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Genetic tests that predict the lifetime risk of common medical conditions are fast becoming more accurate and affordable. The life insurance industry is interested in using predictive genetic tests in the underwriting process, but more research is needed to establish whether this nascent form of genetic testing can refine the process over conventional underwriting factors. Here, we perform Cox regression of survival on a battery of genetic risk scores for common medical conditions and mortality risks in the Health and Retirement Study, without returning results to participants. Adjusted for covariates in a relevant insurance scenario, the scores could improve mortality risk classification by identifying 2.6 years shorter median lifespan in the highest decile of total genetic liability. We conclude that existing genetic risk scores can already improve life insurance underwriting, which stresses the urgency of policymakers to balance competing interests between stakeholders as this technology develops.

Proprietary genetic datasets are valuable for boosting the statistical power of genome-wide association studies (GWASs), but their use can restrict investigators from publicly sharing the resulting summary statistics. Although researchers can resort to sharing down-sampled versions that exclude restricted data, down-sampling reduces power and might change the genetic etiology of the phenotype being studied. These problems are further complicated when using multivariate GWAS methods, such as genomic structural equation modeling (Genomic SEM), that model genetic correlations across multiple traits. Here, we propose a systematic approach to assess the comparability of GWAS summary statistics that include versus exclude restricted data. Illustrating this approach with a multivariate GWAS of an externalizing factor, we assessed the impact of down-sampling on (1) the strength of the genetic signal in univariate GWASs, (2) the factor loadings and model fit in multivariate Genomic SEM, (3) the strength of the genetic signal at the factor level, (4) insights from gene-property analyses, (5) the pattern of genetic correlations with other traits, and (6) polygenic score analyses in independent samples. For the externalizing GWAS, down-sampling resulted in a loss of genetic signal and fewer genome-wide significant loci, while the factor loadings and model fit, gene-property analyses, genetic correlations, and polygenic score analyses are robust. Given the importance of data sharing for the advancement of open science, we recommend that investigators who share down-sampled summary statistics report these analyses as accompanying documentation to support other researchers’ use of the summary statistics.

Contains fulltext : 237013.pdf (Publisher’s version ) (Closed access) Behaviors and disorders related to self-regulation, such as substance use, antisocial behavior and attention-deficit/hyperactivity disorder, are collectively referred to as externalizing and have shared genetic liability. We applied a multivariate approach that leverages genetic correlations among externalizing traits for genome-wide association analyses. By pooling data from ~1.5 million people, our approach is statistically more powerful than single-trait analyses and identifies more than 500 genetic loci. The loci were enriched for genes expressed in the brain and related to nervous system development. A polygenic score constructed from our results predicts a range of behavioral and medical outcomes that were not part of genome-wide analyses, including traits that until now lacked well-performing polygenic scores, such as opioid use disorder, suicide, HIV infections, criminal convictions and unemployment. Our findings are consistent with the idea that persistent difficulties in self-regulation can be conceptualized as a neurodevelopmental trait with complex and far-reaching social and health correlates. 27 p.

Chemokine driven leukocyte recruitment is a key component of the immune response and is central to a wide range of diseases. However, there has yet to be a clinically successful therapeutic approach that targets the chemokine system during inflammatory disease; possibly due to the supposed redundancy of the chemokine system. A range of recent studies have demonstrated that the chemokine system is in fact based on specificity of function. Here we have generated a resource to analyse chemokine gene (ligand and receptor) expression across different species, tissues and diseases; revealing complex expression patterns whereby multiple chemokine ligands that mediate recruitment of the same leukocyte type are expressed in the same context, e.g. the CXCR3 ligands CXCL9, 10 and 11. We use biophysical approaches to show that CXCL9, 10 and 11 have very different interactions with extracellular matrix glycosaminoglycans (GAGs) which is exacerbated by specific GAG sulphation. Finally, in vivo approaches demonstrate that GAG-binding is critical for CXCL9 driven recruitment of specific T cell subsets (e.g. CD4+) but not others (e.g. CD8+), independent of CXCR3 expression. Our data demonstrate that chemokine expression is complex and that multiple ligands are likely needed for robust leukocyte recruitment across tissues and diseases. We also demonstrate that ECM GAGs facilitate decoding of these complex chemokine signals so that they are either primarily presented on GAG-coated cell surfaces or remain more soluble. Our findings represent a new mechanistic understanding of chemokine mediated immune cell recruitment and identify novel avenues to target specific chemokines during inflammatory disease.

ImportanceCharacterizing whether genetic variants for psychiatric outcomes operate via specific versus general pathways provides more informative measures of genetic risk, and, potentially, allows us to design more targeted prevention and interventions.ObjectiveEmploy multivariate methods to tease apart variants associated with problematic alcohol use through either general or specific pathways and compare results to standard univariate genetic analysis of problematic alcohol use.DesignWe compared results from a univariate genome wide association study (GWAS) of problematic alcohol use to those from a previous multivariate GWAS of externalizing phenotypes. We identified genetic variants associated with problematic alcohol use through a broad liability to externalizing, and those that remain after removing shared variance with externalizing. We compared these results across SNP overlap, bioannotations, genetic correlations, and polygenic scores.SettingWe included GWAS summary statistics from existing GWAS, and two US based hold out samples: The National Longitudinal Study of Adolescent to Adult Health (Add Health) and the Collaborative Study on the Genetics of Alcoholism (COGA).ParticipantsPublicly available GWAS of externalizing behaviors and participants in Add Health (N=5,107) and COGA (N=7,483), limited to individuals of European ancestries.Exposure(s)N/AMain Outcome(s) and Measure(s)Outcomes included problematic alcohol use (ALCP-O), shared risk for externalizing (EXT), and problematic alcohol use-specific risk (ALCP-S) for the GWASs; a preregistered list of 99 available phenotypes for genetic correlations; and substance use, substance use disorder criteria, and alcohol misuse in the polygenic score analyses.ResultsThe analysis differentiated SNPs operating through common versus specific risk pathways. While ALCP-O was associated with multiple phenotypes, ALCP-S was predominantly associated with alcohol use and other forms of psychopathology. Polygenic scores for ALCP-O were associated with a variety of other forms of substance use and substance use disorders, polygenic scores for ALCP-S were only associated with alcohol phenotypes. Polygenic scores for both ALCP-S and EXT show differential patterns of associations with alcohol misuse across development.Conclusions and RelevanceFocusing on the differential impacts of shared and specific risk can better characterize pathways of risk for alcohol use disorders. Multivariate methods can be a useful tool for studying many psychiatric conditions. Parsing risk pathways will become increasingly relevant as genetic information is incorporated into clinical practice for psychiatric outcomes.

Treatment resistant depression (TRD), often defined by absence of symptomatic remission following at least two adequate treatment trials, occurs in roughly a third of all individuals with major depressive disorder (MDD). Prior work has suggested a significant common variant genetic component of liability to TRD, with heritability estimates of 8% when comparing to non-treatment resistant MDD. Despite this evidence of heritability, no replicated genetic loci have been identified and the genetic architecture of TRD remains unclear. A key barrier to this work has been the paucity of adequately powered cohorts for investigation, largely because of the challenge in prospectively investigating this phenotype. Using electroconvulsive therapy (ECT) as a surrogate for TRD, we applied standard machine learning methods to electronic health record (EHR) data to derive predicted probabilities of receiving ECT. We applied these probabilities as a quantitative trait in a genome-wide association study (GWAS) over 154,433 genotyped patients across four large biobanks. With this approach, we demonstrate heritability ranging from 2% to 4.2% and significant genetic overlap with cognition, attention deficit hyperactivity disorder, schizophrenia, alcohol and smoking traits and body mass index. We identify two genome-wide significant loci, both previously implicated in metabolic traits, suggesting shared biology and potential pharmacological implications. This work provides support for the utility of estimation of disease probability for genomic investigation and provides insights into the genetic architecture and biology of TRD.

Leukocyte recruitment from the vasculature into tissues is a crucial component of the immune system, but is also key to inflammatory disease. Chemokines are central to this process but have yet to be therapeutically targeted during inflammation, due to a lack of mechanistic understanding. Specifically, CXCL4 (PF4) has no established receptor that explains its function. Here we use biophysical, in vitro and in vivo techniques to determine the mechanism underlying CXCL4 mediated leukocyte recruitment. We demonstrate that CXCL4 binds to glycosaminoglycan (GAG) sugars within the endothelial extracellular matrix resulting in increased vascular permeability and non-specific recruitment of a range of leukocytes. Furthermore, GAG sulphation confers selectivity onto chemokine localisation. These findings represent a new understanding of chemokine biology, providing novel mechanisms for future therapeutic targeting.One sentence summaryCXCL4 binds to extracellular matrix proteoglycans resulting in increased vascular permeability and recruitment of a wide range of different leukocytes via a non-canonical mechanism.

Graphical abstract, SARS-CoV-2 has rapidly spread throughout the world’s population since its initial discovery in 2019. The virus infects cells via a glycosylated spike protein located on its surface. The protein primarily binds to the angiotensin-converting enzyme-2 (ACE2) receptor, using glycosaminoglycans (GAGs) as co-receptors. Here, we performed bioinformatics and molecular dynamics simulations of the spike protein to investigate the existence of additional GAG binding sites on the receptor-binding domain (RBD), separate from previously reported heparin-binding sites. A putative GAG binding site in the N-terminal domain (NTD) of the protein was identified, encompassing residues 245–246. We hypothesized that GAGs of a sufficient length might bridge the gap between this site and the PRRARS furin cleavage site, including the mutation S247R. Docking studies using GlycoTorch Vina and subsequent MD simulations of the spike trimer in the presence of dodecasaccharides of the GAGs heparin and heparan sulfate supported this possibility. The heparan sulfate chain bridged the gap, binding the furin cleavage site and S247R. In contrast, the heparin chain bound the furin cleavage site and surrounding glycosylation structures, but not S247R. These findings identify a site in the spike protein that favors heparan sulfate binding that may be particularly pertinent for a better understanding of the recent UK and South African strains. This will also assist in future targeted therapy programs that could include repurposing clinical heparan sulfate mimetics.

Substance use disorders (SUDs) incur serious social and personal costs. Risk for SUDs is complex, ranging from social conditions to individual genetic variation. We examined whether models that include a clinical/environmental risk index (CERI) and polygenic scores (PGS) are able to identify individuals at increased risk of SUD in young adulthood across four longitudinal cohorts for a combined sample of N = 15,134. Our analyses included participants of European (NEUR = 12,659) and African (NAFR = 2,475) ancestries. SUD outcomes included: 1) alcohol dependence, 2) nicotine dependence; 3) drug dependence, and 4) any substance dependence. In the models containing the PGS and CERI, the CERI was associated with all three outcomes (ORs = 1.37 – 1.67). PGS for problematic alcohol use, externalizing, and smoking quantity were associated with alcohol dependence, drug dependence, and nicotine dependence, respectively (OR = 1.11 – 1.33). PGS for problematic alcohol use and externalizing were also associated with any substance dependence (ORs = 1.09 – 1.18). The full model explained 6% - 13% of the variance in SUDs. Those in the top 10% of CERI and PGS had relative risk ratios of 3.86 - 8.04 for each SUD relative to the bottom 90%. Overall, the combined measures of clinical, environmental, and genetic risk demonstrated modest ability to distinguish between affected and unaffected individuals in young adulthood. PGS were significant but added little in addition to the clinical/environmental risk index. Results from our analysis demonstrate there is still considerable work to be done before tools such as these are ready for clinical applications.

Heparan sulfate (HS) is a cell surface polysaccharide recently identified as a coreceptor with the ACE2 protein for the S1 spike protein on SARS-CoV-2 virus, providing a tractable new therapeutic target. Clinically used heparins demonstrate an inhibitory activity but have an anticoagulant activity and are supply-limited, necessitating alternative solutions. Here, we show that synthetic HS mimetic pixatimod (PG545), a cancer drug candidate, binds and destabilizes the SARS-CoV-2 spike protein receptor binding domain and directly inhibits its binding to ACE2, consistent with molecular modeling identification of multiple molecular contacts and overlapping pixatimod and ACE2 binding sites. Assays with multiple clinical isolates of SARS-CoV-2 virus show that pixatimod potently inhibits the infection of monkey Vero E6 cells and physiologically relevant human bronchial epithelial cells at safe therapeutic concentrations. Pixatimod also retained broad potency against variants of concern (VOC) including B.1.1.7 (Alpha), B.1.351 (Beta), B.1.617.2 (Delta), and B.1.1.529 (Omicron). Furthermore, in a K18-hACE2 mouse model, pixatimod significantly reduced SARS-CoV-2 viral titers in the upper respiratory tract and virus-induced weight loss. This demonstration of potent anti-SARS-CoV-2 activity tolerant to emerging mutations establishes proof-of-concept for targeting the HS-Spike protein-ACE2 axis with synthetic HS mimetics and provides a strong rationale for clinical investigation of pixatimod as a potential multimodal therapeutic for COVID-19.

Low-affinity immunoglobulin gamma Fc region receptor III-A (FcγRIIIa) is a cell surface protein that belongs to a family of Fc receptors that facilitate the protective function of the immune system against pathogens. However, the role of FcγRIIIa in prostate cancer (PCa) progression remained unknown. In this study, we found that FcγRIIIa expression was present in PCa cells and its level was significantly higher in metastatic lesions than in primary tumors from the PCa cohort (P = 0.006). PCa patients with an elevated level of FcγRIIIa expression had poorer biochemical recurrence (BCR)-free survival compared with those with lower FcγRIIIa expression, suggesting that FcγRIIIa is of clinical importance in PCa. We demonstrated that overexpression of FcγRIIIa increased the proliferative ability of PCa cell line C4-2 cells, which was accompanied by the upregulation of androgen receptor (AR) and phosphatidylinositol-4-phosphate 5-kinase alpha (PIP5Kα), which are the key players in controlling PCa progression. Conversely, targeted inhibition of FcγRIIIa via siRNA-mediated knockdown or using its inhibitory antibody suppressed growth of xenograft PC-3 and PC-3M prostate tumors and reduced distant metastasis in xenograft mouse models. We further showed that elevated expression of AR enhanced FcγRIIIa expression, whereas inhibition of AR activity using enzalutamide led to a significant downregulation of FcγRIIIa protein expression. Similarly, inhibition of PIP5K1α decreased FcγRIIIa expression in PCa cells. FcγRIIIa physically interacted with PIP5K1α and AR via formation of protein–protein complexes, suggesting that FcγRIIIa is functionally associated with AR and PIP5K1α in PCa cells. Our study identified FcγRIIIa as an important factor in promoting PCa growth and invasion. Further, the elevated activation of FcγRIII and AR and PIP5K1α pathways may cooperatively promote PCa growth and invasion. Thus, FcγRIIIa may serve as a potential new target for improved treatment of metastatic and castration-resistant PCa.

We conducted genome-wide association studies (GWAS) of relative intake from the macronutrients fat, protein, carbohydrates, and sugar in over 235,000 individuals of European ancestries. We identified 21 unique, approximately independent lead SNPs. Fourteen lead SNPs are uniquely associated with one macronutrient at genome-wide significance (P < 5 × 10-8), while five of the 21 lead SNPs reach suggestive significance (P < 1 × 10-5) for at least one other macronutrient. While the phenotypes are genetically correlated, each phenotype carries a partially unique genetic architecture. Relative protein intake exhibits the strongest relationships with poor health, including positive genetic associations with obesity, type 2 diabetes, and heart disease (rg ≈ 0.15-0.5). In contrast, relative carbohydrate and sugar intake have negative genetic correlations with waist circumference, waist-hip ratio, and neighborhood deprivation (|rg| ≈ 0.1-0.3) and positive genetic correlations with physical activity (rg ≈ 0.1 and 0.2). Relative fat intake has no consistent pattern of genetic correlations with poor health but has a negative genetic correlation with educational attainment (rg ≈-0.1). Although our analyses do not allow us to draw causal conclusions, we find no evidence of negative health consequences associated with relative carbohydrate, sugar, or fat intake. However, our results are consistent with the hypothesis that relative protein intake plays a role in the etiology of metabolic dysfunction. This research was carried out under the auspices of the Social Science Genetic Association Consortium (SSGAC, https://www.thessgac.org/). The research has also been conducted using the UK Biobank Resource under Application Number 11425. The study was supported by funding from the Ragnar Söderberg Foundation (E9/11 and E42/15), the Swedish Research Council (421-2013-1061), The Jan Wallander and Tom Hedelius Foundation, an ERC Consolidator Grant to Philipp Koellinger (647648 EdGe), the Pershing Square Fund of the Foundations of Human Behavior, The Open Philanthropy Project (2016-152872, 010623-00001), and the NIA/NIH through grants P01-AG005842, P01-AG005842-20S2, P30-AG012810, and T32-AG000186-23 to NBER, and R01-AG042568-02 and R56-AG042568-04 to the University of Southern California. CCC was supported by the Intramural Research Program of the NIH/NIDDK and thanks Kevin Hall for informative discussions. PME was funded by Nestlé Nutrition. We thank the DietGen and CHARGE consortia for sharing diet-composition GWAS summary statistics, and we thank 23andMe, Inc., for sharing physical activity GWAS summary statistics. A full list of acknowledgements is provided in Supplementary Information 13.

The SARS-CoV-2 pandemic has caused a significant number of fatalities and worldwide disruption. To identify drugs to repurpose to treat SARS-CoV-2 infections, we established a screen to measure dimerization of ACE2, the primary receptor for the virus. This screen identified fenofibric acid, the active metabolite of fenofibrate. Fenofibric acid also destabilized the receptor binding domain (RBD) of the viral spike protein and inhibited RBD binding to ACE2 in ELISA and whole cell binding assays. Fenofibrate and fenofibric acid were tested by two independent laboratories measuring infection of cultured Vero cells using two different SARS-CoV-2 isolates. In both settings at drug concentrations which are clinically achievable, fenofibrate and fenofibric acid reduced viral infection by up to 70%. Together with its extensive history of clinical use and its relatively good safety profile, these studies identify fenofibrate as a potential therapeutic agent requiring urgent clinical evaluation to treat SARS-CoV-2 infection.TeaserThe approved drug fenofibrate inhibits infection by SARS-COV-2

Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is rapidly growing. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs’ prediction accuracies, we constructed them using genome-wide association studies—some of which are novel—from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the “additive SNP factor.” Regressions in which the true regressor is the additive SNP factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available.

Background and Purpose: Currently, there are no licensed vaccines and limited antivirals for the treatment of COVID-19. Heparin (delivered systemically) is currently used to treat anticoagulant anomalies in COVID-19 patients. Additionally, in the United Kingdom, Brazil and Australia, nebulised unfractionated heparin (UFH) is being trialled in COVID-19 patients as a potential treatment. A systematic comparison of the potential antiviral effect of various heparin preparations on live wild type SARS-CoV-2, in vitro, is needed. Experimental Approach: Seven different heparin preparations including UFH and low MW heparins (LMWH) of porcine or bovine origin were screened for antiviral activity against live SARS-CoV-2 (Australia/VIC01/2020) using a plaque inhibition assay with Vero E6 cells. Interaction of heparin with spike protein RBD was studied using differential scanning fluorimetry and the inhibition of RBD binding to human ACE2 protein using elisa assays was examined. Key Results: All the UFH preparations had potent antiviral effects, with IC50 values ranging between 25 and 41 μg·ml−1, whereas LMWHs were less inhibitory by ~150-fold (IC50 range 3.4–7.8 mg·ml−1). Mechanistically, we observed that heparin binds and destabilizes the RBD protein and furthermore, we show heparin directly inhibits the binding of RBD to the human ACE2 protein receptor. Conclusion and Implications: This comparison of clinically relevant heparins shows that UFH has significantly stronger SARS-CoV-2 antiviral activity compared to LMWHs. UFH acts to directly inhibit binding of spike protein to the human ACE2 protein receptor. Overall, the data strongly support further clinical investigation of UFH as a potential treatment for patients with COVID-19.

Behaviors and disorders related to self-regulation, such as substance use, antisocial conduct, and ADHD, are collectively referred to asexternalizingand have a shared genetic liability. We applied a multivariate approach that leverages genetic correlations among externalizing traits for genome-wide association analyses. By pooling data from ~1.5 million people, our approach is statistically more powerful than single-trait analyses and identifies more than 500 genetic loci. The identified loci were enriched for genes expressed in the brain and related to nervous system development. A polygenic score constructed from our results captures variation in a broad range of behavioral and medical outcomes that were not part of our genome-wide analyses, including traits that until now lacked well-performing polygenic scores, such as opioid use disorder, suicide, HIV infections, criminal convictions, and unemployment. Our findings are consistent with the idea that persistent difficulties in self-regulation can be conceptualized as a neurodevelopmental condition.

Cancer cells facilitate growth and metastasis by using multiple signals from the cancer-associated microenvironment. However, it remains poorly understood whether prostate cancer (PCa) cells may recruit and utilize bone marrow cells for their growth and survival. Furthermore, the regulatory mechanisms underlying interactions between PCa cells and bone marrow cells are obscure. In this study, we isolated bone marrow cells that mainly constituted populations that were positive for CD11b and Gr1 antigens from xenograft PC-3 tumor tissues from athymic nu/nu mice. We found that the tumor-infiltrated cells alone were unable to form tumor spheroids, even with increased amounts and time. By contrast, the tumor-infiltrated cells together with PCa cells formed large numbers of tumor spheroids compared with PCa cells alone. We further utilized xenograft athymic nu/nu mice bearing bone metastatic lesions. We demonstrated that PCa cells were unable to survive and give rise to colony-forming units (CFUs) in media that were used for hematopoietic cell colony-formation unit (CFU) assays. By contrast, PC-3M cells survived when bone marrow cells were present and gave rise to CFUs. Our results showed that PCa cells required bone marrow cells to support their growth and survival and establish bone metastasis in the host environment. We showed that PCa cells that were treated with either siRNA for PIP5K1&alpha, or its specific inhibitor, ISA-2011B, were unable to survive and produce tumor spheroids, together with bone marrow cells. Given that the elevated expression of PIP5K1&alpha, was specific for PCa cells and was associated with the induced expression of VEGF receptor 2 in PCa cells, our findings suggest that cancer cells may utilize PIP5K1&alpha, mediated receptor signaling to recruit growth factors and ligands from the bone marrow-derived cells. Taken together, our study suggests a new mechanism that enables PCa cells to gain proliferative and invasive advantages within their associated host microenvironment. Therapeutic interventions using PIP5K1&alpha, inhibitors may not only inhibit tumor invasion and metastasis but also enhance the host immune system.

Heparan sulfate and heparin are highly acidic polysaccharides with a linear sequence, consisting of alternating glucosamine and hexuronic acid building blocks. The identity of hexuronic acid units shows a variability along their sequence, as D-glucuronic acid and its C5 epimer, L-iduronic acid can both occur. The resulting backbone diversity represents a major challenge for an unambiguous structural assignment by mass spectrometry-based techniques. Here, we employ cryogenic infrared spectroscopy on mass-selected ions to overcome this challenge and distinguish isomeric heparan sulfate tetrasaccharides that differ only in the configuration of their hexuronic acid building blocks. High-resolution infrared spectra of a systematic set of synthetic heparan sulfate stereoisomers were recorded in the fingerprint region from 1000 to 1800 cm-1 . The experiments reveal a characteristic combination of spectral features for each of the four diastereomers studied, and imply structural modularity in the vibrational fingerprints. Strong spectrum-structure correlations were found and rationalized by state-of-the-art quantum chemical calculations. The findings demonstrate the potential of cryogenic infrared spectroscopy to extend the mass spectrometry-based toolkit for the sequencing of heparan sulfate and structurally related biomolecules. Cited

SummaryHeparan sulfate (HS) is a cell surface polysaccharide recently identified as a co-receptor with the ACE2 protein for recognition of the S1 spike protein on SARS-CoV-2 virus, providing a tractable new target for therapeutic intervention. Clinically-used heparins demonstrate inhibitory activity, but world supplies are limited, necessitating alternative solutions. Synthetic HS mimetic pixatimod is a drug candidate for cancer with immunomodulatory and heparanase-inhibiting properties. Here we show that pixatimod binds to and destabilizes the SARS-CoV-2 spike protein receptor binding domain (S1-RBD), and directly inhibits its binding to human ACE2, consistent with molecular modelling identification of multiple molecular contacts and overlapping pixatimod and ACE2 binding sites. Assays with multiple clinical isolates of live SARS-CoV-2 virus show that pixatimod potently inhibits infection of monkey Vero E6 and human bronchial epithelial cells at concentrations within its safe therapeutic dose range. Furthermore, in a K18-hACE2 mouse model pixatimod demonstrates that pixatimod markedly attenuates SARS-CoV-2 viral titer and COVID-19-like symptoms. This demonstration of potent anti-SARS-CoV-2 activity establishes proof-of-concept for targeting the HS-Spike protein-ACE2 axis with synthetic HS mimetics. Together with other known activities of pixatimod our data provides a strong rationale for its clinical investigation as a potential multimodal therapeutic to address the COVID-19 pandemic.

Widespread genetic testing for diseases may cause adverse selection, escalating premiums, or discrimination in various insurance markets. Here, without systematically informing study participants of their genetic predisposition, we estimate to what extent genetic data are informative about differences in longevity, health expectations, and economic behavior. We compute measures of genetic liability (polygenic scores) for 27 common diseases and mortality risks in 9,272 participants of the Health and Retirement Study (HRS). Survival analysis suggests that the highest decile of cumulative genetic risk can distinguish a median lifespan up to 4.5 years shorter, a difference that is similar to or larger than that distinguished by conventional actuarial risk factors, including sex. Furthermore, greater genetic liability is associated with less long-term care insurance, among other economic behaviors. We conclude that the rapid developments in genetic epidemiology pose new challenges for regulating consumer genetics and insurance markets, requiring urgent attention from policymakers.

We develop a polygenic index for individual income and examine random differences in this index with lifetime outcomes in a sample of ~35,000 biological siblings. We find that genetic fortune for higher income causes greater socio-economic status and better health, partly via intervenable environmental pathways such as education. The positive returns to schooling remain substantial even after controlling for now observable genetic confounds. Our findings illustrate that inequalities in education, income, and health are partly due the outcomes of a genetic lottery. However, the consequences of different genetic endowments are malleable, for example via policies that target education.

A positive relationship between brain volume and intelligence has been suspected since the 19th century, and empirical studies seem to support this hypothesis. However, this claim is controversial because of concerns about publication bias and the lack of systematic control for critical confounding factors (e.g., height, population structure). We conducted a preregistered study of the relationship between brain volume and cognitive performance using a new sample of adults from the United Kingdom that is about 70% larger than the combined samples of all previous investigations on this subject ( N = 13,608). Our analyses systematically controlled for sex, age, height, socioeconomic status, and population structure, and our analyses were free of publication bias. We found a robust association between total brain volume and fluid intelligence ( r = .19), which is consistent with previous findings in the literature after controlling for measurement quality of intelligence in our data. We also found a positive relationship between total brain volume and educational attainment ( r = .12). These relationships were mainly driven by gray matter (rather than white matter or fluid volume), and effect sizes were similar for both sexes and across age groups.