today inNature.

“The Y chromosome doesn’t just say you’re a male; it doesn’t just say you’re a male and you’re fertile. It says that you’re a male, you’re fertile, and you’re going to survive,” Bellott explains. His group next plans to look in more detail at what the ancestral Y chromosome genes do, where they’re expressed in the body, and which are required for an organism’s survival.

In a second Nature paper, also published online today, another group of researchers used a different genetic sequencing approach, and a different set of mammals, to ask similar questions about the evolution of the Y chromosome. Like Bellott’s paper, the second study concluded thatone reason that the Y chromosome has remained stable over recent history is the dosage dependence of the remaining genes.

“Knowing now that the Y chromosome can have effects all over the genome, I think it becomes even more important to look at its implications on diseases,” Clark says. “The chromosome is clearly much more than a single trigger that determines maleness.” Because genes on the Y chromosome often vary slightly in sequence—and even function—from the corresponding genes on the X, males could have slightly different patterns of gene expression throughout the body compared with females, due to not only their hormone levels, but also their entire Y chromosome. These gene expression variances could explain the differences in disease risks, or disease symptoms, between males and females, Clark says.


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How Cells Get Their Identity

 How Cells Get Their Identity
Liver, muscle, blood, and all other cells in the body have the same set of genes, yet each looks and functions differently. Specialization is driven by regulatory DNA that helps determine which genes are turned on and when. That regulatory DNA includes promoters, which are located at the start of the gene, and enhancers, which can sit some distance away. Now, an international team led by Japanese researchers has developed comprehensive atlases of the promoters and enhancers, The Scientist reports today. The atlases were published online today in Nature, with several more papers in other journals making use of these atlases.


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Deletion of a gene reduces body fat, slows down aging in mice

A single gene appears to play a crucial role in coordinating the immune system and metabolism, and deleting the gene in mice reduces body fat and extends lifespan, according to new research by scientists at the Jean Mayer USDA Human Nutrition Research Center (USDA HNRCA) on Aging at Tufts University and Yale University School of Medicine. Their results are reported online today in the Proceedings of the National Academy of Sciences.

Based on gene expression studies of fat tissue conducted at the USDA HNRCA, the Tufts University researchers initiated studies of the role of FAT10 in adipose tissue and metabolism. “No one really knew what the FAT10 gene did, other than it was ‘turned on’ by inflammation and that it seemed to be increased in gynecological and gastrointestinal cancers.” said co-author Martin S. Obin, Ph.D., an adjunct scientist in the Functional Genomics Core Unit at the USDA HNRCA at Tufts University. “Turning off the FAT10 gene produces a variety of beneficial effects in the mice, including reduced body fat, which slows down aging and extends lifespan by 20 percent.”
Typically, mice gain fat as they age. The authors observed that activation of the FAT10 gene in normal mice increases in fat tissue with age. Mice lacking FAT10 consume more food, but burn fat at an accelerated rate. As a result, they have less than half of the fat tissue found in normal, aged mice. At the same time their skeletal muscle ramps up production of an immune molecule that increases their response to insulin, resulting in reduced circulating insulin levels, protection against type 2 diabetes and longer lifespan.
The authors note that eliminating FAT10 will not fully address the dilemma of aging and weight gain. “Laboratory mice live in a lab under ideal, germ-free conditions,” said Obin, who is also an associate professor at the Friedman School of Nutrition Science and Policy at Tufts University. “Fighting infection requires energy, which can be provided by stored fat. Mice without the FAT10 gene might be too lean to fight infection effectively outside of the laboratory setting. More research is needed to know how to achieve that balance in mice and then hopefully, at some point, people.”
The possibilities for future research of FAT10 are exciting. Recent high-profile studies reported that FAT10 interacts with hundreds of other proteins in cells. Now the Tufts and Yale researchers have demonstrated that it impacts immune response, lipid and glucose metabolism, and mitochondrial function.
“Now there is dramatic road map for researchers looking at all of the proteins that FAT10 gets involved with,” said co-first and corresponding author Allon Canaan, Ph.D., an associate scientist in the Department of Genetics at Yale. “Blocking what FAT10 does to coordinate immunity and metabolism could lead to new therapies for metabolic disease, metabolic syndrome, cancer and healthy aging, because when we knock it out the net result is mice live longer.”
Canaan and colleagues initially developed the FAT10-deficient mouse to study the role of FAT10 in sepsis. In an attempt to increase sensitivity for sepsis, Canaan aged the FAT10 knockout mice and made the discovery that mice lacking the gene were lean and aged more slowly. The mice appear younger and more robust than comparably-aged normal mice, have better muscle tone, and do not develop age-related tumors.

More information: Canaan A; Defuria J; Perelman E; Schulz V; Seay M; Tuck D; Flavell R; Snyder M; Obin M; and Weissman S. “Extended Lifespan and Reduced Adiposity in Mice Lacking the FAT10 Gene.” Proceedings of the National Academy of Sciences. Published online ahead of print March 24, 2014. http://www.pnas.org/cgi/doi/10.1073/pnas.1323426111


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Equation to describe competition between genes

In biology, scientists typically conduct experiments first, and then develop mathematical or computer models afterward to show how the collected data fit with theory. In his work, Rob Phillips flips that practice on its head. The Caltech biophysicist tackles questions in cellular biology as a physicist would — by first formulating a model that can make predictions and then testing those predictions. Using this strategy, Phillips and his group have recently developed a mathematical model that accounts for the way genes compete with each other for the proteins that regulate their expression.

A paper describing the work appears in the current issue of the journal Cell. The lead authors on the paper are Robert Brewster and Franz Weinert, postdoctoral scholars in Phillips’s lab.
“The thing that makes this study really interesting is that we did our calculations before we ever did any experiments,” says Phillips, the Fred and Nancy Morris Professor of Biophysics and Biology at Caltech and principal investigator on the study. “Just as it is amazing that we have equations for the orbits of planets around stars, I think it’s amazing that we are beginning to be able to write equations that predict the complex behaviors of a living cell.”
A number of research teams are interested in modeling gene expression — accurately describing all the processes involved in going from a gene to the protein or other product encoded by that DNA. For simplicity’s sake, though, most such models do not take competition into consideration. Instead, they assume that each gene has plenty of whatever it needs in order to be expressed — including the regulatory proteins called transcription factors. However, Phillips points out, there often is not enough transcription factor around to regulate all of the genes in a cell. For one thing, multiple copies of a gene can exist within the same cell. For example, in the case of genes expressed on circular pieces of DNA known as plasmids, it is common to find hundreds of copies in a single cell. In addition, many transcription factors are capable of binding to a variety of different genes. So, as in a game of musical chairs, the genes must compete for a scarce resource — the transcription factors.
Phillips and his colleagues wanted to create a more realistic model by adding in this competition. To do so, they looked at how the level of gene expression varies depending on the amount of transcription factor present in the cell. To limit complexity, they worked with a relatively simple case — a gene in the bacterium E. coli that has just one binding site where a transcription factor can attach. In this case, when the transcription factor binds to the gene, it actually prevents the gene from making its product — it represses expression.
To build their mathematical model, the researchers first considered all the various ways in which the available transcription factor can interact with the copies of this particular gene that are present in the cell, and then developed a statistical theory to represent the situation.
“Imagine that you go into an auditorium, and you know there are a certain number of seats and a certain number of people. There are many different seating arrangements that could accommodate all of those people,” Phillips says. “If you wanted to, you could systematically enumerate all of those arrangements and figure out things about the statistics — how often two people will be sitting next to each other if it’s purely random, and so on. That’s basically what we did with these genes and transcription factors.”
Using the resulting model, the researchers were able to make predictions about what would happen if the level of transcription factor and the number of gene copies were independently varied so that the proteins were either in high demand or there were plenty to go around, for example.
With predictions in hand, the researchers next conducted experiments while looking at E. coli cells under a microscope. To begin, they introduced the genes on plasmids into the cells. They needed to track exactly how much transcription factor was present and the rate of gene expression in the presence of that level of transcription factor. Using fluorescent proteins, they were able to follow these changes in the cell over time: the transcription factor lit up red, while the protein expressed by the gene without the transcription factor attached glowed green. Using video fluorescence microscopy and a method, developed in the lab of Caltech biologist Michael Elowitz, for determining the brightness of a single molecule, the researchers were able to count the level of transcription factor present and the rate at which the green protein was produced as the cells grew and divided.
The team found that the experimental data matched the predictions they had made extremely well. “As expected, we find that there are two interesting regimes,” says Brewster. “One is that there’s just not enough protein to fill the demand. Therefore, all copies of the gene cannot be repressed simultaneously, and some portion will glow green all the time. In that case, there are correlations between the various copies of the genes. They know, in some sense, that the others exist. The second case is that there is a ton of this transcription factor around; in that case, the genes act almost exactly as if the other genes aren’t there — there is enough protein to shut off all of the genes simultaneously.”
The data fit so well with their model, in fact, that Phillips and his colleagues were able to use plots of the data to predict how many copies of the plasmid would be found in a cell as it grew and multiplied at various points throughout the cell cycle.
“Many times in science you start out trying to understand something, and then you get so good at understanding it that you are able to use it as a tool to measure something else,” says Phillips. “Our model has become a tool for measuring the dynamics of how plasmids multiply. And the dynamics of how they multiply isn’t what we would have naively expected. That’s a little hint that we’re pursuing right now.”
Overall, he says, “this shows that the assertion that biology is too complicated to be predictive might be overly pessimistic, at least in the context of bacteria.”
Story Source:
The above story is based on materials provided by California Institute of Technology. The original article was written by Kimm Fesenmaier. Note: Materials may be edited for content and length.
Journal Reference:
Robert C. Brewster, Franz M. Weinert, Hernan G. Garcia, Dan Song, Mattias Rydenfelt, Rob Phillips. The Transcription Factor Titration Effect Dictates Level of Gene Expression. Cell, 2014; DOI: 10.1016/j.cell.2014.02.022


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microarray analysis — a complex technology commonly used in many applications such as discovering genes, disease diagnosis, drug development and toxicological research — has just become easier and more user-friendly. A new advanced software program called Eureka-DMA provides a cost-free, graphical interface that allows bioinformaticians and bench-biologists alike to initiate analyses, and to investigate the data produced by microarrays. The program was developed by Ph.D. student Sagi Abelson of the Rappaport Faculty of Medicine at the Technion-Israel Institute of Technology in Haifa, Israel.

DNA microarray analysis, a high-speed method by which the expression of thousands of genes can be analyzed simultaneously, was invented in the late 1980s and developed in the 1990s. Genetic researchers used a glass slide with tiny dots of copies of DNA to test match genes they were trying to identify. Because the array of dots was so small, it was called a “microarray.” There is a strong correlation between the field of molecular biology and medical research, and microarray technology is used routinely in the area of cancer research and other epidemiology studies. Many research groups apply it to detect genetic variations between biological samples and information about aberrant gene expression levels can be used in what is called “personalized medicine.” This includes customized approaches to medical care, including finding new drugs for gene targets where diseases have genetic causes and potential cures are based on an individual’s aberrant gene’s signal.

An article written by Abelson published in the current issue of BMC Bioinformatics(2014,15:53) describes the new software tool and provides examples of its uses.

“Eureka-DMA combines simplicity of operation and ease of data management with the rapid execution of multiple task analyses,” says Abelson. “This ability can help researchers who have less experience in bioinformatics to transform the high throughput data they generate into meaningful and understandable information.”

Eureka-DMA has a distinct advantage over other software programs that only work “behind the scenes” and provide only a final output. It provides users with an understanding of how their actions influence the outcome throughout all the data elucidation steps, keeping them connected to the data, and enabling them to reach optimal conclusions.

“It is very gratifying to see the insightful initiative of Sagi Abelson, a leading ‘out-of-the-box’ thoughtful Technion doctorate student whom I have had the privilege of supervising,” said Prof. Karl Skorecki, the Director of the Rappaport Family Institute for Research in the Medical Sciences at the Technion Faculty of Medicine and Director of Medical and Research Development at the Rambam Health Care Campus. “Over and above his outstanding PhD thesis research project on cancer stem cells, Sagi has developed — on his own — a user-friendly computer-based graphical interface for health and biological research studies. Eureka-DMA enables users to easily interpret massive DNA expression data outputs, empowering researchers (and in the future, clinicians) to generate new testable hypotheses with great intuitive ease, and to examine complex genetic expression signatures of genes that provide information relevant to health and disease conditions. This was enabled by combining outstanding insight and expertise in biological and computer sciences, demonstrating the unique multidisciplinary strengths and intellectual freedom that fosters creative innovation at the Technion.”

According to Abelson, Eureka-DMA was programmed in MATLAB, a high-level language and interactive environment for numerical computation, visualization, and programming. Advanced users of MATLAB can analyze data, develop algorithms, and create models and applications to explore multiple hypotheses and reach solutions faster than with spreadsheets or traditional software. Eureka-DMA uses many of MATLAB’s toolbox features to provide ways to search for enriched pathways and genetic terms and then combines them with other relevant features.

Raw data input is through Windows Excel or text files. This, says Abelson, spares the user from dealing with multiple and less common microarray files received by different manufacturers. Results can then be exported into a ‘txt’ file format,’ or Windows Excel, making Eureka-DMA a unified and flexible platform for microarray data analysis, interpretation and visualization. It can also be used as a fast validation tool for results obtained by different methods.

Eureka-DMA loads and exports genetic data, “normalizes” raw data, filters non-relevant data, and enables pathway enrichment analysis for mapping genes on cellular pathways. The user can browse through the enriched pathways and create an illustration of the pathway with the differentially expressed genes highlighted.

After identifying the differentially expressed genes, biological meaning is ascribed via the software so that the identification of significant co-clustered genes with similar properties — cellular components, a biological process, or a molecular function — can be achieved.

Eureka-DMA software is freely available for academic users and can be downloaded athttp://blue-meduza.org/Eureka-DMA