Monthly Archives: October 2022

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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See more here:
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

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