Category Archives: Transhuman News

The human lipidome, encompassing all the bodys lipids, is gaining attention for its role in human physiology, particularly its direct influence by diet and gut microbes, and its potential in disease intervention, especially in conditions like Type 2 diabetes. A recent study dives deep into the lipidome, revealing its association with health indicators like insulin resistance, aging, and response to infections, and its potential for predicting biological aging and guiding health interventions.

The sequencing of the human genome promised a revolution in medicine, but scientists soon realized that a genetic blueprint alone does not show the body in action. That required understanding the proteome all the proteins, expressed by our genes, forming the cellular machinery that performs the bulk of the bodys functions. Now, another set of molecules known as the lipidome all the lipids in our bodies is filling in more details of human physiology.

Lipids are a broad category of small, fatty, or oily molecules, including triglycerides, cholesterol, hormones, and some vitamins. In our bodies, they make up cell membranes, act as cellular messengers, and store energy; they play key roles in responding to infection and regulating our metabolism.

Our genome is essentially stable. Our proteome, though influenced by our health and environment, is largely dependent on whats encoded by our genes. In contrast, our lipidome can be directly altered, in part, by what we eat and which microbes live inside our gut, making it more malleable and perhaps more responsive to interventions. But the number and variety of lipid molecules there are at least thousands has made them hard to study.

Lipids are very understudied, saidMichael Snyder, PhD, the Stanford W. Ascherman, MD, FACS Professor in Genetics. They are involved in pretty much everything, but because theyre so heterogeneous, and there are so many of them, we probably dont know what most lipids really do.

A new study from Snyders lab, published Sept.11inNature Metabolism, is among the first to deeply dive into the human lipidome and track how it changes under healthy and diseased conditions, particularly in the development of Type 2 diabetes.

More than 100 participants, including many at risk for diabetes, were tracked for up to 9 years, providing blood samples every three months when healthy and every few days during illness.

Using mass spectrometry techniques, which separate compounds by their molecular mass and electric charge, researchers cataloged some 800 lipids and their associations with insulin resistance, viral infection, aging, and more.

The researchers found that although everyones lipidome has a distinctive signature that remains stable over time, certain types of lipids changed predictably with a persons health.

For example, more than half of the cataloged lipids were associated with insulin resistance when the bodys cells cannot use insulin to take up glucose from the blood which can lead to Type 2 diabetes. Though insulin resistance can be diagnosed by measuring blood glucose, understanding changes to the lipidome helps uncover the biological processes at work.

Every molecule that is associated with a disease has a chance of telling us more about the mechanism and may be serving as a target for affecting the disease progression, said Daniel Hornburg, Ph.D., a former post-doctoral scholar in Snyders lab and co-lead author of the study.

The researchers also identified more than 200 lipids that fluctuate over the course of a respiratory viral infection. Rising and falling levels of these lipids matched the bodys higher energy metabolism and inflammation in early infection, and may indicate the trajectory of the disease. Those with insulin resistance showed some anomalies in these responses to infection as well as a weaker response to vaccinations.

The wide age range of the participants 20 to 79 years old and the length of the study allowed the researchers to see how the lipidome changes with aging. They found that most lipids, such as cholesterol, increase with aging, but a few, including omega-3 fatty acids, known for their health benefits, decrease. Moreover, these signs of aging in the lipidome do not occur at the same rate in everyone. Insulin resistance, for example, seems to accelerate them.

It raises the interesting question of whether lipid profiles could predict whether an individual is biologically aging more quickly or more slowly, said Si Wu, PhD, co-lead author of the studyand another former postdoc in Snyders lab.

Another surprising insight, Wu said, was how consistently certain groups of lipids, such as ether-linked phosphatidylethanolamines, which are thought to be antioxidants and involved in cell signaling, were associated with better health. They may be candidates for new ways to monitor health or even taken as dietary supplements.

Next, Snyders lab hopes to follow leads from this broad survey to look at correlations between specific lipids and lifestyle changes.

Reference: Dynamic lipidome alterations associated with human health, disease and ageing by Daniel Hornburg, Si Wu, Mahdi Moqri, Xin Zhou, Kevin Contrepois, Nasim Bararpour, Gavin M. Traber, Baolong Su, Ahmed A. Metwally, Monica Avina, Wenyu Zhou, Jessalyn M. Ubellacker, Tejaswini Mishra, Sophia Miryam Schssler-Fiorenza Rose, Paula B. Kavathas, Kevin J. Williams and Michael P. Snyder, 11 September 2023,Nature Metabolism. DOI: 10.1038/s42255-023-00880-1

See original here:
Stanford Scientists Uncover New Indicators of Health, Disease, and ... - SciTechDaily

The National Academy of Medicine has elected Eric Green, M.D., Ph.D., Director of the National Human Genome Research Institute (NHGRI), part of the National Institutes of Health, as a new member in recognition of his distinguished career using genomics to understand human health and disease.

New members are elected to the National Academy of Medicine based on their professional achievements, in line with the academys mission of improving health for all through scientific advancements and promoting health equity. Dr. Greens membership speaks to the significance of his career in genomics and medicine as a physician scientist.

"Eric Green has been a major architect of efforts to apply genomics to the practice of medicine," said Francis Collins, M.D., Ph.D., former NHGRI Director and NIH Director. "With his characteristic boundless energy, he has also inspired a generation of young scientists and built partnerships across institutions and sectors to accelerate progress in many areas of human genomics. His election to the National Academy of Medicine is a fitting recognition of his sustained leadership."

In 1994, Dr. Green joined the Intramural Research Program of NHGRI, where he continued his work on the Human Genome Project. Specifically, his research program focused on mapping and sequencing the human genome as well as similar efforts with other mammalian genomes which together led to essential discoveries about the structure, function and evolution of the human genome. Later, Dr. Greens group went on to identify the genes involved in several human health conditions, such as hereditary deafness, vascular disease, and inherited peripheral neuropathy.

Eric has catalyzed making genomics mainstream in medicine and public health, both nationally and globally, all the while walking the walk in his deep commitment to diversity, equity, and inclusion in the genomics workforce. His election to the National Academy of Medicine is an incredibly well-deserved honor that puts an exclamation point on the academys reputation for excellence.

Prior to being appointed NHGRI Director in 2009, Dr. Green held other prominent leadership positions, including Director of the NHGRI Genome Technology Branch, Founding Director of the NIH Intramural Sequencing Center, and NHGRI Scientific Director. In these roles, Dr. Green appointed an outstanding cadre of diverse genetic and genomic leaders who worked with him to shape the strategic vision and long-term goals of NHGRI, influencing the direction of the entire human genomics enterprise.

"It is hard to imagine anyone more deserving of this honor than Eric Green," said Daniel Kastner, M.D., Ph.D., NIH Distinguished Investigator in the NHGRI Medical Genetics Branch. "Eric has catalyzed making genomics mainstream in medicine and public health, both nationally and globally, all the while walking the walk in his deep commitment to diversity, equity, and inclusion in the genomics workforce. His election to the National Academy of Medicine is an incredibly well-deserved honor that puts an exclamation point on the academys reputation for excellence."

Prior to his election to the National Academy of Medicine, Dr. Green was elected to the American Society for Clinical Investigation and the Association of American Physicians. He has received numerous other honors for his leadership and research contributions, including the Lucille P. Markey Scholar Award in Biomedical Science; the Cotlove Lectureship Award from the Academy of Clinical Laboratory Physicians and Scientists; and the Wallace H. Coulter Lectureship Award from the American Association for Clinical Chemistry. He has also been honored several times by his medical and graduate school alma mater, Washington University in St. Louis, including being awarded an honorary Doctor of Science degree in 2018.

"Getting elected to the National Academy of Medicine is one of my proudest career honors to date," said Dr. Green. "It signifies the support and admiration of many professional colleagues for my several decades of contributions in genomics. Their collective recognition is both gratifying and humbling."

View post:
NHGRI Director Eric Green elected to the National Academy of ... - National Human Genome Research Institute

A pig (Sus domesticus) kidney is prepared for transplant into a human recipient who had been declared legally dead.Credit: Shelby Lum/AP via Alamy

A kidney transplanted from a genetically engineered miniature pig kept a monkey alive for more than two years one of the longest survival times for an interspecies organ transplant.

The feat brings clinicians one step closer to their goal of relieving the shortage of life-saving human organs, by using animal organs, a practice known as xenotransplantation. The work describes a raft of genome edits that prevent the recipients immune system from attacking the new organs, and that also neutralize ancient viruses lurking in the donors organs crucial steps for harnessing porcine organs for human use.

This is a proof of principle in non-human primates to say our [genetically engineered] organ is safe and supports life, says Wenning Qin, a molecular biologist at the biotech firm eGenesis in Cambridge, Massachusetts, who co-authored the study published in Nature1 on 11 October.

Researchers say that this study will provide more data to regulators such as the US Food and Drug Administration, which is considering whether to approve the first human trials of non-human organ transplants. But scientists say that it will be important to dig into why there was considerable variation in the success of the newly described xenotransplants, and how feasible it will be to mass-produce pigs with such extensive editing.

In the past few years, researchers have transplanted pig hearts into two living people2, and demonstrated that pig hearts3 and kidneys4 can function in people who have been declared legally dead.

Such research is crucial, given the dearth of suitable organ donors, says David Cooper, a xenotransplant immunologist at Massachusetts General Hospital in Boston, who was not involved with the study but is a consultant for eGenesis. In the United States alone, more than 100,000 people are awaiting an organ transplant, and about 17 of them die each day.

Xenotransplantation research has mainly focused on pigs (Sus domesticus), in part because their organs are of a comparable size and anatomy to that of humans. But the immune systems of humans and other primates react to three molecules on the surfaces of pig cells, causing them to reject unaltered pig organs. So, researchers started using the genome-editing technology CRISPRCas9 to disable the genes that encode enzymes that produce those molecules.

A gene-edited pig kidney (fuschia, the human protein CD46; green, kidney endothelial cells; blue, nuclei) transplanted into a monkey kept the animal alive for more than two years.Credit: Violette Paragas, eGenesis.

Qin and her colleagues edited 69 genes, which is the most extensive editing done in live pigs for xenotransplantation. Three edits target the rejection-related molecules, and 59 edits target retrovirus genomes that became embedded in the pig genome long ago. Previous research5,6 has shown that, in a laboratory setting, these embedded genomes can produce viral particles that infect human cells, but the infection risk to human xenotransplant recipients and their transplanted organs is unclear.

The last seven edits are additions of human genes that help to keep the transplanted organ healthy. Two genes, for example, encode proteins that prevent unnecessary blood clotting.

Will pigs solve the organ crisis? The future of animal-to-human transplants

Qin and her colleagues created pigs with these gene edits and transplanted a pig kidney into more than 20 cynomolgus macaques (Macaca fascicularis) that also received an immunosuppressive drug cocktail. None of the monkeys that received kidneys without the seven human genes survived for more than 50 days. By comparison, 9 of the 15 monkeys that received kidneys with the human genes did. Five of those monkeys lived for more than one year, and one of the five lived for more than two. An analysis of kidney biomarkers show that the transplanted organs performed just as well as two native kidneys.

Organs transplanted from conventional pigs grow rapidly in the recipients, threatening to compromise the grafts. Some researchers have tried disabling the pig genes responsible for this growth, but this step comes with unintended complications, says Muhammad Mohiuddin, a xenotransplantation surgeon at University of Maryland School of Medicine in Baltimore. He commends the authors of the Nature study for solving this problem by using kidneys from miniature pigs, whose organs grow at a slower pace.

Although survival times of up to two years are exceptional, Qin acknowledges that the times were more varied than the team had expected. But researchers engineered the pig genomes with people in mind, not non-human primates, so its likely that they would fare better in humans, Mohiuddin says.

Still, the jump to humans will not be small, says Jayme Locke, a transplant surgeon at the University of Alabama at Birmingham. Humans weigh much more and have a higher blood pressure than these monkeys, and its unknown whether the pig organs will withstand that environment, she adds.

First pig kidneys transplanted into people: what scientists think

Not all researchers are convinced that such extensive genetic changes are necessary. Megan Sykes, a transplant immunologist at Columbia University Medical Center in New York City, applauds the researchers for studying the effect of so many genes.

But the survival is not strikingly better than what has been seen before with many fewer gene modifications, she says. With each extra gene modification, they become harder to produce, which might make it more difficult to scale up, she says.

In principle, Mohiuddin agrees that some of these edits might be overkill, but he is optimistic that one day there will be genetically modified pigs that eliminate the need for immunosuppressive drugs.

I dont think we know yet how simple [these gene edits] can be or how complex they need to be, Locke says. Thats really where these clinical trials are going to be very important.

Here is the original post:
Monkey survives for two years after gene-edited pig-kidney transplant - Nature.com

Pictured: RNA/iStock, Artur Plawgo

Based on the significant progress made over the last few decades with RNA therapeutics, RNA editing is widely considered the next generation of promising medicines in this field.

RNA therapies have made significant progress over the last few years, with an increasing number of FDA approvals beginning in 1998 with Vitravene for CMV retinitis, followed by Macugen for macular degeneration in 2004 and Spinraza for spinal muscular atrophy in 2016. There have also been multiple siRNA-based drugs, including Onpattro for polyneuropathy of hereditary transthyretin-mediated amyloidosis in 2018. And finally, in 2020, perhaps the most well-known products in the RNA space were introduced: the mRNA-based COVID-19 vaccines.

All of these demonstrate the strength of RNA therapies and their potential impact on diseases with high unmet need.

RNA therapeutics are indeed elegant approaches to altering RNA and thus protein expression, opening the potential to target a broad array of diseases. The field has seen a renewed and increased interest as reversible changes offer flexibility and RNA approaches introduce therapeutic opportunities that were not accessible before.

RNA editing technology was first known and recognized as an interesting approach to treating genetic conditions and reversing disease-causing mutations at the RNA level. RNA editing is a naturally occurring and highly active process that uses the bodys existing capabilities to perform nucleotide changes. Since 2014, when ProQR Therapeutics invented the technique of using oligonucleotides recruiting endogenous adenosine deaminase action on RNA, known as ADAR-mediated editing, the field has rapidly progressed.

This growth can be attributed to the rapid progression of knowledge about the technology. Alpha-1 antitrypsin deficiency (AATD) is the first indication that many RNA editing companies have decided to pursue, as it provides the opportunity to address liver and lung symptoms of the disease. Clinical trials for AATD run by both Wave Life Sciences and Korro Bio are planned to begin this year and next.

There is great excitement in the field about using learnings from decades of oligonucleotide-based drug development, natural RNA editing, and knowledge of biological pathways to make RNA editing technology a compelling approach to target various pathophysiological processes. This offers, for example, the possibility not only of reducing or restoring protein expression but also of modulating protein activity involved in diseases. This application of RNA editing offers the potential to impact both genetic disorders and common conditions, such as metabolic and cardiovascular diseases.

ProQRs approach differentiates RNA editing, as it provides the opportunity to target conditions that have thus far not been treatable with other technologies. Indeed, RNA editing offers the possibility of introducing protective variants, informed by human genetics, that could address or prevent diseases including certain cholestatic or cardiovascular conditions. For example, it has been reported in the literature that an Old Order Amish-enriched variant in a functional B4GALT1 was associated with lower serum LDL-C and lower plasma fibrinogen. This protective variant can be introduced via ADAR RNA editing technology, which has the potential to simultaneously address the two cardiovascular risk factors.

Delivery is an important aspect of oligonucleotide base therapeutics. RNA therapies, including RNA editing, have again made great progress and generally use conjugation or lipid nanoparticle approaches. As an example, Alnylam made tremendous progress in its siRNA-based treatments for amyloidosis, with Onpattro in 2018 offering an intravenous treatment once every three weeks. Only four years later, Amvuttra arrived on the market for the same condition but with a subcutaneous 3-month dosing approach.

For now, the majority of RNA editing programs are focused on targeting the liver where delivery is relatively de-risked, although progress is also being made in exploring new frontiers such as the central nervous system, as evidenced by the partnerships between Roche and Shape Therapeutics and ProQR and Eli Lilly.

In summary, the RNA editing space is making impressive progress. The recent approvals and clinical results demonstrating the potential of RNA therapy to target a broad array of organs are extremely encouraging for RNA editing. Near term, we expect to see further development of the technology, more programs advancing to clinical development, expansion of therapeutic areas addressed, and ultimately, we are hopeful that the next few years will bring considerable progress for patients in need.

Gerard Platenburg is a cofounder of ProQR and has served as the companys chief scientific officer since 2022. Gerard has an extensive background in RNA modulation and orphan drug discovery and development and currently leads ProQR's Innovation unit.

See original here:
Opinion: Interest in RNA Editing Accelerates as Therapies Approach ... - BioSpace

Materials

The following reagents were used in this study: HNP1, HNP2 and HNP3 (Peptide Institute, Inc., Japan); TGF- (BioLegend Inc., CA, USA); Dulbeccos Modified Eagles Medium (Cytiva, Marlborough, MA, USA); Fetal Bovine Serum (Gibco, Grand Island, NY); ProLong Gold Antifade Mountant with DAPI (Invitrogen, CA, USA); LDH-Cytotoxicity Colorimetric Assay Kit II (BioVision Inc., CA, USA); RNeasy Mini Kit (QIAGEN Inc., Hilden, Germany); iScript Reverse Transcription Supermix, SsoAdvanced Universal Probes Supermix (Bio-Rad Inc., CA, USA); Pierce BCA Protein Assay Kit (Thermo Fisher Scientific Inc., NY, USA); 1X Protease/Phosphatase Inhibitor Cocktail, Rabbit anti-COL1A1 antibody, Mouse anti-Ki-67 antibody, Rabbit anti--actin antibody, Mouse anti-rabbit IgG antibody (HRP conjugate), Anti-rabbit IgG Alexa Fluor 555, Anti-mouse IgG Alexa Fluor 488 (Cell Signaling Technology Inc., MA, USA); Amersham ECL Western Blotting Detection Kit (GE Healthcare Life Sciences Inc., MA, USA); Alliance Q9 chemiluminescence imaging system (Uvitec Inc., UK); Tissue-Tek O.C.T. Compound (Sakura, Alphenaan den Rijn, Netherlands).

Neonatal foreskin tissues were obtained by surgical circumcision of healthy male neonates at the Pediatric Surgery clinic, King Chulalongkorn Memorial Hospital with parental informed consent and assent forms. Ethical approval for this study was granted by the Institutional Review Board of the Faculty of Medicine, Chulalongkorn University (IRB 120/63). We confirm that all methods and experiments were performed in accordance with relevant guidelines and regulations. Dermal fibroblasts were isolated as described previously17 and cultured in medium containing DMEM supplemented with 10% FBS and gentamicin (1mL/L). The cells were incubated in a 5% CO2 incubator at 37C, and the cells derived from the 2nd to 5th passage were used in experiments.

Dermal fibroblasts (5103 and 1104 cells/well) in 100 L of DMEM with 10% FBS were seeded into 96-well clear round bottom, ultra-low attachment plates. The medium was replaced with fresh medium every 3days18. Spheroids were imaged at days 3, 5 and 7 and diameters were measured by ImageJ.

Cell proliferation was analyzed by methylene blue staining. Dermal fibroblasts were seeded into a 96-well plate (3103 cells/well) with 1% FBS DMEM overnight. HNP1-3 (0.62510M) were added into the wells, and the cells were incubated for 24h. The supernatant was collected, and the cells were fixed with 20% (v/v) formaldehyde for 48h and stained with methylene blue for 30min. The cells were washed and eluted with 100 L of ice-cold HCl (0.1M) in absolute ethanol solution (1:1 ratio). The absorbance was measured at 650nm using microplate reader. Cytotoxicity was analyzed using LDH-Cytotoxicity Colorimetric Assay Kit II. Collected supernatants (2.5 L) were mixed with 25 L of LDH reaction mix for 30min. Stop solution (2.5 L) was added and the absorbance was measured at 450nm using microplate reader. Spheroids derived from dermal fibroblasts (5103 cells/well) were treated with HNP1-3 at 10M for 4days. All experiments were performed in triplicates.

Dermal fibroblasts were seeded into a 6-well plate (2.5105 cells/well) in DMEM containing 1% FBS overnight. The cells were treated with HNPs (2.5, 5 and 10M) for 24h. Total RNA was extracted and converted to cDNA with the following conditions: 25C for 5min, 46C for 20min and 95C for 1min. COL1A1 and Ki-67 gene expressions were determined by real-time PCR. ABL gene expression was used as internal control. Primers and probes are listed in Supplementary Table S1 online. Real-time PCR was performed for 40 cycles with the following program: 95C for 2min, 95C for 5s and 60C for 30s.

Dermal fibroblasts were seeded into a 6-well plate (2.5105 cells/well) in 1% FBS in DMEM overnight. HNPs (2.5, 5 and 10M) were added into the wells and the cells were incubated for 48h. Cells were lysed by 1X RIPA Lysis Buffer containing 1X Protease/Phosphatase Inhibitor Cocktail. Total protein concentration was measured by Pierce BCA Protein Assay Kit. Protein lysates (10g) were mixed with 2X SDS dye and heated at 100C for 5min. Proteins were loaded in 7.5% SDS-PAGE and gel electrophoresis was performed at 100V for 1.5h. Proteins were transferred to PVDF membrane with electrophoresis at 15V for 50min. Blotting membranes were blocked with 1X PBS with 0.1% Tween-20 (PBST) containing 5% skimmed milk, followed by incubation with primary antibodies; COL1A1 (1:2000) and -actin (1:4000), overnight at 4C. The membranes were washed with PBST, and mouse anti-rabbit IgG (HRP conjugate) secondary antibody (1:4000) was added. The membranes were incubated for 1h with shaking before washing. The membranes were soaked in chemiluminescent substrate (Amersham ECL Western Blotting Detection Kit) and chemiluminescence signals were directly scanned with Alliance Q9 chemiluminescence imaging system. The band intensity was quantified by densitometry using ImageJ.

Dermal fibroblasts were seeded into a Lab-Tek II Chamber Slide System (1.5104 cells/well) in 1% FBS in DMEM overnight. HNPs (2.5, 5 and 10M) were added into the cells and incubated for 24h. The cells were washed with PBS and fixed with 4% paraformaldehyde for 10min. The cells were treated with 0.2% Triton-100 in PBS for 2min and blocked with 1% BSA in PBS for 30min. Primary antibody: Ki-67 (1:1000), diluted in 1% BSA in PBS was added and the cells were incubated at 4C overnight. After washing, secondary antibody: anti-mouse IgG Alexa Fluor 488 (1:2000), diluted in 1% BSA in PBS was added and the cells were incubated for 1h. After washing, the sections were mounted and proteins were observed.

Spheroids derived from dermal fibroblasts (5103 cells/well) were treated with HNPs (10M) for 4days. The spheroids were collected and covered with Tissue-Tek O.C.T. Compound. Frozen spheroids were cryosectioned into 8m thick layers onto glass slides. The sections were washed with PBS, fixed with 4% paraformaldehyde for 10min and treated with 0.2% Triton-100 in PBS for 2min. The sections were blocked with 5% BSA in PBS for 1h and incubated with primary antibody: COL1A1 (1:400), diluted in 1% BSA in PBS at 4C overnight. After washing, the sections were incubated with secondary antibody: anti-rabbit IgG Alexa Fluor 555 (1:1000), diluted in 1% BSA in PBS for 1h. After washing, the sections were mounted and proteins were observed.

The statistical analyses were determined by paired t-test using GraphPad Prism 9.0.0 (GraphPad Software, Boston, MA, USA). A simple linear regression analyses was performed using STATA version 15.1 (StataCorp, College Station, TX USA). The regression coefficients, 95% confidence intervals (CI), and p-value were demonstrated. The results were expressed as the meanstandard deviation (SD) and differences with a p-value<0.05 were considered statistically significant.

Dermal fibroblasts were seeded into a 6-well plate (2.5105 cells/well) in DMEM containing 1% FBS overnight. The cells were treated with HNP1 (10M) for 24h. Total RNA was extracted and the quality of extracted RNA (RNA Integrity Number6.5) was evaluated using an Agilent 2100 Bioanalyzer. The RNA-seq experiment was conducted by Vishuo Biomedical, Thailand. Purified poly-A mRNA was fragmented, and pair-end RNA sequencing was performed on the Illumina HiSeq platform. The Gene Expression Omnibus (GEO) of raw reads in FASTQ files was GEO ID: GSE230670.

Quality of raw reads in FASTQ files was inspected with the FASTQC program (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). The Trim Galore program (http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/) was used to cut adaptors and sequence reads with a Phred score lower than 30. To estimate abundance of transcript, cleaned raw reads were analyzed with Salmon v1.9.019 by 2 steps; (1) indexing and (2) quantification. First, Salmon with default setting was used to build an index on human reference transcriptome (GRCh38) downloaded from Human Genome Resource at NCBI (downloaded; July 2022) (https://www.ncbi.nlm.nih.gov/projects/genome/guide/human/). Next, Salmon was used for quantification by mapping paired-end reads to the indexed reference sequence in mapping-based mode. Transcript abundances in estimated read counts were imported to R with tximeta v1.12.420 and aggregated to gene-level expression with gene model annotation (GRCh38) for further analysis. Principal component analysis (PCA) was performed on the pre-processed gene expression data, which were first log-transformed and normalized with respect to library sizes by the rlog function in DESeq221 package and standardized so that the expression level of each gene has a zero mean and a unit variance, to visualize the clustering structure of replicates. PCA plots were drawn in R using the ggplot2 package.

Differential gene expression was tested between HNP1 and control groups with DESeq2 v1.34.021 package. Gene expression was normalized with the median of ratios method from DESeq2. Since samples were derived from different donors, statistical design for DESeq2 was accounted for donor factor when fitted generalize linear model to data. Multiple hypothesis testing correction was performed using Benjamini-Hochberg's procedure. Differentially expressed genes (DEGs) were defined as genes with false discovery rates (FDR)<0.01. Boxplots were drawn in R using ggplot2.

Function of genes was analyzed with gene set enrichment analysis (GSEA) from WebGestalt (http://www.webgestalt.org/)22. The values of log fold changes were used to rank genes for the functional enrichment analysis using Gene Set Enrichment Analysis (GSEA) method. KEGG pathway and Gene Ontology databases (biological process, molecular function and cellular component) were used. Multiple hypothesis testing correction was performed using Benjamini-Hochbergs procedure with the FDR cutoff of 0.05 for enriched functions.

Originally posted here:
Regulation of dermal fibroblasts by human neutrophil peptides ... - Nature.com

Cohorts, genotyping and phenotyping Adolescent brain cognitive development (ABCD) ABCD cohort description

The Adolescent Brain Cognitive Development (ABCD) cohort is a longitudinal study of brain development and child health7. Investigators at 21 sites around the USA conducted repeated assessments of brain maturation in the context of social, emotional, and cognitive development, as well as a variety of health and environmental outcomes. We analysed data from release 3.0. At the time of the survey questions, the children ranged in age from 9 to 12 years. Informed written consent was provided by parents and assent was provided by children. The ABCD research protocol approved was approved by the Institutional Review Board of University of California San Diego (IRB# 160091)16.

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children age 910 and follow them over 10 years into early adulthood. The ABCD Study is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.

The ABCD data repository grows and changes over time. The ABCD data used in this report came from https://doi.org/10.15154/1526432) DOIs can be found at https://dx.doi.org/10.15154/1526432. All methods were carried out in accordance with relevant guidelines and regulations.

DNA was extracted from saliva samples of the ABCD participants17. These samples were genotyped on the Affymetrix NIDA SmokeScreen Array (Affymetrix, Santa Clara, CA, USA). The QC procedures are described in full at the following URL: https://doi.org/10.15154/1503209.

ABCD genetic principal components (GPCs) were created using genotyped only SNPs using plink-pca flag.

A set of questions taken from the ABCD Youth NIH Toolbox Positive Affect Items was used. These questions measured aspects of positive emotions and affective well-being in the past week, specifically being attentive, delighted, calm, relaxed, enthusiastic, interested, confident, energetic and able to concentrate. Responses were measured as not true, somewhat true or very true. Each item was analysed separately as well as a combined score that was the sum of responses to the individual questions. In addition to the happiness PGS the models were adjusted for age, sex, and principal genetic components (PGCs) 18.

As the initial UK Biobank GWAS was run in the white British sub-group, testing was performed firstly in the white (as defined by ABCD) participants and secondly in the whole sample, with ancestry treated as a factor variable. The other ancestral backgrounds of this cohort as defined by ABCD are; White, Black, Hispanic, Asian, and Other (Table S6).

Creation of the derived MRI variables from the ABCD cohort has been described in detail elsewhere18. For the purposes of this study, total frontal lobe volume was derived by summing the 22 frontal lobe subsection variables of the left and right hemisphere19. Additionally, we looked at total grey and white matter volume and left and right hippocampus volume. The hippocampal body and tail regions and white matter hyperintensity volume were not available for replication. All outcomes were transformed into z scores and all models were adjusted for the happiness PGS, age, sex, PGCs 18, and MRI site. For models that included participants from different ancestries, a factor variable for ancestry was included (Table S7). Models were weighted to match the American community survey (ACS) data by the weighting variable acs raked propensity score. Relationship filtering was also performed removing one individual at random from any pair of participants with valid phenotypes, who were determined to be related by ABCD.

Add Health is a nationally representative cohort study of more than 20,000 adolescents from the USA who were aged 1219 years at baseline assessment in 199495. They have been followed through adolescence and into adulthood with five in-home interviews in five waves (IV) conducted in 1995, 1996, 20012002, 20082009 and 20162018. In this analysis, participants ranged from 24.3 to 34.7 years old, 53% were female and 62% were non-Hispanic white. The study was approved by the University of California San Diego Institutional Review Board (IRB #190002XX). Informed consent was obtained from all subjects.

Saliva samples were obtained as part of the Wave IV data collection. Two Illumina arrays were used for genotyping, with approximately 80% of the sample genotyped with the Illumina Omni1-Quad BeadChip and the remainder of the group genotyped with the Illumina Omni2.5-Quad BeadChip. After quality control, genotyped data were available for 9974 individuals (7917 from the Omni1 chip and 2057 from the Omni2 chip) on 609,130 SNPs present on both genotyping arrays20. Imputation was performed separately for European ancestry (imputed using the HRC reference panel) and non-European ancestry samples (imputed using the 1000 Genomes Phase 3 reference panel)21. For more information on the genotyping and quality control procedures see the Add Health GWAS QC report online at: https://addhealth.cpc.unc.edu/wp-content/uploads/docs/user_guides/AH_GWAS_QC.pdf.

Add Health Genetic Principal components (variable name pspcN, where N is the number of the PC) were derived centrally by Add Health. To prevent identification of individuals they are randomly reordered in sets of 5, i.e. PCs 15 were reordered so PC1 was may not be the PC with the largest variance. We adjusted models for the first 2 sets of PCs i.e. GPCs 110.

The outcome happiness variable was collected during the at-home interview of Wave IV and was derived from the response to the question: How often was the following true during the past seven days? You felt happy. Responses were given as: never or rarely; sometimes; a lot of the time; most of the time or all of the time; refused; don't know. Those who responded with the latter two options were excluded. Remaining categories were coded from never=0 to all of the time=3.

Ancestry in Add Health is defined in the psancest variable as European, African, Hispanic and East Asian (Table S8). Additionally, Add Health provides a weighting variable to make the results reflective of the US population. In these analyses the models were weighted by the Wave IV variable gswgt4_2.

UK Biobank is a cohort of over half a million UK residents, aged from approximately 4070 years at baseline. It was created to study environmental, lifestyle and genetic factors in middle and older age22. Baseline assessments occurred over a 4-year period, from 2006 to 2010, across 22 UK centres. These assessments were comprehensive and included social, cognitive, lifestyle and physical health measures.

UK Biobank obtained informed consent from all participants, and this study was conducted under generic approval from the NHS National Research Ethics Service (approval letter dated 29 June 2021, Ref 21/NW/0157) and under UK Biobank approvals for application #71392 Investigating complex relationships between genetics, exposures, biomarkers, endophenotypes and cardiometabolic, inflammatory, immune and brain-related health outcomes (PI Rona Strawbridge; GWAS)#17689 (PI Donald Lyall; imaging).

In March 2018, UK Biobank released genetic data for 487,409 individuals, genotyped using the Affymetrix UK BiLEVE Axiom or the Affymetrix UK Biobank Axiom arrays (Santa Clara, CA, USA) containing over 95% common content. Pre-imputation quality control, imputation and post-imputation cleaning were conducted centrally by UK Biobank (described in the UK Biobank release documentation)23.

Several structural and functional brain MRI measures are available in UK Biobank as imaging derived phenotypes (IDPs)24. The brain imaging data, as of January 2021, were used (N=47,920). Participants were excluded if they had responded to either of the happiness questions used for the GWAS meta-analysis, were missing more than 10% of their genetic data, if their self-reported sex did not match their genetic sex, if they were determined by UK Biobank to be heterozygosity outliers, and if they were not of white British ancestry (classified by UK Biobank based on self-report and genetic principal components)23.

Brain imaging data used here were processed and quality-checked by UK Biobank and we made use of the IDPs25,26. Details of the UK Biobank imaging acquisition and processing, including structural segmentation and white matter diffusion processing, are freely available from three sources: the UK Biobank protocol: http://biobank.ctsu.ox.ac.uk/crystal/refer.cgi?id=2367 and documentation: http://biobank.ctsu.ox.ac.uk/crystal/refer.cgi?id=1977 and in protocol publications (https://biobank.ctsu.ox.ac.uk/crystal/docs/brain_mri.pdf).

We investigated key imaging substrates previously associated with psychological health e.g., mood disorder, cognitive health. Total white matter hyperintensity volumes were calculated on the basis of T1 and T2 fluid-attenuated inversion recovery, derived by UK Biobank. White matter hyperintensity volumes were log-transformed due to a positively skewed distribution. We constructed general factors of white matter tract integrity using principal component analysis. The two separate unrotated factors used were fractional anisotropy (FA), gFA, and mean diffusivity (MD), gMD, previously shown to explain 54% and 58% of variance, respectively27. We constructed a general factor of frontal lobe grey matter volume using 16 subregional volumes as per Ferguson et al.27. Total grey matter and white matter volumes were corrected for skull size (by UK Biobank). Models were adjusted for the happiness PGS, age, sex, PGCs 18.

LDpred28 established the LD structure of the genome using a reference panel of 1000 unrelated white British UK Biobank participants (the PGS training set). These participants had not been used in the discovery GWAS or have valid MRI data and passed the same QC as described above. SNPs were excluded if they had MAF<0.01, had HWE P<1 106 or had imputation score<0.8. Scores were then created in the validation set using an infinitesimal model. Models using polygenic scores (PGS) derived using LDpred were adjusted for age, sex, genotyping array and the first eight GPCs.

Due to the lower cohort size of ABCD and Add Health, it would not have been possible to remove 1000 participants from the analyses to use as a training set without markedly reducing the power of the analyses. Therefore, we used the same 1000 unrelated UK Biobank participants as the training set to establish LD and this was used to generate the PGS for the participants in these datasets29. The only additional step was to find the SNPs that were found in both the training (UK Biobank) and validation (ABCD and Add Health) datasets and passed the same SNP filtering criteria in both datasets, with an additional filter that MAF threshold was set at>0.0130. The number of SNPs in each LDpred PGS can be found in supplementary table (S9).

For each pair of related individuals (as determined by ABCD using variables genetic paired subjected 14) one participant was excluded at random. Models were adjusted for age at interview, sex and the first 10 GPCs. For multi-ancestry models, ancestry was treated as a factor variable.

p values for analyses were false discovery rate (FDR)-adjusted31.

View original post here:
Consistent effects of the genetics of happiness across the lifespan ... - Nature.com

As we look in the mirror and find our fathers eyes or our mothers nose, what similarities persist through generations, and how do the unique ways we animate identical traits tell our story? Carl Zimmer, science journalist in genetics and author of the 2018 non-fiction book She Has Her Mothers Laugh: The Powers, Perversions and Potential of Heredity, captures the mixture of deja vu and naturalistic wonder as the machine of heredity reminds us of its unyielding march throughout our day to day lives.

The exigence of the book reflects a semi-memoir in which She Has Her Mothers Laugh has its humble beginnings from family dysfunction and a mid-life crisis very approachable. Zimmer pulls from his own anxieties about his unknown family tree while sitting with a genetics counselor discussing health predispositions for his own children. Zimmer describes his murky background as his mothers genealogy was equated to an elaborate game of telephone and his fathers side was a dead end, showing the reader that the books genetic inquiry is a very intimate relationship researched and traced back to validate those also searching into their genetic identity. Almost like pulling on loose threads to reveal the patchwork of the gene narrative, Zimmer is our tour guide from the birth of genetics in the 1900s to the political and ethical quandaries that have gravitated toward the human genome as technology has become more advanced and robust.

Zimmer bows down to the gene, and because every chromosome borrows from the last, he does the same. Like any true science journalist, Zimmer doesnt shy away from posing the bioethical questions of the future implications of genetic engineering and technology, but he knows that the answers are weaved into the past. As the book follows the relevant threads of heredity, careful to equally recognize non-western and classical concepts of genealogy, Zimmer exemplifies alternate concepts of heredity from the Malaysian Island of Langkawi that recognizes familial ties if children consumed the same food in which kinship was established through shared substance. Conversely, the hierarchy of pedigrees emerged in French society by the 13th century, coined by noble lineage portraiture. The physical proponents of limited gene pools and royal in-breeding have resulted in physical deformities such as the Habsburg Jaw, marked by an elongated lower jaw, which is symbolic of a moment in history where genetics jeopardized lineage and the unknown medical repercussions would continue to haunt an empire.

Few non-fiction books are fast and immersive reads, but She Has Her Mothers Laugh reads like a fairy-tale story as Zimmer turns to colorful anecdotes, historical case studies, and even mythology to answer the looming gene question. Contextualizing the origin of heredity, Zimmer knows that his audience isnt interested in a genetics lesson on Mendelian inheritance and human genome sequencing.

Leaning on cultural and historical phenomena, we learn of kinship bonds, feudal inheritance, and the transfer of culture aided through genes from across, and sometimes in spite of, generations. At its heart, this seemingly dense read about a long-forgotten high school biology class becomes an empathetic study of the confusion and admiration towards our own genetic profile that reveals a way of life. As the information on genetics has amassed, understanding complex inheritance of rare diseases and CRISPR gene editing, Zimmer treats these scientific checkpoints as living and changing entities that have been morphed by social interest, medical breakthroughs, and prejudice.

Link:
Storytelling through the looking glass of genetics The Stute - The Stute