Daily Archives: April 19, 2021

Introduction

Antidepressants are a first-line treatment for major depressive disorder (MDD) and are widely prescribed for other conditions, such as obsessive-compulsive disorder (OCD). However, between 40% and 50% of patients on antidepressants do not respond to treatment or relapse.1,2 This individual variability could be due to the complexity of antidepressant response that involves the interplay of both environmental and genetic factors.3 There are currently no specific sociodemographic or clinical markers to predict the response to antidepressants.4

Pharmacogenetic studies have shown that genetic variation influences antidepressant response, but have not fully explained individual variability.5 Recent reports have indicated that the estimates of heritability due to common genetic variants are lower than expected and that significant associations are poorly replicated.6,7 Thus, the search for biomarkers other than genetic factors that predict antidepressant response is gaining increasing attention,3 with epigenetic markers, especially DNA methylation, attracting a lot of interest.8

DNA methylation involves the addition of a methyl group at position 5 of the cytosine pyrimidine ring, a reaction catalyzed by members of the DNA methyltransferase (DNMT) family that usually occurs in cytosine bases that are immediately followed by a guanine (CpG). Large clusters of CpGs, known as CpG islands, occur in promoter regions. With some exceptions, active promoters are generally unmethylated, while inactive promoters tend to be methylated.

Several studies strongly indicate that antidepressants can induce the epigenetic modification of DNMTs, thus altering methylation levels and, subsequently, gene expression. This could explain how antidepressants modulate several molecular mechanisms and significantly affect synaptic plasticity.3,5.

A number of studies have identified epigenetic biomarkers of antidepressant response, with the majority of these studies using a targeted approach to examine a limited number of CpG sites within a specific gene locus. These gene loci include: the brainderived neurotrophic factor (BDNF);9,10 the sodium-dependent serotonin transporter (SLC6A4);1113 the serotonin receptor 1B (HTR1B);14,15 and the interleukin 11 gene (IL11).16 Recently, a genome-wide methylation study identified a set of CpG sites in specific genes such as PPFIA4 and HS3ST1 that accurately predicted paroxetine response.17

In the present study, we performed a genome-wide study assessing differences in DNA methylation that were characterized at baseline after 8 weeks of fluoxetine treatment in a homogenous sample of child and adolescent patients receiving fluoxetine for the first time.

Twenty-two children and adolescents aged between 13 and 17 years, receiving fluoxetine treatment for the first time participated in the present study. None of the participants had been treated previously with antidepressants or other psychotropic drugs. Patients were diagnosed using the Diagnostic and Statistical Manual of Mental Disorders-V (DSM-V).18 The study was carried out at the Child and Adolescent Psychiatry and Psychology Service of the Institute of Neuroscience in Barcelona. Exclusion criteria were comorbidity with other psychiatric disorders, Tourettes syndrome, autism, somatic or neurological diseases, an intelligence quotient <70, and a non-Caucasian ethnicity. All procedures were approved by the Hospital Clnic ethics committee. Written informed consent was obtained from all the parents and verbal informed consent was given by all the participants following explanation of the procedures involved. All experiments were performed in accordance with relevant guidelines and regulations. This study was conducted in accordance with the Declaration of Helsinki.

Information on illness severity was obtained during the initial phase of the study using the following questionnaires: the Childrens Depression Inventory (CDI) for MDD patients (Kovacs, 1992) and the Childrens Yale-Brown Obsessive Compulsive Scale (CYBOCS) for OCD patients.19,20 The same scales, as well as the CGI-Improvement scale (CGI-I), were administered after 8 weeks of fluoxetine treatment. The clinical response after 8 weeks of fluoxetine treatment was evaluated using the percentage of improvement: ((CDI8weeks-CDIbasal)/CDIbasal)*100 or ((CYBOCS8weeks- CYBOCSbasal)/CYBOCS basal)*100. Patients were classified as Responders or Non-Responders according to CGI-I score after 8 weeks of fluoxetine treatment. The CGI-I scale assesses the adequacy of clinical response since the start of treatment and is rated on a 7-point scale, as follows: 1=very much improved, 2=much improved, 3=minimally improved, 4=no change from baseline, 5=minimally worse, 6=much worse and 7=very much worse. According to this rating, and according to the literature: Responders were patients with CGI-I<2 (Very much improved or much improved) and Non-Responders were patients with CGI-I>3 (from minimally improved to very much worse).

A blood sample from each participant was collected in EDTA (BD Vacutainer K2EDTA tubes; Becton Dickinson, Franklin Lakes, New Jersey, USA) before the start of fluoxetine treatment. Genomic DNA was extracted using the MagNA Pure LC DNA Isolation Kit III and a MagNA Pure LC system (Roche Diagnostics GmbH, Mannheim, Germany). DNA concentration and quality were measured using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Surrey, UK).

Genome-wide DNA methylation was profiled using the Illumina Infinium MethylationEPIC BeadChip Kit carried out at CEGEN-PRB3-ISCIII. Raw.IDAT files were received and bioinformatics processes were conducted in house using the Chip Analysis Methylation Pipeline (ChAMP) Bioconductor package.21 Raw intensity data files were used to load the data into the R environment with the champ.load function, which also allows for probe QC and removal steps to occur simultaneously. Probes with low detected signals (p<0.01) (n=3302), cross reactive probes (n= 11), non-CpG probes (n=2954), probes with <3 beads in at least 5% of samples per probe (n=6891), probes that bound to SNP sites (n=96,621), and sex chromosome probes (n=61,734) are all considered problematic for accurate downstream methylation detection. After removing these probes, 739,405 probes remained for downstream analysis. Beta values were then normalized using the champ.norm function, specifically with the beta mixture quartile method (BMIQ function). Cell counts were measured using the champ.refbase function. The following cells were counted: CD8+ T cells, CD4+ T cells, natural killer (NK) cells, B cells, monocytes, and granulocytes. Next, the singular value decomposition (SVD) method was performed by champ.SVD in order to assess the amount and significance of technical batch components, along with any potential confounding variables (sex, age, diagnosis, cell count, fluoxetine dosage), in our dataset. Using the champ.runCombat function, Combat algorithms were applied in order to correct for slide and array as significant components detected by SVD. No effect of sex, age, diagnosis, cell count, or fluoxetine dosage was detected.

After filtering, normalization, and detection of batches and covariates, differentially methylated positions (DMPs) were identified using the function champ.DMP, which implements the limma package to calculate the p-value for differential methylation using a linear model. The absolute value of the difference between -value medians () of Responders and Non-Responders higher than 0.2 was set as a cut-off value to decrease the number of significant CpGs and identify sites with more biologically relevant methylation differences. Hierarchical cluster analysis of significant DMP was plotted as a heatmap and a dendrogram using the gplot and d3heatmap R packages.

Table 1 shows the sociodemographic and clinical data of the 22 participants of this study classified as Responders or Non-Responders according to the CGI-I scale after 8 weeks of fluoxetine treatment. No significant differences in age, sex, BMI, fluoxetine dose or basal clinical scores were observed between the two groups.

Table 1 Sociodemographic, Clinical and Pharmacological Data of the 22 Study Participants

We classified 47,690 probes as significant DMPs (adjusted p-values FDR<0.05): however, this included DMPs with very small differences in methylation between Responders and Non-Responders. Therefore, a > 0.2 cutoff was applied to identify 21 DMPs with methylation changes that are more likely to be biologically relevant (Table 2).

Table 2 21 Significant (FDR<0.05, > 0.2) Differentially Methylated Probes (DMPs) Between Responders and Non-Responders

We assessed the distribution of these 21 DMPs and the other probes in the array in relation to genomic regulatory elements and CpG islands. The genomic regulatory elements considered were the first exon, 3UTR, 5UTR, the gene body, and promoter-proximal regions (TSS1500 and TSS200). Hypermethylated probes in Responders were enriched in the first exon (27% vs 0.025% of all probes) and hypomethylated probes were enriched in the 5UTR (30% vs 0.08% of all probes) (Figure 1A). Regarding the CpG islands, we differentiated between CpG islands, shores (2 kbp from a CpG site), shelves (2 to 4 kbp from a CpG site) and open sea CpGs (isolated CpG in the genome). Hypermethylated probes in Responders were enriched in CpG islands (45% vs 18%) and hypomethylated probes were enriched in open sea CPGs (90% vs 58%) (Figure 1B).

Figure 1 (A) Distribution of 21 significant (FDR<0.05, > 0.2) DMPs and the rest of the probes of the array relative to regulatory elements including transcription start sites (TSS1500, and TSS200), gene body, untranscribed regions (3UTR and 5UTR) and first exon. (B) Distribution of DMPs and the rest of the probes of the array relative to CpG islands, shores, shelves, and sea.

The 21 significant CpGs mapped to 11 genes (RHOJ, RPTOR, ADAP1, SPAG1, GPR1-AS, SLC15A5, OR2L13, NDUFAF1, PPP5D1, LOX2 and ZNF697) and five intergenic regions. Two genes showed more than two significant DMPs (FDR<0.05, > 0.2) (Figure 2A). RHOJ (Ras Homolog Family Member J) presented four CpGs that were significantly hypermethylated in Non-Responders. These CpGs were in the 5-UTR and first exon of the gene, a region that, according to the UCSF browser, includes a promoter region enriched with H3K27AC marks in all cell lines considered by ENCODE (Figure 2B). Two of these CpGs (cg18771300 and cg07157030) were included in The Blood-Brain Epigenetic Concordance database (BECon; https://redgar598.shinyapps.io/BECon/)22 and showed significant correlation between methylation levels in blood and Brodmann Area 10 (BA10) and Brodmann Area 20 (BA20) (r>0.66). Both CpGs were highly variable in the blood (reference range>0.1) and fitted with the definition of a bloodbrain informative CpG in the BECon.

Figure 2 (A) Genes most enriched by the 21 significant DMPs (FDR<0.05, > 0.2). (B) Distribution of significant DMPs (FDR<0.05, > 0.2) in the RHOJ (Ras Homolog Family Member J) gene, and methylation values in Responders (RES) and Non-Responders (NORES). (C) Distribution of significant DMPs (FDR<0.05, > 0.2) in the OR2L13 (Olfactory Receptor family 2 subfamily L member 13) gene and methylation values in Responders and Non-Responders. (D) Hierarchical cluster analysis of the seven CpG sites in the RHOJ (Ras Homolog Family Member J) and OR2L13 (Olfactory Receptor family 2 subfamily L member 13) genes.

OR2L13 (Olfactory Receptor family 2 subfamily L member 13) presented three CpGs that were significantly hypomethylated in Non-Responders, located on a large CpG island in the first exon of the gene (Figure 2C). According to the BECon database, the three CpGs showed significant correlations between methylation levels in blood and the BA10, BA20 and BA7 areas (r>0.5) and were also highly variable in blood and could be considered bloodbrain informative CpGs.

As a sensitivity analysis, we tested the correlations between the methylation level of the seven CpG sites in the RHOJ (Ras Homolog Family Member J) and OR2L13 (Olfactory Receptor family 2 subfamily L member 13) genes and the percentage of improvement scored using the CDI or the CYBOCS. Significant correlations were obtained in all cases: cg03748376 (r=0.55, p=0.008), cg20507276 (r=0.54, p=0.010), cg08944170 (r=0.54, p=0.010), cg11079896 (r=0.44, p=0.038), cg07157030 (r=0.49, p=0.021), cg07189587 (r=0.48, p=0.024) and cg18771300 (r=0.43, p=0.045).

We conducted a hierarchical cluster analysis of the seven sites in these two genes RHOJ (Ras Homolog Family Member J) and OR2L13 (Olfactory Receptor family 2 subfamily L member 13). The results were expressed as a heat map indicating the methylation level at each CpG, and as a dendrogram (Figure 2D). The dendrogram clearly indicated that Responders and Non-Responders differed from each other.

To our knowledge, the present study is the first to analyze differences in DNA methylation in association with response to fluoxetine in the peripheral blood of children and adolescents using a genome-wide approach. We identified 21 CpG sites significantly (FDR<0.05) associated with fluoxetine response that showed meaningful differences (> 0.2) in methylation level between Responders and Non-Responders. Two genes, RHOJ and OR2L13, were enriched in significant CpG sites that showed a strong correlation in DNA methylation between the blood and brain (The Blood-Brain Epigenetic Concordance database BECon; https://redgar598.shinyapps.io/BECon/).

RHOJ (Ras Homolog Family Member J) is a member of the Cdc42 subfamily of the Rho family of GTPases, a group of small signaling molecules that are major regulators of cytoskeleton properties.23 Rho GTPases are involved in various cellular processes, including adhesion, cell polarization, motility and transformation, gene activation and vesicular trafficking, and have been associated with cytoskeletal organization and the regulation of axon outgrowth.24 Early studies suggested that RhoJ plays a role in modulating the formation of distinct cytoskeletal structures and lamellipodia as well as in actin filaments.25 Also, RhoJ has been shown to regulate the early endocytic pathway, being necessary for the transport of endocytosed receptors.26 Recently, the crp1 gene in Caenorhabditis elegans that encodes a protein that resembles human RhoJ has been linked to axon guidance and neuronal migration.27

OR2L13 (Olfactory Receptor family 2 subfamily L member 13) is responsible for the initialization of the neuronal response to odorants.28 Differential DNA methylation in a CpG site of this gene has been identified in multiple independent studies examining epigenetic modification in neurodevelopmental disorders.29 The CpG of interest in these studies (cg20507276) was also identified in the current study.

Our hierarchical cluster analysis indicated that methylation sites in RHOJ (Ras Homolog Family Member J) and OR2L13 (Olfactory Receptor family 2 subfamily L member 13) could be important for explaining interindividual differences in fluoxetine response. However, experimental research is needed to confirm that the methylation of these genes plays an important role in the pharmacological effect of fluoxetine and to elucidate their involvement in the mechanism of action of antidepressant drugs.

The significant CpGs identified in relation to fluoxetine in our analysis also mapped to other genes. There is some connection with neuronal physiology or pathological mechanisms of neuropsychiatric disorders for some of these genes, including ADAP1 (Stricker and Reiser, 2014), SPAG1, SLC15A5 and RPTOR.3033 For the other genes (GPR1-AS, NDUFAF1, PPP5D1, LOX2 and ZNF697) or intergenic regions identified we have little or no information about their physiological connection with the pharmacological effect of fluoxetine or their role in the pathophysiology of neuropsychiatric disorders.

To our knowledge, this study is the first genome-wide DNA methylation study of fluoxetine response in children and adolescents. The major strength of our study was that several potential confounders were controlled for, such as age, smoking status, pharmacological treatment and the course of the disease. Our sample contained children and adolescents of similar ages who had not previously been treated with antidepressants or other psychotropic drugs and who were at the initial stages of the illness. We also controlled for blood cell composition, as DNA methylation is cell-type specific and different cell compositions between samples could affect the methylation data obtained.

However, the findings of this study should be interpreted by bearing in mind several important limitations. The sample size limited the statistical power of the study and made it difficult to detect small or modest effects on DNA methylation. Given that the study was hypothesis-driven and due to the small sample size, our results should be seen as preliminary and should be considered as exploratory findings that require further confirmation. Our study had several limitations. We used peripheral blood even though DNA methylation is known to be tissue-specific. However, blood is considered to be a useful proxy for detecting changes across tissues and is the most appropriate tissue in which to look for biomarkers. Moreover, there is a moderate correlation between blood and the brain for non-specific regulatory regions across the methylome.22 Third, the observation period was eight weeks, which could not be enough to detect long-term epigenetic changes. Finally, our study included patients with different diagnoses, MDD and OCD. For this reason, in the primary analysis, Responders and Non-Responders were defined according to the CGI-I scale. However, the sensitivity analysis, replacing the dichotomous classification of patients according to the CGI by the symptoms improvement scored using the CDI and the CYBOCS, confirms our significant findings.

In conclusion, our findings provide new insights into the molecular mechanisms underlying the complex phenotype of antidepressant response and suggest that methylation at specific genes, such as (RHOJ and OR2L13) could become potential biomarkers for predicting antidepressant response. However, the replication of our results in large samples is necessary in order to include the methylation level of these specific genes as biomarkers to develop predictors for clinical applications.

The authors thank the Language Advisory Service at the University of Barcelona for manuscript revision. The authors also thank all subjects and their families for the time and effort spent on this study.

Rodriguez N and Martnez-Pinteo A participated carrying out the experimental procedures, performing the bioinformatic analyses and the interpretation of results and wrote the first draft of the manuscript.

Gass P helped in performing the statistical analyses and the interpretation of results and helped in drafting the manuscript.

Blzquez A, Varela E and Plana MT participated in the recruitment and assessment of the sample and helped in drafting the manuscript.

Lazaro L participated in the coordination of the recruitment and assessment of the sample, the maintenance of the database, acquisition of funding, and helped in drafting the manuscript.

Lafuente A participated in helping in conceiving, designing and coordinating the whole study, interpreting the results and drafting the manuscript.

Mas S conceived and designed the whole study and participated in performing the statistical analysis, interpretation of results and wrote the first draft of the manuscript.

All authors made substantial contributions to conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; agreed to submit to the current journal; gave final approval for the version to be published; and agree to be accountable for all aspects of the work.

This work was supported by the Alicia Koplowitz Foundation; Ministerio de Economa y Competitividad-Instituto de Salud Carlos III-Fondo Europeo de Desarrollo Regional (FEDER)-Unin Europea (PI16/01086). Support was also given by the CERCA Programme/the Government of Catalonia, Secretaria dUniversitats i Recerca del Departament dEconomia i Coneixement to the Child Psychiatry and Psychology Group (2017SGR881) and to the Clinical Pharmacology and Pharmacogenetics Group (2017SGR1562). Funding sources had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication.

Dr Natalia Rodriguez reports grants from Alicia Koplowitz Foundation, Ministerio de Economa y Competitividad-Instituto de Salud Carlos III-Fondo Europeo de Desarrollo Regional (FEDER)- Unin Europea, and non-financial support from CERCA Programme/the Government of Catalonia, Secretaria dUniversitats i Recerca del Departament dEconomia i Coneixement, during the conduct of the study. The authors reported no other potential conflicts of interest for this work.

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[Full text] DNA Methylation of Fluoxetine Response in Child and Adolescence: Preli | PGPM - Dove Medical Press

Building a family tree depends on access to historical records, which can be problematic for Black Americans whose ancestors were enslaved. Before the Civil War and emancipation in 1863, slaves were considered property and werent included by name in many records. In the absence of clear records about genealogy, DNA can help fill in the gaps regarding family relationships.

LaBrenda Garrett-Nelson wanted to establish that Samuel and Nancy Garrett, an enslaved couple, were the parents of Isaac Garrett, her great-great-grandfather.

Once a practicing lawyer, she was used to painstakingly searching for evidence. Now working as a certified genealogist specializing in tracing African-American families that came out of slavery, she knew how difficult it could be. Even after years of effort, she couldnt fill out every branch of her own family tree. In the search for documents about the couple, there were 28 years unaccounted for before they turned up in official records after the end of slavery.

Ms. Garrett-Nelson hoped DNA tests could help her fill in the gaps and connect Isaac with his parents.

DNA tests are a critical tool to help identify and establish family ties disrupted or severed by slavery, said Melvin Collier, a genealogist in Washington, D.C., who shares tips about researching African enslaved ancestors on his blog, Roots Revealed, and has written three books on the subject.

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Proving a Connection to Enslaved Ancestors Through DNA - The Wall Street Journal

Marveling at the size of the elephants or squealing at the cuteness of the meerkats, visitors come regularly to the Houston Zoo for the chance to observe exotic animals up-close. Meanwhile, just a few blocks away, a laboratory houses a markedly different sort of zoo. Instead of furry and feathered creatures in enclosures, there are thousands of blood samples in a pair of freezers surrounded by dozens of white boards covered by mathematical equations.

This is the DNA Zoo, where a team of thirty-plus scientists use cutting-edge genomic technology in service of boosting the survival chances for countless endangered specieswork that could contribute to human health as well. The lab has acquired genetic samples of 4,234 animals representing 1,105 species, largely obtained from zoos and parks including the Houston Zoo, San Antonio Zoo, Sea World, and the Texas State Aquarium. In 2019, they opened a counterpart lab in Australia to focus on species unique to that continent.

Humans are essentially one of natures experiments, says Erez Lieberman Aiden, founder of the Aiden Lab at the Center for Genome Architecture at Baylor College of Medicine, which runs the DNA Zoo. Nature has performed many, many, many experiments, and we can learn from the experiments that nature has performed on other species.

Over the course of five years, beginning in 2011, Aiden and his team developed technology that allows them to sequence DNA in days, instead of the usual weeks, and at a cost of hundreds of dollars, rather than hundreds of thousands.

It took thirteen years to sequence the human genome and another four years for the corn genome, but you ended up with a similar quality of work to what the DNA Zoo is doing now in a matter of days, says Blake Hanson, an associatedirectorof microbial genomics at the University of Texas Health Science Center in Houston.

Aiden, who built a scale model of DNA for a science fair in high school, remembers becoming truly fascinated during graduate schoolhe holds doctorates from Harvard and MITwith how a DNA strand as long as six feet could fold inside a single cell. His studies led to genome mapping technology he dubbed Hi-C (after the fruity drink, a favorite of his) and Juicebox, software that facilitates the three-dimensional assembly of a DNA strand.

Genome sequencing requires disassembling and reassembling strands of DNA in order to fully understand how each of those segments relate to one another. Some segmentsknown as repeats or regions of low complexityare nearly impossible to distinguish from others. If you have a jigsaw puzzle thats like pure black, it becomes really hard because a piece can go anywhere, Aiden says. The algorithms built into Hi-C and Juicebox allow his team to solve that puzzle by ferreting out subtle patterns in the DNA that are otherwise extremely difficult to detect.

The DNA Zoos work assists zoos and other wildlife parks in their conservation efforts. For instance, Aidens team has sequenced the genes of all the elephants at the Houston Zoo, which helps in determining which of the animals should be bred with one another to keep the gene pool diverse, preventing the animals that mate from being too closely related. The DNA Zoo wont be the only piece we need to preserve the genomes of these animals to push forward the idea of conservation, but it is a huge piece in that puzzle, Hanson says.

Furthermore, by making its collection of sequenced genomes publicly available, the DNA Zoo provides vital data to researchers looking to combat diseases, both in animals and humans. In 2015, Aiden and his team helped scientists map the DNA of the mosquito species that carried the Zika virus, an epidemic at the time. Aiden is enthusiastic in his explanation of how genomic technology is already changing the face of health care. Its literally a very straight line from the release of the genome of the SARS-CoV-2 virus to the vaccine, he says of how similar knowledge has been deployed by others in the COVID-19 pandemic.

The DNA Zoo is hardly the first program to sample and store animal blood for scientific study. The Cryo-Zoo at the MD Anderson Cancer Center, which collaborates with the DNA Zoo, was founded by biologist T.C. Hsu in the 1970s. Hsu was a pioneer in studying animal chromosomes and collected them from thousands of species. Forty years ago, most zoos were much smaller and less involved in conservation efforts than they are today. As a result, many animal samples had to be acquired in the wild, which could be rather difficult.

They would go places, like classical adventure-type stories, says Olga Dudchenko, co-founder of the DNA Zoo. Somebody would get stranded on a boat for several days without water and food trying to get some cells from some rodent in South Africa.

Even today, its not always as simple as drawing blood from animals at zoos. Scientists sometimes have to get creative. In the case of the southern right whaleso named because whalers considered it a good target (i.e. the right whale to hunt), nearly hunting it to extinction in the twentieth centurythere are none in captivity. So how do you get a DNA sample from a sixty-foot, ninety-ton animal swimming through the ocean? Out of the blowhole. Just as humans expel DNA when we sneeze, whales expel it when they breathe, and that can be collected by nearby scientists.

The highly technical efforts of the DNA Zoo are sometimes difficult to explain to the layperson, so the lab has employed more down-to-earth approaches in reaching out to both the scientific community and the world at large. Lab members write regular blog posts about the new sequences they have completed, which contain the complex language of scientists, but also fun facts about the species. A typical post, in February, featured an adorable photo of a mouse. Weighing about as much as six paper clips, the text explained, the endangered Pacific pocket mouse (PPM) aka Perognathus longimembris pacificus is the among the smallest rodents in the world.

The DNA Zoo has even produced a comic strip called ChromoGnomes, drawn by Adam Fotos, a comic book artist in Chicago. It tells of a pair of gnomes attempting to create various animals from their genetic code with varying degrees of success. The lab hopes it will make what they do more accessible to those without advanced degrees in biology and computer science.

Similar to what we do with blog posts, we can do this in a more fun and visual way, Dudchenko says. So far, theyve only published four installments of the strip, though more are promised. It turns out that writing comics is more difficult right now for us then creating genome assemblies.

Fortunately, the Aiden team appears quite skilled at assembling DNA sequences. They convey excitement about where it all might lead, perhaps even into the realm of what sounds like science fiction. The ultimate goal is that if we read through the genomes really, really well, and probably in a few years, well be able to not just read but also generate DNA, Dudchenko says. In theory, you can re-create just from the seed, re-create species just from the sequence.

This notion conjures images from Jurassic Park, the Michael Crichton novel that envisioned a world where dinosaurs could be replicated from the DNA of long-dead mosquitoes trapped in amber. It might someday not be quite as far-fetched as when the book came out.

Recently, in fact, a group of scientists who are collaborators with the DNA Zoo set a record by reading the DNA from a sample that was estimated to be a million years old. Its not like we would say no to Jurassic Park-level DNA, Dudchenko says. Its too juicy a topic.

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A DNA Zoo Maps the Mysteries of All Creatures Great and Small - Texas Monthly

More than 7,000 living donors give a kidney or part of a liver each year to a blood relative in the U.S., according to the United Network for Organ Sharing. In the era of DNA testing, as families circles widen to discover new blood relations, the potential for donor matches will increase.

On a Thursday morning in 2016, Mindy Towns prepared to make a phone call that would end up taking her to a hospital bed halfway across the country. She couldnt have known it, but she was about to connect with a seriously ill half-brother who had lost nearly everything in a plane crasheven, perhaps, his will to live.

On this February morning, Ms. Towns was simply looking to connect with the man she believed to be her birth father. Now 56 years old, she had grown up in an era when most adoption records were secret. It had taken her more than 30 years of sleuthing to find Daryl Wedan.

To prove it, I was going to have to call him, Ms. Towns said. It was the longest day of my life. I set up my computer with the family tree and papers. I was so nervous, I wrote myself a script.

When he got the call, Mr. Wedan hung up on her. I was actually at my grandsons karate class, he said. I thought it was a sales call or something, and I didnt quite hear her properly so I hung up on her. Later I saw it was a Florida number, and I thought I better call that number back.

The rest is here:
DNA Test Leads to a Kidney Donation and Second Chance at Life - The Wall Street Journal

Leopards are among the most widespread carnivores today, living in a wide range of habitats, from deserts to rainforests, and from the lowland plains to the mountainous highlands.

Over the past century, theyve experienced extreme habitat losses due to human activity, both directly from hunting and indirectly from habitat reduction and prey competition. This has led to the land they occupy being reduced by over 50% in Africa, and over 80% in Asia, involving the local extinction of many populations.

Genetic analysis of leopards is important to understand their population history, structure and dynamics. Particularly important is the analysis of whole nuclear genomes, which means all the DNA contained in the cell core approximately 2.5 billion DNA bases (pairs of DNA building blocks).

In new research, we studied the genomes of modern and historical leopards, using samples gathered from an unusual place natural history museums. And we found a surprising level of genetic separation between leopards from different parts of the world.

Normally, genetic analysis involves collecting fresh tissue samples. For leopards, doing this would be extremely difficult. The animals are hard to track down, particularly in areas where they are rare, and invasive sampling can be bad for the animal.

Animals bred in zoos may not be a good option as they may be mixtures of multiple wild populations. Getting samples from areas where they have been eradicated is not possible at all. For these reasons, we turned our sampling efforts to museums.

Natural history museums across the world are filled with skins, skeletons and even complete taxidermy specimens, often collected decades and decades ago. Its a lot more challenging to extract genetic material from these old specimens, both from a technical and a financial point of view, because the DNA in such samples is more degraded, and sometimes includes large amounts of contaminant DNA in addition to the leopard DNA. But doing so allowed us to collect data from leopards covering their entire distribution, both current and historical.

This would have been near impossible if we only looked for fresh tissue samples. The collection of this genetic data allowed us to investigate the global population dynamics of leopards, with unprecedented resolution.

We collected material from many museum specimens, and investigated the DNA quality in each. Then, we selected the best samples from which to sequence hundreds of billions of bases of DNA. Using high powered computational resources we compared the DNA from all leopards to each other, and ran a range of different types of analyses to better understand how they differ.

One of the most striking revelations we found was a marked distinction between African and Asian leopards. In fact, at the genome wide scale across most of the leopards 2.5 billion DNA bases Asian leopards are more genetically separated from African leopards than brown bears are from polar bears.

Adding to the puzzle is the comparatively recent divergence of African and Asian leopards, approximately 500,000 to 600,000 years ago, which is comparable to that between modern humans and Neandertals. Brown bears and polar bears, in contrast, diverged around 1 million years ago.

Read more: We sequenced the cave bear genome using a 360,000-year-old ear bone and had to rewrite their evolutionary history

The cause of this genetic differentiation of Asian leopards is their out-of-Africa dispersal. Although the evidence suggests that leopards in south-western Asia carry DNA thats relatively similar to African leopards, which could be due to occasional interbreeding, the overall distinctiveness of leopards on the two continents has been maintained. We would have expected Asian and African leopards to show more similarities in their DNA, as there has been (and possibly still is) mixing between the populations.

This level of separation is unexpected within a single species. Such a genetic distinction is not even always clear between different species. It also shows a brief event with relatively few individual leopards the out-of-Africa dispersal has had a massive influence on shaping the genetic patterns of these animals across the world.

A second important result is that African and Asian leopards have had a very different population history since their separation. African leopards show higher genetic variability, and their populations are less genetically distinct from one another.

In Asia, theres a much stronger effect of geography, meaning that the correlation between genetic distance and geographic distance is stronger. Leopards are generally genetically more similar to other leopards that live close by, than those that live far away. This suggests less gene flow and dispersal between different parts of the continent than in Africa.

Despite the extensive encroachment by humans on leopard habitats, the historical samples didnt necessarily have a higher genetic diversity than the modern samples included in the study. This shows that the differences we see in Asian leopards is not due to recent human impacts. Although humans have driven some local leopard populations to extinction, the impact of humans on the species as a whole is not yet severe enough to be reflected in the entire genome.

The leopard samples from the museum shelves have given us valuable new insights into their evolutionary history, as well as current populations across the world even populations weve driven to extinction. Leopards are listed on the IUCN red list of threatened species, and classed as critically endangered for some of their range.

Considering the impact we humans have had on wildlife in recent centuries, there may be many species for which there are exciting genetic discoveries hidden among the shelves of natural history museums around the world.

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We studied the DNA of African and Asian leopards and found big differences between the two - The Conversation Africa

TUCSON, ARIZONAAccording to a Science News report, traces of a viral epidemic some 25,000 years ago have been detected in the DNA of present-day East Asians. Evolutionary geneticist David Enard of the University of Arizona and his colleagues analyzed more than 2,000 publicly available DNA samples from Chinese Dai, Vietnamese Kinh, and African Yoruba people for more than 400 proteins known to interact with coronaviruses. The researchers found that only the East Asian groups showed substantially increased production of all of the proteins. Analysis of the genes related to the production of these proteins suggests they became more common about 25,000 years ago and then leveled off about 5,000 years ago. This indicates that East Asians could have adapted to the infection, or the virus became a less potent cause of disease, Enard explained. Some of the gene variants would have also been useful for fighting other types of viruses as well, he added. Further study is needed to determine if these gene variants offer any protection against SARS-CoV-2, the virus that causes COVID-19. To read about a sixteenth-century epidemic in Mexico, go to "Conquistador Contagion."

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Traces of Ancient Epidemic Detected in DNA - Archaeology

Aim small, miss small.

The application of that phrase originally applied to aiming a muzzle-loader rifle. The concept being that instead of aiming broadly at your target, aim at something very small on your target so that should you miss, you still hit your target. While I have no muzzle-loader experience, I still find the phrase applies to my draft preparation.

Ive always been fond of player scouting dating back to the late 1990s, and Ive been a fan of statistical modeling for long before that. Combining these two passions has made for a great foundation for success at fantasy football, where leveraging my objectivity and available data has allowed me to gain an edge over my competition. Look around the fantasy multiverse and youll see a broad array of data points, metrics and applications bandied about as the secret sauce to winning.

Read more from the original source:
Dynasty draft DNA: Identifying the traits that make up the future elite fantasy football stars - The Athletic

The human genome has long been a difficult book to read. Modern technological advances have recently opened doors for researchers to begin asking a big question: What parts of our DNA sequences might influence disease? Mary Lauren Benton, Ph.D., recently joined the Baylor Engineering and Computer Science faculty as an assistant professor of bioinformatics, and she is working to answer that question.

Mary Lauren Benton, Ph.D.

If you think of the genome like an instruction manual, Im interested in the grammar, Benton said. Im interested in understanding how short DNA sequences turn genes on and off in different cells and allow for many different outcomes. If we know how a particular sequence influences risk of heart disease, for example, we can use that information to help us guide clinical decisions, whether thats applying different treatments, prescribing different medications or scheduling more preventative care. All of these things can help clinicians to better prioritize and care for patients.

Benton uses computer modeling to look through large data sets of genetic information. Bioinformatics allows for processing of these large data in ways not possible previously, giving room for biological researchers to find patterns and solutions using methods and tools from computer science.

I think of bioinformatics as the intersection of computer science and biology, Benton said. I take tools and methods from computer science, and I apply them to solve fundamental biological questions. We have a lot of really big data sets in biology. The human genome is 3 billion base pairs long, which we cant analyze by hand. The tools from computer science and statistics give us a way to ask questions that we wouldnt be able to otherwise. They open the doors to analyses that would have been impossible even 10 or 20 years ago.

Benton most recently authored The Influence of Evolutionary History on Human Health and Disease, which was published in the Nature Reviews Genetic Journal and takes a look at the evolutionary origins of disease. Being diagnosed with a disease or health problem may feel like a present problem; however, Benton explained that looking at the foundations of a disease is important to understanding how to move forward with treatment.

The foundations and the systems that are involved in disease have really deep evolutionary origins, she said. Cancer might be something that youre diagnosed with today, but the foundation of cancer can be traced back to the idea that we have cells that are able to grow and divide, which also provides the opportunity for tumors to grow.

Benton explained that its important to consider the history of the disease and the systems involved alongside any variants or environmental factors that help to cause the disease. A holistic understanding of disease can influence how patients are treated as well as provide information about how their diseases came to be.

Its not enough to understand whats happening in a person right now or in the last five years, Benton said. Understanding the million-year history of how people got here is equally important to make advances in personalizing medicine, especially genomic medicine. Having that long lens is something that is often lost in the day-to-day operations of a doctors office.

Benton is excited to be evaluating the way that researchers think about decoding genetic information. While a common approach is to think of genes as being able to be turned off or on with a simple switch, that may not be the most accurate approach.

We study these sets of genetic switches and how they turn genes on and off at the right times. Often, we think about these switches working one-at-a-time; the gene is either on or its off, Benton said. But it is much more complicated than that. There are often multiple switches that act more like a dashboard of knobs and dials that all work together to properly tune the output of the genome.

Bentons research is moving toward the development of new models and ways of thinking about how known individual elements are combined and factored into this much larger, more accurate dashboard. Differences based on demographic histories, environmental variables and evolutionary processes all influence the risk of disease in different ways. A better understanding of genomes and how genetic variants relate to disease has major implications for precision medicine.

Its really vital for precision medicine to take into account the full diversity of the human experience. We cant focus on one particular kind of person or one population. People of European ancestry are over-represented in genetic studies, Benton said. Improving diversity and representation in our genomic studies is vital to understanding how the genome relates to disease and to learning how to appropriately treat all of the patients that might walk through the doors of a clinic.

Precision medicine, in some ways, seems futuristic and far-off. But, in other ways, precision medicine is already being used to protect at-risk individuals from diseases like cancer. While widespread precision medicine may not be seen for a long time, research like Bentons plays a role in better understanding disease risk broadly and providing context for clinical solutions moving forward.

Precision medicine is both happening right now and is something that well probably always be working toward, Benton said. There are things that we understand right now about specific genetic variants that might predispose you to a certain kind of breast cancer, for example. We already have diseases that we can test for or treat differently based on someones genotype. But, because the genome is such a complicated thing, walking into the clinic and handing your DNA sequence to the doctor, who would then read it and prescribe the right treatments on the spot, is a goal that well always be working toward. Still, I expect well see big changes in the next five to 10 years given the current rate of progress.

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Grammar of the Genome: Reading the Influence of DNA on Disease - Baylor University