Category Archives: Human Genetics

Genetic diversity in human populations

Human genetic variation is the genetic differences in and among populations. There may be multiple variants of any given gene in the human population (alleles), a situation called polymorphism.

No two humans are genetically identical. Even monozygotic twins (who develop from one zygote) have infrequent genetic differences due to mutations occurring during development and gene copy-number variation.[1] Differences between individuals, even closely related individuals, are the key to techniques such as genetic fingerprinting.

As of 2017, there are a total of 324 million known variants from sequenced human genomes.[2]

As of 2015, the typical difference between an individual's genome and the reference genome was estimated at 20 million base pairs (or 0.6% of the total of 3.2 billion base pairs).[3]

Comparatively speaking, humans are a genetically homogenous species. Although a small number of genetic variants are found more frequently in certain geographic regions or in people with ancestry from those regions, this variation accounts for a small percentage of the human genome (~15%). For comparison, rhesus macaques exhibit 2.5-fold greater DNA sequence diversity compared to humans.[4]

The lack of discontinuities in genetic distances between human populations, absence of discrete branches in the human species, and striking homogeneity of human beings globally, imply that there is no scientific basis for inferring races or subspecies in humans, and for most traits, there is much more variation within populations than between them.[5][6][7][8][9][10][11][12] Despite this, modern genetic studies have found substantial average genetic differences across human populations in traits such as skin colour, bodily dimensions, lactose and starch digestion, high altitude adaptions, and predisposition to developing particular diseases.[11] The greatest diversity is found among populations in Africa,[13] and gradually declines with increasing distance from the African continent, consistent with the Out of Africa theory of human origins.[14]

The study of human genetic variation has evolutionary significance and medical applications. It can help scientists reconstruct and understand patterns of past human migration. In medicine, study of human genetic variation may be important because some disease-causing alleles occur more often in certain population groups. For instance, the mutation for sickle-cell anemia is more often found in people with ancestry from certain sub-Saharan African, south European, Arabian, and Indian populations, due to the evolutionary pressure from mosquitos carrying malaria in these regions.

New findings show that each human has on average 60 new mutations compared to their parents.[15][16]

Causes of differences between individuals include independent assortment, the exchange of genes (crossing over and recombination) during reproduction (through meiosis) and various mutational events.

There are at least three reasons why genetic variation exists between populations. Natural selection may confer an adaptive advantage to individuals in a specific environment if an allele provides a competitive advantage. Alleles under selection are likely to occur only in those geographic regions where they confer an advantage. A second important process is genetic drift, which is the effect of random changes in the gene pool, under conditions where most mutations are neutral (that is, they do not appear to have any positive or negative selective effect on the organism). Finally, small migrant populations have statistical differences called the founder effect from the overall populations where they originated; when these migrants settle new areas, their descendant population typically differs from their population of origin: different genes predominate and it is less genetically diverse.

In humans, the main cause is genetic drift.[17] Serial founder effects and past small population size (increasing the likelihood of genetic drift) may have had an important influence in neutral differences between populations.[citation needed] The second main cause of genetic variation is due to the high degree of neutrality of most mutations. A small, but significant number of genes appear to have undergone recent natural selection, and these selective pressures are sometimes specific to one region.[18][19]

Genetic variation among humans occurs on many scales, from gross alterations in the human karyotype to single nucleotide changes.[20] Chromosome abnormalities are detected in 1 of 160 live human births. Apart from sex chromosome disorders, most cases of aneuploidy result in death of the developing fetus (miscarriage); the most common extra autosomal chromosomes among live births are 21, 18 and 13.[21]

Nucleotide diversity is the average proportion of nucleotides that differ between two individuals. As of 2004, the human nucleotide diversity was estimated to be 0.1%[22] to 0.4% of base pairs.[23] In 2015, the 1000 Genomes Project, which sequenced one thousand individuals from 26 human populations, found that "a typical [individual] genome differs from the reference human genome at 4.1 million to 5.0 million sites affecting 20 million bases of sequence"; the latter figure corresponds to 0.6% of total number of base pairs.[3] Nearly all (>99.9%) of these sites are small differences, either single nucleotide polymorphisms or brief insertions or deletions (indels) in the genetic sequence, but structural variations account for a greater number of base-pairs than the SNPs and indels.[3][24]

As of 2017[update], the Single Nucleotide Polymorphism Database (dbSNP), which lists SNP and other variants, listed 324 million variants found in sequenced human genomes.[2]

A single nucleotide polymorphism (SNP) is a difference in a single nucleotide between members of one species that occurs in at least 1% of the population. The 2,504 individuals characterized by the 1000 Genomes Project had 84.7 million SNPs among them.[3] SNPs are the most common type of sequence variation, estimated in 1998 to account for 90% of all sequence variants.[25] Other sequence variations are single base exchanges, deletions and insertions.[26] SNPs occur on average about every 100 to 300 bases[27] and so are the major source of heterogeneity.

A functional, or non-synonymous, SNP is one that affects some factor such as gene splicing or messenger RNA, and so causes a phenotypic difference between members of the species. About 3% to 5% of human SNPs are functional (see International HapMap Project). Neutral, or synonymous SNPs are still useful as genetic markers in genome-wide association studies, because of their sheer number and the stable inheritance over generations.[25]

A coding SNP is one that occurs inside a gene. There are 105 Human Reference SNPs that result in premature stop codons in 103 genes. This corresponds to 0.5% of coding SNPs. They occur due to segmental duplication in the genome. These SNPs result in loss of protein, yet all these SNP alleles are common and are not purified in negative selection.[28]

Structural variation is the variation in structure of an organism's chromosome. Structural variations, such as copy-number variation and deletions, inversions, insertions and duplications, account for much more human genetic variation than single nucleotide diversity. This was concluded in 2007 from analysis of the diploid full sequences of the genomes of two humans: Craig Venter and James D. Watson. This added to the two haploid sequences which were amalgamations of sequences from many individuals, published by the Human Genome Project and Celera Genomics respectively.[29]

According to the 1000 Genomes Project, a typical human has 2,100 to 2,500 structural variations, which include approximately 1,000 large deletions, 160 copy-number variants, 915 Alu insertions, 128 L1 insertions, 51 SVA insertions, 4 NUMTs, and 10 inversions.[3]

A copy-number variation (CNV) is a difference in the genome due to deleting or duplicating large regions of DNA on some chromosome. It is estimated that 0.4% of the genomes of unrelated humans differ with respect to copy number. When copy number variation is included, human-to-human genetic variation is estimated to be at least 0.5% (99.5% similarity).[30][31][32][33] Copy number variations are inherited but can also arise during development.[34][35][36][37]

A visual map with the regions with high genomic variation of the modern-human reference assembly relatively to aNeanderthal of 50k[38] has been built by Pratas et al.[39]

Epigenetic variation is variation in the chemical tags that attach to DNA and affect how genes get read. The tags, "called epigenetic markings, act as switches that control how genes can be read."[40] At some alleles, the epigenetic state of the DNA, and associated phenotype, can be inherited across generations of individuals.[41]

Genetic variability is a measure of the tendency of individual genotypes in a population to vary (become different) from one another. Variability is different from genetic diversity, which is the amount of variation seen in a particular population. The variability of a trait is how much that trait tends to vary in response to environmental and genetic influences.

In biology, a cline is a continuum of species, populations, varieties, or forms of organisms that exhibit gradual phenotypic and/or genetic differences over a geographical area, typically as a result of environmental heterogeneity.[42][43][44] In the scientific study of human genetic variation, a gene cline can be rigorously defined and subjected to quantitative metrics.

In the study of molecular evolution, a haplogroup is a group of similar haplotypes that share a common ancestor with a single nucleotide polymorphism (SNP) mutation. The study of haplogroups provides information about ancestral origins dating back thousands of years.[45]

The most commonly studied human haplogroups are Y-chromosome (Y-DNA) haplogroups and mitochondrial DNA (mtDNA) haplogroups, both of which can be used to define genetic populations. Y-DNA is passed solely along the patrilineal line, from father to son, while mtDNA is passed down the matrilineal line, from mother to both daughter or son. The Y-DNA and mtDNA may change by chance mutation at each generation.

A variable number tandem repeat (VNTR) is the variation of length of a tandem repeat. A tandem repeat is the adjacent repetition of a short nucleotide sequence. Tandem repeats exist on many chromosomes, and their length varies between individuals. Each variant acts as an inherited allele, so they are used for personal or parental identification. Their analysis is useful in genetics and biology research, forensics, and DNA fingerprinting.

Short tandem repeats (about 5 base pairs) are called microsatellites, while longer ones are called minisatellites.

The recent African origin of modern humans paradigm assumes the dispersal of non-African populations of anatomically modern humans after 70,000 years ago. Dispersal within Africa occurred significantly earlier, at least 130,000 years ago. The "out of Africa" theory originates in the 19th century, as a tentative suggestion in Charles Darwin's Descent of Man,[46] but remained speculative until the 1980s when it was supported by the study of present-day mitochondrial DNA, combined with evidence from physical anthropology of archaic specimens.

According to a 2000 study of Y-chromosome sequence variation,[47] human Y-chromosomes trace ancestry to Africa, and the descendants of the derived lineage left Africa and eventually were replaced by archaic human Y-chromosomes in Eurasia. The study also shows that a minority of contemporary populations in East Africa and the Khoisan are the descendants of the most ancestral patrilineages of anatomically modern humans that left Africa 35,000 to 89,000 years ago.[47] Other evidence supporting the theory is that variations in skull measurements decrease with distance from Africa at the same rate as the decrease in genetic diversity. Human genetic diversity decreases in native populations with migratory distance from Africa, and this is thought to be due to bottlenecks during human migration, which are events that temporarily reduce population size.[48][49]

A 2009 genetic clustering study, which genotyped 1327 polymorphic markers in various African populations, identified six ancestral clusters. The clustering corresponded closely with ethnicity, culture and language.[50] A 2018 whole genome sequencing study of the world's populations observed similar clusters among the populations in Africa. At K=9, distinct ancestral components defined the Afroasiatic-speaking populations inhabiting North Africa and Northeast Africa; the Nilo-Saharan-speaking populations in Northeast Africa and East Africa; the Ari populations in Northeast Africa; the Niger-Congo-speaking populations in West-Central Africa, West Africa, East Africa and Southern Africa; the Pygmy populations in Central Africa; and the Khoisan populations in Southern Africa.[51]

Because of the common ancestry of all humans, only a small number of variants have large differences in frequency between populations. However, some rare variants in the world's human population are much more frequent in at least one population (more than 5%).[52]

It is commonly assumed that early humans left Africa, and thus must have passed through a population bottleneck before their African-Eurasian divergence around 100,000 years ago (ca. 3,000 generations). The rapid expansion of a previously small population has two important effects on the distribution of genetic variation. First, the so-called founder effect occurs when founder populations bring only a subset of the genetic variation from their ancestral population. Second, as founders become more geographically separated, the probability that two individuals from different founder populations will mate becomes smaller. The effect of this assortative mating is to reduce gene flow between geographical groups and to increase the genetic distance between groups.[citation needed]

The expansion of humans from Africa affected the distribution of genetic variation in two other ways. First, smaller (founder) populations experience greater genetic drift because of increased fluctuations in neutral polymorphisms. Second, new polymorphisms that arose in one group were less likely to be transmitted to other groups as gene flow was restricted.[citation needed]

Populations in Africa tend to have lower amounts of linkage disequilibrium than do populations outside Africa, partly because of the larger size of human populations in Africa over the course of human history and partly because the number of modern humans who left Africa to colonize the rest of the world appears to have been relatively low.[53] In contrast, populations that have undergone dramatic size reductions or rapid expansions in the past and populations formed by the mixture of previously separate ancestral groups can have unusually high levels of linkage disequilibrium[53]

The distribution of genetic variants within and among human populations are impossible to describe succinctly because of the difficulty of defining a "population," the clinal nature of variation, and heterogeneity across the genome (Long and Kittles 2003). In general, however, an average of 85% of genetic variation exists within local populations, ~7% is between local populations within the same continent, and ~8% of variation occurs between large groups living on different continents.[54][55] The recent African origin theory for humans would predict that in Africa there exists a great deal more diversity than elsewhere and that diversity should decrease the further from Africa a population is sampled.

Sub-Saharan Africa has the most human genetic diversity and the same has been shown to hold true for phenotypic variation in skull form.[48][56] Phenotype is connected to genotype through gene expression. Genetic diversity decreases smoothly with migratory distance from that region, which many scientists believe to be the origin of modern humans, and that decrease is mirrored by a decrease in phenotypic variation. Skull measurements are an example of a physical attribute whose within-population variation decreases with distance from Africa.

The distribution of many physical traits resembles the distribution of genetic variation within and between human populations (American Association of Physical Anthropologists 1996; Keita and Kittles 1997). For example, ~90% of the variation in human head shapes occurs within continental groups, and ~10% separates groups, with a greater variability of head shape among individuals with recent African ancestors (Relethford 2002).

A prominent exception to the common distribution of physical characteristics within and among groups is skin color. Approximately 10% of the variance in skin color occurs within groups, and ~90% occurs between groups (Relethford 2002). This distribution of skin color and its geographic patterning with people whose ancestors lived predominantly near the equator having darker skin than those with ancestors who lived predominantly in higher latitudes indicate that this attribute has been under strong selective pressure. Darker skin appears to be strongly selected for in equatorial regions to prevent sunburn, skin cancer, the photolysis of folate, and damage to sweat glands.[57]

Understanding how genetic diversity in the human population impacts various levels of gene expression is an active area of research. While earlier studies focused on the relationship between DNA variation and RNA expression, more recent efforts are characterizing the genetic control of various aspects of gene expression including chromatin states,[58] translation,[59] and protein levels.[60] A study published in 2007 found that 25% of genes showed different levels of gene expression between populations of European and Asian descent.[61][62][63][64][65] The primary cause of this difference in gene expression was thought to be SNPs in gene regulatory regions of DNA. Another study published in 2007 found that approximately 83% of genes were expressed at different levels among individuals and about 17% between populations of European and African descent.[66][67]

The population geneticist Sewall Wright developed the fixation index (often abbreviated to FST) as a way of measuring genetic differences between populations. This statistic is often used in taxonomy to compare differences between any two given populations by measuring the genetic differences among and between populations for individual genes, or for many genes simultaneously.[68] It is often stated that the fixation index for humans is about 0.15. This translates to an estimated 85% of the variation measured in the overall human population is found within individuals of the same population, and about 15% of the variation occurs between populations. These estimates imply that any two individuals from different populations are almost as likely to be more similar to each other than either is to a member of their own group.[69][70]"The shared evolutionary history of living humans has resulted in a high relatedness among all living people, as indicated for example by the very low fixation index (FST) among living human populations." Richard Lewontin, who affirmed these ratios, thus concluded neither "race" nor "subspecies" were appropriate or useful ways to describe human populations.[54]

Wright himself believed that values >0.25 represent very great genetic variation and that an FST of 0.150.25 represented great variation. However, about 5% of human variation occurs between populations within continents, therefore FST values between continental groups of humans (or races) of as low as 0.1 (or possibly lower) have been found in some studies, suggesting more moderate levels of genetic variation.[68] Graves (1996) has countered that FST should not be used as a marker of subspecies status, as the statistic is used to measure the degree of differentiation between populations,[68] although see also Wright (1978).[71]

Jeffrey Long and Rick Kittles give a long critique of the application of FST to human populations in their 2003 paper "Human Genetic Diversity and the Nonexistence of Biological Races". They find that the figure of 85% is misleading because it implies that all human populations contain on average 85% of all genetic diversity. They argue the underlying statistical model incorrectly assumes equal and independent histories of variation for each large human population. A more realistic approach is to understand that some human groups are parental to other groups and that these groups represent paraphyletic groups to their descent groups. For example, under the recent African origin theory the human population in Africa is paraphyletic to all other human groups because it represents the ancestral group from which all non-African populations derive, but more than that, non-African groups only derive from a small non-representative sample of this African population. This means that all non-African groups are more closely related to each other and to some African groups (probably east Africans) than they are to others, and further that the migration out of Africa represented a genetic bottleneck, with much of the diversity that existed in Africa not being carried out of Africa by the emigrating groups. Under this scenario, human populations do not have equal amounts of local variability, but rather diminished amounts of diversity the further from Africa any population lives. Long and Kittles find that rather than 85% of human genetic diversity existing in all human populations, about 100% of human diversity exists in a single African population, whereas only about 70% of human genetic diversity exists in a population derived from New Guinea. Long and Kittles argued that this still produces a global human population that is genetically homogeneous compared to other mammalian populations.[72]

There is a hypothesis that anatomically modern humans interbred with Neanderthals during the Middle Paleolithic. In May 2010, the Neanderthal Genome Project presented genetic evidence that interbreeding did likely take place and that a small but significant portion, around 2-4%, of Neanderthal admixture is present in the DNA of modern Eurasians and Oceanians, and nearly absent in sub-Saharan African populations.[73][74]

Between 4% and 6% of the genome of Melanesians (represented by the Papua New Guinean and Bougainville Islander) are thought to derive from Denisova hominins a previously unknown species which shares a common origin with Neanderthals. It was possibly introduced during the early migration of the ancestors of Melanesians into Southeast Asia. This history of interaction suggests that Denisovans once ranged widely over eastern Asia.[75]

Thus, Melanesians emerge as the most archaic-admixed population, having Denisovan/Neanderthal-related admixture of ~8%.[75]

In a study published in 2013, Jeffrey Wall from University of California studied whole sequence-genome data and found higher rates of introgression in Asians compared to Europeans.[76] Hammer et al. tested the hypothesis that contemporary African genomes have signatures of gene flow with archaic human ancestors and found evidence of archaic admixture in the genomes of some African groups, suggesting that modest amounts of gene flow were widespread throughout time and space during the evolution of anatomically modern humans.[77]

New data on human genetic variation has reignited the debate about a possible biological basis for categorization of humans into races. Most of the controversy surrounds the question of how to interpret the genetic data and whether conclusions based on it are sound. Some researchers argue that self-identified race can be used as an indicator of geographic ancestry for certain health risks and medications.

Although the genetic differences among human groups are relatively small, these differences in certain genes such as duffy, ABCC11, SLC24A5, called ancestry-informative markers (AIMs) nevertheless can be used to reliably situate many individuals within broad, geographically based groupings. For example, computer analyses of hundreds of polymorphic loci sampled in globally distributed populations have revealed the existence of genetic clustering that roughly is associated with groups that historically have occupied large continental and subcontinental regions (Rosenberg et al. 2002; Bamshad et al. 2003).

Some commentators have argued that these patterns of variation provide a biological justification for the use of traditional racial categories. They argue that the continental clusterings correspond roughly with the division of human beings into sub-Saharan Africans; Europeans, Western Asians, Central Asians, Southern Asians and Northern Africans; Eastern Asians, Southeast Asians, Polynesians and Native Americans; and other inhabitants of Oceania (Melanesians, Micronesians & Australian Aborigines) (Risch et al. 2002). Other observers disagree, saying that the same data undercut traditional notions of racial groups (King and Motulsky 2002; Calafell 2003; Tishkoff and Kidd 2004[23]). They point out, for example, that major populations considered races or subgroups within races do not necessarily form their own clusters.

Racial categories are also undermined by findings that genetic variants which are limited to one region tend to be rare within that region, variants that are common within a region tend to be shared across the globe, and most differences between individuals, whether they come from the same region or different regions, are due to global variants.[80] No genetic variants have been found which are fixed within a continent or major region and found nowhere else.[81]

Furthermore, because human genetic variation is clinal, many individuals affiliate with two or more continental groups. Thus, the genetically based "biogeographical ancestry" assigned to any given person generally will be broadly distributed and will be accompanied by sizable uncertainties (Pfaff et al. 2004).

In many parts of the world, groups have mixed in such a way that many individuals have relatively recent ancestors from widely separated regions. Although genetic analyses of large numbers of loci can produce estimates of the percentage of a person's ancestors coming from various continental populations (Shriver et al. 2003; Bamshad et al. 2004), these estimates may assume a false distinctiveness of the parental populations, since human groups have exchanged mates from local to continental scales throughout history (Cavalli-Sforza et al. 1994; Hoerder 2002). Even with large numbers of markers, information for estimating admixture proportions of individuals or groups is limited, and estimates typically will have wide confidence intervals (Pfaff et al. 2004).

Genetic data can be used to infer population structure and assign individuals to groups that often correspond with their self-identified geographical ancestry. Jorde and Wooding (2004) argued that "Analysis of many loci now yields reasonably accurate estimates of genetic similarity among individuals, rather than populations. Clustering of individuals is correlated with geographic origin or ancestry."[22] However, identification by geographic origin may quickly break down when considering historical ancestry shared between individuals back in time.[82]

An analysis of autosomal SNP data from the International HapMap Project (Phase II) and CEPH Human Genome Diversity Panel samples was published in 2009.The study of 53 populations taken from the HapMap and CEPH data (1138 unrelated individuals) suggested that natural selection may shape the human genome much more slowly than previously thought, with factors such as migration within and among continents more heavily influencing the distribution of genetic variations.[83]A similar study published in 2010 found strong genome-wide evidence for selection due to changes in ecoregion, diet, and subsistenceparticularly in connection with polar ecoregions, with foraging, and with a diet rich in roots and tubers.[84] In a 2016 study, principal component analysis of genome-wide data was capable of recovering previously-known targets for positive selection (without prior definition of populations) as well as a number of new candidate genes.[85]

Forensic anthropologists can assess the ancestry of skeletal remains by analyzing skeletal morphology as well as using genetic and chemical markers, when possible.[86] While these assessments are never certain, the accuracy of skeletal morphology analyses in determining true ancestry has been estimated at 90%.[87]

Gene flow between two populations reduces the average genetic distance between the populations, only totally isolated human populations experience no gene flow and most populations have continuous gene flow with other neighboring populations which create the clinal distribution observed for most genetic variation. When gene flow takes place between well-differentiated genetic populations the result is referred to as "genetic admixture".

Admixture mapping is a technique used to study how genetic variants cause differences in disease rates between population.[88] Recent admixture populations that trace their ancestry to multiple continents are well suited for identifying genes for traits and diseases that differ in prevalence between parental populations. African-American populations have been the focus of numerous population genetic and admixture mapping studies, including studies of complex genetic traits such as white cell count, body-mass index, prostate cancer and renal disease.[89]

An analysis of phenotypic and genetic variation including skin color and socio-economic status was carried out in the population of Cape Verde which has a well documented history of contact between Europeans and Africans. The studies showed that pattern of admixture in this population has been sex-biased and there is a significant interactions between socio economic status and skin color independent of the skin color and ancestry.[90] Another study shows an increased risk of graft-versus-host disease complications after transplantation due to genetic variants in human leukocyte antigen (HLA) and non-HLA proteins.[91]

Differences in allele frequencies contribute to group differences in the incidence of some monogenic diseases, and they may contribute to differences in the incidence of some common diseases.[92] For the monogenic diseases, the frequency of causative alleles usually correlates best with ancestry, whether familial (for example, EllisVan Creveld syndrome among the Pennsylvania Amish), ethnic (TaySachs disease among Ashkenazi Jewish populations), or geographical (hemoglobinopathies among people with ancestors who lived in malarial regions). To the extent that ancestry corresponds with racial or ethnic groups or subgroups, the incidence of monogenic diseases can differ between groups categorized by race or ethnicity, and health-care professionals typically take these patterns into account in making diagnoses.[93]

Even with common diseases involving numerous genetic variants and environmental factors, investigators point to evidence suggesting the involvement of differentially distributed alleles with small to moderate effects. Frequently cited examples include hypertension (Douglas et al. 1996), diabetes (Gower et al. 2003), obesity (Fernandez et al. 2003), and prostate cancer (Platz et al. 2000). However, in none of these cases has allelic variation in a susceptibility gene been shown to account for a significant fraction of the difference in disease prevalence among groups, and the role of genetic factors in generating these differences remains uncertain (Mountain and Risch 2004).

Some other variations on the other hand are beneficial to human, as they prevent certain diseases and increase the chance to adapt to the environment. For example, mutation in CCR5 gene that protects against AIDS. CCR5 gene is absent on the surface of cell due to mutation. Without CCR5 gene on the surface, there is nothing for HIV viruses to grab on and bind into. Therefore, the mutation on CCR5 gene decreases the chance of an individual's risk with AIDS. The mutation in CCR5 is also quite common in certain areas, with more than 14% of the population carry the mutation in Europe and about 610% in Asia and North Africa.[94]

Apart from mutations, many genes that may have aided humans in ancient times plague humans today. For example, it is suspected that genes that allow humans to more efficiently process food are those that make people susceptible to obesity and diabetes today.[95]

Neil Risch of Stanford University has proposed that self-identified race/ethnic group could be a valid means of categorization in the US for public health and policy considerations.[96][92] A 2002 paper by Noah Rosenberg's group makes a similar claim: "The structure of human populations is relevant in various epidemiological contexts. As a result of variation in frequencies of both genetic and nongenetic risk factors, rates of disease and of such phenotypes as adverse drug response vary across populations. Further, information about a patient's population of origin might provide health care practitioners with information about risk when direct causes of disease are unknown."[97] However, in 2018 Noah Rosenberg released a study arguing against genetically essentialist ideas of health disparities between populations stating environmental variants are a more likely cause[98]

Human genome projects are scientific endeavors that determine or study the structure of the human genome. The Human Genome Project was a landmark genome project.

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Human genetic variation - Wikipedia

DNA

Through news accounts and crime stories, were all familiar with the fact that the DNA in our cells reflects each individuals unique identity and how closely related we are to one another. The same is true for the relationships among organisms. DNA, or deoxyribonucleic acid, is the molecule that makes up an organisms genome in the nucleus of every cell. It consists of genes, which are the molecular codes for proteins the building blocks of our tissues and their functions. It also consists of the molecular codes that regulate the output of genes that is, the timing and degree of protein-making. DNA shapes how an organism grows up and the physiology of its blood, bone, and brains.

DNA is thus especially important in the study of evolution. The amount of difference in DNA is a test of the difference between one species and another and thus how closely or distantly related they are.

While the genetic difference between individual humans today is minuscule about 0.1%, on average study of the same aspects of the chimpanzee genome indicates a difference of about 1.2%. The bonobo (Pan paniscus), which is the close cousin of chimpanzees (Pan troglodytes), differs from humans to the same degree. The DNA difference with gorillas, another of the African apes, is about 1.6%. Most importantly, chimpanzees, bonobos, and humans all show this same amount of difference from gorillas. A difference of 3.1% distinguishes us and the African apes from the Asian great ape, the orangutan. How do the monkeys stack up? All of the great apes and humans differ from rhesus monkeys, for example, by about 7% in their DNA.

Geneticists have come up with a variety of ways of calculating the percentages, which give different impressions about how similar chimpanzees and humans are. The 1.2% chimp-human distinction, for example, involves a measurement of only substitutions in the base building blocks of those genes that chimpanzees and humans share. A comparison of the entire genome, however, indicates that segments of DNA have also been deleted, duplicated over and over, or inserted from one part of the genome into another. When these differences are counted, there is an additional 4 to 5% distinction between the human and chimpanzee genomes.

No matter how the calculation is done, the big point still holds: humans, chimpanzees, and bonobos are more closely related to one another than either is to gorillas or any other primate. From the perspective of this powerful test of biological kinship, humans are not only related to the great apes we are one. The DNA evidence leaves us with one of the greatest surprises in biology: the wall between human, on the one hand, and ape or animal, on the other, has been breached. The human evolutionary tree is embedded within the great apes.

The strong similarities between humans and the African great apes led Charles Darwin in 1871 to predict that Africa was the likely place where the human lineage branched off from other animals that is, the place where the common ancestor of chimpanzees, humans, and gorillas once lived. The DNA evidence shows an amazing confirmation of this daring prediction. The African great apes, including humans, have a closer kinship bond with one another than the African apes have with orangutans or other primates. Hardly ever has a scientific prediction so bold, so out there for its time, been upheld as the one made in 1871 that human evolution began in Africa.

The DNA evidence informs this conclusion, and the fossils do, too. Even though Europe and Asia were scoured for early human fossils long before Africa was even thought of, ongoing fossil discoveries confirm that the first 4 million years or so of human evolutionary history took place exclusively on the African continent. It is there that the search continues for fossils at or near the branching point of the chimpanzee and human lineages from our last common ancestor.

Due to billions of years of evolution, humans share genes with all living organisms. The percentage of genes or DNA that organisms share records their similarities. We share more genes with organisms that are more closely related to us.

Humans belong to the biological group known as Primates, and are classified with the great apes, one of the major groups of the primate evolutionary tree. Besides similarities in anatomy and behavior, our close biological kinship with other primate species is indicated by DNA evidence. It confirms that our closest living biological relatives are chimpanzees and bonobos, with whom we share many traits. But we did not evolve directly from any primates living today.

DNA also shows that our species and chimpanzees diverged from a common ancestor species that lived between 8 and 6 million years ago. The last common ancestor of monkeys and apes lived about 25 million years ago.

Link:
Genetics | The Smithsonian Institution's Human Origins Program

Fluent BioSciences showcasing breakthrough solutions to enable unprecedented scale, cost-efficiency and access for single-cell RNA sequencing at the 2022 American Society of Human Genetics (ASHG) conference  PR Newswire

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News Release

Tuesday, September 27, 2022

The Molecular Phenotypes of Null Alleles in Cells program will first look at protein-coding genes.

The National Institutes of Health is launching a program to better understand the function of every human gene and generate a catalog of the molecular and cellular consequences of inactivating each gene. The Molecular Phenotypes of Null Alleles in Cells (MorPhiC) program, managed by the National Human Genome Research Institute, aims to systematically investigate the function of each gene through multiple phases that will each build upon the work of the previous.

The program will be funded initially for five years for a total of $42.5 million, pending the availability of funds. Phase 1 of the program will focus on 1,000 protein-coding genes and serve as a pilot phase and has three goals: exploring multiple methods of inactivating, or knocking out, gene function; developing molecular and cellular systems that model multiple human tissues and developmental stages; and developing molecular and cellular approaches to catalog gene function that other researchers can reproduce.

The function of thousands of genes is still a mystery, and they likely serve vital biological roles," said Colin Fletcher, Ph.D., NHGRI program director in the Division of Genome Sciences. Understanding fundamental biology can help us figure out why certain diseases occur and how can we develop drugs to target and treat those diseases.

Projects funded by the program will use versions of genes that do not make functional proteins, called null alleles. In the absence of making its functional protein, a given genes function can be more readily deduced by studying the resulting biological characteristics, or phenotype. The researchers expect this process to make it easier to interpret the results.

Currently, over 6,000 out of the estimated 19,000 protein-coding genes have not been well-studied. Among the genes that have been studied, only a subset of their functions is well-characterized.

Creating a catalog of what all human genes do is no easy feat. Most genes are likely to have more than one function and behave differently depending on the type of cell in which they are expressed. In addition, genes may turn on or off depending on the cells relationship to surrounding cells, environment and age.

Research funded by the MorPhiC program will use cell culture models such as organoids, which are miniature, three-dimensional models composed of multiple cell types that mimic the function of real tissues and organs. Research that works with cells in culture has a major advantage: it can more robustly study human cells, and therefore, human genes. All data will be made available to the broader research community. If Phase 1 is successful, NIH will activate a second phase to characterize a larger set of human genes.

MorPhiC is meant to add another layer of functional information between the gene knock-out at the DNA level and the organism-level effects. We want to catalog the effects of knocking out each gene within cells and together with information from other studies use that to understand how genes function to produce an organism, said Adam Felsenfeld, Ph.D., NHGRI program director in the Division of Genome Sciences.

The MorPhiC program offers a new approach to understanding gene function when compared to other programs at NIH and NHGRI. For example, a well-established NIH effort to probe gene function has been investigating the consequences of knocking out genes in mice at the level of tissues and organs as part of the Knockout Mouse Program. Another effort is applying new technologies, genome-sequencing strategies and analytical approaches to significantly increase the proportion of human genetic diseases with an identified genetic cause, as part of the Genomics Research to Elucidate the Genetics of Rare Diseases Consortium. Research into understanding how genomic variation affects genome function and phenotype is also an area of ongoing NHGRI investment through the Impact of Genomic Variation on Function Consortium.

MorPhiC complements these other efforts by examining the impact of human gene knock outs at the molecular and cellular level, said Carolyn Hutter, Ph.D., director in the Division of Genome Science. Ultimately, catalytic advances will come when we are able to collaborate across these different programs.

Funding for Phase 1 of the MorPhiC program will be awarded to support the following investigators:

The National Human Genome Research Institute (NHGRI) is one of the 27 institutes and centers at the NIH, an agency of the Department of Health and Human Services. The NHGRI Division of Intramural Research develops and implements technology to understand, diagnose and treat genomic and genetic diseases. Additional information about NHGRI can be found at: http://www.genome.gov.

About the National Institutes of Health (NIH):NIH, the nation's medical research agency, includes 27 Institutes and Centers and is a component of the U.S. Department of Health and Human Services. NIH is the primary federal agency conducting and supporting basic, clinical, and translational medical research, and is investigating the causes, treatments, and cures for both common and rare diseases. For more information about NIH and its programs, visit http://www.nih.gov.

NIHTurning Discovery Into Health

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NIH initiative to systematically investigate and establish function of every human gene - National Institutes of Health (.gov)

Newswise Why do some people become addicted to drugs and alcohol while others dont?

What role does genetics play? Which genes or networks of genes are key?

Geneticists Trudy Mackay and Robert Anholt lead a team of researchers from theClemson University Center for Human Geneticsworking to unravel those mysteries usingDrosophila melanogaster, or the common fruit fly.

The work, funded by a five-year, nearly $2.5 million grant renewal from theNational Institutes of Healths National Institute on Drug Abuse (NIDA), builds upon previous work by Mackay and Anholt to identify the genetic underpinnings of cocaine and methamphetamine consumption. The research could lay the groundwork for developing new drugs or repurposing already approved drugs to treat or prevent addiction in humans.

Substance abuse is one of the costliest public health problems in the nation. The U.S. Department of Health and Human Servicesestimates Illicit drug use accounts for $193 billionin health care, productivity loss, crime, incarceration and drug enforcement.

Scientists know genetics plays a role in human susceptibility to drug addiction.

Not everybody becomes addicted. Some people become very easily addicted while others can be social drinkers or users and never become addicted, so we know theres a genetic component, said Anholt, Provosts Distinguished Professor of Genetics and Biochemistry.

The researchers use fruit flies in their research because approximately 70 percent of fruit fly genes have human counterparts. Plus, unlike humans, the flies genetic background and environment can be precisely controlled.

In a previous study, Mackay and Anholt found cocaine use elicits rapid, widespread changes in gene expression throughout the fruit fly brain and that the differences are more pronounced in males than females.

That study allowed male and female flies to ingest a fixed amount of sucrose or sucrose supplemented with cocaine over no more than two hours. Researchers then dissected the brains and dissociated them into single cells. Using next-generation sequencing technology, they constructed an atlas of gene expression changes after cocaine exposure.

Through the previous grant, we learned a lot about the genetic basis of flies consuming cocaine or sucrose when they werent given a choice. But as the field is evolving, it is thought that preference is a better model of what could be considered addictive behaviors in humans, said Mackay, the director of the CHG and the Self Family Endowed Chair in Human Genetics.

Mackays lab developed theDrosophila melanogasterGenetic Reference Panel (DGRP), which consists of inbred fly lines with fully sequenced genomes derived from a natural population. The DGRP allows researchers to use naturally occurring variations to examine genetic variants that contribute to susceptibility to various stressors.

Using those fly lines and a high throughput method CHG Ph.D. student Spencer Hatfield and former postdoctoral fellow Joshua Walters developed to measure preference (choosing sucrose containing cocaine over plain sucrose when given the choice), the researchers will map variants associated with preference and the genes associated with those variants.

We can look at those lines that have an innate preference and ask whether we can further develop the model for addiction. In other words, if they are exposed repeatedly, will they start to prefer it more and develop adverse behavioral or physiological reactions? And despite that adversity, will they continue to show a preference for cocaine? That will be a real measure of addiction, Anholt said.

A small-scale Mackay lab study involving 46 genetically diverse lines of flies showed a genetic variation for preference that changed over time.

That shows that the larger experiment were doing now is likely to succeed, Mackay said. It showed that, even on a small scale, there is genetic variation.

Genes identified as important in cocaine preference that have human counterparts could be potential targets for therapeutics that could treat or prevent addiction.

Continued here:
Researchers seek to unravel the mystery of susceptibility to drug addiction - Newswise

Obesity can seriously compromise a persons physical and mental health. It is defined as abnormal or excessive fat accumulation that may impair health and is a known risk factor for heart disease, type 2 diabetes, and certain cancersall of which are leading causes of preventable, premature death.

Rates of obesity have tripled since 1975, over 41% of adults and almost 20% of children in the U.S. are classed as obese. People are considered obese if they have an excess of body fat and a Body Mass Index (BMI) over 30.

BMI is a simple but rather controversial measurement, defined as a persons weight in kilograms divided by the square of their height in meters (kg/m2).

Recently, researchers at Baylor College of Medicine suggested that obesity risk in humans may be determined by environmental and genetic factors during early development and argue that obesity should be considered a neurodevelopmental disease.

Study lead Dr. Robert A. Waterland, professor at Baylor College of Medicine, told Medical News Today:

[] genetic variation certainly contributes to individual differences in body weight, early environmental influences on the development of body weight regulatory mechanisms (developmental programming) may, in general, play a bigger role in determining individual propensity to obesity.

The work published in the journal Science Advances uses epigenetics to show that obesity is linked to nutrition during certain phases of development.

A number of things such as poor diet, lack of physical exercise, and a lack of good sleep, are known to increase the risk of obesity.

The type and amount of food eaten are also directly linked to obesity risk, consuming an excess of calories and burning very few will create a calorie surplus leading to weight gain. That said, the public health message to eat less and exercise more hasnt stemmed the tide of obesity.

Once seen as a result of a lack of will and self-restraint, the biological nature of obesity has been shown to be much more complex. Indeed, prenatal and early life studies have linked undernutrition to obesity in rats.

The effect of nutrition during early development in human studies has shown famine during the first trimester of pregnancy resulted in higher obesity rates, but famine during the last trimester and the first months of life was linked with lower levels of obesity.

It is widely accepted that body weight is also influenced by genetics. The CDC reports over 50 different genes that have been associated with obesity. Genes determine the signals that are transmitted by hormones to the brain, where they direct the body to eat or move.

Large-scale human genome studies have found changes in genes linked to BMI are expressed in the developing brain.

Epigenetics studies the way genes work, allowing scientists to study how behavior and environment can alter how genes work. Epigenetic changes dont change the sequence of the DNA, they change how the body reads the DNA sequence.

For this study, mice ages 2 to 4 months were monitored through pregnancy and their pups were studied through post-natal development.

Whole genome analysis and RNA sequencing were completed on neuron and glia cells and studied for epigenetic markers and gene expression. Specifically, the researchers used tissue from the arcuate nucleus of the hypothalamus of the brain, the area that controls hunger and satiety.

The researchers noted the post-natal period in mice is critical for epigenetic changes linked to obesity and energy balance regulation, suggesting obesity could be a consequence of dysregulated epigenetic maturation, according to Dr. Harry MacKay, the studys first author.

Interestingly, when comparing the epigenetic data with data from human genome studies the investigators found a strong correlation between regions of the human genome linked to BMI and the areas of epigenetic changes in mice, leading to the suggestion that adult obesity may be determined in part by epigenetic development in the arcuate nucleus.

The authors propose this new understanding may create effective interventions to prevent obesity this work provides the argument that prenatal and early postnatal development can at least in part determine human obesity risk.

[E]vidence from the last several decades indicates that once an individual is obese, it is extremely difficult to achieve a normal body weight. And, when obese adults do succeed in losing substantial weight, it is extremely difficult to maintain the weight loss in the long term. It is our hope that an improved understanding of the developmental neuroepigenetic mechanisms underlying the establishment of body weight regulation will enable effective approaches to prevent obesity. Dr. Robert A. Waterland

When asked if the work could lead to new nutritional recommendations for pregnancy, Dr. Waterland commented that the current research, which was conducted in mice, does not provide a basis for making nutritional recommendations for humans. Although we dont yet have the data, it is a reasonable guess that the postnatal epigenetic maturation we cataloged in this mouse study occurs during late fetal development in humans.

[] such data would bolster existing recommendations that women try to achieve a healthy body weight prior to becoming pregnant, as maternal obesity during pregnancy not only raises the risk of pregnancy complications like preterm birth and gestational diabetes but also appears to promote lifelong positive energy balance in her developing child, he added.

The study is not without limitations.

The nature of the ever-changing cell population during early development makes interpreting the data complicated, it is possible that changes in the cell population between the time points may affect the results.

The authors plan to overcome this in future studies by using more time points and using computational modeling.

The next step for the research is to extend it into human studies.

[] an obvious next step is to determine when this BMI-associated epigenetic maturation occurs in humans. Because many neurodevelopmental processes occur earlier in humans than in mice, it is likely that this hypothalamic epigenetic maturation occurs during late fetal development in humans, said Dr. Waterland.

[A]n obvious next step would be to try to determine if maternal obesity during pregnancy somehow impairs these developmental changes, resulting in persistently impaired regulation of energy balance in her child. Dr. Robert A. Waterland

See more here:
Does obesity have more to do with the brain than we initially thought? - Medical News Today

Ethics approval and informed consent

All participants provided informed consent, and studies were approved by the individual IRBs at the respective institutions. UK Biobank has approval from the North West Multi-center Research Ethics Committee (MREC), which covers the UK. It also sought the approval in England and Wales from the Patient Information Advisory Group (PIAG) for gaining access to information that would allow it to invite people to participate. The DiscovEHR study was approved by the Institutional Review Board (IRB) at Geisinger. The BioMe Biobank is an ongoing research biorepository approved by the Icahn School of Medicine at Mount Sinais IRB. The Ethical Committee at Lund University approved the Malmo Diet and Cancer Study (LU 5190) and all the participants provided a written informed consent. The CGPS study (H-KF-01-144/01) was approved by the Ethics Committee of the Capital Region and from the Danish Data Protection Agency. Research at Estonian Biobank is regulated by Human Gene Research Act and all participants have signed a broad informed consent. IRB approval for current study was granted by Research Ethics Committee of University of Tartu, approval nr 236/T-23. For the POAAGG study, approval to enroll and to recontact subjects was obtained from the University of Pennsylvania IRB. The Finngen Biobank was evaluated and approved by the Coordinating Ethics Committee of the Helsinki and Uusimaa Hospital District.

Association with IOP was tested on a total of 101,678 individuals and 27,529 individuals of European ancestry from the United Kingdom Biobank (UKB) and the MyCode Community Health Initiative cohort from Geisinger Health System (GHS), respectively. The UKB is a population-based cohort study of people aged between 40 and 69 years recruited through 22 testing centers in the UK between 2006 and 201040. The GHS MyCode study is a health system-based cohort of patients from Central and Eastern Pennsylvania (USA) recruited in 2007201941. For IOP association tests in African ancestry individuals, we included 4114 individuals from UKB and 3167 individuals from the Primary Open Angle African-American Glaucoma Genetics (POAAGG) study conducted at the University of Pennsylvania Perelman School of Medicine42. We excluded all participants with a glaucoma diagnosis code (ICD-10 H40) or self-reported glaucoma (UKB field IDs: 6148 and 20002) from IOP analyses.

Association of ANGPTL7 variants with glaucoma was tested in 8 studies: UKB, GHS, Mt. Sinai BioMe cohort (SINAI), the Malm Diet and Cancer study (MALMO)43, the Estonia Biobank (EstBB)44, The Trndelag Heath Study (HUNT)45, FinnGen, a study from Finland, and the Copenhagen General Population Study and the Copenhagen City Heart Study (CGPS-CCHS)46. We had, in total, up to 40,042 cases (UKB: 12,377, GHS: 8032, SINAI: 409, MALMO: 2395, EstBB: 7629, HUNT: 3874; CPGS-CCHS: 1863; FinnGen: 3463) and 947,782 controls of European ancestry, and 5153 cases (UKB: 448, POAAGG: 3444, SINAI: 1261) and 21,650 controls of African ancestry in glaucoma analyses.

IOP in UKB was measured in each eye using the Ocular Response Analyzer (Reichert Corp., Buffalo, New York). Participants were excluded from this test if they reported having eye surgery in the preceding 4 weeks or having an eye infection. The Ocular Response Analyzer calculates two forms of IOP, a Goldmann-correlated IOP (IOPg) and a corneal-compensated IOP (IOPcc). IOPg most closely approximates the IOP measured by the Goldmann Applanation Tonometer(GAT), which has been the gold standard for measuring IOP, while IOPcc provides a measure of IOP that is adjusted to remove the influence of corneal biomechanics47. For this study, we focused on IOPg as this measurement is the most comparable to IOP measurements in other cohorts, and herein IOPg will be referred to as IOP. IOP in POAAGG was measured using a GAT. In GHS, IOP measurements were obtained from several instruments including GAT, Tono-pen and I-Care, which are correlated with IOPg readings from the Ocular Response Analyzer48. For GHS individuals who were not prescribed any IOP medications, we used the median of all IOP measurements available. For individuals who had an IOP medication prescribed, we used the median of IOP measurements available preceding the start date for IOP medications (if available). Individuals for whom we did not have non-medicated IOP values were excluded from the IOP genetic analyses. For association analyses of IOP, we excluded individuals with: (1) a glaucoma diagnosis; (2) IOP measures that were more than 5 standard deviations away from the mean; (3) more than a 10-mmHg difference between both eyes. We derived a mean IOP measure between both eyes for each individual. IOP of only one eye was used in instances where IOP measures for both eyes were not available.

Details on glaucoma definition in each cohort are given in the Supplementary Methods. In brief, glaucoma cases in GHS, SINAI, MALMO, HUNT, EstBB, FinnGen (v.R3) and CGPS-CCHS were defined by the presence of an ICD-10 H40 diagnosis code in either outpatient or inpatient electronic health records. In UKB, glaucoma cases were defined as individuals with either an ICD-10 H40 diagnosis or self-reported glaucoma (UKB field ID: 6148 or 20002). In the POAAGG cohort, glaucoma cases and controls were classified based on an ophthalmic examination by glaucoma specialists, and glaucoma suspects were also included in the cases42.

High coverage whole exome sequencing and genotyping was performed at the Regeneron Genetics Center49,50 as described in Supplementary Methods. We estimated the association with IOP and glaucoma of genetic variants or their gene burden using REGENIE v1.0.4351 (UKB, GHS, MALMO, SINAI), SAIGE52 (HUNT, EstBB, FinnGen) or logistic regression (CGPS-CCHS). Analyses were adjusted for age, age2, sex, an age-by-sex interaction term, experimental batch-related covariates, and genetic principal components, where appropriate. Cohort-specific statistical analysis details are provided in Supplementary Methods. Results across cohorts were pooled using inverse-variance weighted meta-analysis. Details on the PheWAS analysis conducted in UKB and GHS are provided in Supplementary Methods. Western blotting and ELISA analyses were repeated on three independent biological replicates and data are presented as meanSEM. Technical replicates (n=3) were run for the ELISA analysis. P values were calculated by one-way ANOVA with Tukeys multiple comparison test for multiple groups analysis (Supplementary Data1). A total of 12 eyes were used to test the effect of increasing mAngptl7 levels in mouse eyes and Students t test was used to calculate the significance of the resulting change in IOP. The IOP was measured on 33 WT, 41 Angptl7 KO and 15 Angptl7 Het mice and conventional outflow facility was measured on 4 WT and 7 Angptl7 KO mice. Unpaired Students t-test was used to calculate the statistical significance of the results between the different genotypes. For in vivo siRNA knockdown of mAngptl7, we used 8, 6, 6 and 5 mouse eyes for siRNA#3, siRNA#5, PBS-treated and Nave controls, respectively. Statistical significance was calculated using one-way ANOVA with Dunnetts post hoc analysis (Supplementary Data1).

HEK293 cells, derived within Regeneron, were cultured in DMEM media 4.5g/L D-Glucose, (+) L-Glutamine, () Sodium Phosphate, () Sodium Pyruvate supplemented with 10% FBS and 1% Penicillin-Streptomycin-Glutamine (Invitrogen), at 37C in a humidified atmosphere under 5% CO2. The day before transfection, HEK293 cells were seeded in OptiMEM supplemented with 10% FBS. After 24h, the cells were transfected with FuGENE 6, and 10g of pcDNA 3.1(+) encoding the following proteins: ANGPTL7 WT, Gln175His, Arg177* and Trp188*. After 24h, the media was changed with 2% FBS OptiMEM. The following day, the cells were collected in RIPA buffer, supplemented with protease and phosphatase inhibitors (BRAND) or TRIzol reagent (Invitrogen) for protein and RNA analysis, respectively. The supernatants were transferred to an Eppendorf tube and immediately flash frozen for downstream protein analysis. Western blot analysis was performed using a rabbit polyclonal antibody against ANGPTL7 at 1:1000 dilution (10396-1-AP ProteinTech), using standard procedures. ANGPTL7 was quantified by ELISA according to manufacturers instructions (LS-F50425 Life Sciences). The cell lysates were diluted 1:1000. The supernatants were diluted 1:10,000. The ELISA plate was read at 450nm via SpectraMax M4 plate reader (Molecular Devices).

Total RNA was extracted using TRIzol reagent (Invitrogen) and RNeasy kit (Qiagen) according to manufacturers instructions and treated with RNase-free DNase I (Promega). cDNA was synthesized using Superscript VILO cDNA synthesis kit (Invitrogen). Taqman analysis was performed using TaqMan Fast Advanced Master Mix (Applied Biosystems) in a QuantStudio 6 Flex (Applied Biosystems) and commercially available primers and probes for ANGPTL7 (Hs00221727Applied Biosystems) and GAPDH (Hs02786624_g1Applied Biosystems).

All animal protocols were approved by the Institutional Animal Care and Use Committee in accordance with the Regenerons Institutional Animal Care and Use Committee (IACUC) and the Association for Research in Vision and Ophthalmology (ARVO) Statement for the Use of Animals in Ophthalmic and Vision Research. Angptl7/ mice, on 63% C57BL/6NTac and 37% 129SvEvTac background, were generated by Regeneron Pharmaceuticals using the VelociMouse technology53. Heterozygous mice (Angptl7+/) were bred to generate age-matched wild-type, het and KO littermates that were used for experimentation at 3-4 months of age (mixed gender). Ocular anatomy in these mice was characterized using optical coherence tomography. Detailed methods on generation and characterization of KO mice are provided in Supplementary Methods. For in vivo siRNA experiments, we used C57BL/6J male mice, 3-4 months old, from Jackson Labs.

Mice were anesthetized and IOP was measured in both eyes using a TonoLab rebound tonometer (Colonial Medical Supply, Franconia, NH) before the start of Angptl7 injection and every day afterwards for six days54,55,56. When testing Angptl7 siRNAs, IOPs were measured in each eye before then start of experiment and then every week until end of study. IOP measurements for both eyes were completed within 35min. Six correct single measurements were done on each eye to generate one IOP reading. We took five IOP readings for each eye and used the average of those readings at each time-point.

Aqueous humor outflow facility (C) was measured by using our constant flow infusion technique in live mice55,56,57,58. Mice were anesthetized by using a 100/10mg/kg ketamine/xylazine cocktail. A quarter to half of this dose was administered for maintenance of anesthesia as necessary. One to two drops of proparacaine HCl (0.5%) (Bausch+Lomb) were applied topically to both eyes for corneal anesthesia. The anterior chambers of both eyes were cannulated by using a 30-gauge needle inserted through the cornea 12mm anteriorly to the limbus and pushed across the anterior chamber to a point in the chamber angle opposite to the point of cannulation, taking care not to touch the iris, anterior lens capsule epithelium, or corneal endothelium. Each cannulating needle was connected to a previously calibrated (sphygmomanometer, Diagnostix 700; American Diagnostic Corporation, Hauppauge, NY, USA) flow-through BLPR-2 pressure transducer (World Precision Instruments [WPI], Sarasota, FL, USA) for continuous determination of pressure within the perfusion system. A drop of genteal (Alcon) was also administered to each eye to prevent corneal drying. The opposing ends of the pressure transducer were connected via further tubing to a 1ml syringe loaded into a microdialysis infusion pump (SP200I Syringe Pump; WPI). The tubing, transducer, and syringe were all filled with sterile DPBS (Gibco). Signals from each pressure transducer were passed via a TBM4M Bridge Amplifier (WPI) and a Lab-Trax Analog-to-Digital Converter (WPI) to a computer for display on a virtual chart recorder (LabScribe2 software; WPI). Eyes were initially infused at a flow rate of 0.1 l/min. When pressures stabilized within 1030min, pressure measurements were recorded over a 5-min period, and then flow rates were increased sequentially to 0.2, 0.3, 0.4, and 0.5l/min. Three stabilized pressures at 3-minute intervals at each flow rate were recorded. C in each eye of each animal was calculated as the reciprocal of the slope of a plot of mean stabilized pressure as ordinate against flow rate as abscissa.

A 33-gauge needle with a glass microsyringe (5-uL volume; Hamilton Company) was used for injections of Angptl7 protein/siRNA into mice eyes. For intravitreal injections, the eye was proptosed, and the needle was inserted through the equatorial sclera and into the vitreous chamber at an angle of approximately 45 degrees, taking care to avoid touching the posterior part of the lens or the retina. Angptl7 protein (catalog# 4960-AN-025; R&D Systems, Minneapolis, MN) or siRNA (from Alnylam Pharmaceuticals, Supplementary Methods) or PBS (1uL) was injected into the vitreous over the course of 1minute. The needle was then left in place for a further 45s (to facilitate mixing), before being rapidly withdrawn. siRNA sequences for all six probes tested are provided in Table2. Before and during intracameral injections of Angptl7 protein, mice were anesthetized with isoflurane (2.5%) containing oxygen (0.8L/min). For topical anesthesia, both eyes received one to two drops of 0.5% proparacaine HCl (Akorn Inc.). Each eye was proptosed and the needle was inserted through the cornea just above the limbal region and into the anterior chamber at an angle parallel to the cornea, taking care to avoid touching the iris, anterior lens capsule epithelium, or corneal endothelium. Up to 1L of Angptl7 protein or PBS was injected into each eye over a 30-s period before the needle was withdrawn. Only one injection was administered at day 0.

Further information on research design is available in theNature Research Reporting Summary linked to this article.

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ANGPTL7, a therapeutic target for increased intraocular pressure and glaucoma | Communications Biology - Nature.com

image:Microscopy image of actine. (Actine is yellow, cell core is blue) view more

Credit: Peter Haarh, Netherlands Cancer Institute

I'm a professional pin-in-a-haystack seeker, geneticist Thijn Brummelkamp responds when asked why he excels at tracking down proteins and genes that other people did not find, despite the fact that some have managed to remain elusive for as long as forty years. His research group at the Netherlands Cancer Institute has once again managed to track down one of these "mystery genes" - the gene that ensures that the final form of the protein actin is created, a main component of our cell skeleton. These findings were published today in Science.

Cell biologists are very interested in actin, because actin a protein of which we produce more than 100 kilograms in our lifetime - is a main component of the cell skeleton and one of the most abundant molecules in a cell. Large quantities can be found in every cell type and it has many purposes: it gives shape to the cell and makes it firmer, it plays an important role in cell division, it can propel cells forward, and provides strength to our muscles. People with faulty actin proteins often suffer from muscle disease. Much is known about the function of actin, but how the final form of this important protein is made and which gene is behind it? "We didn't know," says Brummelkamp, whose mission is to find out the function of our genes.

Genetics in haploid human cellsBrummelkamp has developed a number of unique methods for this purpose over the course of his career, which allowed him to be the first to inactivate genes on a large scale for his genetics research in human cells twenty years ago. You cant crossbreed people like fruit flies, and see what happens. Since 2009, Brummelkamp and his team have been using haploid cells - cells containing only one copy of each gene instead of two (one from your father and one from your mother). While this combination of two genes forms the basis of our entire existence, it also creates unwanted noise when conducting a genetics experiment because mutations usually occur in just one version of a gene (the one from your father, for example) and not the other.

Multi-purpose method for genetics in human cellsTogether with other researchers, Brummelkamp uses this multi-purpose method to find the genetic causes of particular conditions. He has already shown how the Ebola virus and a number of other viruses, as well as certain forms of chemotherapy, manage to enter a cell. He also investigated why cancer cells are resistant to certain types of therapy and discovered a protein found in cancer cells that acts as a brake on the immune system. This time he went looking for a gene that matures actin - and as a result, the skeleton of the cell.

In search of scissorsBefore a protein is completely "finished" - or mature, as the researchers describe it in Science - and can fully perform its function in the cell, it usually has to be stripped of a specific amino acid first. This amino acid is then cut from a protein by a pair of molecular scissors. This is also what occurs with actin. It was known on which side of the actin the relevant amino acid is cut off. However, no one managed to find the enzyme that acts as scissors in this process.

Peter Haahr, postdoc in Brummelkamp's group, worked on the following experiment: first he caused random mutations (mistakes) in random haploid cells. Then he selected the cells containing the immature actin by adding a fluorescently labeled antibody to his cells that fit in the exact spot where the amino acid is cut off. As a third and final step, he investigated which gene mutated after this process.

They called it ACTMAPThen came the "eureka"-moment: Haahr had traced down the molecular scissors that cut the essential amino acid from actin. Those scissors turned out to be controlled by a gene with a previously unknown function; one no researcher had ever worked with. This means that the researchers were able to name the gene themselves, and they settled on ACTMAP (ACTin MAturation Protease).

To test whether a lack of ACTMAP leads to issues in living things, they switched off the gene in mice. They observed that the actin in the cell skeleton of these mice remained unfinished, as expected. They were surprised to find that that the mice did stay alive, but suffered from muscle weakness. The researchers conducted this research together with scientists from the VU Amsterdam.

More scissors found in the skeleton of the cellACTMAP is not the first mystery gene discovered by Brummelkamp that plays a role in our cell skeleton function. Using the same method, his group has been able to detect three unknown molecular scissors over recent years that cut an amino acid from tubulin, the other main component of the cell skeleton. These scissors allow tubulin to perform its dynamic functions properly inside the cell. The last scissors (MATCAP) were discovered and described in Science this year. Through this earlier work on the cell skeleton, Brummelkamp managed to arrive at actin.

Mission: mapping out all 23.000 genesUnfortunately, our new discovery about actin doesnt tell us how to cure certain muscular conditions, says Thijn Brummelkamp. But we have provided new fundamental knowledge about the cell skeleton that may be useful to others later. Moreover, Brummelkamp, whose mission is to be able to map out the function of all of our 23,000 genes one day, can tick another new gene off his gigantic list. After all, we don't know what half of our genes do, which means that we cannot intervene when something goes wrong.

Actin maturation requires the ACTMAP/C19orf54 protease

30-Sep-2022

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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Mystery gene matures the skeleton of the cell - EurekAlert

We live in a time where the rate of medical and superlative scientific advances is accelerating by more than 1,300% since 1985, according to one recent estimate. With so many unprecedented, transformative breakthroughs happening, forecasting which one will be awarded top research honors isnt getting any easier. But with the naming of this years Nobels fast approaching the medicine award will be announced on Oct. 3, physics on Oct. 4, chemistry on Oct. 5 prize prognosticating for the World Series of Science is once again in full swing.

Public polls, tallies of other elite awards, and journal citations have helped betting-minded people collect the names of whos most likely in the running. The shortlist includes researchers who elucidated how cells make energy, those who discovered the chemical chatter of bacteria, many of the brilliant minds who shepherded us into the era of the genome, and most prominently, the pioneers behind the mRNA Covid vaccines.

How Nobels are decided is a matter of grave secrecy records of who nominated and voted for whom are sealed for 50 years making forecasting new winners even more of a challenge. Still, some experts have developed systems that do a decent job.

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David Pendlebury of Clarivate looks at how often a scientists key papers are cited by peers and awarded so-called predictive prizes like the Lasker or Gairdner awards. Each year he comes up with a group of Citation Laureates, and since 2002, 64 of his picks have gone on to receive a Nobel Prize.

Using that strategy, Pendlebury thinks the medicine Nobel could go to the researchers who discovered that different kinds of malformed protein aggregates, in different cell types, underlie a number of neurological diseases including Parkinsons, ALS, and frontotemporal dementia. Virginia Man-Yee Lee of the University of Pennsylvania published a seminal Science paper in 2006, which has now been cited more than 4,000 times. When Pendlebury dug into those citations, he noticed that researchers almost always mentioned that paper in tandem with a very similar but much lower-profile study published a few months later by Masato Hasegawa of the Tokyo Metropolitan Institute of Medical Science.

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This phenomenon of simultaneous independent discovery is very common in science, more than I think people understand, Pendlebury told STAT. So the citations tend to go to the first mover, but they are really a pair. And since their papers, the field has blossomed in many directions, because it was a big step forward for trying to find therapies for these kinds of diseases.

For similar reasons, Pendlebury also has his eyes on two scientists who made groundbreaking discoveries about the genetic basis of disease: Mary-Claire King of the University of Washington for uncovering the role of mutations in the BRCA genes in breast and ovarian cancers, which revolutionized cancer screening, and Stuart Orkin of Harvard Medical School for identifying the genetic changes behind the various types of thalassemia leading to promising new gene-based therapies for inherited blood disorders.

Another thing that Pendlebury takes into account in his predictions is periodicity. The committees tend to take turns rewarding different disciplines; neuroscience, cancer, or infectious-disease discoveries win every decade or so. For the medicine prize, periodicity also shows up between discoveries of basic molecular biology and ones that lead to people actually being treated or cured of the things that ail them.

In the past decade, the medicine prize has more times than not gone back to basics. In 2013, it went to intra-cell transportation, in 2016 to the process of cellular self-destruction, in 2017 to the genetic clocks that control circadian rhythms, in 2019 to how cells sense and adapt to oxygen availability, and last year to how cells sense temperature and touch. Prizes with a more clinical focus have been awarded in 2015, (roundworm and malaria therapy), 2018 (immuno-oncolgy), and 2020 (hepatitis C).

Thats just one reason why cancer biologist Jason Sheltzer of the Yale School of Medicine is so bullish on this years medicine prize going to Katalin Karik of BioNTech and Drew Weissman of Penn Medicine for taking messenger RNA, or mRNA, on a 40-year journey from an obscure corner of cell biology to a pandemic-halting vaccine technology. Its such a radical change in vaccine technology, at this point billions of doses have been given, and it has incontrovertibly saved millions of people from dying of Covid, Sheltzer said. To me, its just a slam dunk.Sheltzer has been making Nobel predictions on Twitter since 2016 and correctly chose immuno-oncology pioneer James Allison for the 2018 medicine prize. His methodology is a bit more straightforward; he tracks winners of seven major science prizes the Horwitz, Wolf, Albany, Shaw, and Breakthrough Prize, in addition to the Lasker and Gairdner because the data show that theres only so long the Nobel Committee can ignore people whove won at least two. Karik and Weissman have won five of the six. Its not a question of if it will happen, its just a question of when, he said.

Hes less certain about the chemistry prize. Might David Allis of Rockefeller and Michael Grunstein of UCLA finally get the call to Stockholm? They discovered one way genes are activated through proteins called histones for which they shared a 2018 Lasker and a 2016 Gruber Prize in genetics. The control of gene expression, otherwise known as epigenetics, is a fundamental process in cell biology that researchers and industry are just beginning to harness to treat human disease. But the last time epigenetics got the Nobel nod was in 2006, with Roger Kornbergs win in chemistry for his work unlocking the molecular mystery of how RNA transcripts are assembled.

Its been nearly 20 years since that field has been recognized with a prize, so you could make the case that its very much due this year, said Sheltzer.

Thats even more true for DNA sequencing, which was last awarded a Nobel in 1980 to Wally Gilbert and Frederick Sanger for their work developing the first (eponymously named) method for determining the order of base pairs in nucleic acids. But so much has happened in the field since then, that the slate of worthy sequencing successors is practically overflowing.

Should it go to the scientists who gave us the first-ever draft of the human genome, and if so, which ones? Hundreds of researchers all over the world aided in the effort, which was a feat of engineering and mass production as much as scientific innovation. If the chemistry or medicine Nobel committees takes a cue from their physics counterpart, who in 2017 honored the organizers of the international project that discovered gravitational waves, then the top contenders would likely be the Human Genome Projects cat-herder-in-chief and recently departed director of the National Institutes of Health, Francis Collins, and Eric Lander, whose lab at the Broad Institute churned out much of the draft sequence. A third might be Craig Venter, whose competing private sequencing push at Celera raced the public effort to a hotly contested draw.

Perhaps a more deserving trio would be Marvin Caruthers of the University of Colorado, Leroy Hood of the Institute for Systems Biology, and Michael Hunkapiller, former CEO of DNA-sequencing behemoth Pacific Biosciences. They invented the technology behind the first automated sequencers, which powered the Human Genome Project (and were Pendleburys pick for the chemistry Nobel in 2019).

Or perhaps the call from Stockholm will go out to David Klenerman and Shankar Balasubramanian of the University of Cambridge, who developed the sequencing-by-synthesis technology that came after the Human Genome Project and is now the workhorse of the modern sequencing era (and for which they won the 2020 Millennium Technology Prize and this years Breakthrough Prize in life sciences). More recent inventions, like the nanopore sequencing technologies that have enabled the construction of the first actually complete human genomes in the last few years are also in the running, but probably a longer shot, despite their obvious contributions to both chemistry and medicine. Thats because the Nobel committees tend to tilt toward true trailblazers and away from those who extend an initial, foundation-laying discovery or insight.

The Human Genome Project, a perennial topic of conversation among Nobel-casters, has inspired even more intrigue than usual this year, following the surprise exit of Eric Lander from his position as White House science adviser in the wake of workplace bullying allegations.

Although the rare Nobel has been awarded to well-known jerks or kooks Kary Mullis, the eccentric inventor of PCR, and James Watson, the dubious co-discoverer of the double-helix structure of DNA (and frequent maker of racist, sexist remarks) come to mind the Royal Swedish Academy of Sciences, which selects the physics and chemistry laureates, and the Nobel Assembly at the Karolinska Institute, which chooses the physiology/medicine winner, tend to steer clear of controversy.

Its hard to find many examples of a Nobel being awarded to someone whos been super controversial, said Sheltzer.

Among Pendleburys picks, the person who skirts closest is perhaps Stephen Quake of Stanford University and the Chan Zuckerberg Initiative, who provided advice to He Jiankui, the Chinese scientist who created the worlds first CRISPR babies. Stanford later cleared Quake of any misconduct. Quake has made important discoveries in microfluidics which led to rapid advances in noninvasive testing and single cell sequencing, and Pendlebury sees him as a favorite for a physics Nobel.

In chemistry, Pendlebury likes another Stanford University engineer, Zhenan Bao, for her paradigm-shifting work in the field of semiconducting polymers making stretchable electronic skin. Hes also got his eye on Daniel Nocera at Harvard University for foundational work illuminating the proton-coupled electron transfer process that powers cells, and the team of Bonnie Bassler from Princeton University and E. Peter Greenberg of the University of Washington for their discovery of quorum sensing a chemical communication system between bacteria.

Besides citations, prediction prizes, and periodicity, Pendlebury is also playing the long game. I pay special attention to papers that are 15, 20, 25, 30 years old, because it usually takes a decade or two for research to be selected by the Nobel Prize Committee, he said.

That might complicate things for one of the leading vote-getters in an online poll for the chemistry Nobel John Jumper of the Alphabet-owned company DeepMind and a 2023 Breakthrough Prize in life sciences winner. His work leading the AlphaFold artificial intelligence program stunned the world two years ago by essentially solving one of biologys most enduring challenges: quickly and accurately predicting the 3D structure of a protein from its amino acid sequence.

Thats why this first-time Nobel forecaster is betting on another top vote-getter for the chemistry prize, Carolyn Bertozzi of Stanford University, who has spent much of her illustrious career devising methods to understand an elusive but critical class of sugar-coated molecules called glycans found on the surface of almost all living cells. Shes been a member of the National Academy of Sciences since 2005 and won the Wolf prize earlier this year, in recognition of founding the field of bioorthogonal chemistry a term Bertozzi coined two decades ago that refers to reactions scientists can perform within living organisms without interfering with their normal functions.

Sticking with dark-horse picks (because, why not), Im going with Yuk Ming Dennis Lo of the Chinese University of Hong Kong for the medicine prize. In 1997, he reported that a growing fetus sheds cell-free DNA into the mothers blood. Ten years later, he found a way to use that DNA to detect the signature abnormalities associated with Down syndrome. Together, these discoveries revolutionized clinical practice of screening for fetal genetic abnormalities, leading to the development of non-invasive prenatal testing now used by millions of people every year. Lo has only just begun to be recognized for that work, winning last years Breakthrough Prize for life sciences and this years Lasker Award for clinical medical research, which was announced on Wednesday. He also founded companies based on this same principle for the early detection of multiple cancers, one of which was acquired by pioneering liquid biopsy giant Grail.

Other crowdsourced efforts to predict Nobel winners arent making a return appearance, including the March Madness-style brackets run for many years by the scientific research honors society Sigma Xi. (Last year saw Bertozzi lose in the finals to Omar Yaghi and Makoto Fujita, pioneers of metal-organic self-assembling structures.) Sigma Xi couldnt be reached for comment, but the change comes amid increasingly loud criticism of the Nobel Prizes, for the way they distort the collaborative nature of the scientific enterprise and overlook many of its important contributors (including many women and people of color).

Even Nobel obsessives like Sheltzer admit those arguments are becoming more compelling. But he likes how, at least for a few days every October, he can count on scientific discoveries splashing across the front page of the New York Times and leading the hour on the nightly news. There are amazing things happening in the scientific world right now, like CRISPR gene editing and immunotherapy for cancer, that I think should really be front-page news much more frequently than they are, said Sheltzer. But Im glad that the Nobel Prize shines a spotlight on them and elevates them into the national consciousness, even if just for a brief period of time.

Excerpt from:
Who will get the call from Stockholm? It's time for STAT's 2022 Nobel Prize predictions - STAT

Nazira Kelly first noticed something was different about her newborn son, Ezra, when he was two weeks old. While many newborns develop splotchy red rashes that go away after a few days or weeks, Ezras skin had an unusual, swirling pigmentation. Then, when Ezra was about four weeks old, Kelly noticed that his head had grown unusually large, and he was having trouble lifting it.

A registered nurse who worked in the Labor and Delivery unit, Kelly immediately brought her concerns to his pediatrician. But a head ultrasound came back normal, and the dermatologist recommended he see a neurologist, who advised the Kellys to track Ezras head growth and other symptoms.

Kelly held onto hope that maybe these unusual symptoms would sort themselves out. Then, when Ezra was six months old, Kelly awoke at 5:30 a.m. to see her baby having a seizure.

Our lives just changed right there, she says.

Ezra was put through two rounds of genetic testing, but his condition eluded the geneticist. Over the next three months, Ezra had two more seizures but no clear diagnosis.

Around this time, the family moved from Kansas to North Carolina and set Ezra up with a new slate of specialists. It was their neurologist at Duke University Medical Center in Durham who suggested they apply to the Undiagnosed Diseases Network (UDN), a consortium of 12 academic medical centers across the country funded by the National Institutes of Health (NIH) and dedicated to combining expertise, technology, and shared data to try to solve the most challenging medical mysteries. Duke happened to be one of the networks clinical sites.

About three months after being accepted to the program, the UDN team at Duke had cracked the case. Ezra had a rare genetic disorder called Smith-Kingsmore syndrome caused by changes to the MTOR gene, which plays an essential role in cell growth and function. Only about 10 in 10,000 individuals are affected by MTOR disorders.

Although there is no cure or treatment, Kelly was relieved to finally have some answers.

In the beginning, the ocean is so vast, you dont know what to ask or where to go for information, Kelly says. The UDN really streamlined everything.

Since its inception in 2014, the UDN has reviewed more than 5,800 applications, accepted more than 2,300 participants, and discovered 50 new conditions. Its done so by bringing together a multidisciplinary team of specialists and researchers and using innovative diagnostic technologies.

The UDN has seen extensive collaborations develop between clinicians and researchers. That doesnt typically happen in a clinical setting, says Kimberly LeBlanc, MS, a genetic counselor and director of the UDN Coordinating Center at Harvard Medical School. Its been amazing to see all of these different clinicians and researchers come together to try to help undiagnosed patients.

The UDN will likely operate differently in coming years, however, as NIH funding for the network which was always intended to launch, rather than sustain, the program will sunset in June 2023.

And while the funding mechanisms and organization may change, the investigators and scientists who have pioneered the networks advancement are determined to continue to build on the UDNs success.

I dont think theres any doubt that the UDN will continue, says Vandana Shashi, MD, a pediatrician and principal investigator of the UDN clinical site at Duke. Were all committed to doing this work.

One key to the success of the UDN is its focus on leveraging cutting-edge genetic analysis and modeling, explains Brendan Lee, MD, PhD, who chairs the Department of Molecular and Human Genetics and is director of the Undiagnosed Diseases Center at Baylor College of Medicine in Houston, Texas.

While genomic sequencing has been around for decades, laboratories involved in the UDN have advanced genetic analysis capabilities, including multi-omic approaches that map out not only the patients DNA but also the molecules responsible for protein-coding and for metabolic function. They can also analyze RNA, which carries genetic information, informs gene expression, and translates different proteins into essential functions. Mapping out the patients genes with these functionalizing approaches can help to identify the disease-causing variants with more precision and sensitivity that more basic sequencing might overlook.

Sometimes things that are cryptic then become very obvious when we look directly at the RNA, says Stanley Nelson, MD, a pediatrician specializing in rare diseases and the principal investigator of the UDN clinical site at the University of California, Los Angeles David Geffen School of Medicine.

Baylor serves as the UDNs sequencing core, aiding the 12 clinical sites in the advanced genetic analyses needed to pinpoint more specific variations in the patients genes. It also hosts one of the networks two model organism screening centers, where researchers test human genetic variations in animal models specifically fruit flies, worms, and zebrafish to see how the variation may or may not contribute to disease presentation.

Depending on whether the disease-causing variants have been observed previously, diagnosis can take anywhere from weeks to years, with the UDN successfully diagnosing about 30% of the patients accepted to the program.

But the UDN never closes an undiagnosed case, says Shashi. Rather, it stores all its data analysis at the coordinating center at Harvard and maintains biological samples at the UDN biorepository at Vanderbilt University Medical Center in Nashville.

This allows us to do ongoing research into diagnoses for patients, LeBlanc says.

So as technology advances or if new patients accepted to the network shed further light on specific conditions, the UDN may be able to solve old cases with new information.

The other key to the UDNs work is its multidisciplinary, collaborative approach to each case. Once all necessary testing has been done, experts from a variety of specialties and across multiple institutions get on a call together to discuss and sometimes debate potential diagnoses.

Many times, people go to a specialist and they receive care for their heart, but not their kidney, says Nicola Longo, MD, PhD, chief of the Division of Medical Genetics at the University of Utah School of Medicine, which is one of the UDNs clinical sites. The nice thing about having a team of people is that if you have an immunological condition, we have an immunologist. If you have a kidney condition, we have a nephrologist. If you have an intestinal condition, we have a gastroenterologist. The capacity of having multiple specialists talking together and having a general practitioner or a geneticist, in our case coordinating the care of multiple specialists will ensure that we are taking care of the patient, not of a specific organ.

When managing a patient with complex needs, one issue that often hampers the patients diagnosis and care is a lack of communication among medical professionals, Lee says. Furthermore, each physician across multiple specialties, and even within the same specialty brings a distinct perspective to the case. Bringing these minds together to study cases in real time multiplies the chances of a successful diagnosis, he says.

The collaboration component is so key, Lee says. There is a maximum increase in productivity, efficiency, and brainpower.

At times, bringing together specialists from different institutions has also helped to connect the dots for extremely rare conditions by identifying multiple patients with the same or similar conditions.

Its a repository of clinical knowledge, Nelson says, explaining that he had just completed a weekly UDN call where researchers from the clinical site at the University of Washington in Seattle had presented a case that was reminiscent of a UCLA case on which Nelson was the lead clinician. Both cases involved a previously healthy teenager who suddenly began to develop abnormal movements, trouble swallowing, and difficulty finding words. Though there hasnt been a solution for either case, now the UDN can examine the cases jointly.

We can start to join these cases together to make small cohorts, Nelson says.

For Kelly, being able to put a name to Ezras condition has been life changing. After receiving the Smith-Kingsmore syndrome diagnosis, Kelly promptly Googled the condition and found a support group on Facebook. She soon became involved with the Smith-Kingsmore Syndrome Foundation and attended a conference where she was able to connect with other families of people with the condition.

When you dont have a diagnosis, you really just feel lost, and its lonely, Kelly says. Seeing these other kids [who] looked like your child physically, [I thought,] These people get me. They understood the frustration and feelings, the tears. That was exactly how I felt, those are the same things we went through. It was like finding a lost family member.

Kellys experience is not unique.

Shashi says that her patients who receive a diagnosis for themselves or a family member often feel immense relief, even without an available treatment, especially since the diagnosis allows them to connect with others who have the same condition.

I dont think anybody understands how hard it is to live without having a diagnosis, she says. We will never have a treatment if we dont know whats wrong. As one patient put it to me, For me, the diagnosis is hope.

Many patients and their families start or join foundations and advocate for research and funding, Shashi says.

Through the Smith-Kingsmore Syndrome Foundation, Kelly has helped fundraise for a postdoctoral candidate who is studying the syndrome. She doesnt expect these efforts will pay off in time to benefit Ezra, but she hopes that todays work could help children diagnosed in the future.

And this is both a great challenge and a great potential for the work the UDN does. Most of the conditions it identifies dont have known treatments or cures. Though occasionally physicians can recommend a therapy approved by the Food and Drug Administration or connect patients with a clinical trial, more often the diagnoses are only laying the foundation for further research into treatments, says Nelson.

Its not as satisfying as any of us would like, he says.

Still, he believes the UDNs progress has been accelerating in recent years.

The UDN was formed using money from the NIHs Common Fund, a mechanism intended for short-term, goal-driven strategic investments, according to its website. Although consistent NIH funding for the network will sunset next year, the NIH will continue to fund certain aspects, such as a coordinating center, through grants. The intent is to replace the UDN with a network of Diagnostic Centers of Excellence that can be sustained through different funding mechanisms, LeBlanc explains.

Four of the 12 clinical sites have paused reviewing applications for new patients, according to the UDN website. The UDN has committed to continue to analyze data for existing patients and is still reviewing all applications. Several of the clinical sites are pursuing alternative funding sources, such as institutional funding, philanthropy, and grants, in order to continue and expand on the work that the UDN facilitated.

I think all of the parts of UDN need to adapt to the reality and every site will have a different solution, Lee says. We are committed to this. We were doing it before the UDN, and we will do it in the new version of the UDN.

LeBlanc says that the coordinating center is supporting the startup of a foundation to have sustainable funding and oversight for the initiative, and Lee says he expects academic institutions will dedicate funding for the work.

The major medical centers that are engaged in gene discovery are the medical centers where people want to bring their children and their adult relatives [with undiagnosed conditions], Nelson adds.

This is such an important enterprise; [discovering] what are the genes that contribute to meaningful diseases in humans. Its a very natural integration of what our academic medical centers do well.

Excerpt from:
Solving medical mysteries: Physicians and researchers collaborate to study the most challenging cases - AAMC