Accurate and Scalable Construction of Polygenic Risk Scores (PRS) in Large Biobank Data Sets; Predictive Utility of PRS for Coronary Heart Disease in 3 Racial and Ethnic Groups

As these GEITP pages have often discussed, there are relatively simple monogenic (Mendelian) traits — in which one or only a few genes contribute to the phenotype (trait) — and multifactorial traits (e.g. human complex diseases and quantitative traits such as height, serum cholesterol levels, body mass index) — in which hundreds or thousands of small-effect genes contribute to the trait. For the latter, polygenic risk scores (PRS) are being promoted as the best approach, because of the complex interactions of so many genes being involved. In simple terms, the PRS for a phenotype is a weighted summation of the estimated gene-effect sizes across genome-wide single-nucleotide variants (SNVs). By means of aggregating the contribution of many SNVs toward the phenotype of interest, PRS can be used to construct an individual’s inherited component (which is his/her genetic predisposition, underlying the phenotype of interest).

By estimating the genetic predisposition, PRS serves both as the earliest measurable (and the most stable) predictor for disease and disease-related complex traits. [“PGS” is commonly used for a polygenic score of a quantitative trait, whereas PRS is the preferred term for polygenic risk scores, when the phenotype of interest is a complex disease.] We will use only the “PRS term” in this discussion.

PRS have been widely used in a range of genetic applications, including: disease risk prediction, genetic prediction of complex traits, prioritization of preventive interventions, understanding missing heritability, modeling polygenic adaptation, genomic selection in animal and plant breeding programs, transcriptome-wide association studies, and Mendelian randomization analysis (i.e. a method of using measured variation in genes of known function — to examine the causal effect of a modifiable exposure on disease in observational studies). Accurate construction of PRS can facilitate disease prevention and intervention — at an early stage — and might be helpful in developing personalized medicine.

Authors [see first attachment] developed a method called Deterministic Bayesian Sparse Linear Mixed Model (DBSLMM); this method relies on flexible modeling assumptions about effect-size distribution to achieve robust and accurate prediction performance — across a range of genetic architectures (the underlying genetic basis of the phenotypic traits of interest, and their variational properties). Using simulations, authors show that DBSLMM achieves scalable and accurate prediction performance across a range of realistic genetic architectures. Analyzing 25 traits in UK Biobank and comparing this method with previously existing approaches, authors determined that DBSLMM achieves an average of 2.03% to 101% accuracy gain in internal cross-validations. In external validations on two separate datasets [including one from Bio-Bank Japan], DBSLMM achieved a 14.7% to 523% accuracy gain. In these real-life applications, DBSLMM was 1.03 to 28.1 times faster [and used only 7.4% to 25% of physical memory] — compared to other multiple regression-based PGS methods. Overall, authors believe that DBSLMM represents the most accurate and scalable method for constructing PRS in biobank scale datasets.

Going from simulations to a real-life disease phenotype — authors [see second attachment] investigated associations of ‘‘restricted’’ vs genome-wide PRS with coronary heart disease (CHD) in three major racial and ethnic groups in the U.S. — comprising 45,645 European-Americans (EA), 7,597 African-Americans (AA), and 2,493 Hispanic ethnicity (HE) individuals. Over a median follow-up of 11.1 years, 2,652 incident CHD events occurred. Hazard ratio and odds ratio for the association of restricted PRS with CHD were similar in EA and HE cohorts, but lower in AA cohorts. Genome-wide PRS were more strongly associated with CHD than restricted PRS were [see the article for more details]. These findings highlight the potential clinical utility of PRS for CHD, as well as the need to assemble diverse cohorts to generate ancestry-PRS and ethnicity-PRS. 😊

DwN

Am J Hum Genet May 2020; 106: 679-693 & 707-716

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An epigenome-wide association study of posttraumatic stress disorder (PTSD) in US veterans implicates several new DNA methylation loci

This fascinating topic fits perfectly the theme of gene-environment interactions. The environmental “signal” is severe stress, and the response by the genome of some (but not all) individuals is to develop post-traumatic stress disorder (PTSD). In the early 1990s, “a genetic cause of PTSD” was being explored at the University of Cincinnati; “genetics thinking at that time”, of course, was that the trait (disease phenotype) is likely caused by one gene — which we now appreciate was very naïve. ☹ Now we know that multifactorial traits (e.g. PTSD) always reflect the contributions of genetics (i.e. DNA squence differences), epigenetic events (DNA methylation, RNA-interference, histone modulation, chromatin remodeling), environmental effects (life style, smoking), endogenous influences (e.g. cardiopulmonary disease, kidney function status), and each individual’s microbiome [commensal (i.e. long-term biological interaction in which members of one species gain benefits — while those of the other species neither benefit nor are harmed) microbes living in many places within our bodies].

In recent years, studies — using candidate-gene and genome-wide approaches — have focused on epigenetic changes in DNA methylation (DNAm) associated with PTSD. Authors [see attached article] performed an epigenome-wide association study (EWAS) of PTSD in a cohort of military veterans (N = 378 lifetime PTSD cases vs 135 controls), using the Illumina EPIC Methylation BeadChip (which assesses DNAm at >850,000 DNA-methylation sites throughout the genome). This model included covariates for ancestry, cell heterogeneity, gender, age, and a smoking score — based on DNAm at 39 smoking-associated CpGs (regions of DNA where cytosine nucleotide is followed by guanine nucleotide in linear sequence of bases, in the 5’→3′ direction). Authors also examined EPIC-based DNAm data generated from prefrontal cortex (PFC) tissue from the National PTSD Brain Bank (N = 72).

In blood samples, authors discovered one genome-wide significant association with PTSD in the G0S2 (P = 1.2 x 10–7) gene ( “G0/G1 growth switch-2” pertaining to cell cycle); this association was replicated in the independent prefrontal-cortex (PGC)-PTSD-EWAS consortium meta-analysis of military cohorts (P = 0.0024). Authors also observed an association with the smoking-related AHRR (aryl hydrocarbon receptor repressor) gene, evidence suggestive of a smoking-independent effect. The top 100 EWAS loci were then examined in the prefrontal cortex (PFC) data. One of the blood-based PTSD loci, in the CHST11 (carbohydrate sulfotransferase-11) gene — (which was in the top 10 loci in blood, but which did not reach genome-wide significance) — was significantly associated with PTSD in brain tissue. Gene-set-enrichment analysis of the top 500 EWAS loci yielded several significant overlapping GO (gene ontology) terms involved in pathogen response, including “response to lipopolysaccharide” (P = 7.0 x 10–6).

For those interested — previous genome-wide association studies (GWAS) have reported PTSD-related differences in DNAm levels in genes associated with the hypothalamic-pituitary-adrenal axis (e.g. ADCYAP1, FKBP5, NR3C1), with inflammation (e.g. BDNF, HTR2A, IL-18), and with neurotransmission (e.g. BDNF, HTR2A, HTR3A) [see Refs 9-14 of attached article]. In contrast, five published PTSD EWAS [see Refs 4-6 & 16, 17 of attached article] have reported single-site associations within the genes ACP5, ANXA2, CLEC9A, TLR8, TPR, BRSK1, DOCK2, LCN8, NGF, LCN8, HIST1H2APS2 (pseudogene), RNF39, ZFP57, CCDC88C, AHRR, and several intergenic loci. For anyone curious as to what these genes represent, please check with the web site https://www.genenames.org/ .

These GEITP pages suggest that one might be a bit wary of some of the (above) ‘statistically significant loci’ data. Authors (of the present more solid study), however, concluded that their cross-replication — observed in independent cohorts — is evidence that DNA methylation in peripheral tissue can yield consistent and replicable PTSD associations. Their data also suggest that that some PTSD associations — observed in peripheral tissue — might mirror associations detectable in the brain. 😊

DwN

Clin Epigenet Mar 2020; 12: 46

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Monkeys on a raft — is the only way to explain species on two continents ???

This mind-boggling story is way TOO COOL, bordering on heresy. 😉 Scientists have long been skeptical with the suggestion that small mammals crossed large oceanic barriers to populate faraway lands — millions of years ago…!! However, progress in phylogenetics during the 1980s has forced researchers to reconsider that North American fossil records showed no relatives of South American caviomorph rodents or platyrrhine (New World) monkeys, and that their closest relatives lived on the Afro-Arabian landmass during the Eocene epoch (56 to 34 million years ago). Therefore, to reach South America, these animals would have had to cross the South Atlantic Ocean — which probably was more than ~1500 to 2000 km (~930 to 1240 miles) wide during this period.

Authors [see attached article & editorial] report on fossils from Santa Rosa in Amazonian Perú, that provide evidence of a

third mammalian lineage of African origin that briefly appeared in South America in the early Oligocene (35 to 32 million years ago): a now-extinct parapithecid anthropoid monkey (genus: Ucayalipithecus). These data: (i) provide the most compelling phylogenetic link yet of a South American fossil mammal to an Afro-Arabian clade; (ii) substantially constrain the timing of the transatlantic dispersal that gave rise to this South American parapithecid lineage and possibly other South American primate lineages; and (iii) suggest that eustatic sea level fall [‘eustatic sea level’ is the distance from the center of Earth to the sea surface; increases in eustatic sea level can be generated by decreasing glaciation, increasing spreading rates of mid-ocean ridges, or creation (upsurging) of more mid-oceanic ridges] that was coincident with the onset of Antarctic glaciation — might have played a role in facilitating transatlantic dispersal of these species.

The Santa Rosa locality is just south of the border between Perú and Brazil, so it’s in Perú. This locality has (mysteriously) previously yielded: a single upper molar of a possible stem platyrrhine (Perupithecus); fragmentary teeth of an unnamed second anthropoid taxon; numerous isolated teeth and maxillary and mandibular fragments of caviomorph rodents and marsupials; and rare remains of bats, noto-ungulates, and enigmatic mammals of Afro-Arabian origin.

Bayesian clock–based phylogenetic analysis [‘Bayesian inference of phylogeny’ uses a likelihood function (integration of Markov chain Monte Carolo algorithms) to create a quantity called the posterior probability of trees — using a model of evolution, based on various probabilities, producing the most likely phylogenetic tree for the given data] nests this genus (Ucayalipithecus) deep within the otherwise Afro-Arabian clade Parapithecoidea and indicates that transatlantic rafting of the lineage leading to Ucayalipithecus likely took place between ~35 and ~32 million years ago — a dispersal window that includes a major worldwide drop in sea level that is known to have occurred near the Eocene-Oligocene boundary. 😊

DwN

Science 10 Apr 2020; 368: 194-197 & editorial pp 136-137

COMMENT:David, the most amazing portion of this story is that these “precursors of rodents” and “precursors of marsupials” also joined the primate precursors on these trips. Sounds like a story right out of (a precursor version of) Noah’s Ark. 😉

And I see the book is available on Amazon.com and also available for anyone’s Kindle.

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‘CO2 levels’ and your ‘carbon footprint’ — NOT the problem you’ve been told

Uh oh. Look what I just found online yesterday… but it looks like an interesting read. 😊

https://www.cfact.org/2020/05/28/

CO2 at the Yale Bowl

‘CO2 levels’ and your ‘carbon footprint’ — NOT the problem you’ve been told

By Dan Nebert | May 28th, 2020 | Climate

Last year a student at a nearby university complained she couldn’t focus in class; she was convinced high levels of carbon dioxide (CO2) were the cause. The entire building was immediately evacuated and tested for “toxic levels of this dangerous gas.” After determining the CO2 levels were less than 500 parts-per-million (ppm), the classroom air was considered “safe” and classes again resumed.

Recently, this same school advertised that you can now “offset carbon emissions from previously completed university-funded ground-transportation and air travel trips” — by filling out a “travel carbon offsets” form, available in their “Sustainability Office.” Plus, this school is offering a course on “how to lower your carbon footprint.”

National Association of Scholars is planning a meeting to discuss indoor CO2 levels, because they “may reach levels harmful to cognition by the end of this century, and the best way to prevent this hidden consequence of climate change is to reduce fossil fuel emissions.” A publication this week in Nature Climate Change states that “government policies and human activity data, due to decreases in travel during forced COVID-19 confinements, have decreased daily global CO2 emissions by ~17% to ~25% by early April 2020, compared with mean 2019 levels.”

As I read this nonsense in the news every day, I feel like screaming: “This nonsensical obsession with CO2 and the ‘carbon footprint’ is absolute insanity! Where has common sense gone?” Doesn’t anyone remember — from grade school and high school biology — what they learned about plant photosynthesis requiring CO2 and all animals requiring oxygen (O2) and exhaling CO2? Life on this planet is carbon-based; if we were not carbon-based, the next available tetrahedral element (having four chemical bonds) in Mendeleev’s Periodic Chart is silicon — in which case we would be able to live on the sun’s surface!

CO2 levels in our lungs reach ~40,000-50,000 ppm, which causes us to inhale our next breath. One of the first things medical students learn in respiratory physiology — is that the carotid body (small cluster of chemoreceptor cells, located at bifurcation of the common carotid artery running along both sides of neck) detects changes in arterial blood flow pO2 (partial pressure of oxygen), pCO2, blood pH, and temperature. When the blood pCO2 reaches a critical level, this message is quickly sent to the medulla oblongata in the brainstem, which then sends signals our diaphragm to breathe; more O2 is needed, and excessive CO2 must be expelled.

The human breathing reflex is controlled by blood CO2 levels, not O2 levels. Too little CO2, which can happen from hyperventilating, leads to respiratory alkalosis. This is called hyperventilation syndrome — usually brought on by stress and anxiety. Symptoms include light-headedness; tingling in the fingers, toes and face; and chest pain; sometimes people fear they’re having a heart attack. Treatment for hyperventilation syndrome is to breathe into a paper bag, which increases your blood CO2 back to normal.

As the only physician on a commercial airlines cross-country flight, I was asked to examine a ~35-year-old woman who thought she was having a heart attack; the obvious diagnosis was hyperventilation syndrome (due to anxiety of meeting her inlaws for the first time). I had her breathe intermittently into a paper bag to increase her blood pCO2 levels; within ~20 minutes she was no longer symptomatic. Had no physician been on that flight, they would have diverted the aircraft to St. Louis to a waiting ambulance, rather than proceeding to Portland, OR, the scheduled destination.

Breathing is automatic (controlled by our autonomic nervous system) — meaning that we don’t think about it; it “just happens” about 16 times a minute. This is one of God’s many miracles in all animals with lungs. Heart rate, kidney blood flow, and digestion of our food — are other examples of autonomic-nervous-system regulation that constantly functions while we don’t think about it.

Today’s global atmospheric CO2 levels are about 415 ppm; at these levels CO2 remains a limiting factor for growth of farm crops and trees. Plants today are “at least 25% CO2-starved.” In fact, standard procedures for commercial greenhouse growers are to elevate CO2 to 800­-1200 ppm; this enhances growth and yield ~20-50%. Indoor air routinely ranges between 500 and 2,000 ppm of CO2. Submarines regularly operate with ambient CO2 levels between 2,000 and 5,000 ppm.

In past ages, ice-core data suggest CO2 levels have been as high as 10,000-15,000 ppm (this was before humans; in fact, before mammals evolved), and plant life flourished. In recent times, “normal” CO2 ranges between ~150-180 ppm during Glacial Periods and ~280-300 ppm during Inter-Glacial Periods. Industrialization during the past 130 years has probably increased global atmospheric CO2 levels by ~135 ppm, which has improved crop growth.

To paraphrase Professor Zbigniew Jaworowski (Chair, Scientific Council of Central Laboratory for Radiological Protection; Warsaw, Poland) who testified before the US Senate Committee on Commerce, Science, and Transportation in 2004: “The basis — of most conclusions by United Nation’s Intergovernmental Panel on Climate Change (IPCC) on anthropogenic (man-made) causes, and their projections of climatic change — relies on the assumption that low levels of CO2 in the pre-industrial atmosphere represent the ‘normal’ baseline. From glaciological studies, we know this assumption is false. Therefore, IPCC projections should not be used for national and global economic planning and governmental policy.”

The atmospheric impact of CO2 on climate is overstated. Since the Little Ice Age (1300-1860), Earth has been warming naturally. As temperatures rise, CO2 in the liquid phase (oceans) moves to the gaseous phase (air); we learn this in introductory chemistry. Hence, rising global atmospheric temperatures cause CO2 to increase — not the other way around!

“Carbon emissions” and “carbon footprint” as causes of global warming are nothing more than scaremongering buzzwords — created by global warming alarmists, insincere environmentalists, certain manipulative dishonest politicians, and misinformed journalists. Earth has undergone climate change and local severe weather since its formation ~4.54 billion years ago. Causes of natural variations in climate include: solar activity; cloud type and amount; radiative forcing and insolation (amount of sunlight absorbed vs amount radiated back into space); Earth’s rotation and interplay between its atmosphere and oceans; variations in precession, eccentricity and axial tilt of our planet; gravitational pull of other planets of substantial mass (especially Jupiter); and volcanic eruptions both on land and underwater.

CO2 is an odorless, tasteless, invisible non-polluting gas on which all life on Earth depends. “Smoke” from factory chimneys usually represents water vapor, not CO2. Dirty industrial fossil-fuel pollution is, of course, undesirable and causes health problems. However, many scientific lines of evidence — including geological history and basic radiation-transfer physics — show that anthropogenic CO2 emissions have negligible influence on climate, in comparison to the natural factors listed above.

Author

·

· Dan Nebert

Daniel (“Dan”) Walter Nebert is an American physician-scientist, molecular biologist, and geneticist. He has authored/coauthored publications in fields of biochemistry, molecular biology, pediatrics, developmental biology, pharmacology, drug metabolism, toxicology, mouse genetics, human genetics, evolutionary genomics, gene nomenclature, and cancer.

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HGNC spring newsletter 2020

For those interested — here is the Spring 2020 NewsLetter from the HUGO-based Human Gene Nomenclature Committee (HGNC), located in London. Their web site is https://www.genenames.org/ and should be bookmarked by every scientist who is dealing with any gene or any RNA or protein product from that gene. Using standardized nomenclature helps cut down on the confusion created in the scientific literature by the use of so many trivial and jargon names. 😊

DwNSearch
Spring newsletter 2020

HGNC, VGNC, Newsletters · 28 May 2020

Progress on replacing placeholder symbols

Here is a selection of placeholder symbols that we have managed to update while working from home:

C15orf41 to CDIN1, CDAN1 interacting nuclease 1
C11orf88 to HOATZ, HOATZ cilia and flagella associated protein
C12orf49 to SPRING1, SREBF pathway regulator in golgi 1
C1orf61 to MIR9-1HG, MIR9-1 host gene

Note that this final rename was triggered following an agreement of a change in locus type for the gene between the RefSeq and Havana manual annotation groups and the HGNC. The locus type changed from protein coding to long non-coding RNA and was based on a lack of conservation across species and lack of support for the predicted protein in mass spectrometry data. The new symbol and name reflect the fact that the lncRNA gene hosts the microRNA gene MIR9-1 within one of its introns.
New HGNC gene groups

We have curated several new gene groups for genenames.org within the past few months as part of our ongoing curation work. Highlights include:

IFT-B1 complex
Nuclear factor I family (NFI)
Ras related GTP binding proteins (RRAG)
HIRA histone chaperone complex subunits
Linker of nucleoskeleton and cytoskeleton complex subunits

Gene Symbols in the News

The coronavirus pandemic has dominated our lives and the news in recent times. We couldn’t include this regular newsletter section without mentioning the following genes that have recently appeared in many more news articles than we can possibly feature due to their association with COVID-19. We are pleased to see that all of these genes are being referred to by their approved symbols, allowing unambiguous communication about them.

The product of the ACE2 gene was immediately a prime suspect as a SARS-CoV-2 receptor following its previous association with the SARS-CoV virus. Early reports suggested that SARS-CoV-2 could be more infectious than SARS-CoV due to its possible enhanced binding to ACE2. There have since been reports linking levels of this ACE2 protein to differences in the severity of Covid-19 between males and females and across age groups. There are now many drug trials going ahead that aim to disrupt the association of SARS-CoV-2 with ACE2, as mentioned in this CORDIS report and on this news site for the Massachusetts Institute of Technology.

The involvement of a number of proteases, needed for activation of the virus upon cell entry, have been reported. The most regularly mentioned is the enzyme encoded by TMPRSS2, and there is hope that a pre-existing drug that can inhibit this protease may block viral entry. Although TMPRSS2 has been found to be the major protease for infection in nasal cells, TMPRSS4 encodes a protease that has been identified as potentially playing a role in the infection of digestive tissues. It has also been suggested that the FURIN cleavage site in the spike protein of the SARS-CoV-2 virus could be linked to its high transmission rates because this cleavage site does not exist in the SARS-CoV virus.

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Assessing Digital Phenotyping to Enhance Genetic Studies of Human Diseases

Every genotype (genetic constitution of an individual organism) results in one or more phenotypes (traits). Long ago, it was assumed by many that one DNA variant (genotype) would be responsible for one phenotype (e.g. a disease, clinical presentation, or quantitative trait such as height or weight, etc.); this can be true for certain monogenic (Mendelian) diseases. In fact, the Human Phenome Project was initiated in about 1996, before the genome was realized to be so complicated. However, it is now abundantly clear that — with virtually all multifactorial traits — one DNA variant is often associated with multiple phenotypes (this is called ‘pleiotropic’, also called ‘polyphenic’). The opposite of pleiotropism is “polygeny” or “polygenic” (e.g. two or more genes affecting one trait, such as the trait of eye color).

The attached article is studying digital-phenotyping and unstructured-phenotype data, showing how this can be combined with structured data such as hospital records to identify cases for genome-wide association studies (GWAS). These GEITP pages believes these attempts are extremely difficult and perhaps futile. [For example, recall yesterday’s GEITP blog on autism spectrum disorder (ASD); large-effect variants associated with ASD were also found to be associated with numerous other neurological disorders.] ☹

GWAS for binary phenotypes (e.g. ‘patient has disease X’, ‘patient does not have disease X’) typically obtain cases by way of recruitment through medical systems or archived medical samples; cases can then be compared to controls, or to random population controls (in which the disease has a certain prevalence in the population). Recent studies, however, have begun to rely on self-reported phenotypes — collected via questionnaires, or internet or mobile phone applications. Such ‘‘digital phenotyping’’ may be faster and cheaper than standard cohort study approaches, but the extent to which this approach agrees with more traditional phenotyping approaches for GWAS is largely unknown, because previous attempts to estimate the agreement between the two phenotyping approaches have focused on a small number of top associations and have not systematically assessed agreement across the hundreds or thousands of variants likely associated with most complex polygenic traits.

For instance, a GWAS of self-reported thrombosis events found strong agreement between the top associations displayed in Manhattan plots (a type of scatter plot — usually used to display data with a large number of data-points, many of non-zero amplitude, and with a distribution of higher-magnitude values; these are commonly used in GWAS to display significant DNA variants) from their self-reported thrombosis GWAS compared to previous cohort-based studies. Other studies have reported overlaps with genome-wide significant loci from cohort studies, the studies have not investigated the extent to which genetic effects that did not reach genome-wide significance agree with one another.

Authors [see attached article] used genetic parameters (including genetic correlation) to evaluate whether GWAS — performed using cases in the UK Biobank ascertained from hospital records, questionnaire responses, and family history of disease — implicate similar disease genetics across a range of effect-sizes. Authors found that hospital-record-and-questionnaire GWAS are largely able to identify similar genetic effects for many complex phenotypes, and that combining together both phenotyping methods improves statistical power to detect genetic associations. Authors also showed that family-history GWAS — using cases ascertained on family history of disease — agree with combined-hospital-record-and-questionnaire GWAS; authors also demonstrate that family-history GWAS have greater statistical power to detect genetic associations for some phenotypes. Overall, authors believe this study demonstrates that digital-phenotyping and unstructured-phenotype data — can be combined with structured data such as hospital records to identify cases for GWAS in biobanks, and this improves the ability of such studies to identify genetic associations.

DwN

Am J Hum Genet 7 May 2020; 106: 611-622

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Insufficient Evidence for “Autism-Specific” Genes !!

As these GEITP pages have continued to emphasize, there are (relatively) monogenic diseases (Mendelian disorders) and there are human complex diseases [multifactorial traits that reflect contributions by genetics (DNA sequence alterations), epigenetic factors (not involving DNA mutations), environmental effects (e.g. diet, smoking), endogenous influences (e.g. cardiopulmonary disease or renal function), and each individual’s microbiome]. Autism spectrum disorder (ASD) is a great example of a “complex disease” — clinically and etiologically heterogeneous, and for which a unifying pathophysiology has not yet been identified — for either the disorder as a whole, or its core behavioral components. Heritability estimates are high (between 0.65 and 0.91), based on family and twin studies. Elucidation of the complex genetic architecture (i.e. the underlying genetic basis of a any phenotypic trait and its variational properties) of ASD has, in recent years, revealed contributions from both rare and common DNA variants.

Chromosomal microarray, and next-generation sequencing (NGS), studies have identified many de novo and inherited rare variants of large-effect size that contribute substantially to the etiology of ASD. It has also become clear that pathogenic variants in the same genes are identified in individuals with a variety of different clinically defined brain disorders [i.e. not only ASD but also intellectual disability (ID), epilepsy, schizophrenia, and other neurodevelopmental and neuropsychiatric conditions]. The known collective contribution of rare large-effect pathogenic variants is greatest for neurodevelopmental disorders (NDDs) — such as ID, ASD, and epilepsy — but they are also important etiologic factors in other conditions with onset in childhood (e.g. attention-deficit/hyperactivity disorder [ADHD]) or adolescence (e.g. schizophrenia) and, to a lesser degree, to later-onset neuropsychiatric conditions (e.g. mood disorders).

Authors [see attached Commentary], in a very objective fashion, review the advances and limitations of recent efforts to identify relatively ‘‘autism-specific’’ genes — efforts which focus on rare variants of large-effect size that are thought to account for the observed phenotypes. Authors rigorously question some of the interpretations of published evidence. They also discuss practical and theoretical issues related to studying the relationships between rare large-effect deleterious variants and neurodevelopmental phenotypes. Finally, authors describe potential future directions of this research. Authors argue that there is currently insufficient evidence to establish meaningful ASD specificity of any genes — based on all the large-effect rare-variant data…!!! ☹

DwN

Am J Hum Genet May 2020; 106, 587–595

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Smoking May Exacerbate SARS-CoV-2 Infection by Increasing ACE2 Expression

This article, summarizing a paper just published in Developmental Cell, describes an interesting finding that fits with GEITP’s theme of gene-environment interactions: the environmental signal is “smoking” and the genome response includes “up-regulation of the ACE2 (angiotensin-converting enzyme-2) gene.” These data would suggest that ACE2 is part of the AHR-CYP1 axis that governs the expression of dozens if not hundreds of genes that are turned on in response to specific environmental and endogenous signals. In fact, induced ACE2 expression likely is secondary to stimulation of the pro-inflammatory processes, mediated by lipid mediator [e.g. downstream of arachidonic acid (AA), eicosopentaenoic acid (EPA), and docosahexaenoic acid (DHA) signaling] second-messenger pathways. [Reviewed in Progr Lipid Res 2017; 67: 38-57]

DwN

Smoking May Exacerbate SARS-CoV-2 Infection by Increasing ACE2 Expression

May 20, 2020

Whether smokers have a harder time dealing with a COVID-19 infection remains, like many questions surrounding this disease, a question without a firm answer. Previous data have suggested that cigarette smokers are more likely than non-smokers to have health complications. Now, a group of researchers present findings that may explain why.

Scientists from Cold Spring Harbor Laboratory (CSHL) found that smoking increases the gene expression of ACE2—the protein that binds SARS-CoV-2. The study suggests that prolonged smoking could cause an increase of the ACE2 protein in the lungs, possibly resulting in a higher rate of morbidity in patients.

“Our results provide a clue as to why smokers who develop COVID-19 tend to have poor clinical outcomes,” said senior author Jason Sheltzer, PhD, an independent fellow at CSHL. “We found that smoking caused a significant increase in the expression of ACE2, the protein that SARS-CoV-2 uses to enter human cells.”

The work appears in a paper in Developmental Cell titled, “Cigarette smoke exposure and inflammatory signaling increase the expression of the SARS-CoV-2 receptor ACE2 in the respiratory tract.”

ACE2, or angiotensin-converting enzyme-2, is a regulatory protein that has been linked to vulnerability to the 2003 SARS (2003) virus. “Evidence from mouse experiments has shown that higher levels of ACE2 make mice more susceptible to SARS,” said Sheltzer. More recent work with SARS-CoV-2 found that when human ACE2 was highly expressed in mice infected with the SARS-CoV-2 virus, they died more quickly.”

In humans, the lungs act as one of the primary locations of ACE2 production. To assess the direct impact of smoking on ACE2 expression in the lungs, Sheltzer compared ACE2 gene expression from the lung epithelial tissue of people who smoked cigarettes regularly to those who had never smoked. “We found that smoking caused a significant increase in the expression of ACE2,” said Sheltzer, who noted that smokers produced 30–55% more ACE2 than their non-smoking counterparts. This change was also dose dependent, with heavy smokers having the greatest ACE2 values.

The effects of smoking on ACE2 may be tied to the goblet cells in the lungs—one of the few lung cell types that Sheltzer found to actively express the ACE2 gene. “Goblet cells produce mucous to protect the respiratory tract from inhaled irritants. Thus, the increased expression of ACE2 in smokers’ lungs could be a byproduct of smoking-induced secretory cell hyperplasia,” said Sheltzer. An uptick in ACE2 was also associated with the inflammatory pulmonary diseases, COPD and idiopathic pulmonary fibrosis.

Additionally, Sheltzer’s results indicate that other viral infections, such as influenza, as well as interferon signaling—the part of the body’s virus defense system—also increase ACE2 expression. “Because of this, it’s conceivable that SARS-CoV-2 could trigger the upregulation of its own receptor, thereby creating a positive feedback loop leading to more infections,” Sheltzer said.

While the impact of cigarette smoke and ACE2 expression is compelling, it is not permanent. By comparing the lungs of current smokers to those who quit smoking for at least 12 months, Sheltzer found “a significant decrease in ACE2 expression, demonstrating that the effects of smoking on ACE2 can be reversed.” Further, other studies on the effects of cigarette smoke have shown mixed results. “Cigarette smoke contains hundreds of different chemicals. It’s possible that certain ingredients (like nicotine) have a different effect than whole smoke does,” said Sheltzer.

And while Sheltzer finds strong support for the upregulation of ACE2 gene expression from smoking, the actual ACE2 protein may be regulated in ways not addressed in this study. “One could imagine that having more cells that express ACE2 could make it easier for SARS-CoV-2 to spread in someone’s lungs, but there is still a lot more we need to explore.”

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Invitation to become a member of the EU Academy of Sciences (EUAS)

Last night this email was received, which was an invitation to “join the society of the EU Academy of Sciences (EUAS) [ http://www.interpaper.org
Interpaper Research Organization – EU Academy of Sciences
Interpaper Research Organization Interpaper is a Research Organization dealing with research projects and consulting engineers projects in several fields of engineering mechanics, like elasticity, plasticity, fracture mechanics, structural analysis, fluid mechanics, aerodynamics, petroleum science and elastodynamics.
www.interpaper.org
] of distinguished members worldwide” [see email far below]. Because I had never heard of this organization, I inquired among a half dozen EU GEITP-ers and received varying answers (most had never heard of it). The most complete email response included a 1-page editorial from 2002 [see attached] — suggesting that this society “might not be quite legitimate.” Because these GEITP pages include fraud and corruption as a topic, we will discuss this issue.

It might feel flattering to be asked in an email to join what the organizers claim to be the “most distinguished group of scientists of this century,” but — if you receive this invitation — you should become suspicious. Eminent researchers across the world have received letters, congratulating them on being elected to the European Academy of Sciences, and then, once you agree to join, they they ask for “a US$115 membership fee” (which, 18 years later, has likely increased to ~US$150 per year). However, an investigation by Nature Publishing Group has established that the organization may not be … all that it is described to be. ☹

According to the invitation letter, election to the Academy is “one of the highest honours that can be accorded any scientist or engineer.” Researchers will be able to “take part in academy-funded projects and submit papers to its publications.” The Academy’s website says the organization “promotes the establishment of new scientific laboratories and institutions” and collaborates with “national academies, universities and research centres of various European countries.”

However, Nature has not been able to find any record of the Academy’s publications, projects or meetings … and cannot confirm the scientific credentials of those behind this organization. This academy — which is NOT related to the Vienna-based European Academy of Sciences and Arts (Academia Scientiarum et Artium Europaea) — claims to be “represented in almost every European country,” but it could only be traced to a rented office in Brussels. It also claims to have “recruited some 600 members, including 40 Nobel Laureates,” yet other scientific organizations in Europe say that they are not aware of it. For further entertaining reading, please examine the attached editorial from 2002 (which means that this allegedly fraudulent ‘society’ continues to persist, 18 years later). ☹

DwN

Nature 31 Oct 2002; 419: 865

From: academy@interpaper.org
Sent: Wednesday, May 20, 2020 1:10 AM
To:
Subject: INVITATION TO BECOME A MEMBER OF THE EU ACADEMY OF SCIENCES (EUAS)

Dear Professor XXXXX,

We would be pleased to invite you to join our society of the EU Academy of Sciences (EUAS) ( http://www.interpaper.org ) of distinguished members worldwide. You join some special people in many countries all over the world, who are members of this unique society, committed to advancing science and technology.

The EU Academy of Sciences is an international scientific organization and among the most prestigious in Europe. It is composed of the world’s leading scientists, scholars and business people, aiming to promote excellence in science and technology.

So, the EU Academy of Sciences is an independent, non-profit international organization with many members in several countries all over the world, including Nobel Prize (Physics, Chemistry, Medicine & Economics) and Outstanding Scientists winners.

The membership newsletter, EU ACADEMY ANNUAL REPORT, would be your best source of news and information about the EU Academy and its members. Besides, within the scope of the Academy is to organize an annual symposium called Next Generation Sciences of the 21st Century.

We hope that you will take advantage of your membership, you will participate in our research programs, you will keep to be informed about the activities of our Academy and you will invite your colleagues to join the EU Academy.

Please confirm that you are interested to join us, as a member of the EU Academy of Sciences and then we will send you immediately the Membership Card. Also, attached you will find your personalized Membership Certificate and the Invitation Letter.

With my very best regards, Prof. E.G. Ladopoulos

President & CEO of the EU Academy of Sciences (EUAS)

New High Quality Scientific Journals by PaperSciences Research Publisher

http://www.papersciences.com

Editor-in-Chief : Prof. E.G.Ladopoulos
President & CEO of the EU Academy of Sciences

Universal Journal of Engineering Mechanics (ISSN: 2241 – 7672)

Universal Journal of Structural Analysis (ISSN: 2241 – 7664)

Universal Journal of Fracture Mechanics (ISSN: 2241 – 7656)

Universal Journal of Computational Analysis (ISSN: 2241 – 7648)

Universal Journal of Fluid Mechanics (ISSN: 2241 – 763X)

Universal Journal of Hydraulics (ISSN: 2241 – 7621)

Universal Journal of Aerodynamics (ISSN: 2241 – 7613)

Universal Journal of Petroleum Sciences (ISSN: 2241 – 7605)

Universal Journal of Renewable Energy (ISSN: 2241 – 7591)

Universal Journal of Aeronautical & Aerospace Sciences (ISSN: 2241 – 7583)

Universal Journal of Non-linear Mechanics (ISSN: 2241 – 7575)

Universal Journal of Integral Equations (ISSN: 2241 – 7567)

Universal Journal of Applied Mathematics & Computation (ISSN: 2241 – 7559)

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Targets of T cell responses to SARS-CoV-2 coronavirus in humans with COVID-19 disease and unexposed individuals

These GEITP pages continue to stay updated on papers dealing with SARS-CoV-2 transmission and possible preventions and treatments. Vaccines against this new virus are just beginning to be developed. An understanding of human T cell responses to SARS-CoV-2 is lacking, due to the rapid emergence of the pandemic; certainly, there is an urgent need for foundational information about T cell responses to this virus. The first steps for such an understanding would be the ability to quantify virus-specific CD4+ and CD8+ T cells. Such knowledge is of immediate relevance — because it will provide insights into immunity and pathogenesis of SARS-CoV-2 infections, and this knowledge would assist vaccine design and evaluation of candidate vaccines.

Estimations of immunity are also central to epidemiological-model calibration of future social-distancing pandemic-control measures. Such projections are stikingly different — depending on whether SARS-CoV-2 infection creates substantial immunity, and whether any crossreactive immunity exists between SARS-CoV-2 and circulating seasonal “common cold” human coronaviruses. Definition and assessment of human antigen-specific SARS-CoV-2 T cell responses are best made with direct ex vivo (i.e. cells taken from a live patient) T cell assays using broad-based epitope pools and assays capable of detecting T cells of any cytokine polarization. Authors [see attached preprint] have completed such an assessment with blood samples from COVID-19 patients.

There is also great uncertainty about whether adaptive immune responses to SARS-CoV-2 are protective or pathogenic, or whether both scenarios can occur depending on timing, composition, or magnitude of the adaptive immune response. Hypotheses range the full gamut — based on available clinical data from severe acute respiratory disease syndrome (SARS) or Middle East respiratory distress syndrome (MERS), as well as animal model data with SARS in mice, SARS in nonhuman primates, or feline infectious peritoneitis virus (FIPV) in cats.

Using HLA Class I and II predicted peptide “mega-pools”, authors [see attached] identified circulating SARS-CoV-2-specific CD8+, and CD4+ T cells, in ~70%, and 100%, respectively, of COVID-19 convalescent patients. CD4+ T cell responses to the virus spike protein (the main target of most vaccine efforts) were robust and correlated with the

magnitude of the anti-SARS-CoV-2 IgG and IgA titers. The M, spike, and N proteins each accounted for 11-27% of the total CD4+ response, with additional responses commonly targeting nsp3, nsp4, ORF3a, and ORF8, among others. For CD8+ T cells, spike and M were recognized, with at least eight SARS-CoV-2 ORFs targeted. It was most noteworthy that the authors detected SARS-CoV-2-reactive CD4+ T cells in ~40-60% of unexposed individuals. These intriguing data suggest cross-reactive T cell recognition between various circulating “common cold” coronaviruses and SARS-CoV-2. 😊

DwN

Cell, preprint in press, May 2020; https://doi.org/10.1016/j.cell.2020.05.015

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