An artist’s rendition of a ribosome. Credit: C. BICKLE/SCIENCE

Genes to Genomes asked Dr. Maria Barna (Stanford University), a recipient of the Rosalind Franklin Award for Young Investigators, to tell us about her research and what it means to receive the award. She will be recognized for her Rosalind Franklin Award along with the other 2016 recipient, Dr. Carolyn McBride, at the 2015 American Society of Human Genetics meeting this week.

It is an incredible honor to be selected as the recipient of a 2016 Rosalind Franklin Young Investigator Award. I am so grateful to be receiving this prestigious award, and I hope to follow in the footsteps of all the amazing women scientists who have received it before me. This award will be extremely helpful in supporting our research as we delve deeply into an exciting, newly-discovered mechanistic program for controlling how the mammalian genome is converted into final effector proteins, which execute all of the decisions that a cell makes during its life. The central dogma of biology, which Rosalind Franklin was so instrumental in establishing, has for decades served as an explanation for the flow of genetic information within a biological system. In our current understanding of the “normal” flow of biological information from DNA to RNA to protein, the ribosome—the central protein synthesis machinery of the cell—decodes the genome with machine-like precision, serving as an integral but largely passive participant in the synthesis of effector proteins across all kingdoms of life (whether a cat, carp, cholera or Caesar).

There are millions of ribosomes situated in every cell’s cytoplasm, churning out the proteins essential for cellular life. To a large extent , the ribosome has been viewed as a backstage participant in translating the genetic code, despite being recognized as spectacular molecular machine. Our research has fundamentally changed this view by demonstrating that not all of the millions of ribosomes within each cell are the same, and that ribosome heterogeneity provides a novel means for diversity of the proteins that can be produced in specific cells, tissues, and organisms from the same DNA sequence. Collectively, we have termed this additional layer of gene regulation as a “ribocode,” which adds important diversity to how gene products can be converted into proteins in time and space.

I believe that this interesting discovery has been made possible through mouse genetics. By employing an unbiased forward genetic screen, we realized that the activity of core components of the ribosome machinery were unexpectedly tailored to execute highly specific developmental decisions and were “tuned” to translating specific subsets of key developmental mRNAs. My favorite example comes from a mouse mutant we first characterized which harbored a loss-of-function mutation in one of the 80 ribosomal proteins (RPs) that constitute the core of the mammalian ribosome, known as RPL38. It was extremely unexpected when we discovered that RPL38  was selectively required for the formation of the mammalian body plan. In Rpl38 mutant mice, the stereotyped arrangement of vertebral elements were altered in a profound way, including the formation of extra pairs of ribs growing out of vertebrae in the neck! This led to the realization that RPL38 can be considered a regulatory element, or “filter,” of the ribosome—one that is selectively required to convert a subset of critical genes into the proteins, including a key group of  Homeodomain transcription factors  that establish  the body plan.

Since I started as an Assistant Professor at Stanford, my laboratory has taken a highly genetic approach to deconstructing the impact of individual ribosome components on the translational code of gene expression in specific cell and tissue types. At present, we are creating one of the largest series of conditional knock-out mice for each of the 80 core RPs belonging to the mammalian ribosome. Our findings have uncovered distinct and striking tissue-specific phenotypes, which are evident upon conditional deletion of specific RPs, during eye development, facial patterning, limb development, and spermatogenesis, among many others. An additional outstanding question raised by our initial studies is the nature of regulatory elements in target mRNAs that interface with what we called “specialized ribosomes,” which may contain a unique RP composition and/or activity. Akin to a transcription factor binding site or micro-RNA seed sequence, how might ribosome-mediated control of gene regulation be encoded within the sequence and structure provided by the transcribed genome? We recently identified unique structured elements within the untranslated regions of transcripts that are recognized by specialized ribosomes. We are starting to understand the grammatical rules for how such elements guide the ribosome in decoding the genome with newfound specificity.  We anticipate that this ‘ribocode’ will be a vital  additional layer of gene regulation guiding cell specification, tissue patterning, mammalian development, and human disease—that  my laboratory hopes to study for decades to come.

I’m extremely excited by the recognition and support given to our work by the Rosalind Franklin Young Investigator Award. This recognition is particularly meaningful to me, as my lab has embarked on work that was initially perceived as very provocative and potentially too risky. As with any research that breaks convention or dogma, my lab has worked tirelessly to explore largely uncharted areas of research into gene regulation. Our investigations have always been aided by the elegance and strength of mouse genetics, which has the power to overturn what we think is known and what is still left to be discovered. I’m particularly grateful for the extraordinary cadre of junior scientists in my lab that make this research possible and who, in many ways, follow the pioneering spirit for discovery embodied by Rosalind Franklin.

Dr. Barna received her bachelor’s degree in anthropology from New York University and her PhD in molecular biology from Cornell University and Sloan-Kettering Institute. Her doctoral research focused on the genetic basis of limb development. Building on her post-doctoral research conducted at the University of California, San Francisco, Dr. Barna currently studies the ribosome molecular machine in her own laboratory at Stanford University.

The views expressed in guest posts are those of the author and are not necessarily endorsed by the Genetics Society of America.

Members of the GSA staff and leadership will be participating in the annual meeting of our sister society, the American Society of Human Genetics (ASHG). If you’ll be attending ASHG 2015 in Baltimore, please look out for us, including at the following events:

Exhibit Hall

GSA will be at Booth 2008 in the exhibit hall. Come by to learn about the Society or publishing in one of our peer-edited journals, GENETICS and G3: Genes|Genomes|Genetics.

Invited session on model organisms

GSA President Jasper Rine and Past President Phil Hieter have organized an invited session on “Understanding Disease Pathogenesis: A Grand Challenge for Model Organisms.” This session will accent the current relevance of model organism studies for the understanding, diagnosis, and treatment of human disease, and anticipate the future role of model organisms in human disease research.

Saturday, October 10
1:45 PM – 3:45 PM

Concurrent Invited Sessions II
83. Understanding Disease Pathogenesis: A Grand Challenge for Model Organisms

Room 318/321, Level 3, Convention Center

1:45 pm – Fruit fly/mouse: A fly approach to personalized cancer therapeutics. R. Cagan. Mount Sinai Hosp, New York.

2:15 pm – Fruit fly/mouse: Molecular genetics of tumor suppressor genes and oncogenes. D. Pan. Johns Hopkins Univ Sch Med, Baltimore.

2:45 pm – Nematode worm: Nutritional regulatory networks. M. Walhout. U Mass Med Sch, Worcester.

3:15 pm – Yeast/zebrafish: Genetic models to determine gene function and a potential therapy for an inherited anemia. C. McMaster. Dalhousie Univ, Halifax, Canada.

Publications Workshop

GENETICS Editor-in-Chief Mark Johnston will be participating in the sold-out “Behind the Scenes: Publications Workshop.” Friday, October 9, 12:45 pm, Room 349.

Awards

Two GSA members will be honored with awards from ASHG:

• GSA member Huntington Willard will receive the ASHG Arno Motulsky–Barton Childs Award for Excellence in Human Genetics Education. Friday, October 9, 9:30 am, Hall F, Level 1.
• GSA member Leonid Kruglyak will receive ASHG’s Curt Stern Award, which recognizes genetics and genomics researchers who have made significant scientific contributions during the past decade. Friday, October 9, 10 am, Hall F, Level 1.

In addition, GSA member Carolyn McBride and Maria Barna will be formally honored with the Rosalind Franklin Young Investigator Award. Funded by The Gruber Foundation and administered by GSA and ASHG, the award is presented to promising early-career women geneticists. Friday, October 9, 8:45 am, Fall F, Level 1H

Finally, Jennifer Doudna, who will be delivering a keynote address at The Allied Genetics Conference next year will be honored with the Gruber Genetics Prize. Doudna and co-recipient Emmanuelle Charpentier will receive a $500,000 cash award in recognition of their development of the CRISPR-Cas9 genome editing technology.

• Presentation: Friday, October 9, 8:45 am, Hall F, Level 1
• Gruber Lecture: Friday, October 9, 1 pm, Hall F, Level 1

One of the major risk factors for autoimmune diseases is being born with two copies of the X chromosome. For example, women—who typically carry two Xs—face around ten times the risk of lupus, while men with lupus are around 15 times more likely than the general population to carry two Xs and a Y (Klinefelter syndrome), rather than the usual single X and Y.

The luck of the sex-chromosome draw affects many other complex diseases in prevalence, severity, symptoms, and rates of progression. Women face about twice the risk of depression, for example, while men are three to five times more prone to liver cancer. Though environmental factors like sex hormones and alcohol consumption are behind some of the differences, Alon Keinan (Cornell University) argues that genetic variation carried on sex chromosomes must also play a role.

But how big is that role? He says there is little conclusive evidence. The problem is, the sex chromosomes have been largely ignored in the flood of human genome-wide association studies (GWAS) of recent years.

“We are conducting all these genome-wide studies, but sometimes forget that the genome also includes sex chromosomes,” he says. There are many other unknowns in how sex chromosomes affect complex disease, including the population-wide variation in X-chromosome inactivation, a crucial factor in the expression of X-linked variants.

For the 2014 American Society for Human Genetics meeting in October, Keinan teamed up with Melissa Wilson Sayres (Arizona State University) to organize a session called “The X-Factor of Complex Disease: From Evolution to Association Studies of the X Chromosome” to discuss these issues and more. The idea was to bring together speakers with expertise ranging from evolutionary to functional genetics to present research on sex chromosomes and their relevance to interpretations of human medical genetic data.

“Part of the motivation is that medicine today is often driven by diagnosis and treatment of males. We would like to better understand the genetic risk factors that affect males and females differentially, to make at least one step towards sex-specific diagnosis and treatment,” says Keinan.

GSA Journals Assistant Editor Cristy Gelling caught up with Wilson Sayres and Keinan to discuss X-linked disease associations and the evolution of X-inactivation.

Bringing X back

Although the X chromosome makes up about 5% of the human genome and carries 4% of all genes, it is linked to fewer than 0.5% of associations in NHGRI’s GWAS catalog. For example, chromosome 7, similar in size to the X, has about 10 times as many associations. In model organisms, sex chromosomes are responsible for more phenotypic variation than you would predict from their size. But however you do the math, we’ve likely missed the vast majority of the X-linked associations with complex disease.

The main problem is that most GWAS discard all data from the X chromosome. “It’s understandable,” says Keinan. “You can imagine when researchers are competing to publish quickly, it’s easy to ignore something that is only 5% of the genome and needs extra time and special expertise.”

Even when the X is included, it’s typically analyzed using methods designed for autosomes. But standard statistical models for detecting significant associations don’t perform well for X chromosomes because they have unique patterns of diversity. For example, because recombination is suppressed in sex chromosomes, they evolve very differently from autosomes. One speaker at the session, Krishna R. Veeramah of Stony Brook University, compared the effects of natural selection on the X chromosomes to the autosomes in humans and apes. The X chromosomes in most of these species (including humans) show evidence for greater purifying selection and faster adaptive evolution rates compared to autosomes.

Sex-biased social practices, such as polygyny and male migration also affect variation on the X chromosome in ways that can distort association tests. One particularly recent example of a sex-biased demographic effect was caused by human slavery in the United States. Today’s African-American populations carry, on average, a higher proportion of African ancestry on their X chromosomes compared to autosomes. This shows much of the European genetic heritage of African-Americans is derived from male, rather than female ancestors—likely the legacy of white men fathering children with the women they enslaved.

It’s not only the different models of variation that create headaches when analyzing X-linked GWAS data. All the other steps of analysis, including determining an individual’s genotype, filtering out poor quality data, and imputation of missing genotype values, can be distorted by the difference in copy number between sexes. The end result of applying autosomal pipelines to the X chromosome is lower statistical power and a higher rate of false positives.

In his presentation, Keinan described a new “XWAS” analysis pipeline developed by several graduate students and postdoctoral fellows in his lab  tailored to account for the biology of X chromosomes. To test the performance of the pipeline, the group used it to reanalyze existing autoimmune disease GWAS data for X-linked associations. The results replicated several previously found associations and revealed new candidate disease risk genes on the X chromosome with plausible links to immune function.

So can we now reanalyze the back-catalog of previously performed GWAS and unearth all those missing X-linked associations? Not quite. Although many GWAS datasets are available for reanalysis, they are usually deposited as processed data that have already been through quality control steps. In most cases, these QC steps have not been corrected for sex, and in some cases, the X chromosome data have even been discarded. Keinan hopes more geneticists will start depositing their GWAS results as raw data.

“All I would like to be able to do at this point is take a look at data that’s already available. A huge amount of taxpayer money has been invested in GWAS, which has paid off in many discoveries and can pay off further if the data generated is available for reanalysis,” he says.

Escape from X-inactivation

Another major complication for modelling how an X-linked variant contributes to disease is X inactivation, a process in which one of the two X-chromosomes in genetic females is randomly silenced. Which of the two chromosomes gets silenced varies from tissue to tissue and cell to cell. That means a particular X-linked variant may or may not be expressed in the tissues relevant to the disease.

X-inactivation doesn’t affect the entire chromosome; around 15 percent of genes “escape” inactivation and are expressed. Adding to this complexity, around ten percent of the genes that escape silencing only do so in some individuals and not in others. At the last talk of the session, Christine Disteche (University of Washington) presented results suggesting the genes that escape X inactivation also vary between tissues, resulting in sex-specific differences in phenotypes. But taking these phenomena into account in models of disease association is not possible because we simply don’t know enough about X-inactivation and its variation in humans, says Wilson Sayres.

“Right now, what we know about X-inactivation heterogeneity is based on nine cell lines, and we don’t even know which populations the lines came from,” she says. She argues that assaying patterns of X-inactivation across populations and within individuals is one of the challenges the field needs to address before it can fully understand disease phenotype variation.

Interestingly, understanding the evolution of sex chromosomes may shed some light on patterns of variation in X inactivation. One of the topics Wilson Sayres discussed at the meeting was how gene loss on the Y chromosome influences escape from silencing on the X.

In the evolutionary past, the X and Y chromosomes were homologous, carrying mostly the same set of genes. But over time, the Y has lost many genes, leaving it much smaller than the X. Wilson Sayres set out to test the idea that X inactivation of a gene only evolves when the gene’s homolog on the Y (technically a “gametolog”) is lost. Silencing one copy of the gene in females would balance out the mismatch between the sexes.

If this is the case, genes on the X chromosome were once expressed from both copies in females (i.e. they “escaped” silencing), but only became subject to X inactivation when their Y gametolog lost function. By re-examining the X inactivation data from the nine cell lines derived from different people, Wilson Sayres and colleagues confirmed that X linked genes with a Y gametolog escape silencing. In contrast, genes with no gametolog on the Y were, on average, silenced in most cell lines.

Crucially, genes with non-functional gametologs—which are earlier along in the process of being lost from the Y—escaped silencing at a greater rate than those that had completely lost the Y gametolog. That suggests an evolutionary lag between loss of the gene on the Y, and the onset of silencing of the gametolog on the X. This model predicts that the genes that still “escape” silencing even though their Y gametolog is no longer function, will eventually be silenced. “This process is still happening in us,” says Wilson Sayres. “There’s ongoing evolution.”

One implication of this continuing adjustment is that different species are at different stages in the evolution of X inactivation. The process is more tightly regulated in mice, perhaps because the mouse Y chromosome has lost comparatively more of its genes than the human Y. A much smaller proportion of genes escape silencing, and there may be lower variation between individuals.

“It might actually be less messy in mice than in humans,” says Wilson Sayres. This has implications for mouse models of human disease, particularly in cases where the phenotype is modified by genes on the X chromosome.

Solve for X

There is much work to do, Keinan and Wilson Sayres agree. Keinan says researchers need to re-analyze the 2000 or so available GWAS, not to mention the newer sequence-based datasets, most of which still do not consider X. He wonders what proportion of the infamous “missing heritability” of complex human traits might be explained by X-linked variants, but more importantly, hopes we can pin down specific variants and pathways that contribute to the sexual dimorphism of human disease.

Wilson Sayres argues that we need to survey human variation in X-inactivation not only across populations, but within individuals, to get a handle on the patterns of tissue mosaicism in X silencing. She would also like to see more diversity incorporated into association studies. One example would be to include individuals with sex chromosome aneuploidies, instead of excluding them from analysis.

“In Turner Syndrome — that’s where you have only a single X — there’s huge variation in phenotype. Some people don’t even know they have it until they do a cheek swab,” she says.  “A huge challenge in studying sex-chromosome aneuploidies is that we don’t understand X variation in karyotypically-common people, let alone for those with single or multiple copies.”

But that’s for the long term, she says. “The first step is just to include the X!”