StemCell Therapy

INTRODUCTION

The spread of antibiotic resistance is outpacing the development of new antibiotics. On average, new antibiotics cost upward of $500 million USD and take 10 years to develop, only to have widespread resistance appear in less than 3 years (1, 2). Horizontal gene transfer (HGT) is often implicated in this rapid decline in efficacy, mobilizing reservoirs of resistance long established in the environment by natural antibiotic producers (3). In this way, targeting mobilization to restore antibiotic efficacy may be more successful than trying to eliminate resistance outright (4, 5).

Conjugation, the direct transfer of DNA from a donor to a recipient, is considered the HGT mechanism most responsible for mobilizing resistance among Enterobacteriaceae (2, 6). Conjugative plasmids are exchanged across broad host ranges, harbor resistance to virtually all antibiotics, and may be maintained in hosts with little cost, or in spite of costs, to fitness (7, 8). Furthermore, resistance genes tend to cluster on plasmids, allowing acquisition of multidrug resistance in a single transfer and making antibiotic selection for one now select for all (2).

Modulating conjugation to curtail the spread and maintenance of resistance requires an understanding of the environmental and genetic factors underlying plasmid mobility. Multiple factors have been proposed, including nutrient levels (9, 10), cell-cell signaling (1113), and even antibiotics (11, 1417), potentially accelerating the spread of resistance. Entire environments (e.g., animals, sewage, and biofilms) have also been labeled conjugative hotspots, uniting compatible donors, recipients, and plasmids under conditions that encourage HGT (6). However, growth dynamics are often coupled with conjugation modulation, making conjugation-specific effects difficult to interpret (Fig. 1A). For instance, in contrast to the prevailing conclusion that antibiotics promote conjugation, a recent study found no notable antibiotic effects when controlling for growth dynamics (9). Together with limited strain diversity and low-resolution genomic analysis, this ambiguity in conjugation quantification leaves the determinants of plasmid mobility in question.

(A) The influence of environmental and genetic factors on conjugation is confounded by growth dynamics between donor (red), recipient (blue), and transconjugant (purple) populations. By decoupling conjugation modulation from growth dynamics, we can identify and use the determinants of plasmid mobility to fight antibiotic resistance. (B) Assembling a library of natural isolates with quantifiable rates of conjugation is a substantial undertaking. Starting from a library of 219 clinical E. coli pathogens from patient bloodstream infections, we screened for the ability to transfer -lactam resistance commonly found on plasmids native to the Enterobacteriaceae family. Approximately 25% of the carbenicillin-resistant (CarbR) isolates exhibited detectable transfer to chromosomally kanamycin (KanR) or chloramphenicol (CmR)resistant recipients. These and seven extended spectrum -lactamase (ESBL) donors were subsequently used to examine environmental and genetic determinants of plasmid mobility. (C) The diversity present in the E. coli pathogen library is maintained through conjugation screening. A phylogenetic tree of the library was constructed from 200 genome assemblies (BioProject accession nos. PRJNA290784 and PRJNA551684) to reveal the breadth of our analysis throughout each phase of screening. Genome assemblies for the remaining 19 isolates were either unavailable or of insufficient quality. E. coli strain EC958 (GenBank accession no. HG941718.1) was used as a reference genome for alignment. Isolates are color labeled by their final phase. Major multilocus sequence types (5 isolates in common) present in the library are highlighted in gray.

To address these issues, we carried out the largest-scale analysis of conjugation phenotype paired with genotype in clinical Escherichia coli pathogens known for plasmid-borne multidrug resistance. In doing so, we introduce a new method for quantifying conjugation phenotype that increases throughput and sensitivity while reducing ambiguity due to growth dynamics. In general, we find that antibiotics exert little to no effect on conjugation efficiency, with one exception displaying significant promotion in the presence of macrolides and chloramphenicol (Cm). Conversely, conjugation efficiency strongly correlates with plasmid maintenance as indicated by the incompatibility (Inc) group. As our understanding of the environmental and genetic determinants of conjugation efficiency improves, modulation of HGT may become an important new tool for the fight against antibiotic resistance and control of bacterial evolution at large.

To study conjugation modulation, we focus on a library of 219 E. coli isolates collected from patient bloodstream infections at Duke University Hospital over 2002 to 2014. The Enterobacteriaceae family of Gram-negative bacteria serves as an ideal model to study HGT. Worldwide, it causes hundreds of millions of infections per year and harbors plasmids bearing multidrug resistance, most notably extended-spectrum -lactamases (ESBLs) that cleave the most widely used class of antibiotics (18). Of these 219 isolates, 197 were chosen at random from the Duke Bloodstream Infection Biorepository (BSIB), and the remaining 22 were chosen for multidrug resistance.

We first screened the isolates for kanamycin (Kan) and Cm susceptibility to contrast with established MG1655 E. coli plasmid recipients with Kan or Cm resistance (Fig. 1B). All but 18 (8%) isolates were susceptible to at least one antibiotic, with 69 (32% total, 25% random) exhibiting resistance to Kan (50 g/ml) and 31 (14% total, 13% random) exhibiting resistance to Cm (50 g/ml). With Kan or Cm susceptibility established for each isolate, we screened for the ability to transfer -lactam resistance to susceptible recipient E. coli. Of the Kan- or Cm-susceptible isolates, 143 were found to be resistant to carbenicillin (100 g/ml; Carb). Clinical data from the BSIB indicating susceptibility to -lactam antibiotics other than Carb were used to infer Carb susceptibility for 51 isolates. The 143 Carb-resistant isolates were then tested for the ability to generate Carb- and Cm/Kan-resistant transconjugants under dual-antibiotic selection. Subsequent rounds of dilution and repeat selection were performed to ensure that any growth under dual selection was from transconjugants, instead of donors or recipients. Of the 143 Carb-resistant isolates tested, 35 (24% total, 25% random) were capable of producing Carb- and Cm/Kan-resistant transconjugants under the experimental conditions. Phylogenetic analysis revealed that much of the genetic diversity present in the starting library was maintained throughout screening (Fig. 1C). Only plasmid-free recipients were used to prevent incompatibility issues with unknown isolate plasmids and retrotransfer, which generates two transconjugants per conjugative pairing (19).

In the absence of growth dynamics, the conjugation efficiency (), or the rate constant of conjugation, can be determined as=T/(DRt)where D, R, and T, respectively, represent the densities of the donor, recipient, and transconjugant populations after a short incubation time (t) (9). This relationship can be disrupted via selection (e.g., media and antibiotics) that leads to differential growth or death (Fig. 1A). To avoid these confounding factors, we restrict growth during conjugation (fig. S1). Under these conditions, D and R can be readily determined from starting cultures, which poses no technical challenges.

Quantifying T after incubation traditionally relies on selective plating from mixed culture, which is error prone and tedious. Relatively small T and variation over multiple orders of magnitude make this more challenging, and restricting growth exacerbates these issues. To overcome these limitations, we developed a simple, yet robust method that allows transconjugant outgrowth for improved quantification. Consider the exponential growth of the transconjugant starting from an initial density of T0. T0 is uniquely related to , the time for the population to reach a set threshold TClnT0=lnTCwhere is the specific growth rate (Fig. 2A). With proper calibration, this relationship provides a high-throughput approach to quantify T0 by tracking bacterial growth via optical density (OD) in a plate reader. Experimentally, we establish a standard curve relating T0 and by growing transconjugant cultures from known starting densities. The standard curve is then used to determine T0 in samples subjected to the same growth conditions. For the same TC, the standard curves for different transconjugants (generated from different donors, but the same recipient) were highly similar to each other, reflecting a similar for these strains (fig. S2A). For the same transconjugant, we found quantification to be robust to the choice of OD thresholds that fall in the logarithmic growth phase (fig. S2B).

(A) The principle of time to threshold quantification. Consider the exponential growth of the transconjugant from an initial density T0 (top panel). The time () required for the population to reach a set threshold (TC) is uniquely determined by T0 and the specific growth rate (). This defines a log-linear relationship between T0 and : lnT0 = ln TC (bottom panel). (B) Quantification of T0 is complicated by the presence of donor and recipient cells. Top panel: Although strong antibiotic selection is applied against donor and recipient cells during transconjugant outgrowth, death is not instantaneous (i.e., conjugation may still occur). Bottom panel: Modeling reveals conjugation of variable efficiency () during outgrowth causes a deviation from the log-linear relationship. This effect is amplified with smaller T0, where transconjugants produced from outgrowth conjugationnot outgrowth alonemay comprise a sizeable proportion of the total transconjugant population. (C) Correcting for outgrowth conjugation. Top panel: The growth contribution from the transconjugant alone can be approximated by the difference (N) between the growth curves originating from the conjugation mixture (T0 > 0) and conjugation control (T0 = 0). Darker curves represent higher T0. Bottom panel: Using N, the log-linear relationship between T0 and is maintained even in the presence of conjugation during outgrowth. (D) Applying the time to threshold method to experimental data. T0, spanning six orders of magnitude, maintains a strong correlation (R2 > 0.99) with from a OD threshold. Darker curves represent higher T0.

The reliability of the standard curve depends on a critical assumption: All the growth originates from the transconjugants at time zero. This can be approximated by imposing strong double selection to suppress growth of donor and recipient cells during outgrowth. However, this suppression is not instantaneous, and new transconjugants can still be produced through conjugation (Fig. 2B).

To examine this contribution, we built a kinetic model that accounts for growth and conjugation dynamics during T outgrowth. In the absence of conjugation during outgrowth ( = 0), the model predicts a linear relationship between lnT0 and , similar to the case where exponential growth is assumed (Fig. 2A). At > 0, the correlation deviates from the log-linear relationship, where the same T0 would correspond to a smaller than what would be expected for = 0. A larger leads to a larger deviation. Under this condition, a given value of would lead to an overestimate of T0 if the log-linear relationship is directly used. However, we found that the deviation from outgrowth conjugation can be eliminated by subtracting the growth curve produced by parents mixed only at the start of outgrowth, yielding N value (Fig. 2C). The early phase of N provides an approximation of T0 growth in the absence of outgrowth conjugation, and simulated standard curves established using N do not significantly deviate from the log-linear correlation even in the presence of substantial outgrowth conjugation. Experimental results using OD to approximate N emulate the model, and displays a strong log-linear correlation with T0 over multiple orders of magnitude (Fig. 2D and fig. S2).

Together with seven previously identified ESBL donors (20), we measured the effect of antibiotics on conjugation in clinical E. coli pathogens (Fig. 3). We display the difference in time to threshold () between antibiotic and no-antibiotic control conditions to better reflect conjugation effects across isolates and minimize unnecessary data processing. The absolute magnitude of is the result of many variables that affect growth rate (e.g., conjugation and media conditions). Five antibiotics with different mechanisms of action were tested, covering inhibition of translation, DNA replication, and cell wall synthesis. Three sublethal concentrations were used, based on the 50% inhibitory concentrations (IC50) of the susceptible recipients, to capture concentration-dependent effects (9). Donor minimum inhibitory concentrations (MICs) and IC50s are shown in fig. S3A and table S3. We erred on the side of concentrations too low for the multidrug-resistant pathogens to ensure the survival of the recipients and subsequent transconjugants. Because of experimental constraints chosen to restrict background conjugation and growth dynamics, donors with conjugation efficiencies less than 1016 produced too few transconjugants for quantification.

The effect of five antibiotics on conjugation in clinical E. coli pathogens was assessed via the time to threshold method. Antibiotics of differing therapeutic mechanism were dosed in three concentrations (0.5, 1, and 2) based on 50% inhibitory concentrations for a MG1655 E. coli recipient standard. IC50 values for Carb, Cm, Kan, erythromycin (Ery), and Norf were as follows: 1.91, 1.92, 2.13, 20.20, and 0.05 g/ml. Displayed are averages of triplicate measurements SE, and normalized by subtracting the no-antibiotic control . Promotion of conjugation is indicated by < 0, while > 0 indicates inhibition. Only GN02766 displayed major modulation of conjugation when exposed to Ery (all concentrations, P < 0.01, Tukey post hoc test) and Cm (2 concentration, P < 0.05). Results from two separate GN02766 experiments are shown.

A global analysis of variance (ANOVA) revealed significant effects for both antibiotic isolate [F(94,1374) = 4.96, P < 0.0001] and antibiotic concentration isolate [F(282,999) = 2.28, P < 0.0001] interactions, prompting post hoc testing via Tukey post hoc test. We find that the majority of pathogen donors display little to no significant antibiotic modulation of conjugation, and where there is statistically significant , it corresponds to approximately less than a fivefold change in transconjugants. In addition, no correlation was seen between donor IC50 and (fig. S3B). However, isolate GN02766 exhibited a drastic increase in conjugation when exposed to certain antibiotics. GN02766 produced ~3.5-fold more transconjugants in the presence of 2 IC50 of Cm [t(7.5) = 3.7, P < 0.05, Welchs modified t test], while all concentrations of erythromycin (Ery) yielded up to 31-fold more transconjugants [t(8.5) = 18, P < 0.0001] (fig. S4A). Of the five antibiotics tested, only Cm and Ery are considered bacteriostatic and both inhibit translation via the 50S ribosomal subunit, suggesting a common mechanism. Kan, which also inhibits translation, did not have an effect, possibly due to bactericidal effects, targeting the 30S ribosomal subunit, or concentration dependence.

To distinguish a general bacteriostatic effect from a macrolide- and phenicol-specific mechanism, we retested GN02766 with azithromycin (Az) and sulfamethoxazole (Sm). Solvent- and donor-recipient strain interactions were also tested and ruled out (fig. S4B). Also a macrolide, Az differs from Ery in its 15-membered ring structure. Sm is bacteriostatic, but differs from Cm and Ery in mechanism of action by inhibiting folate and, subsequently, DNA synthesis. Az produced a virtually identical ~31-fold increase in transconjugants to Ery [t(8.4) = 15.5, P < 0.0001], while Sm produced no significant effect [t(2) = 0.04, P = 0.76] (fig. S4C). Together, these results suggest macrolide- and, to a lesser degree, phenicol-specific promotion of conjugation in GN02766.

To reveal plasmid genotype and identify features associated with antibiotic modulation and conjugation efficiency, we performed whole-genome sequencing on 19 isolates of varying conjugative phenotypes (BioProject no. PRJNA551684). For large plasmids, standard plasmid purification kits yielded poor results, and sequence assembly from short reads has been shown to be difficult. Instead, we used long-read sequencing from Pacific Biosciences (PacBio) capable of accurately discerning mobile elements from the chromosome. As predicted from empirical findings, plasmid-borne -lactamases were detected in each isolate. A summary of sequencing results can be found in table S2, complete with plasmid number, size, replicon (Inc), mobility relaxase (MOB), and identified resistances present in each isolate.

GN02766 was found to harbor two plasmids: p2766-1, a 137-kb plasmid identified as IncFIB/IncFII/Col156, and a 29-kb plasmid without designation. Without identifiable MOB or transfer (tra) machinery on the 29-kb plasmid, only p2766-1 appears to be mobile and carries a -lactamase, blaSHV, relevant to our conjugation assay. By comparing the plasmids of isolates displaying no antibiotic modulation to p2766-1, we can identify unique features that may give insight into how macrolide and phenicol antibiotics promote conjugation. Here, we show 24 of these plasmids that bear the greatest similarity to p2766-1 (nucleotide identity, 70%), although much diversity remains (Fig. 4). Much of the tra region for conjugation was conserved across plasmids with traJ, a key activator of tra gene expression, as a notable exception. We also aligned plasmid sequences to p168-1, the closest match to p2766-1 that displayed no antibiotic modulation, to see what common features p2766-1 might lack (fig. S5). No major commonalities among the majority of plasmids appear to be lost, suggesting the conjugation phenotype of p2766-1 may be gained instead.

Plasmid sequences from strains displaying no antibiotic modulation were aligned to p2766-1 via BLASTn and BLAST Ring Image Generator (BRIG) (47). Nucleotide identity 70% is indicated by a band colored according to the Inc group, with darker shading corresponding to higher sequence match. Blank regions indicate <70% nucleotide identity. Inner GC content plots, size map, and outer coding sequences (CDSs) are for p2766-1. Antibiotic resistance genes are highlighted in red, and transposon repeat region in gray. Notable features include five-plasmid maintenance systems (pemKI, relBE, hok-sok, srnBC, and parAB), a mobilized enterotoxin and colicin J receptor operon (cjrABC-senB), and five consecutive tnpA transposon repeats carrying blaSHV.

There are several features of note on p2766-1: five plasmid maintenance systems, a mobilized enterotoxin and colicin J receptor operon, and five consecutive tnpA transposon repeats. Of the five plasmid maintenance systems, four function through addiction (pemKI, relBE, hok-sok, and srnBC), with toxins that kill daughter cells if they lack the plasmid-encoded antitoxin. For comparison, ESBL-producing strains only have 2.38 addiction systems on average with a range of 0 to 6 (21). The fifth maintenance system, parAB, aids in plasmid segregation (22). In common with two other IncFIB/FII/Col156 plasmids, p2766-1 also includes cjrABC and senB, which play important roles in E. coli pathogenesis (23). Unique to p2766-1 is a repetitive region of five consecutive tnpA transposon repeats, each bearing a deoR, blaSHV, recF, and truncated lacY gene, respectively, encoding a transcriptional repressor of sugar metabolism (24), ESBL, DNA recombination and repair protein, and -galactoside permease. This repetitive region is genuine as we see no decrease in sequence coverage indicative of assembly error (fig. S6). It is particularly interesting that deoR is implicated in limiting plasmid copy number (25), and recF facilitates recombination (26, 27) and mutagenesis (28). A closely related IS26-flanked blaSHV-5 amplicon was found to spread via recombination, not transposition, with up to 11 consecutive amplicons forming under high -lactam antibiotic stress (29).

The conjugation phenotypes we observed with clinical E. coli pathogens can arise from the plasmids, chromosomes, or both. To control for chromosomal effects, we used Kan-susceptible DA28102 transconjugants (denoted as strain #T) as donors to Kan-resistant fAYC002 recipients. With plasmids as the only differences between the transconjugants, any change in conjugation efficiency may therefore be attributable to the plasmids themselves. In this way, the macrolide promotion seen in GN02766 appears to be transferrable, with 0.5 and 1 IC50 of Ery significantly reducing (P < 0.0001 and P < 0.05, respectively, Tukey post hoc test) when 2766T is used as a donor (fig. S7A). No effect was seen with other DA28102 transconjugants, further supporting a p2766-1based mechanism (fig. S7B). Donor, recipient, and growth conditions likely play a role, however, as the degree of macrolide promotion was diminished in 2766T.

Compared to the maximum 31-fold change we saw with antibiotics, the baseline conjugation efficiencies of plasmids have the potential to differ over several orders of magnitude. Even relatively minor changes, such as growth stage or nutrient concentration, can cause orders of magnitude shifts in conjugation efficiency (9, 10, 30). Here, we focus on the origin of these more fundamental distinctions between conjugative plasmids. Plasmids are typically classified by two major systems: Inc group and MOB relaxase. Inc groups are based around plasmid maintenance (e.g., replication controls), where two plasmids of the same Inc group could not be stably maintained in the same cell (31). MOB relaxase typing, centered on the protein that nicks oriT to initiate conjugative transfer, was proposed to avoid problems in the Inc system, such as multiple replicons or maintenance systems that differ greatly in how they work (32).

MOB relaxases are thought to accurately represent tra machinery based on high congruence between their phylogenetic trees (32). Therefore, we wondered whether MOB relaxase might correlate with conjugation efficiency with the donor, recipient, and environment held constant. However, we find that Inc groups are more predictive of a plasmids conjugation efficiency than MOB relaxase [F(6,53) = 111.6, P < 0.0001 versus F(2,57) = 1.26, P = 0.291, respectively, ANOVA; Fig. 5). The strong correlation between the Inc group and conjugation efficiency remains when the 2766T outlier is removed [F(5,48) = 62.36, P < 0.0001]. Inc groupings were made at the most general level (i.e., IncF includes IncFI, FII, etc.) for plasmids carrying a -lactamase marker by which we measured conjugation. Given the apparent difference between the IncF/Col and IncF groups, we paid particular attention to mobilizable colicin plasmids that can transfer alongside the -lactamase (i.e., from GN05696 to 5696T and beyond). Further subdivisions in Inc groups and MOB types may yield greater resolution between conjugation efficiencies. Our results suggest that the factors involved in plasmid maintenance play a more significant role in its transfer than previously thought.

Plasmids carrying -lactamases from clinical E. coli pathogens were classified by Inc grouping and MOB relaxase families. To eliminate host effects, conjugation efficiencies were only measured using DA28102 transconjugants and the fAYC002 recipient. There is no significant difference among conjugation efficiencies when grouped by MOB relaxase (P = 0.291, ANOVA), whereas grouping by Inc is highly significant (P < 0.0001). Individual Inc groups that could not be distinguished from one another at P 0.05 are bracketed. Data were log transformed to normalize variation across orders of magnitude. Error bars represent SD from at least three replicates.

Drawing from the same fundamental principles as quantitative polymerase chain reaction, we developed time to threshold as a simple and effective new method for quantifying plasmid transfer rates in the absence of confounding growth dynamics. The time to threshold method improves upon agar plating in both throughput and sensitivity, returning transconjugant density in as little as 5 hours without the ambiguity from subjective colony counting. Amid conflicting reports for antibiotic modulation of conjugation (9, 11, 15, 16), we find little to no effect in this largest study of conjugation phenotype in clinical E. coli pathogens. The lack of a general antibiotic effect suggests that modulation, if present, arises from more specific interactions and highlights the potentially confounding impact of growth dynamics on conjugation quantification. Yet, even minor modulation of conjugation maintained over time may be sufficient to push plasmids above or below critical maintenance thresholds (7). As more conjugation inhibitors and promoters are discovered, we may begin to harness and incorporate these dynamics into prophylactic strategies that address the source of antibiotic resistance for many pathogens (5).

Isolate GN02766 is an exceptional case of conjugation modulation with the ability to spread antibiotic resistance faster in the presence of antibiotics. Through the time to threshold method, we detect up to 31-fold promotion of conjugation upon exposing GN02766 to macrolides or Cm, both of which target the 50S ribosomal subunit. We link macrolide promotion to a single IncFIB/FII/Col156 plasmid (i.e., p2766-1) bearing five plasmid maintenance systems and tnpA transposon repeats notably encoding deoR, blaSHV, and recF. Unique to p2766-1 and the conjugation promotion phenotype, deoR and recF are involved in suppressing copy number (25) and promoting recombination of plasmids, respectively. Translation inhibition from macrolides and Cm may relieve metabolic suppression from multiple copies of deoR and destabilizing oligomerization from recF (33), increasing p2766-1 copy number, tra gene expression, and, ultimately, conjugation efficiency. As the number of unique -lactamases continues to rise, the potential this transposon offers for blaSHV diversification through gene duplication is concerning (29). Both intra- and intercellularly mobile through recombination and antibiotic-promoted conjugation, this blaSHV transposon represents a threat for the evolution of antibiotic resistance and warrants further study.

Going beyond special cases, understanding the determinants underlying plasmid mobility is critical to predicting how they spread as vehicles of antibiotic resistance and HGT. By removing host and growth variables, we find that Inc groups based on plasmid maintenance appear predictive of conjugation efficiency, whereas MOB relaxase classification does not. This comes as a surprise given MOB relaxases more direct connection to conjugation (32). However, the dominance of MOBF and MOBP among E. coli isolates with detectable conjugation falls in stark contrast to the diversity of Inc groups, suggesting that MOB may dictate plasmid prevalence at a high level (31, 34). The apparent predictive power of incompatibility groups, as ubiquitous features of plasmids, could nevertheless hold considerable value for understanding gene flow through HGT networks, especially if it extends beyond E. coli and the plasmids studied herein. Knowing which plasmids and bacterial strains are most adept at mobilizing antibiotic resistance could better guide strategies to inhibit its spread and improve the life span of antibiotics (5, 7, 35).

A full description of all strains used in this study can be found in table S1.

Unless otherwise stated, donor or recipient strains were cultured at 37C in standard Luria-Bertani (LB; Miller) broth with shaking. Conjugation experiments were performed in M9 media containing casamino acid (2 mg/ml), thiamine (0.1 mg/ml), 2 mM MgSO4, 0.1 mM CaCl2, and 0.4% (w/v) glucose. We call this M9CA media in subsequent text. Rich LB or terrific broth (TB) was used to generate high cell density for subsequent conjugation in M9CA, which was used to control for growth dynamics. Shaking is applied for oxygenation, accurate OD measurements, and breaking up conjugation pili.

We screened the pathogen library for Carb, Cm, and Kan resistance to establish selection markers for conjugation quantification. The BSIB had previously collected disk diffusion data on -lactam antibiotics other than Carb, which we used to infer Carb susceptibility (CarbS) for 51 isolates. Isolates without prior data or with ambiguous resistance were inoculated in 1 ml of LB media in 96deep well plates, covered in a gas-permeable membrane, and grown overnight. Once grown, cultures were diluted 1000 into secondary 96-well plates with and without added antibiotic. Concentrations for antibiotics were as follows: Carb (100 g/ml), Cm (50 g/ml), and Kan (50 g/ml). Secondary plates were then grown for approximately 16 hours. At this point, the OD600 of each culture was measured in a plate reader. Antibiotic culture OD was normalized to corresponding no-antibiotic cultures. Resistance to each antibiotic was defined as 1% maximal growth.

The MIC, which we defined as the first antibiotic concentration to yield 10% or less of the no-antibiotic controls maximum OD600, and IC50 were also determined for plasmid donors tested for antibiotic effects on conjugation (fig. S3A and table S3). Plasmid donors were grown for 16 hours, diluted 100-fold into M9CA with antibiotic concentrations ranging from 0 to 64 g/ml, and then grown for 24 hours before taking endpoint OD600 measurements.

To find pathogens capable of conjugation, we screened Carb-resistant isolates for the ability to transfer -lactamase into Carb-susceptible recipients. Pathogen cultures were grown overnight in 1 ml of LB media with Carb (100 g/ml) in 96deep well plates covered in a gas-permeable membrane. Recipients were grown overnight in 3 ml of LB media with either Kan (50 g/ml) or Cm (70 g/ml). Pathogen isolates were then diluted 10 in LB media and regrown under similar conditions for 2 hours to enter exponential phase. Exponential-phase pathogen isolates and recipient were diluted 100 into 200 l of LB media and mixed 1:1 in a 96-well plate with dual antibiotic selection for transconjugants. Mixed cultures were then covered with 50 l of mineral oil and grown in a plate reader for 24 hours.

After 24 hours of exposure to transconjugant selection, mixed pathogen and recipient cultures that displayed growth were diluted 1000 into fresh LB media with antibiotic selection for transconjugants. These cultures were covered with 50 l of mineral oil and regrown in a plate reader for 24 hours. Cultures that repeated growth under high dilution and strong transconjugant selection were plated on agar also selecting for transconjugants. Individual colonies were isolated and grown overnight in 3 ml of LB media with transconjugant selection. These clonal transconjugant cultures were glycerol stocked for later analysis.

Donor E. coli strains were inoculated into 3 ml of TB media with Carb (100 g/ml) and grown for approximately 16 hours at 37C without shaking. An appropriate recipient MG1655 E. coli strain (CmR DA28102 or KanR fAYC002) with contrasting resistance markers was similarly grown, but with 50 g/ml selecting antibiotic and shaking. Following growth, donor cultures were diluted 10 and regrown for 2 hours to enter the exponential phase. Recipient and exponential-phase donor cultures were pelleted at 2000 relative centrifugal force (rcf) for 10 min at 25C and resuspended in M9CA media, equalizing OD600. At this point, aliquots of donor and recipient cultures were taken for quantification via agar plating and controls for outgrowth conjugation.

Donor and recipient cultures were then mixed in a 1:1 ratio for conjugation, distributed in 500-l volumes, and incubated for 1 hour at room temperature while being exposed to antibiotic or control test conditions. Antibiotic test conditions were applied in 0.5, 1, and 2 multiples of IC50s determined for the primary MG1655 E. coli recipient. IC50 values for Carb, Cm, Kan, Ery, and Norf were as follows: 1.91, 1.92, 2.13, 20.20, and 0.05 g/ml (9). These growth conditions were chosen to restrict growth during conjugation. After 1 hour, cell mixtures were vortexed to disrupt conjugation, pelleted, and resuspended in 500 l of M9CA. The separated donor and recipient conjugation control aliquots were then mixed to track outgrowth conjugation.

The experimental and outgrowth conjugation control curves must be displaced in time; otherwise, OD will not reach target OD600 thresholds. For this reason, all cell mixtures were diluted 150 in M9CA with dual antibiotic selection [Carb (100 g/ml), Cm (70 g/ml), and Kan (50 g/ml)] according to the donor-recipient pairs and then distributed three times in 150-l volumes onto a microtiter plate. Microtiter cultures were covered with 50 l of mineral oil to prevent evaporation and grown in a plate reader for 24 hours at 37C, shaking before each OD600 measurement. The 150 dilution was chosen on the basis of an estimated of ~1014 from previous growth-restricted E. coli measurements. For best results, T0 should be maximized while minimizing outgrowth . Outgrowth dilutions and temperature may need to be adjusted on a strain-by-strain basis to optimize separation between experimental and control curves. For E. coli, the most common plasmids belong to the IncF family. Therefore, to maximize T0 with the E. coli library, we used rich, buffered TB media for optimal conjugative pili formation and exponential-phase donor cultures for activation of F plasmid transfer machinery (30). Conversely, recipients are kept in stationary phase for decreased motility or cell wall modifications thought to improve pili tip searching (9, 36).

Transconjugant growth curves were passed through a moving average filter in MATLAB to reduce noise. Time to specified OD600 threshold was found via linear interpolation. OD600 thresholds were typically chosen between 0.03 and 0.05 in early exponential phase to reduce background noise, maintain the cell density:OD relationship, and lessen postantibiotic effects (37). Within this range, changes in OD threshold had little effect on the correlation between and T0 (fig. S2B).

Transconjugant cultures were grown following the time to threshold protocol to accurately recreate postconjugation outgrowth conditions. Once resuspended in M9 media with Carb (100 g/ml) and Cm (70 g/ml), the cultures were serially diluted by 107 and plated on LB agar to determine T0 via colony-forming units (CFUs). These dilutions were then covered in 50 l of mineral oil and grown in a plate reader as before for 24 hours. With known T0, we constructed standard curves for each transconjugant strain by calculating from growth curves at varied OD thresholds. We find that defined M9 with Carb (100 g/ml) and Cm (70 g/ml) antibiotic concentrations minimizes standard curve variation across strains.

Time to threshold results () were normalized by subtracting from the no-antibiotic control yielding a value. An outlier for both ESBL41 and ESBL168 was removed due to unambiguous plate reader and experimental error, respectively, to preserve data integrity. Triplicate measures were parsed with ANOVA in R to reveal significant global interactions among strain, antibiotic, and concentration variables. Tukey post hoc test was performed for significant interactions. All tests are two tailed, and all replicates are technical unless indicated otherwise.

We selected 19 strains for whole-genome sequencing covering a range of conjugation dynamics. Genomic DNA was extracted using the QIAGEN MagAttract highmolecular weight DNA kit (catalog ID: 67563) from cultures grown for 16 hours until a density of approximately 109 cells/ml. DNA libraries were prepared according to PacBios recommendations and sequenced using the PacBio Sequel system.

All samples were assembled and polished using the PacBio SMRT Analysis assembly pipeline (SMRTLink version 5.1.0.26412), which uses HGAP 4 for assembly and Quiver for assembly polishing. Default parameters were applied, with the following exceptions: genome length was set to 5 Mb, the aggressive option was set to true, no read quality filters were applied, and the default falcon configuration parameters were supplanted with the following parameters: pa_DBsplit_option = -x500 -s200; ovlp_DBsplit_option = -x500 -s200. For samples with initial suboptimal assemblies, seed coverage was modified to increase the proportion of corrected reads. Seed coverage was set to 60 for ESBI168, GNO2448, and GNO4540, and 35 for GNO2766.

For constructing the phylogenetic tree, single-nucleotide polymorphisms (SNPs) were identified from genome assemblies (BioProject accession nos. PRJNA290784 and PRJNA551684) following the North Arizona SNP Pipeline (NASP) pipeline (38) using E. coli strain EC958 (GenBank accession no. HG941718.1) as a reference. Duplicated regions of the reference genome, including repeat regions and multiple gene copies, were determined by aligning the reference sequence to itself using the NUCmer-3.23 (39). SNPs that fell within these duplicate regions were excluded from further analysis to avoid false SNP calls due to ambiguous read alignment. Each query genome assembly was aligned to the reference with NUCmer-3.23. The best SNPs in all genomes compared to the reference were concatenated in a matrix. Phylogenetic trees were inferred using the maximum likelihood (ML) method available in RAxML (40, 41). This returned the best-found tree from 100 replicates inferred using the general time-reversible substitution model.

For Inc typing, the plasmid sequences were queried against a locally downloaded version (retrieved 22 May 2018) of the PlasmidFinder database (http://www.genomicepidemiology.org), using the recommended percentage coverage threshold of 60% and a more stringent percentage identity and e-value thresholds of 90% and 0.00001, respectively (42). MOB typing was performed as previously described, and default value was chosen for all parameters (43). Specifically, the e-value threshold of each MOB type was chosen as follows: MOBC, 0.001; MOBF, 0.01; MOBH, 0.01; MOBP, 1; MOBQ, 0.0001; and MOBV, 0.01. In addition, 247,882 protein sequences were extracted from a National Center for Biotechnology Information dataset of 6952 complete Enterobacteriaceae plasmids (44) and were compiled into a database in combination with the sequences of 2423 antibiotic resistance proteins from ResFinder database (retrieved 22 May 2018) (http://www.genomicepidemiology.org) (45) as predicted by prodigal v2.6.3 (46). Genome assemblies were further annotated using Prokka v1.13 (47) against the above compiled protein database. For comparison, plasmids were aligned to one another via BRIG, displaying sequence matches with nucleotide identity 70% (48).

Transconjugant conjugation was performed similarly to the time to threshold protocol with a few exceptions. Transconjugant donors were grown with shaking as DA28102 displays poor growth otherwise and fAYC002 was the sole recipient used. Donor, recipient, and second-generation transconjugant cell densities were quantified through LB agar plating and CFU counting.

Acknowledgments: We thank D. Anderson for contributing clinical isolates. We also thank the Duke Center for Genomic and Computational Biology, Duke Sequencing core facility, Duke Bioinformatics core facility, and J. Modliszewski, N. Devos, and G. Alexander Jr. specifically for their support with whole-genome sequencing of the isolates. Funding: L.Y. acknowledges support from the NIH (R01GM098642 and R01AI125604), Army Research Office (W911NF-14-1-0490), and the David and Lucile Packard Foundation. J.H.B. acknowledges a graduate fellowship from the NSFs IBIEM Training Grant. Author contributions: J.H.B. designed and performed the modeling and experimental analyses, interpreted the results, and wrote the manuscript. A.D. developed the analysis pipeline for time to threshold measurements and assisted with conjugation experiments. L.C., W.S., and M.X. processed, annotated, and analyzed the whole-genome sequencing results. A.J.L. conceived the research and assisted with the manuscript revisions. J.T.T. and V.G.F.J. provided clinical isolates and assisted with manuscript revisions. L.Y. conceived the research and assisted with the modeling, experimental design, data analyses, and manuscript revisions. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Genomic sequence data are available from GenBank with BioProject accession no. PRJNA551684. Additional data related to this paper may be requested from the authors.

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Environmental and genetic determinants of plasmid mobility in pathogenic Escherichia coli - Science Advances

New UC Riverside research shows soybean oil not only leads to obesity and diabetes, but could also affect neurological conditions like autism, Alzheimers disease, anxiety, and depression.

Used for fast food frying, added to packaged foods, and fed to livestock, soybean oil is by far the most widely produced and consumed edible oil in the U.S., according to the U.S. Department of Agriculture. In all likelihood, it is not healthy for humans.

It certainly is not good for mice. The new study, published this month in the journal Endocrinology, compared mice fed three different diets high in fat: soybean oil, soybean oil modified to be low in linoleic acid, and coconut oil.

The same UC Riverside research team found in 2015 that soybean oil induces obesity, diabetes, insulin resistance, and fatty liver in mice. Then in a 2017 study, the same group learned that if soybean oil is engineered to be low in linoleic acid, it induces less obesity and insulin resistance.

However, in the study released this month, researchers did not find any difference between the modified and unmodified soybean oils effects on the brain. Specifically, the scientists found pronounced effects of the oil on the hypothalamus, where a number of critical processes take place.

The hypothalamus regulates body weight via your metabolism, maintains body temperature, is critical for reproduction and physical growth as well as your response to stress, said Margarita Curras-Collazo, a UC Riversideassociate professor of neuroscience and lead author on the study.

The team determined a number of genes in mice fed soybean oil were not functioning correctly. One such gene produces the love hormone, oxytocin. In soybean oil-fed mice, levels of oxytocin in the hypothalamus went down.

The research team discovered roughly 100 other genes also affected by the soybean oil diet. They believe this discovery could have ramifications not just for energy metabolism, but also for proper brain function and diseases such as autism or Parkinsons disease. However, it is important to note there is no proof the oil causes these diseases.

Additionally, the team notes the findings only apply to soybean oil not to other soy products or to other vegetable oils.

Do not throw out your tofu, soymilk, edamame, or soy sauce, said Frances Sladek, a UC Riverside toxicologist and professor of cell biology. Many soy products only contain small amounts of the oil, and large amounts of healthful compounds such as essential fatty acids and proteins.

A caveat for readers concerned about their most recent meal is that this study was conducted on mice, and mouse studies do not always translate to the same results in humans.

Also, this study utilized male mice. Because oxytocin is so important for maternal health and promotes mother-child bonding, similar studies need to be performed using female mice.

One additional note on this study the research team has not yet isolated which chemicals in the oil are responsible for the changes they found in the hypothalamus. But they have ruled out two candidates. It is not linoleic acid, since the modified oil also produced genetic disruptions; nor is it stigmasterol, a cholesterol-like chemical found naturally in soybean oil.

Identifying the compounds responsible for the negative effects is an important area for the teams future research.

This could help design healthier dietary oils in the future, said Poonamjot Deol, an assistant project scientist in Sladeks laboratory and first author on the study.

The dogma is that saturated fat is bad and unsaturated fat is good. Soybean oil is a polyunsaturated fat, but the idea that its good for you is just not proven, Sladek said.

Indeed, coconut oil, which contains saturated fats, produced very few changes in the hypothalamic genes.

If theres one message I want people to take away, its this: reduce consumption of soybean oil, Deol said about the most recent study.

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America's most widely consumed cooking oil causes genetic changes in the brain - University of California

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"Is my blood teal?"

It may sound like a surprising question for a doctor to hear - but this isn't just any patient. This is Blake McCaughey, a Belfast Giants superfan.

The 12-year-old, from Tandragee in County Armagh, has a rare genetic disorder that means he will spend the foreseeable future in hospital, hooked up to pumps, for up to 20 hours a day.

But ice hockey has become "the reason he fights so hard through all these challenges".

He spends hours every day watching the sport from his hospital bed, which has been brightened up with teal pillows, in support of his beloved team.

When player Spiro Goulakos broke his ice hockey stick, it was used to make Blake a new pair of crutches.

"For Blake, life is hockey - everything that drives him through rough times will be about hockey and the Giants," his mum, Christine, told BBC News NI.

Blake was born with two chromosome deletions and abnormal muscle fibres.

By the time he was 16 months old, he had had 33 hospital admissions and battled pneumonia 19 times. Since then, he has been designated "nil by mouth".

In May 2017, he had open-heart surgery.

"When he was coming round from surgery, one of the Belfast Giants was there, holding his hand," said Christine.

Blake recovered well but, two years later, his health declined: His gut and bowel stopped absorbing nutrients and his heart rate, blood pressure and body temperature dangerously lowered.

Just last week he had more surgery to help with feeding and he will remain in hospital until he has gained weight.

"When Blake has a bad day, a simple photo or video call from one of his hockey buddies helps him keep fighting," said Christine.

"The Giants have always been a rock to Blake and us, and Blake has no bones in saying that Belfast Giants head coach Adam Keefe is his best mate, although he doesn't like Adam getting cross on game nights on the bench."

When Blake is well enough to leave the hospital, watching ice hockey is always at the top of his to-do list. It's even better when he gets to go with his little sister Pixie.

"He hates to miss a game," said Christine.

"Unlike most 12 year olds, Blake will never get to experience playing hockey but, with friends like the Belfast Giants, they make the impossible very possible and on a few occasions have had Blake out of his wheelchair, pull on the skates and be held whilst he skates in the SSE Arena."

His love for ice hockey has even caught the attention of other teams in the Elite Ice Hockey League (EIHL), the UK's premier competition, who send him videos and messages of love and support.

"Social media has brought us many friends on our journey from far and wide, and especially the hockey community," said Christine.

"Every team in the EIHL has looked after Blake so well and he's got good friends on every team. They have all reached out when Blake has needed a little encouragement along the way.

"Players for the Giants come and go, depending on their contracts, but some of our closest friends have been found through the Giants and no matter where they are in the world, they still keep very much in touch," she added.

"We use social media to spread awareness of Blake's condition and to share his amazing attitude to life, the strength and courage he has pulls us all through. His personality is one of a kind.

"When you meet him for the first time, you will see a shy little boy in a wheelchair but he's an absolute character and fond of the girls, especially nurses and doctors!

"He is a mischievous, fun-loving, hockey-crazed boy who spends hours watching hockey, talking hockey and even sleeps in hockey bedding.

"Blake loves everything teal and they are the reason he fights so hard through all these challenges."

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Ice hockey: Belfast Giants give boy with genetic disorder 'reason to fight' - BBC News

CARMEL, Ind., Jan. 24, 2020 /PRNewswire/ --AgReliant Genetics, a leader in seed research, production and provider of seed solutions, signed a contract with GyanSys Inc. ("GyanSys"), a leading IT services provider headquartered in Indiana, to implementSAP S/4HANA on HANA Enterprise Cloud (HEC) as part of their digital transformation journey to replace their legacy ERP systems.

Steve Thompson, CIO of AgReliant Genetics "GyanSys led our team to conduct S/4HANA Best Practice workshops, gap analysis, and recommended the right SAP software bill-of-materials. AgReliant is excited to start our digital transformation journey partnering with GyanSys to build a scalable digital core for our Finance, Purchasing, Planning, Sales, Manufacturing, and Warehouse Management systems."

Rajkishore Una, President & CEO of GyanSys "GyanSys is committed to successfully deliver AgReliant Genetics' new SAP environment with our global delivery approach and our best practice-led implementation methodology. We are bringing our expertise in SAP S/4HANA digital core, alongside BPC, EWM, aATP, Manufacturing for Planning & Scheduling, and Analytics Cloud, for AgReliant to derive the most value from this strategic investment."

About AgReliant Genetics:

AgReliant Genetics offers corn, soybean, sorghum, and alfalfa seed solutions to farmers through their product brands. Contact your local AgriGold, LG Seeds, or PRIDE Seeds representative for more information.

Learn more about AgReliant Geneticsat http://www.agreliantgenetics.com.

About GyanSys Inc.:

GyanSys is a mid-tier global systems integrator specializing in SAP, Salesforce, Microsoft, and ServiceNow Platforms to improve the Sales, Finance, Supply Chain, Manufacturing, Operations, and HR business processes to support digital transformation.

Headquartered in Indiana, GyanSys was founded in 2005 and has approximately 1,000+ professionals globally serving 125+ customers across various industries, including the manufacturing, automotive, high-tech, CPG, and life sciences industries.

For more information about GyanSys, visit http://www.gyansys.com.

For press inquiries and more information, contact:Cliff SaitoDigital Marketing ManagerE-mail: cliff.saito@gyansys.com

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GyanSys Selected by AgReliant Genetics as the Primary Partner for Their Implementation of SAP S/4HANA as Part of Their Digital Transformation -...

Thursday, January 23, 2020

Florida could be the first state to deny life and long-term care insurers access to genetic test results. Under a new bill titled Genetic Information for Insurance Purposes (HB 1189), life insurers and long-term care insurers are prohibited from canceling, limiting, or denying coverage, or establishing differentials in premium rates based on genetic information. In addition, HB 1189 would prevent life insurers and long-term care insurers from requiring or soliciting genetic information, using genetic test results, or considering a person's decisions or actions relating to genetic testing in any manner for any insurance purposes.

On Jan. 16, the Florida House Health & Human Services Committee passed HB 1189 without any debate. The bill is now being reviewed by the Commerce Committee, which will have to clear the bill before it would be ready to go to the full House.

HB 1189 is sponsored by Representative Chris Sprowls, the incoming Speaker-designate. It is the only bill he has filed this year. In the Senate, the bill is sponsored by Kelli Stargel, who is part of Senate leadership. Given HB 1189s sponsors, the issue will likely be a high profile one, and will have a good chance of passing in the next year or two.

Existing federal law, the Genetic Information Nondiscrimination Act (GINA), protects Americans from discrimination in health insurance, employment decisions, and employee benefit decisions on the basis of genetic information. Under GINA, U.S. insurance companies and health plans (including both group and individual insurers, as well as federally regulated plans) are prohibited from:

looking at predictive genetic information or genetic services before enrollment;

requesting or requiring that individuals or their family members take a genetic test;

restricting enrollment based on genetic information;

changing premiums based on genetic information.

GINA, however, does not cover life, long-term care, or disability insurance providers. As a result, those companies can ask about health, family history of disease, or genetic information, and reject those that are deemed too risky.

2020 Greenberg Traurig, LLP. All rights reserved.

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Genetic Non-Discrimination Bill Advances in Florida - The National Law Review

Washington: A group of researchers from Australia has formulated a genetic test that could detect peoples susceptibility towards developing glaucoma, which is a debilitating ocular disease that can potentially make its sufferers go blind.

The team of scientists suggests that there are 107 genes that are responsible for the onset of this condition.

They are looking forward to 20,000 peoples participation in their Genetics of Glaucoma Study in order to help them find more genes involved in the disease.

Glaucoma is characterised by progressive damage and degeneration of the optic nerve which also causes gradual loss of vision. It is the leading cause of irreversible blindness worldwide and is predicted to affect 76 million people by 2020.

There is still no proven cure for the disease, but treatment can reliably slow or halt deterioration in most cases. Up to 50 percent of those affected are not even aware.

Stuart MacGregor, lead researcher and the head of QIMR Berghofers Statistical Genetics Group, Associate Professor, said that identifying new genes allowed them to develop a glaucoma polygenic risk score (PRS) that can predict who is likely to get the eye disease.

Glaucoma is a genetic disease and the best way to prevent the loss of sight from glaucoma is through early detection and treatment, MacGregor defined.

Our study found that by analysing DNA collected from saliva or blood, we could determine how likely a person was to develop the disease and who should be offered early treatment and/or monitoring, he added.

He also feels that unlike existing eye health checks that are based on eye pressure or optic nerve damage, the genetic test can be done before damage begins so that regular screening can be put in place.

Clinical lead researcher and academic head of the Department of Ophthalmology at Flinders University, Professor Jamie Craig, said that the study results gave hope that mass screening for glaucoma could be offered in the future.

There are Australians who, if theyd had appropriate treatment a few years earlier, wouldnt have gone blind, said Professor Craig, who is also a consultant ophthalmologist.

One in 30 Australians has glaucoma, but most people only find out they have it when they go to the optometrist because they are losing vision, or for a general eye check, shares Craig, continuing, Early detection is paramount because existing treatments cant restore vision that has been lost, and late detection of glaucoma is a major risk factor for blindness.

He said that glaucoma can arise at any age but most of those affected are in their 50s or older, so their aim is to offer blood tests to people of that age to find out if they are at risk, and then hopefully act on it.

This test is likely to be helpful in identifying those who would benefit from a more aggressive intervention such as surgery rather than simple eyedrops.

The researchers are hoping to get in touch with people with a family history of the disease. We want to know who will get glaucoma, and for those who are susceptible, we want to be able to pinpoint at what age theyre going to get it, said Associate Professor MacGregor.

The researcher concluded, That would allow us to develop a personalised approach for earlier treatment of high-risk individuals, and means people at lower risk could have less intensive monitoring and treatment. This would have benefits for patients, doctors and the health care system with reduced interventions and reduced costs.

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Genetic test developed to predict onset of glaucoma - The Siasat Daily

Hyderabad: Creating super humans through genetic mutations is a red line that should not be crossed, said Prof. Virjinijus Siksnys, the biochemist who developed the gene-editing tool CRISPR, here on Friday.

CRISPR is a very powerful technology and people are now just testing boundaries of this technology. Scientists, in general, agree that you can use the gene-editing tool to correct mutations to cure diseases. But, improving humans is a Pandoras Box, which should be avoided, he said.

Prof Sriksnys, a biochemist from Institute of Biotechnology, Vilnius University, Lithuania, while delivering Dr Manohar VN Shirodkar Memorial Lecture of Telangana Academy of Sciences (TAS) at IICT, said there was a definite need for regulatory authorities from the US and European Union to come together and put in place a framework for the CRISPR technology.

Many discussions are ongoing on having regulations in the US and EU. Hopefully, in the coming few years, there will be some regulations in place. CRISPR is a tool that can be used for different applications and it depends on us on how we are going to use it, he said.

CRISPR enables geneticists to edit parts of the genome by removing and adding or altering sections of the DNA sequence. I believe such mutations can be introduced in plants very quickly. Usually, it takes a long time to develop better plant varieties. But, CRISPR technology can help speed up the process of generating better plant varieties, he said.

Senior office-bearers of TAS including its president Prof K Narasimha Reddy, former president Dr CH Mohan Rao, IICT Director S Chandrasekhar, CCMB Director RK Mishra and students from different schools were present.

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'Genetic mutations must be regulated' - Telangana Today

The International Parkinson Disease Genomics Consortium (IPDGC) has now been in existence for ten years. In an open accessarticlepublished in theJournal of Parkinson's Diseasethe consortium reviews the progress made over the past decade in the genomics of Parkinson's disease (PD) and related disorders including Lewy body diseases, progressive supranuclear palsy, and multiple system atrophy and looks ahead at its future direction and research priorities.

Since PD was first defined, it has been suspected that there was a genetic component. In June 2009, a small group of investigators met to discuss a potential research alliance focused on the genetics of PD. The outcome was the creation of the IPDGC, a group focused on collaborative genetics research, enabled by trust, sharing, and as little paperwork as possible. This article summarizes the efforts of the IPDGC to date and places these in the context of a decade of progress in PD genomics. It also discusses the future direction of IPDGC and its stated research priorities for the next decade.

The IPDGC was born out of a realization that no single investigator could deliver on the promise of modern human genetics in isolation, explained lead author Andrew Singleton, PhD, Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA. We realized that to truly leverage the incredible gains in genetic technologies in the PD space, at scale, would require a highly collaborative approach. This notion brought a small group of PD geneticists together with the common goal of building an effective, transparent, and functional collaboration.

Since its inception, the IPDGC has grown considerably, now including more than 100 scientists from around the world with meetings at least once a year. The focus has also expanded to include clinical and functional investigation of PD at scale. Most recently, the IPDGC initiated major research efforts in East Asia and Africa and has prioritized collaborations with ongoing major efforts in India and South America.

The coordinated analysis of genome-wide association (GWA) data was perhaps the first success for IPDGC and has continued to be a mainstay of our work, noted Dr. Singleton. This work has centered on available genome-wide SNP genotyping of IPDGC members case and control cohorts from the USA, Canada, England, Wales, The Netherlands, France, Germany, Italy, Spain, Austria, Finland, Norway, Estonia, and Australia. These studies have involved collaboration within IPDGC and with groups from industry, including Genentech and 23andMe. The source diversity and size of these sample series have grown considerably, from the first efforts that centered on around 1,500 cases and a similar number of controls, to the most recent effort that included dense genotyping in more than 50,000 cases and proxy-cases, and around 1.4 million controls. As in other disorders, as sample size has grown, so has power and the number of loci detected. Currently, there are about 90 known risk variants for PD.

Collaboration among IPDGC members has furthered knowledge, including:

Future challenges the consortium has identified include expanding the known genetic architecture; genetics in diverse ancestries; advanced cohort building; and creating PD resources for the research community.

The importance of the dissection of genetic risk in non-European ancestry populations has led the consortium to invest more in establishing research in underrepresented groups. With the support of the Michael J. Fox Foundation for Parkinsons Research, the IPDGC has initiated largescale efforts in South East Asia and China and across Africa. It is also working closely with collections centered in India, LUX-GIANT, and LARGE-PD.

The field of PD genetics is one that has changed dramatically over the last ten years, commented Dr. Singleton. There has been an exponential growth in our appreciation of the genetic architecture of the disease and a greater understanding of how to proceed with genetic prosecution of PD.

Our future path promises to expand this work and leverage its clinical, mechanistic, and biological potential. Thus, while we believe the work of the IPDGC has had a significant and lasting impact on our field over the last ten years, we are even more excited by the course we have charted for the next decade.

Reference:The International Parkinson Disease Genomics Consortium (IPDGC). (2020).Ten Years of the International Parkinson Disease Genomics Consortium: Progress and Next Steps. The Journal of Parkinson's Disease.DOI: 10.3233/JPD-191854.

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source.

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Ten Years of Unraveling the Genomics of Parkinson's Disease - Technology Networks

Today, Cobb-Vantress appointed Dr Mark Cooper as managing director of genetics to oversee the companys global genetic program. Effective immediately, Dr Cooper will continue work to achieve genetic gains and competitive advantage through alignment of Cobbs breeding program with its product strategy, developing a portfolio of products to meet growing global market needs. He will report to Dr Aldo Rossi, vice president of research and development (R&D).

In his new role, Dr Cooper will lead a global, multifunctional team, including Dr Rachel Hawken, senior director of genetics; Dr Manouchehr Katanbaf, senior geneticist; and Dr Sriram Krishna, senior geneticist. Prior to this appointment, Dr Cooper previously worked as director of product testing. Since joining Cobb, he has also served as pedigree geneticist responsible for male line development, European director of genetics, director of genetics for all of Cobbs breeding programs, and director of product management.

Cobb has been dedicated to genetic research and the responsible use of technology for over 100 years, said Dr Rossi. Dr Cooper has made a big impact in his nearly 20 years with Cobb, and were looking forward to the continued advancements we expect him to accomplish in this new position.

In his time at Cobb, Dr Coopers research has focused on technology development and implementation in the breeding program, welfare parameters and meat quality. He has also spent time with global business leaders and customers to understand and update the R&D team on the product portfolio needed for the future. Most recently, he led Cobbs product testing team, helping to evaluate the companys product performance and development.

Im honored to take on the position of managing director of genetics, said Dr Cooper. Im fortunate because Cobb invests a significant percentage revenue into research and development, allowing us to continue leading the way in genetic progress.

Dr Cooper earned a bachelors degree in poultry science from Texas A&M University, a masters degree in poultry genetics from the University of Georgia, and a PhD in poultry genetics from the University of Arkansas.

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Cobb-Vantress appoints genetics executive - The Poultry Site

A genome-wide association study has identified several human genome regions related to anxiety risk. Researchers published their findings online in The American Journal of Psychiatry.

The findings are an important step forward in understanding anxiety disorders and the contribution of genes, said lead author Daniel F. Levey, PhD, of Yale University in New Haven, Connecticut, and the Veterans Affairs Connecticut Healthcare Center.

Dr. Levey and colleagues tapped data from the VA Million Veteran Program, a biobank that includes genetic, environmental, and medical information, to compare the genomes of almost 200,000 people.

The study identified 5 locations on the human genome associated with anxiety in Americans of European descent and 1 location in African Americans. Gene variants at the locations, researchers explained, could increase the risk for anxiety.h

Scans Show Shared Brain Abnormalities With Mood, Anxiety Disorders

The strongest locations were near genes involved with global regulation of gene expression (SATB1) and the estrogen receptor alpha (ESR1), according to the study. Another location (near MAD1L1) was previously linked with bipolar disorder and schizophrenia risk.

The study provides the first significant genome-wide findings regarding anxiety in people of African ancestry, Dr. Levey noted.

Minorities are underrepresented in genetic studies, and the diversity of the Million Veteran Program was essential for this part of the project, he said. The genetic variant we identified occurs only in individuals of African ancestry and would have been completely missed in less diverse cohorts.

Some 18% of participants in the Million Veteran Program are African American.

Jolynn Tumolo

References

Levey DF, Gelernter J, Polimanti R, et al. Reproducible genetic risk loci for anxiety: results from 200,000 participants in the Million Veteran Program. The American Journal of Psychiatry. 2020 January 7;[Epub ahead of print].

Million Veteran Program study sheds light on genetic basis of anxiety [press release]. Baltimore, Maryland: Veterans Affairs (VA) Research Communications; January 7, 2020.

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Study Yields Insight Into Genetic Architecture of Anxiety - Psych Congress Network

Abstract

High alcohol consumption is a risk factor for morbidity and mortality, yet few genetic loci have been robustly associated with alcohol intake. Here, we use U.K. Biobank (n = 125,249) and GERA (n = 47,967) datasets to determine genetic factors associated with extreme population-level alcohol consumption and examine the functional validity of outcomes using model organisms and in silico techniques. We identified six loci attaining genome-wide significant association with alcohol consumption after meta-analysis and meeting our criteria for replication: ADH1B (lead SNP: rs1229984), KLB (rs13130794), BTF3P13 (rs144198753), GCKR (rs1260326), SLC39A8 (rs13107325), and DRD2 (rs11214609). A conserved role in phenotypic responses to alcohol was observed for all genetic targets available for investigation (ADH1B, GCKR, SLC39A8, and KLB) in Caenorhabditis elegans. Evidence of causal links to lung cancer, and shared genetic architecture with gout and hypertension was also found. These findings offer insight into genes, pathways, and relationships for disease risk associated with high alcohol consumption.

Alcohol consumption is associated with over 60 diseases, with the risk of these comorbidities generally increasing with greater exposure (1). Excessive consumption of alcohol is considered a result of complex interactions between genetic and nongenetic risk factors. Nongenetic factors associated with levels of alcohol intake include gender (2), age at first alcohol use (3), duration of poverty and involuntary unemployment (4), and other lifestyle risk factors (5).

Meta-analysis from twin and adoption studies has shown that half of the variance for alcohol use disorder (AUD) is explained by genetic factors (6). The discovery of well-replicated risk loci, however, has been limited to the alcohol metabolizing genes alcohol dehydrogenase (ADH) and aldehyde dehydrogenase (ALDH). Missense variants, rs1229984 (G-->A; p.Arg48His) in ADH1B and rs671 (G-->A; p.Glu504Lys) in ALDH2, are protective against higher alcohol consumption and alcohol misuse phenotypes (7). For example, in a meta-analysis of ~3800 European ancestry individuals, the ADH1B rs1229984 variant was strongly associated with reduced risk of alcohol dependence and lower number of maximum drinks in 24 hours (8). The ADH1B and other ADH and ALDH variants that are associated with alcohol consumption occur at low frequency among European ancestry populations but are more common in East Asian ancestry populations, where the standardized population prevalence of alcohol misuse is lower (9).

Larger samples and genome-wide screens have been used to identify previously unidentified loci beyond the ADH-ADLH cluster. Alcohol consumption phenotypes are of specific interest to the field as they are often more applicable to the wider population than the AUD criteria. Through genome-wide association studies (GWAS), single-nucleotide polymorphisms (SNPs) mapping to/near KLB, AUTS2, SERPINC1, ANKRD36, GCKR, PXDN, CADM2, HGFAC, SLC39A8, and TNFRSF11A have been associated with alcohol consumption in European ancestry populations at genome-wide significance (P < 5 108) (1015). However, apart from association signals at KLB and GCKR, strong evidence of replication has been limited.

In this study, our aim was to determine factors associated with heavy alcohol consumption in white British individuals from the U.K. Biobank (UKB) (www.ukbiobank.ac.uk/), alongside exploring the functional relevance of genome-wide significant variants using model organisms and data mining techniques.

The application of the phenotype definition resulted in the identification of 21,967 cases and 103,282 controls that had complete data for all covariates. The covariates included in the final logistic regression model and carried forward to the GWAS analysis were (table S1) age at recruitment, sex, smoking status (anytime versus never), property ownership (own versus rent), body mass index (BMI), Townsend deprivation index at recruitment, adopted as a child, and long-standing illness, disability, or infirmity (yes or no).

We tested for SNP-level association with our high alcohol consumption phenotype in UKB. A total of 11,141,077 SNPs survived central quality control (QC) by UKB and post-GWAS filtering for imputation quality and minor allele frequency. The GWAS data test statistics showed modest deviation from the null (GC = 1.09; Fig. 1, inset), although linkage disequilibrium (LD) score regression intercept = 1.02 suggests most of the inflation is consistent with polygenic architecture. We then carried forward lead SNPs at P < 5 106 from UKB to Genetic Epidemiology Research in Adult Health and Aging (GERA) for replication. We report validated associations that meet genome-wide significance in the meta-analysis of UKB and GERA, which also demonstrate nominal association with the same direction of effect in GERA (Table 1). A summary of all SNPs reaching P < 5 108 in UKB can be found in table S2. We identified six loci attaining genome-wide significant association with alcohol consumption after meta-analysis and meeting our criteria for replication: ADH1B (rs1229984; P_meta = 2.3 1066); KLB (rs13130794; P_meta = 5.7 1016); BTF3P13 (rs144198753; P_meta = 4.1 1029); GCKR (rs1260326; P_meta = 1.5 1013); SLC39A8 (rs13107325; P_meta = 6.7 109); and DRD2 (rs11214609; P_meta = 4.3 109) (Table 1).

Inset: QQ plot of expected versus observed GWAS results from UKB, demonstrating modest deviation from the null, GC = 1.09.

OR, odds ratio; CI, confidence interval.

Multiple distinct signals of association observed at alcohol consumption loci. Conditional analyses revealed an additional signal (P < 1 105) (Table 2) at the SLC39A8 locus (NFKB1). Given ADH1B and BTF3P13 are located <1 Mb apart on q23 of chromosome 4, we conducted a wider conditional analysis across a 1.5-Mb region, which included both SNPs. The analysis identified eight independent SNPs mapping to/near ADH1A, ADH1B, ADH4, ADH5, TSPAN5, and EIF4E. The signal mapping to BTF3P13 did not meet locus-wide significance in conditional analysis, suggesting a false positive for this variant.

Joint models refer to the estimated joint effects of all selected SNPs in a region (i.e., all independent SNPs are fitted together).

Previously reported loci. The signals described in this section meet our validated association criteria and have been reported for various alcohol phenotypes by other groups. The lead SNP at ADH1B, rs1229984 [risk allele frequency (RAF), 0.980; P = 3.3 1036; fig. S1A], is the missense variant (G-->A; p.His48Arg) that has been widely replicated. The lead SNP rs13130794 (RAF, 0.632; P = 4.0 109; fig. S1B) is located in the KLB locus and has been reported to be associated with alcohol intake in the UKB (11) and a separate European cohort of >98,000 individuals (10). The lead variant in chromosome 2, rs1260326 (RAF, 0.612; P = 2.6 108; fig. S1C), is in GCKR, a glucokinase regulatory gene. This specific SNP has been reported as genome-wide significant for alcohol consumption (i.e., drinks/week) in large-scale European ancestry (11, 13) and transethnic populations (15). The lead SNP rs13107325 (RAF, 0.928; P = 1.6 108; fig. S1D) is in the zinc transporter gene, SLC39A8, which has been linked in Europeans to AUD Identification Test (AUDIT) (14) and AUDIT-C outcomes, and to AUD diagnosis (15). Last, rs11214609 (RAF, 0.395; P = 4.3 109; fig. S1E) was the SNP in the DRD2 locus. DRD2 often has been cited in addiction phenotypes and has been identified for AUD, but not alcohol consumption (15).

Nonreplicated signals reported elsewhere. We also observed genome-wide significant evidence of association in UKB at FTO and CRHR1, but these signals could not be validated in GERA. There is, however, evidence for association with alcohol-related phenotypes at these loci from other studies. The lead SNP rs55872725 (RAF, 0.599; P = 2.6 108) is in the FTO gene. This locus has recently been reported to be associated with AUDIT-C and AUD diagnosis in European ancestry individuals (15). Different index variants were reported between studies, rs62033408 for AUDIT-C and AUD diagnosis outcomes, but the SNPs are in strong LD with each other (r2 = 0.92). The FTO locus has been strongly associated with BMI, obesity, and, subsequently, type 2 diabetes as a clinical end point. Our lead SNP in this locus is in complete LD (r2 = 1.0) with rs1558902 in Europeans, which is the lead SNP for BMI in the largest published GWAS to date (16). The CRHR1 locus, with rs1635291 (RAF, 0.754; P = 4.5 1010) as the lead SNP, has been identified through gene-based analysis in a previous alcohol consumption GWAS where never drinkers were excluded. However, no other groups have reported this locus directly through GWAS. Given the previous associations for these loci with covariates included in our analysis but not in the GERA dataset, we explored the potential for collider bias at rs55872725 when not adjusting for BMI, and rs1635291 when not adjusting for smoking; the results were consistent at 6.5 106 and 2.8 108, respectively. We also found our lead SNP in the CRHR1 locus to be in strong LD (r2 = 0.87) with a tag SNP rs1800547 for a common inversion polymorphism in 17q21.31 (17).

Of the six validated variants from the UKB and GERA cohorts, three were identified as expression quantitative trait loci (eQTLs) through the Genotype-Tissue Expression (GTEx) database (table S3). rs11214609 showed evidence of being an eQTL in various tissues for nearby genes, ANNK1 and TTC12. rs13130794 was associated with the expression of RFC1 in the cerebellar hemisphere and skeletal muscle and UDGH in blood. rs1260326 was a broader eQTL with evidence across eight loci and various tissues including skeletal muscle, thyroid, and adrenal glands. Table S4 describes the LD between the top eQTL SNP for any eQTL signal and the GWAS SNP. None of the SNP pairs demonstrated evidence of colocalization based on a threshold of LD r2 > 0.8.

The validated SNPs were submitted to Gene ATLAS to explore phenome-wide association study (PheWAS) outcomes in disease phenotypes via International Statistical Classification of Diseases, 10th Revision (ICD-10) codes. Evidence suggests that these SNPs contribute to a range of diseases including alcohol dependence, hypertension, skeletal disorders, gout, alcoholic liver disease, ischemic heart diseases, metabolic disorder, obesity, and diabetes mellitus (table S5). The ADH1B variant is associated with lipid metabolism disorder, giving further link between alcohol intake and liver fat accumulation.

A set of 37 loci, which reached 5 106 with heavy drinker status phenotype in UKB, were submitted to the Reactome Knowledgebase for pathway analysis (table S6). Six pathways across three distinct processes were found to be significant. The most prominent outcome related to signaling of phosphatidylinositol 3-kinase (PI3K) and PI3K/AKT pathways, particularly in reference to cancer. Dysfunction of the PI3K/AKT pathway is widely implicated in many cancers and is a key regulator of cell survival through downstream targets (18). The genes implicated in these pathways were KLB and ESR1 (fig. S2). The other two pathways were neurexins and neuroligins, driven by LRRTM4 and NRXN3, and TFAP2 (AP-2) family regulation of transcription of growth factors and their receptors, driven by ESR1.

Through genetic correlation analysis of the entire genome, we identified 21 significant correlations that survived multiple testing correction. These outcomes are summarized in Fig. 2. The traits with the strongest correlations included smoking variables [e.g., ever versus never smoked (rg = 0.48, PFDR = 2.60 1013) and age of smoking initiation (rg = 0.41, PFDR = 0.006)], several lung cancer outcomes [e.g., squamous cell lung cancer (rg = 0.37, PFDR = 0.006) and lung cancer (rg = 0.36, PFDR = 1.20 104)], and mothers age at death (rg = 0.41, PFDR = 1.60 104). Several education measures and mental health conditions were also found to have significant correlations.

Mendelian randomization (MR) was used to examine the causal relationship between our heavy drinker case-control phenotype and 111 selected traits and clinical outcomes. The number of SNPs used for instrumental variables for each outcome test varied between two and six. Twelve outcomes including four insulin-related and two lung cancer outcomes demonstrated nominal significance using the inverse varianceweighted (IVW) method, although only evidence of a protective effect for ischemic stroke survived multiple testing correction (table S7). The MR-Egger regression intercept demonstrated no evidence of horizontal pleiotropy for the 12 outcomes (P 0.11). Single SNP analysis revealed that rs1229984 was not included in the instrumental variable for ischemic heart disease (SNP or appropriate proxy not available in the outcome dataset). Given rs1229984 demonstrates a consistent and large effect size across genetic studies of alcohol-related phenotypes, it is questionable whether the outcome can be considered as truly representative for this disease.

To further explore the potential causal effect of heavy alcohol consumption on lung cancer outcomes and allow for potential pleiotropy that might be driven by smoking, we repeated our GWAS analysis stratified for smoking status (ever versus never) and performed MR to assess potential collider bias. The SNPs used as the instrumental variable in the original analysis were retained, and lung adenocarcinoma and lung cancer were the only outcomes investigated. Evidence of consistent outcomes was observed in both stratified groups using IVW, although lung cancer in never smokers was the only outcome that did not reach the statistical significance threshold (P = 0.085).

To verify whether validated genetic targets (i.e., ADH1B, GCKR, SLC39A8, and KLB) had a conserved role in phenotypic responses to alcohol, we investigated the acute effects of ethanol on the nematode worm, Caenorhabditis elegans. In comparison to wild-type animals, those with a loss-of-function mutation in the worm ortholog for ADH (sodh-1 in C .elegans) had a statistically enhanced ethanol response (Fig. 3) as has been previously described in detail (19). The effect of intoxicating ethanol on coordinated locomotion was next quantified for loss-of-function mutations in C. elegans glucokinase (GK; hxk-1) and solute carrier family 39 member 8 (SLC39A8; zipt-15) (Fig. 3). Without an ortholog for GCKR in C. elegans, we instead analyzed its downstream effector protein glucokinase itself. Loss-of-function mutations in these genes significantly reduced the effect of ethanol for GK and SLC39A8 (Fig. 3), underlining a conserved role for these genes in whole-animal responses to alcohol. We also quantified single mutations in the C. elegans orthologs for the -Klotho protein (KLB; klo-1 and klo-2) and found that individual mutations did not alter the ethanol phenotype (fig. S3A). A compound mutation of both klo-1 and klo-2 (20), however, did have a significantly enhanced ethanol effect (Fig. 3).

C. elegans with loss-of-function mutations in worm orthologs to ADH (sodh-1), glucokinase (hxk-1), solute carrier family 39 member 8 (zipt-15), and -Klotho protein (klo-2;klo-1) were exposed to ethanol, and the resultant effect on locomotion rate was determined. Results are presented normalized to locomotion of untreated worms [basal locomotion rate: 99.03 1.47 (Bristol N2 controls), 103.13 3.66 (sodh-1), 87.37 1.91 (hxk-1), 31.43 2.97 (zipt-15), and 99.90 21.7 (klo-2;klo-1)]. *P < 0.05.

To validate the effects seen in individual mutant strains, we performed RNA interference (RNAi) experiments to knock down expression of the contraindicated genes. In comparison to control, RNAi knockdown of GK (hxk-1) and SLC39A8 (zipt-15) resulted in the same phenotypic effects as did the mutations (Fig. 4). In our RNAi experiments, knockdown of ADH (sodh-1) did not result in a significant decrease. Similar to the KLB mutations, individual knockdown of C. elegans KLB (klo-1 or klo-2) did not statistically enhance the ethanol phenotype and neither did knocking down both klo-1 and klo-2 simultaneously (Fig. 4). The lack of effect in the double knockdown is perhaps expected given that RNAi efficiency can be reduced with multiple targets (21). To validate the alcohol effect of KLB in C. elegans in an alternative method, we performed RNAi on individual KLB genes in the mutant strain of the other ortholog (i.e., klo-1 RNAi in the klo-2 background; klo-2 RNAi in the klo-1 background). In both cases, there were exceptionally enhanced effects of ethanol similar to that seen with the compound mutant strain (fig. S3B).

RNAi knockdown of worm orthologs to glucokinase (hxk-1) and solute carrier family 39 member 8 (zipt-15) phenocopies the loss-of-function mutations. Results are presented as locomotion of worms treated with ethanol normalized to untreated worms [basal locomotion rate: 87.63 21.6 (empty vector control), 94.17 2.91 (sodh-1), 77.60 2.34 (hxk-1), 60.0 2.34 (zipt-15), 90.97 3.56 (klo-1), 99.13 2.78 (klo-2), and 110.0 3.40 (klo-2;klo-1)]. *P < 0.05; n.s., not significant.

We report here a large alcohol consumption GWAS, including 125,249 white British participants, with subsequent replication and meta-analysis in an additional 47,967 individuals. Moreover, and as promoted by Salvatore and colleagues in this field (22), we conducted a post-GWAS study to investigate the biological implications of our findings. This includes providing evidence of a conserved role in phenotypic responses to alcohol for all targets available for investigation (ADH1B, GCKR, SLC39A8, and KLB) in C. elegans.

The primary strengths of this study are the (i) large sample size; (ii) replication and subsequent meta-analysis; (iii) post-GWAS analysis, including functional assessment using C. elegans; and (iv) use of a mixed-model approach in GWAS to account for relatedness. There are, however, several limitations that require discussion. First, the alcohol data and, therefore, the case-control phenotypes are based on self-reported alcohol intake. It is well documented that individuals underreport their alcohol consumption for a number of reasons. This presents risk of cases being mislabeled as controls, alongside the granularity of the data being reduced by the categorical approach. There are also differences in the measurement scale between discovery and replication cohorts. This difference was handled by applying a z score approach to meta-analysis. Second, we restricted analysis to those of white British ancestry to limit population structure variability on outcomes. This restricts generalizability outside of European populations. Third, we recognize limitations to our MR approach: (i) MR is considered most powerful when instrumental variables are from a continuous trait. This is of greater concern, however, when a disease-specific phenotype is used for instrument selection because of the likely contribution of various factors in disease pathology; and (ii) an inherent assumption of MR is that variants show no pleiotropy or direct effects on the outcome. This requires knowledge of the underlying biology under investigation, although this is rarely complete. Last, we were unable to undertake functional assessment of all genome-wide significant loci due to there being either no specific C. elegans orthologs, or too many nonspecific orthologs, or fatal consequences of gene knockdown.

The largest and most robust effects were observed in ADH1B, including replicated findings from the work in C. elegans for ADH (19), providing confidence for the selected phenotype. The biological validity of polymorphisms in ADH loci is well documented and discussed in detail in other GWAS publications (12).

KLB has been previously associated with alcohol phenotypes in European populations (10, 12). A biological basis for KLB has been proposed in mice, where those lacking -Klotho had increased alcohol consumption (10). This behavior was refractory to recombinant fibroblast growth factor 21 (FGF21), a hormone involved in sugar intake regulation and for which -Klotho is an obligate coreceptor. Hence, down-regulation of KLB may lead to sustained intake of alcohol and/or high-sugar food. Moreover, loss of both KLB isoforms in C. elegans caused an enhancement in the ethanol effects. Further evidence for energy processing pathways being implicated in alcohol consumption is demonstrated by the genome-wide significant outcomes for GCKR and SLC39A8, with these findings being consistent with recent publications (11, 13, 14). The data from our functional work in hexokinase and ZRT/IRT-like protein transporter supports the role of glucose metabolism pathways in the susceptibility to heavy alcohol consumption by demonstrating attenuation of the depressive effects of high-dose alcohol when hxk-1 and zipt-15 are independently knocked down. Although we failed to demonstrate replication between the UKB and GERA cohorts, potentially due to variation in phenotype, evidence from other GWAS showed consistent effects for FTO (23). The suggestive association with this pleiotropic locus adds further plausibility of common pathways implicated in the consumption of food and alcohol. The purported shared pathogenic architecture may result in dysregulation of brain reward pathways leading to excess consumption (24). Controlling for BMI within our GWAS suggests that the associations for alcohol consumption are independent of BMI, adding weight to the hypothesis of a potentially shared, rather than mediated, pathways.

DRD2 encodes the dopamine receptor 2 subtype and is linked to several neurobiological processes, including functional activation of reward circuits (25). Data from in vivo and in vitro experiments show DRD2 to be a susceptibility gene for alcohol dependence (26), and altering DRD2 expression leads to differential responses to substances and stimuli (27), conferring increased risk for addiction. Moreover, evidence suggests increased risk of relapse in alcohol and cocaine dependence, and heightened heroin, nicotine, and glucose craving when polymorphisms of DRD2 are present or there is low D2 receptor availability (28). The association of DRD2 with alcohol was confirmed in GWAS findings for AUD but not alcohol consumption, with authors proposing that the central nervous system is a fundamental element in the progression to clinical diagnosis (15). Our findings are somewhat contradictory given that participant categorization is based on U.K. alcohol units consumed per week, although the quantities for cases are often associated with high risk of AUD.

Together, the loci outside of the ADH/ALDH cluster suggest several common pathways associated with different types of compulsive behavior and addiction phenotypes. Considerable evidence from animal models and from humans supports convergence of these common etiologies in the brains limbic system regardless of the prior distinct mechanism of action and ultimate observable phenotype (29, 30). This suggests that addiction might be better considered as a pathobiological risk with different endotypes, rather than each specific phenotype (e.g., alcohol dependence, drug addiction, and gambling addiction) being independently characterized. From a therapeutics perspective, these outcomes provide additional and supportive evidence toward a number of targets that might be amendable to pharmacological intervention. Further investigation is required to determine which sites have the greatest potential. Data from the Open Targets resource (www.opentargets.org/) suggest that 49 drugs have reached phase IV investigation for DRD2 across a range of indications, including mental health disorders and cocaine dependence; no drugs are in development for ADH1B, KLB, GCKR, or SLC39A8. FGF21 has been explored due to links with KLB, but no drugs are in the market yet.

Using the GWAS outcomes from UKB enabled us to examine the relations between key variants/loci and traits and disease phenotypes. Genetic correlation analysis and MR consistently demonstrated an association with lung cancer. Determining alcohols contribution to lung cancer often has been limited by the strong positive correlation between alcohol intake and smoking. However, the outcomes from the MR provide potential evidence of a causal relationship in our overall sample and when stratified by smoking status. Alcohol is a known carcinogen and is implicated in cancers of the liver, colon, rectum, head and neck, and breast, for example (31), while evidence for lung is variable (32, 33). Lung cancer is a complex and multifactorial disease involving genetic and a range of measurable and nonmeasurable environmental and lifestyle factors. Hence, heavy alcohol consumption is one potentially modifiable risk factor to reduce disease incidence. An alternative hypothesis is through a joint risk locus in KLB that independently drives alcohol consumption and cancer risk. In addition to the above, -Klotho inhibits PI3K and, subsequently, AKT, an important pathway in normal cell function. The dysfunction of the PI3K/AKT pathway, identified in our pathway analysis, has been cited in cancerous cells and as a risk factor in cancer onset (18, 34). Down-regulation of KLB has been reported across several cancers (35, 36). However, some variations in findings exist (37), and no evidence is available in lung cancer. Basic cell line study would provide initial data on -Klotho expression in lung tumor cells.

Links to other diseases were also found. Drinking heavily was suggested as a protective factor for ischemic stroke. This is not consistent with traditional epidemiological findings or other MR findings using rs1229984 as the instrumental variable (38). However, the lack of rs1229984 in our instrumental variable for this analysis means the outcome should be interpreted with caution. The nominal evidence in several insulin measures suggests a wider biological association with glucose regulation, linking back to the potential importance of energy metabolism pathways in alcohol consumption. ADH1B and GCKR were associated with gout, and ADH1B alone with hypertension. The lead SNP at GCKR, rs1260326, has been shown to be a risk variant for gout in a separate GWAS (39), and rs1229984 in ADH1B has been identified for systolic blood pressure using a functional enrichment approach. Increasing alcohol consumption is a known risk factor for both gout and hypertension (40). Last, there was evidence for several skeletal complications with identified alcohol consumption variants. Alcohol intake represents a dose-dependent risk factor for fragility fractures due to the direct effects of alcohol on bone cell metabolism. Chronic alcohol consumption has been associated with a twofold increased risk of hip fracture in prospective cohort studies involving more than 16,000 subjects (41).

Our findings offer insight into genes, pathways, and causal relationships for disease risk associated with heavy alcohol consumption. The inclusion of model organism work to investigate the conserved role of loci alongside GWAS outcomes is novel in the alcohol field and adds validity for relevant outcomes. In addition, the correlation between the C. elegans phenotypic data with genome-wide association in humans reinforces a link between the acute physiological effect of alcohol and predisposition to excessive alcohol consumption. Specific findings suggesting joint reward/addiction pathways, the role of energy metabolism, casual links to lung cancer, and shared genetic architecture with gout and hypertension are of particular interest. Further investigation is required, however, to realize the potential of these outcomes and result in meaningful populationor clinical-level impact.

UKB is a large population cohort of ~502,000 individuals from the United Kingdom aged 40 to 69 years at the time of recruitment. Baseline assessment was undertaken at one of 22 centers across the United Kingdom between 2006 and 2010. Each participant completed a comprehensive demographic, lifestyle, and health questionnaire, underwent clinical phenotyping, provided biological samples (i.e., blood, urine, and saliva), and agreed to have his or her health record accessed for baseline and follow-up outcomes (42). Ethical approval for UKB was gained from the Research Ethics Service (REC reference: 16/NW/0274), and written informed consent was obtained from all participants. The current analyses were conducted under approved UKB data application number 15110.

Phenotype definition. Questions from the UKB baseline assessment were used to develop two study groups: heavy drinkers (cases) and drinkers not reaching criteria for cases (controls). All participants who indicated they consumed alcohol were asked to quantify their intake per week or per month using standard drink sizes [e.g., In an average WEEK, how many glasses of RED wine would you drink? (there are six glasses in an average bottle)]; pictures accompanied these questions to provide visual representation of each measure. We then applied a standardized number of U.K. alcohol units to each drink to enable an estimated number of units per week to be calculated (see the Supplementary Materials for more detail). Sex-specific heavy drinking was then defined as >35 U/week for women and >50 U/week for men. Any cases with values >4 SDs above the gender-specific means were removed. Controls were individuals who were not current abstainers from alcohol (i.e., 1 U/week) but did not reach the sex-specific criteria for heavy drinking and were drinking at similar levels to 10 years previous.

Genotyping, imputation, QC, and GWAS. In July 2017, UKB released genetic information (directly typed and imputed genotypes) for the entire cohort (n = 487,406) to approved collaborators. Most (90%) of the participants were genotyped on the UKB Axiom Array, with the remaining 10% genotyped on the Affymetrix UK BiLEVE Axiom Array. There is >95% content overlap between arrays. Genotyping, QC, and imputation were performed centrally by UKB and has been previously described (43). Imputation was performed up to combined reference panels from the 1000 Genomes Project (Phase 3), UK10K, and Haplotype Reference Consortium (44). Analyses were restricted to a subset of white British individuals, defined on the basis of self-reported ethnicity and genetic data.

Using UKB data, univariate and multivariate logistic regressions were used to determine covariates to be included in the GWAS analysis. Variables only available for the entire cohort and implicated in previous research were considered, and any values >4 SDs from the mean were removed (n = 7649 participants removed due to missing data). Variables reaching P < 0.01 in separate univariate analysis were carried forward to a multivariate model, where the stepAIC function of the MASS R package was used to determine stepwise entry of variables into the model. Collinearity was determined using variance inflation factor, and variables were accordingly removed from the final model (Variance Inflation Factor >10). An a priori decision was made to include age and sex in all models. Our rationale for using this approach is twofold: first, to account for confounding factors that may bias effect estimates, and second, to improve power by reducing residual variance.

Genetic association analysis in autosomes was conducted using a linear mixed model in BOLT-LMM v2.3.1 (45), adjusted for age at recruitment, sex, genotyping array, and nongenetic covariates identified in the logistic regression model. The BOLT-LMM model includes a random effect derived from a genetic relationship matrix to account for population structure and relatedness. Potential P value inflation due to residual population structure and relatedness was checked using genomic control following filtering of variants based on imputation quality (INFO 0.4) and minor allele frequency of 0.005. Distance-based clustering was used for defining loci, such that genome-wide significant SNPs were ranked from most significant to least significant, and SNPs were retained if they did not map 500 kb of a more significant SNP. Variants reaching P < 5 106 and surviving distance-based clustering (i.e., lead SNPs) in the UKB cohort were explored in the GERA cohort for the purposes of replication.

The GERA cohort was used for replication. GERA is part of the Kaiser Permanente Research Program on Genes, Environment, and Health (RPGEH) and has been described in detail elsewhere (46). In short, the cohort comprises 110,266 adult men and women who are consented participants in the RPGEH, an unselected cohort of adult participants who are members of Kaiser Permanente Northern California, an integrated health care delivery system. All study procedures were approved by the Institutional Review Board of the Kaiser Foundation Research Institute.

For this replication analysis, 47,967 GERA participants of non-Hispanic white ethnicity who had alcohol consumption information were included. Alcoholic drinks consumed per week as a quantitative trait (drinks/week) was assessed on the basis of the RPGEH survey as previously described (12) and as part of the Supplementary Materials. Genotyping using Affymetrix Axiom arrays (Affymetrix, Santa Clara, CA, USA) (47, 48), imputation using the cosmopolitan 1000 Genomes Project reference panel, and GWAS analysis were undertaken as detailed in the Supplementary Materials.

METAL was used to perform a fixed-effects meta-analysis between the UKB and GERA cohorts using Stouffers method to account for the effect sizes in discovery and replication being on different scales (49). An overall z-statistic and P value were calculated from a weighted sum of the individual statistics. Weights are proportional to the square root of the number of individuals examined in each sample and selected such that the squared weights sum to 1.0.

A validated association was defined as follows: (i) reaching P < 5 106 in the discovery cohort, (ii) demonstrating nominal association with the same direction of effect in the replication cohort, and (iii) meeting genome-wide significance in the meta-analysis of both datasets sets.

Conditional analysis was performed on validated associations using Genome-wide Complex Trait Analysis (50) (http://cnsgenomics.com/software/gcta/) and the GWAS outcomes from the UKB to identify independent signals in the same region as each lead SNP (500 kb); one model was fitted per region. A set of 5000 randomly selected UKB white British participants was used to develop a reference set to approximate LD. A threshold of P < 1 105 was used to select index SNPs for independent signals in each region, where the conditional estimates were derived from fitting all independent SNPs jointly (i.e., joint model).

Expression quantitative trait loci. The GTEx Portal (http://www.gtexportal.org) was used to assess whether the lead SNP at each locus was an eQTL for local genes across the range of available tissues (51). This approach uses gene expression information across various human tissue types and genotype data to build information on eQTLs using a 1-Mb cis-window around the transcription start site. All tissue types with more than 70 samples available within GTEx were evaluated in our analysis including the brain, heart, liver, skeletal muscle, and skin. Significant eQTLs were based on a false discovery rate (FDR < 0.05) correction. The LD between the top eQTL SNP for any eQTL signal and the GWAS SNP was assessed to explore whether the two signals colocalize with each other; an LD r2 > 0.8 in Europeans from the 1000 Genomes was considered evidence of colocalization.

Genetic correlations. LD Hub v1.9.0 (http://ldsc.broadinstitute.org/ldhub/) was used to identify genetic correlations through LD score regression between the binary alcohol phenotype and other complex traits (52). This method uses individual SNP allele effect sizes from GWAS summary statistics and the average LD in a region to estimate bivariate genetic correlations. We tested for genetic overlap between alcohol consumption from our GWAS and disease outcomes and related traits in European cohorts available in the LD Hub, except for UKB outcomes and metabolites due to the large number of potential comparisons. FDR < 0.05 was used to account for multiple comparisons.

Mendelian randomization. MR-Base v0.4.21 was used for performing two-sample MR to explore the causal relationship between alcohol consumption and other disease outcomes and related traits (53). Outcomes were selected from the NHGRI-EBI GWAS catalog and filtered for European ancestryonly populations. All genome-wide significant SNPs were initially considered. Before MR analysis, the identified SNPs were explored for independence using estimated LD scores from the 1000 Genomes Project European sample, where r2 0.001 among SNPs in a 10,000-kb region resulted in only the SNP with the lowest P value being retained. One hundred eleven outcomes were selected on the basis of being diseases of interest, metabolites influenced by alcohol and prominent in subsequent alcohol-related disease onset or progression (e.g., triglycerides), or other consequences of heavy alcohol consumption. Harmonization between exposure data and outcome data was undertaken to ensure effects corresponded to the same allele. Causal estimates between exposure and outcomes were obtained using the two-sample MR IVW method with FDR for multiple comparisons. Sensitivity analyses to account for pleiotropy were performed using MR-Egger regression and weighted median approaches. The weighted median test has been suggested as an alternative to the MR-Egger when the instrumental variable contains a small number of SNPs.

PheWAS. Gene ATLAS (http://geneatlas.roslin.ed.ac.uk/) was used as a lookup for outcomes from PheWAS analysis performed on UKB traits (54). The database contains data from >450,000 white British individuals, >31 million variants, and 778 traits; only ICD-10 traits were considered (n = 496). This information was used to derive a phenome-wide significance threshold, divided by the number of independent tests, i.e., 1.68 105 [0.05/(496*6)].

Pathway analysis. Reactome pathway knowledgebase (https://reactome.org/) was used to undertake pathway analysis (55). The Reactome Knowledgebase systematically links human proteins to their molecular functions, providing a resource that operates both as an archive of biological processes and as a tool for discovering unexpected functional relationships. Loci identified through distance-based clustering at a relaxed threshold of P < 5 106 from the GWAS analysis were included. These loci were mapped to pathways, and a P value was calculated on the basis of the overlap between the query and the pathway expression; an FDR correction was applied by the software.

C. elegans is an excellent genetic model for investigating whole-animal effects of alcohol (5658). Similar to humans, acute exposure to intoxicating alcohol induces a dose-dependent reduction in coordinated movement of C. elegans both in solution (59) and on solid agar (60). Strains of C. elegans were selected on the basis of the outcomes from the present GWAS at the level of P < 5 108 and having evidence of replication in GERA or being reported as genome-wide significant in other alcohol phenotype studies.

Phenotypic and RNA interference experiments were performed at 20C in a temperature-controlled room on young adult hermaphrodites selected from sparsely populated NGM (nematode growth media) plates. As we and others have previously demonstrated (59, 60), exposure to 400 mM external ethanol reduces coordinated locomotion of wild-type (Bristol N2) animals by ~70%. An external concentration of 400 mM ethanol is equivalent to an internal concentration of ~20 to 70 mM, which is equivalent to a blood alcohol level of ~0.1 to 0.4% and is consistent with levels of intoxication experienced by humans. Locomotion rate was the outcome of interest and was quantified by thrashing in Dents solution [140 mM NaCl, 6 mM KCl, 1 mM CaCl2, 1 mM MgCl2, and 5 mM Hepes (pH 7.4) with bovine serum albumin at 0.1 mg/ml] as previous described (59, 60). See the Supplementary Materials for full details.

All functional data are expressed as means SE. Thirty treated and untreated animals were analyzed and compared per strain per experiment. Statistical significance was assessed by one-way analysis of variance (ANOVA) with post hoc Bonferroni correction for multiple comparisons.

Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/6/3/eaay5034/DC1

Supplementary Methods

Table S1. Summary of final multivariable logistic regression model.

Table S2. Summary of genome-wide significant SNPs following distance-based clumping on the UKB cohort, and the replication cohort and meta-analysis outcomes.

Table S3. eQTL analysis outcomes.

Table S4. LD between the top eQTL SNP for any eQTL signal and the GWAS SNP.

Table S5. Variant-trait significant outcomes from PheWAS.

Table S6. Variants at 5 106 and submitted to the Reactome Knowledgebase.

Table S7. Mendelian randomization results for nominally significant outcomes in the IVW analysis and FDR outcomes using the IVW method.

Fig. S1. LocusZoom plots for lead SNPs from GWAS on alcohol phenotype in the entire cohort.

Fig. S2. Constitutive signaling by aberrant PI3K in cancer.

Fig. S3. Individual C. elegans b-Klotho genes outcomes.

References (6171)

This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

H. Kranzler, H. Zhou, R. Kember, R. V. Smith, A. Justice, S. Damrauer, P. S. Tsao, D. Klarin, D. J. Rader, Z. Cheng, J. P. Tate, W. C. Becker, J. Concato, K. Xu, R. Polimanti, H. Zhao, J. Gelernter, Genome-wide Association Study of Alcohol Consumption and Use Disorder in Multiple Populations (N = 274,424). bioRxiv 527929v1 [Preprint] (2019).

Acknowledgments: Funding: This work was supported by the Medical Research Council (grant number MR/S000607/1). Genotyping of the GERA cohort was funded by a grant from the National Institute on Aging, National Institute of Mental Health, and National Institute of Health Common Fund (RC2 AG036607). Analysis of GERA data was supported by NIH grants National Eye Institute grants R01 EY027004, R01 DK116738, and R21 AA021223 (E.J.). Author contributions: A.T., A.P.M., and M.P. conceived the project. A.T., J.C., and A.P.M. performed the discovery, replication, and in silico analysis. E.J., H.C., and J.Y. provided access to the replication cohort and data analysis. J.B. and T.K. provided access to C. elegans assays and performed the experiments. A.T., J.C., and J.B. produced data visualization. A.P.M. and M.P. supervised the project. A.T. wrote the original draft. All authors critically reviewed the drafts of the manuscript and approved the final version. Competing interests: The authors declare that they have no competing interests Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. The data from UKB were provided under license by UKB, who is the owner of the data. Requests for access to the data should be directed to UKB as per the material transfer agreement. Additional data related to this paper may be requested from the authors.

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Functional validity, role, and implications of heavy alcohol consumption genetic loci - Science Advances

(TNS) Incoming House Speaker Chris Sprowls had little trouble Thursday convincing members of a House health-care panel to approve legislation that would prohibit life-insurance, long-term care insurance and disability-insurance companies from using customers genetic information in changing, denying or canceling policies.

Florida would become the first state to have such a law if Sprowls proposal is ultimately passed by the Legislature and signed by Gov. Ron DeSantis.

Members of the House Health & Human Services Committee passed Sprowls bill (HB 1189) without any debate, and committee Chairman Ray Rodrigues, R-Estero, praised Sprowls for introducing the bill.

I think our privacy is important. And I think its equally important to be a visionary, to look forward and I 'm happy that Florida is going to be the state that leads the way on this issue, Rodrigues said.

Insurance industry lobbyists, who opposed the measure, sat quietly, agreeing to waive their speaking time.

Curt Leonard, regional vice president for state relations for the American Council of Life Insurers, said his association had expressed concerns on the issue for the past two years.

Weve expressed our concerns with Speaker Sprowls and other interested parties on this issue going back to 2018. So theres no point in repeating the same things over and over again, in the interest of the committee's time, Leonard said. That being said, we do share the speaker-designates (Sprowls) concerns about privacy. I think it's a concern for everybody.

The bill will have to clear the Commerce Committee before it would be ready to go to the full House. Sprowls, R-Palm Harbor, is slated to become speaker after the November elections.

In addition to preventing insurers from using the information in making policy decisions, Sprowls bill also would block the companies from requiring or soliciting genetic information from applicants.

Sprowls said insurance companies have for years been able to sell policies without having access to the genetic data.

Insurance carriers have been successful without access to genetic information. They have been able to provide affordable coverage to consumers without genetic information. Insurance is about spreading risk, not guaranteeing the outcomes or rewards to the (carriers). And affordable life, disability, and health insurance should not be available simply to the genetic elite, Sprowls said.

While Sprowls influence looms large in the House, he must convince the Florida Senate to go along. For that, Sprowls said he will look to Sen. Kelli Stargel, R-Lakeland, to spearhead the issue.

Senate President Bill Galvano, though, told The News Service of Florida that he supports a potential compromise on the issue.

Leonard said a compromise would authorize consumers to use their private information any way they want to. And that might include them wanting to share their genetic science or genetic testing information, he said. So we dont like the idea that consumers will be handcuffed in how they use that information.

2020 The Orlando Sentinel (Orlando, Fla.) Distributed by Tribune Content Agency, LLC.

Link:
Florida Genetic Information Bill Advances in House - Government Technology

St. Elizabeth Healthcare

If you could take a simple test that would identify your risks of developing a potentially deadly disease so you could prevent it or treat it sooner, wouldnt you?

A proactive genetic test can do just that. Caroline Ewart, Genetic Counselor in the Center for Precision Medicine and Genomic Health at St. Elizabeth Healthcare, says, Understanding your family tree is important for your future health. Genetics play a big role in what diseases we will develop in the future. The more we understand the family, the more you can proactively do to protect your health.

Many people dont consider genetic testing because they think it is too expensive, not accurate enough, or doesnt screen for the diseases that run in their family. Ewart is helping us bust some of the common myths of genetic testing.

Myth #1: Genetic Testing is Too Expensive

When proactive genetic testing started, it was very expensive, and only a few select laboratories across the country performed the testing. Today, genetic testing is very affordable. Many health insurance companies will provide some coverage for testing, and the laboratories now have a limit on what they can charge.

A proactive screening in the Center for Precision Medicine and Genomic at St. Elizabeth Healthcare is just $395. That includes an initial genetic counseling visit, coordination of blood tests, carrier status screening, and a comprehensive consultation discussing your results. Individuals with a Flexible Spending Account (FSA) or Health Savings Account (HSA) may be able to use these funds to pay for the cost of the screening.

Myth #2: Genetic Testing Only Finds Breast Cancers

Its true, when genetic testing was in its infancy, we only tested for BRCA1 and 2 genes which detect breast and ovarian cancers, says Ewart. But the tests today are far more sophisticated. We now test for over 100 different gene mutations looking for a range of diseases and cancers.

Inherited conditions the tests screen for, include:

Breast cancer

Cardiovascular diseases

Colorectal cancer

Cutaneous melanoma

Gastric cancer

Ovarian cancer

Pancreatic cancer

Renal cell cancer

Thyroid cancer

Myth #3: Genetic Testing Doesnt Help the Treatment of Diseases like Cancer

Genetic testing is used not only to proactively screen for certain diseases, but it is used to treat cancer as well.

If you have been diagnosed with cancer, the gene mutation may guide treatment. It can also help your team manage increased risks of developing other types of cancers, says Ewart.

More importantly, the results of proactive genetic testing can guide your healthcare teams recommendations for screenings of cancer and cardiovascular diseases. This may include starting screenings at an earlier age, increasing the frequency of screenings or suggesting more advance screenings.

By screening early, we can find the disease early, when it is most treatable, says Caroline.

Myth #4: Genetic Testing isnt Accurate

Ewart says, Certainly there are limitations to testing, but our process is more than just a blood test. By gathering a thorough family history we can determine your risk factors for developing certain diseases, even if a blood test comes back negative.

At St. Elizabeth Healthcare, if you are found to be at high risk or test positive for genetic cancers, you are referred to the Heredity Cancer Clinic to develop a plan for future cancer screenings. They may also recommend your family members be tested, so you can get a full picture of your familys health. St. Elizabeth has many types of genetic screenings. To find the one that best fits your needs, pleasestelizabeth.com/dna or call 859-301-GENE (4363).

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Healthy Headlines: Four common myths about genetic testing and why they are not accurate - User-generated content

CHICAGO (CBS) Despite exhaustive work by detectives, the murder of Pamela Maurer was left unsolved for more than four decades. Last year, a relatively new form of genetic sleuthing began to put together the pieces of the mystery in a matter of days.

DuPage County investigators provided genetic material preserved from the Maurer crime scene to Maryland-based Parabon NanoLabs. Pamelas body was found in Lisle in January of 1976. She had been sexually assaulted and strangled. She was last seen alive the night before her body was found, when she told friends she was going to a restaurant to buy a soft drink.

First, Parabon, led by chief genetic genealogist CeCe Moore, used the DNA to create a snapshot genotypewhich predicts a persons physical traits, such as eye, skin and hair color, and even the shape of a face.

The composite created from that test looks remarkably similar to Bruce Lindahl, a suspected serial killer who police now say killed Maurer. Lindahl died in 1981.

But the testing didnt stop with just a picture. The hard work had only just begun.

Moores team used the DNA to reverse engineer Lindahls family tree.

Parabon loaded the DNA sample from the Maurer crime to a website called GEDmatch and began a form of genetic treasure hunting. GEDMatch is a site where users can upload their genetic testing results, done by companies like 23AndMe and Ancestry.

Typically, Moore said, they find similar DNA from distant cousins of a suspect and build back from there.

We are looking just for people who are second, third, fourth, fifth cousins and beyond, Moore said. Typically we are not getting close matches to close family members.

Basically, Parabon is reverse engineering the family tree of the suspect based on who they are sharing DNA with, Moore said.

Moore said she found multiple distant cousins that led to Lindahl, up to 20 matches and put those puzzle pieces together.

It is almost never a single match that leads to an identity. Its a group of matches to see how they all connect to each other.

My work, and my teams work is really about providing answers to these families for years and decades, Moore said.

She said part of the hunt is luck. In this case, the data allowed them to find a suspected match to Lindahl in a few days.

But Parabons work didnt solve the case. Detectives still needed more proof. So, they got a court order to exhume Lindahls body and extract DNA from his remains.

The result was a match.

The odds of the DNA belonging to somebody else are 1 in 1.8 quadrillion, DuPage County States Attorney Robert Berlin said this week.

This was the second case Parabon has done in Illinois, but the first in the Chicago area.

Last year, Moores work led to murder charges against Michael Henslick, who police say killed Holly Cassano. She was found fatally stabbed in her home in Mahomet, Ill., on Nov. 2, 2009.

That case is expected to go to trial next month, Moore said. Parabon has so far worked on 93 cases with police across the country in the past two years. The most famous charges against the suspected Golden State Killer, Joseph James DeAngelo.

Critics find the practice controversial and a potential invasion of private DNA data. Moore says the benefit to the public, ensuring that killers are put behind bars, and the fact that families get some resolution, far outweigh those concerns.

I feel that the good that has been done is really immeasurable to public safety, Moore said.

Lindahl died at age 28 in 1981 after he bled to death while stabbing another victim, Charles Huber. The coroner said his knife wounds were accidentally self inflicted.

RELATED: Those Who Remember Lindahl Say He Gave Them The Creeps

Police now say he may have killed at least two other women.

Lindahl was charged with raping Deborah Colliander, who manged to escape from the attack. However, two weeks before Lindahls trial, Colliander disappeared after leaving her job at a hospital.

The case against Lindahl was dropped.Collianders body was found on April 28, 1982in a field on Oswego Township.

Investigators also think Lindahl may have something to do with the disappearance of Deborah McCall, a student at Downers Grove North. She was last seen alive in November 1979. Photos of her were found in one of Lindahls residences.

And there may be other victims in the 1970s and before his death, police said. The new evidence will be used to open additional investigations.

Investigators set up two tip lines: (630) 407-8107 (DuPage States Attorney) and (630) 271-4252 (Lisle police).

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The Case Of Pamela Maurers Murder Went Cold For Decades, Then Genetic Sleuthing By Parabon Helped Crack It - CBS Chicago

The Mayo Clinic has identified a genetic problem believed to have caused sudden death in 18 of 23 Amish young people who had it, according to media reports.

The study published recently in JAMA Cardiology said researchers studied two large Amish extended families that reported multiple sudden deaths, including four siblings with exercise-associated sudden deaths.

"With the help of new technology that wasn't around when they first started looking into the case, the team learned that these Amish children had all inherited the same genetic mutation from both of their parent," CNN reported.

Now that the problem has been identified, Popular Science reported, potential couples can be tested to see whether they are both carriers, and people who are at risk of sudden death because of the problem can have a defibrillator implanted.

One of the best-known medical facilities working with the Amish and other Plain community people to identify and treat genetic diseases is in Lancaster County the Clinic for Special Children in Strasburg.

The clinic wasn't involved in the study, according to spokeswoman Kelly Cullen. However, she said, lab director Dr. Erik Puffenberger checked its thousands of records for RYR2, the gene involved. None were affected, she wrote, only three were carriers of the gene, and only one of those people was local to Lancaster County, "demonstrating that the RYR2 genetic variant is very rare in our area."

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However, she wrote, the clinic is planning to add RYR2 to the next version of its Plain Insight Panel, which screens for thousands of genetic variants known to cause problems across the Plain communities of North America.

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18 Amish young people died suddenly; Mayo Clinic identified genetic problem believed to be responsible - LancasterOnline

Giselle Newton, a PhD research student at UNSW, is one of 16 donor-conceived and surrogate-born people from around the world who are leading a renewed push to change laws which govern their access to information about their genetic heritage.

For the first time, the group told their own stories at a historic visit to the United Nations to mark the 30-year anniversary of the Convention for the Rights of the Child in Geneva on November 19.

They presented their five recommendations* to the Human Rights High Commissioner Michele Bachelet, and received a standing ovation from the audience.

We highlighted the consequences of ignoring the voices of those most affected by these practices, Ms Newton says. Donor-conceived people are experts on this issue and our voices need to be listened to and acted upon.

International Social Services representative Mia Dambach led the workshop on biotechnology at the UN, and says there is an urgent need for the development of global standards.

It is hoped that this will not be the last time the voices of those most affected are heard representing as many experiences as possible, Ms Dambach says.

Ms Newton was conceived in the Northern Territory by sperm donated in Western Australia, before being born in NSW.

She says her parents did the right thing by telling her at a very young age that she was donor-conceived.

From day dot, its really important for children to know they were donor-conceived, Ms Newton says. And that they have the ability to contact donors and donor-conceived siblings.

Laws

In Australia, laws differ between the states.Victoria is leading with legislation implemented in 2017 that allows all donor-conceived people the chance to retrospectively access information about their biological parents.

It would be good if other states really pull up their socks and follow suit, Ms Newton says.

But in NSW, recent changes to legislation allow donor-conceived children born after January 1, 2010 the ability to track down their biological parent(s), once they turn 18. Those born before this date can only access non-identifying markers such as the ethnicity, physical characteristics, medical history, and the sex and year of each of the donors offspring.

In WA, once a donor child turns 16, records relating to his or her lineage are open for them to access. But for those born before 2004, they need to have the donors permission before they can obtain the missing pieces to their biological puzzle.

What Ms Newton and others like her want is the power to access information about their identity and origins including information about their donors and donor-conceived siblings.Being disconnected from your biological history can have drastic consequences in terms of identity, she says.

It is this emotional element that US doctor William Pancoast did not foresee when he made the first successful experiment by artificially inseminating a woman with the sperm of one of his most attractive medical students in 1866.

UNSW PhD study

While estimates have suggested that there are between 20,000-60,000 donor-conceived people in Australia, Ms Newton says that there has been very limited empirical research that explores life as a donor-conceived adult in Australia.

In her doctoral thesis at UNSWs Centre for Social Research in Health, she will examine what services and support are available to donor-conceived people with a particular focus on the role of peer support in online and offline contexts.

I am really fascinated by the idea of how connection with peers can contribute to a sense of belonging, she says.

UNSW Associate Professor Christy Newman has highlighted the importance of Ms Newtons research for the support needs of donor-conceived adults. She hopes it will help inform policy and practice responses to this increasingly growing group of people in Australia and around the world.

Ms Newton will be recruiting donor-conceived people to participate in a national online survey and interviews in early 2020.

(To participate or find out more about the study)

An international concern

Netherlands-based Joey Hoofdman was among those who participated at the UN conference in Geneva. Mr Hoofdman found out he has at least 75 half-siblings via the doctor who treated his parents for fertility problems in the 1980s. The 32-year-old only discovered in 2017 that he was donor-conceived.

Mr Hoofdman says he blames his biological father for having crossed a medical-ethical boundary by using his own sperm during a time when there was insufficient supervision and a lack of regulation.

We need to make agreements on an international level so we can prevent this from ever happening again.

*Recommendations

Ms Newton, Mr Hoofdman and others put forward the following to the UN:

1) Ensure the right of donor-conceived and surrogate-born children to access information about their identity and origins regardless of when these children were conceived and born, and to preserve relations with their biological, social and gestational families.

2) Ensure that comprehensive and complete records of all parties involved in the conception of the child be held by the State in perpetuity for future generations.

3) Respect and promote the full and effective enjoyment of all the rights of donor-conceived and surrogate-born children in both the immediate and longer terms.

4) Ensure that the best interests of the child be the paramount consideration in all relevant laws, policies and practices and in any judicial and administrative decisions. This requires a best interests assessment pre-conception on a case-by-case basis.

5) Prohibit all forms of commercialisation of gametes, children and surrogates including, but not limited to, the sale and trafficking in persons and gametes.

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Donor-conceived people lobby UN for access to their genetic heritage - UNSW Newsroom

We visited theBT Young Scientist and Technology Exhibition again this year, getting the chance to chat to some of the countrys brightest students.

There were project posters stretching to all corners of the main hall at the RDS, filled with excited participants waiting for their visit from the judging panel.

Hugh Murtagh from Coliste Mhuire in Westmeath took us through hisA-Ok project a discreet wristband that students with autism can wear to let their teacher know if theyre feeling overwhelmed at school.

I have autism and I know just how hard it is to try and focus in class when you feel overwhelmed. I want to try and help people like me and people who also have autism, he said.

We also learned about an eco-powered dollhouse with the potential to teach children about climate action, how mental health can be impacted by climate change, and whether genetics affects how clever a dog breed is.

>> READ MORE

Words by Lisa Ardill

More:
BTYSTE 2020: From eco-friendly dollhouses to the genetics of clever dogs VIDEO - Siliconrepublic.com

Imagine receiving customized nutrition advice based on your personal biologic or genetic profile. That's the "future of food," according to a UBS analyst, who sees diet personalization as the next plant-based meat.

Personalized nutrition could generate annual revenues as high as $64 billion by 2040, the firm said. Plus, big-name companies such asApple, Uber and Amazon could benefit from the massive growth opportunity.

"With heightened health awareness among consumers, yet also more people suffering from ailments which are attributable to poor nutrition, there is growing demand for solutions that can improve individual nutritional choices," said UBS analyst Charles Eden in a note to clients on Tuesday. "Personalised nutrition ... represents a potential such solution."

Personalization is a theme that has swept many industries in recent years. An increasing number of businesses are sending out questionnaires to customers to create profiles for their likes, dislikes and needs. Customized weight loss programs, clothing and shopping companies, makeup brands, vitamin providers, are just a few to have delved into an industry with massive growth potential, said Eden.

UBS's theory is that food, medical diagnosis, technology and food delivery companies can all benefit from this industry. From services as simple as questionnaires, blood samples and genetic profiling, companies can capitalize on society's shift in support to improved nutritional habits.

UBS said it sees four major industries capitalizing on this opportunity: Medical diagnosis firms to extract and interpret test results; Technology companies to develop wearable tech and integrated platforms for users to receive ongoing interactive feedback; Food producers to meet nutritional demand; And, food delivery companies to meet consumers' increasing demand for convenience.

Illumina, Thermo Fisher Scientific, Apple, FitBit, Nestle, 23andMe, Ancestry.com, Unilever, Amazon Fresh and Uber Eats are some of the companies UBS mentioned as being in the game.

The personalized nutrition opportunity has not been lost on current food company incumbents.

"Nestl, the world's largest food company, has identified personalised nutrition as a major growth opportunity and has made a number of investments in the space," said Eden.

Nestle puts money into research from brain health, pediatrics, chronic medical conditions, obesity, malnutrition, and gastro-intestinal health.

Companies like Apple have bet big on personalized health, which could make the Tim Cook-led tech giant a potential pioneer in the personalized nutrition industry. Apple has identified the health care industry as an area of innovation, with its popular Apple Watch providing real-time personal health data to its wearers.

"The Apple Watch is already being used to study heart rates, perform ECGs, study eating disorders, track fitness and many other health metrics. Health data in Apple Watch could be combined with genetic information to offer personalized nutrition," said Eden.

Even Amazon Fresh, the e-commerce giant's grocery delivery service, and Uber Eats are well-positioned to win in this budding industry, said UBS.

"Delivery will allow for increased convenience and time savings in food preparation (e.g. partnering with Delivery Hero or Uber Eats to deliver the exact meal which has been freshly prepared to meet the needs of that individual consumer)," said Eden.

Eden said affordability is the most obvious constraint on the personalized nutrition scenario in the near term. Healthier foods can be more expensive than mass produced box items, and the personalization will also come with a cost.

Scientific evidence on the merits of personalizaiton are also lacking, UBS said.

Data privacy is a hurdle as well if consumers don't want their medical, biological or genetic information shared with other parties.

with reporting from CNBC's Michael Bloom.

Continued here:
Personalized nutrition could be the next plant-based meat, worth $64 billion by 2040, says UBS - CNBC

Over the past two decades, the field of medical genomics underwent nothing less than a revolution in terms of both technological advancement and accumulated knowledge. This revolution holds the promise of changing the entire medical practice, and while the industrycontinues to improve genome sequencing technologies and decrease the price of sequencing a genome, other challenges are lurking that hinder the prospects of this revolution and undermine the efforts of wide-scale integration of genomics into mainstream medicine.

To emphasize this point further, even though the technologies to help diagnose patients with rare genetic diseases exist, the rate of underdiagnoses and misdiagnoses is still alarmingly high, and patients who receive diagnoses end up waiting too long for them, sometimes years. These extensive diagnostic journeys directly impact the ability to recruit patients for clinical trials, and thus the ability to develop more treatments for rare diseases. To date, only 5% of more than 7,000 known rare genetic diseases have FDA-approved treatments.

At my company, a leading digital health company, our mission is to end the diagnostic odyssey for undiagnosed pediatric patients with rare diseases. I've seen that the main contributors to this state of affairs are the excruciatingly long wait times for genetics appointments, coupled with the significant workforce shortage of experts in the field.

To reach more than 400 million patients globally (50% of whom are estimated to be young kids) with earlier intervention to improve outcomes and help many of them live relatively healthy and productive lives, the diagnosis must shift from the geneticists clinic to the primary point of care, or at least it must be initiated much earlier by primary care physicians.

Without adopting technological solutions that will support the integration of genomics into mainstream medicine, genomics will never live up to its promise and become a standard of care. In my opinion, realizing that vision will be a balancing act between the affordability for payers, accessibility for providers and actionability for patients, and it will depend on technological solutions combining AI-based phenotyping, as well as connecting front-line providers with human experts in genetics, alongside the most advanced genome sequencing technologies.

High Throughput Genetic Testing

As noted, genome sequencing technologies have made huge strides over the last two decades. The affordability of genomics is now increasingly dependent on the ability to sift through and interpret vast amounts of data produced from a genome, and to determine which data is pertinent for a medical diagnosis and for disease treatment a task fitting for AI.

Indeed, in the last few years, we have witnessed many AI-driven solutions sprouting to address this problem. Some of these solutions are home-grown, in leading laboratories such as Invitae, GeneDx and PerkinElmer Genomics. (Full disclosure: PerkinElmer Genomics uses FDNA's technology in its genetic analysis.) Others are developed as software platforms by vendors such as Sophia Genetics, Fabric Genomics, Congenica and Emedgene.

Harnessing AI to perform data analysis challenges has proven to be very successful and is a direct contributor to the affordability of genetic testing today, as well as the gradually increasing rate of reimbursement by payers. I believe AI will continue to play a key role in driving down prices to the $100 range, which will make genomics extremely affordable, both for health systems and for individuals paying out-of-pocket.

Phenotyping Driven By AI

AIs impact goes far beyond applying machine learning algorithms that sift through genetic variations and proprietary knowledge bases of pathogenicity. As more OMICS technologies stack up with genomics, and more AI modalities like natural language processing and computer vision image analysis are integrated directly into the genome analysis pipeline, we will see an increasing standardization of data across disparate data silos and a closing of the genotype-phenotype gap between the clinic and the lab. This trend will drive genomic data to become more actionable for patients and allow them to make informed decisions about their health.

Much of todays phenotyping is performed by humans and is inherently subject to biases such as age, gender and ethnicity. If we approach this problem with legal and ethical rigor, care and are cautious of patient privacy, and with respect to the providers and their workflow, AI could enhance human skills and capabilities. I think that helping primary care providers collect, structure and analyze phenotypic information of patients with rare diseases is an area worth prioritizing.

Connecting The Expert Community

Finally, technology is more than AI. Technology is also an enabler for fostering connections and interactions between humans. Some tasks in practicing medicine must be left to humans, but even then, technology can assist. An alternative abbreviation of AI (augmented intelligence) is my preferred one. It implies a symbiotic relationship between people and machines, making each other stronger, rather than threatening to replace each other.

Tailoring a solution combining all three components (genomics, AI-based phenotyping and community connection) like the one described above is not an easy task, and it depends on the ability of stakeholders from many disciplines to work together, share data and collaborate on research and development.

To achieve this, a best-of-breed approach should be taken, and not only should data be shared, but a global collaboration between commercial companies, academic research institutions and caregivers should occur. The integrity of the data, ethical and privacy policies, and trust in workflow should be established. This requires an open dialogue between all parties involved, as well as a fast-pace framework to allow developers to move quickly in building these tools.

Certainly, working with different stakeholders with sometimes conflicting interests is challenging, but the one common goal we all have is helping patients, especially kids with rare and undiagnosed genetic diseases.

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Access And Actionability Are Key For Genetic Testing And Precision Medicine - Forbes

Nima Sharifi, M.D.

A new Cleveland Clinic study has uncovered a genetic anomaly associated with poor response to a common asthma treatment. The findings, published in Proceedings of the National Academy of Sciences, showed that asthmatic patients with the gene variant are less likely to respond to glucocorticoids and often develop severe asthma.

The research team, led by Nima Sharifi, M.D., of Cleveland Clinics Lerner Research Institute, identified that the gene variant HSD3B1(1245A) is associated with glucocorticoid response and may be clinically useful to identify patients most likely to benefit from other treatments.

Glucocorticoids, which modulate systemic inflammatory response, are commonly prescribed to treat severe asthma. However, until now we have not understood why many patients do not benefit from them, said Dr. Sharifi, senior author of the article. These findings make the case for genetic testing and personalized treatment and provide important information for identifying which patients should be treated using different therapies.

In the study, Dr. Sharifi and his collaborators retrospectively analyzed the association between patient genomes and lung function in more than 500 asthmatic patients who received daily oral glucocorticoids treatment or no glucocorticoids treatment.

Joe Zein, M.D.

They found that a change to the gene HSD3B1 specifically the HSD3B1(1245A) variant is associated with poor lung function and glucocorticoid treatment resistance. The analysis revealed that among patients receiving glucocorticoids, those with the variant had poorer lung function than those who did not have the genetic anomaly, suggesting that it contributes to resistance and helps drive the progression to severe asthma.

Previous studies have shown that HSD3B1 encodes an enzyme that converts less active hormones called androgens into more powerful androgens. While additional research is necessary, the team suspects that HSD3B1(1245A)s effect on lung function may be attributed to inhibition of this process.

This study is the first to provide genetic evidence suggesting that variants related to androgen synthesis affect glucocorticoids treatment resistance in asthma or any other inflammation-related disease, said Joe Zein, M.D., first author on the study and a practicing pulmonologist in Cleveland Clinics Respiratory Institute. These findings provide us with important new information that may lead to more tailored treatments for asthma patients and the ability to prevent the development of severe disease.

Asthma is a chronic condition that causes the airways of the lungs to narrow, the lining of the airways to become inflamed and the cells that line the airways to produce more mucus, making it difficult to take in enough air. According to the CDC, about 25 million people in the U.S. have asthma, including more than six million children. Asthma accounts for nearly two million emergency department visits each year.

Previously, Dr. Sharifis laboratory has extensively studied the role of HSD3B1 in prostate cancer. In 2013, he made the seminal discovery that prostate cancer cells with the HSD3B1(1245C) variant survive androgen deprivation therapy, the first line of defense against prostate cancer, by producing their own disease-fueling androgens. He has spent more than seven years studying and publishing peer-reviewed articles on the variants effect in prostate cancer.

Dr. Sharifi holds the Kendrick Family Chair for Prostate Cancer Research at Cleveland Clinic and directs the Cleveland Clinic Genitourinary Malignancies Research Center. He has joint appointments in the Glickman Urological & Kidney Institute and Taussig Cancer Institute. In 2017, he received the national Top Ten Clinical Achievement Award from the Clinical Research Forum for his discoveries linking HSD3B1(1245C) with poor prostate cancer outcomes.

Dr. Zein is a member of the Cleveland Clinic Asthma Center, which provides a comprehensive approach to asthma management and care along with innovative research, offering patients access to the most advanced diagnostic testing and innovative treatments.

This study was supported by the National Heart, Lung, and Blood Institute and the National Cancer Institute, both of the National Institutes of Health.

Link:
Cleveland Clinic Study Identifies Genetic Anomaly Associated with Poor Response to Common Asthma Treatment - Health Essentials from Cleveland Clinic