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- Data presented at the 18th Annual WORLDSymposiumTM 2022 -

TOKYO, Japan I February 7, 2022 I Astellas Pharma Inc. (TSE: 4503, President and CEO: Kenji Yasukawa, Ph.D., "Astellas") today announced positive interim safety data from FORTIS, the Phase I/II clinical trial evaluating AT845, an investigational adeno-associated virus (AAV) gene replacement therapy to deliver a functional alpha-glucosidase (GAA) gene to express acid alpha-glucosidase (GAA) directly in muscle cells in adults with Late-Onset Pompe Disease (LOPD) (Presentation & Poster: 206).

Pompe disease, a rare, severe, autosomal recessive metabolic disease characterized by progressive muscular degeneration, results from a mutation in the GAA gene that interferes with the production or function of the GAA protein. GAA is responsible for metabolizing glycogen, and dysfunction or absence of this protein results in the accumulation of glycogen, primarily in the skeletal and cardiac muscles, where it causes damage to tissue structure and function. Currently, the only approved treatment for Pompe disease is enzyme replacement therapy (ERT), which is delivered via chronic intravenous infusions every two weeks and relies solely on tissue uptake of GAA from plasma.

"There is significant unmet need for patients with Pompe diseasedue to the short half-life, inefficient uptake in the key tissues affected by the disease and the immunogenicity of ERT," said Tahseen Mozaffar, M.D., Professor of Neurology at UC Irvine. "AT845 has the potential to be a best-in-class approach as a muscle-directed gene therapy using an AAV8 capsid serotype. It is being investigated to determine whether it can deliver a functional GAA gene that is efficiently transduced to express GAA directly in tissues affected by the disease, including skeletal and cardiac muscle."

FORTIS is an ongoing multicenter, open-label, ascending dose Phase I/II first-in-human clinical trial to determine if AT845 is safe and tolerable in adults with LOPD. Enrolled participants receive a one-time peripheral intravenous infusion of AT845, followed by one year of frequent monitoring of safety, clinical and biochemical endpoints including GAA activity and protein level in muscle and four additional years of long-term safety monitoring. The primary endpoints of the trial are safety and tolerability, as well as efficacy measures, including change in muscle GAA protein expression and enzyme activity from baseline. Secondary endpoints evaluate improvements in respiratory, endurance and quality of life measures.

As of the December 3, 2021 data cut-off date, four participants have been enrolled in FORTIS, with two participants dosed at 3 x 1013 vg/kg (Cohort 1) and two participants dosed at 6 x 1013 vg/kg (Cohort 2). The reported data includes interim safety and tolerability assessments, as well as up to 24 weeks of follow-up for the two participants in Cohort 1 and preliminary data from the two participants in Cohort 2.

"We are pleased that AT845 has been well-tolerated so far in the four adults with LOPD who have received treatment," said Weston Miller, M.D., Senior Medical Director, Clinical Development at AstellasGene Therapies. "In the two participants in Cohort 1 with follow-up duration through week 24 after dosing, AT845 demonstrated an encouraging safety profile. Importantly, there have been no serious adverse events reported following dosing in any of the four participants as of the time of the data cut. One participant experienced elevated transaminases, which is considered a common immune-mediated treatment response based on the time of onset after dosing, its presentation during steroid taper initiation and its reversal with steroid re-initiation. These safety data are encouraging, and the program continues to enroll participants."

With the establishment of the Astellas Gene Therapies Center of Excellence following the 2020 acquisition of Audentes Therapeutics Inc., Astellas is a leader in genetic medicines, working alongside its world-renowned partners to build a portfolio of potentially life-changing gene therapies. Astellas strives to identify, develop and deliver transformative therapies for patients with genetic diseases who currently have few or no effective treatment options.

About Pompe DiseasePompe disease is a rare, severe, autosomal recessive metabolic disease characterized by progressive muscular degeneration. The overall incidence is estimated to be approximately 1 in 40,000 births,ialthough frequency and disease progression varies with age of onset, ethnicity and geography.iiThe disease is caused by mutations in the alpha-glucosidase (GAA) genethat prevent the production and function of a protein called acid alpha-glucosidase (GAA). GAA is responsible for metabolizing glycogen, and dysfunction or absence of this protein results in the accumulation of glycogen in tissues, primarily in the skeletal and cardiac muscles, where it causes damage to tissue structure and function. Currently, the only approved treatment for Pompe is enzyme replacement therapy (ERT), which is a chronic treatment delivered in bi-weekly infusions and relies solely on tissue uptake of GAA from plasma.

About AT845 for the treatment of Late-Onset Pompe Disease (LOPD)Astellas is developing AT845, a novel gene replacement therapy using an AAV8 vector, under a cardiac- and skeletal muscle-specific promotor, to deliver a functional copy of the GAA gene, for the treatment of Late-Onset Pompe Disease (LOPD). AT845 is being investigated to determine whether it can deliver a functional GAA gene that is efficiently transduced to express GAA directly in tissues affected by the disease, including skeletal and cardiac muscle.

About FORTISFORTIS is an ongoing multicenter, open-label, ascending dose Phase I/II first-in-human clinical trial to determine if AT845 is safe and tolerable in adults with Late-Onset Pompe Disease (LOPD). The primary endpoints of the trial are safety and tolerability, as well as efficacy measures, including change in muscle GAA protein expression and enzyme activity from baseline. Secondary endpoints evaluate improvements in respiratory, endurance and quality of life measures.

About AstellasAstellas Pharma Inc. is a pharmaceutical company conducting business in more than 70 countries around the world. We are promoting the Focus Area Approach that is designed to identify opportunities for the continuous creation of new drugs to address diseases with high unmet medical needs by focusing on Biology and Modality. Furthermore, we are also looking beyond our foundational Rx focus to create Rx+ healthcare solutions that combine our expertise and knowledge with cutting-edge technology in different fields of external partners. Through these efforts, Astellas stands on the forefront of healthcare change to turn innovative science into value for patients. For more information, please visit our website at https://www.astellas.com/en.

About Astellas Gene TherapiesAstellas integrated its wholly owned subsidiary, Audentes Therapeutics, Inc., as of April 1, 2021 and established "Astellas Gene Therapies" within the organization as an Astellas Center of Excellence to develop genetic medicines with the potential to deliver transformative value for patients. Based on an innovative scientific approach and industry leading internal manufacturing capability and expertise, we are currently exploring three gene therapy modalities: gene replacement, exon skipping gene therapy, and vectorized RNA knockdown and hope to also advance additional Astellas gene therapy programs toward clinical investigation. We are based in San Francisco, with manufacturing and laboratory facilities in South San Francisco and Sanford, North Carolina.

iKishnani, PS, et al. Pompe disease diagnosis and management guideline. Genetics in medicine: official journal of the American College of Medical Genetics, 2006. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3110959/ii Ausems MG, et al. Frequency of glycogen storage disease type II in The Netherlands: implications for diagnosis and genetic counselling. European Journal of Human Genetics, 1999. Available from: https://www.nature.com/articles/5200367.pdf?origin=ppub; Lin CY, et al. Pompe's disease in Chinese and prenatal diagnosis by determination of alpha-glucosidase activity. Journal of Inherited Metabolic Disease, 1987. Available from: https://pubmed.ncbi.nlm.nih.gov/3106710/; Hirschhorn R, et al. Pediatric Research, 2004; Bashan N, et al. Glycogen storage disease type II in Israel. Israel Journal of Medical Sciences, 1988. Available from: https://europepmc.org/article/med/3132435; Meikle PJ, et al. Prevalence of Lysosomal Storage Disorders. JAMA, 1999. Available from: https://jamanetwork.com/journals/jama/article-abstract/188380

SOURCE: Astellas Pharma

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GENETICS Correction for Chromosome 10q26driven age-related macular degeneration is associated with reduced levels of HTRA1 in human retinal pigment epithelium, by Brandi L. Williams, Nathan A. Seager, Jamie D. Gardiner, Chris M. Pappas, Monica C. Cronin, Cristina Amat di San Filippo, Robert A. Anstadt, Jin Liu, Marc A. Toso, Lisa Nichols, Timothy J. Parnell, Jacqueline R. Eve, Paul L. Bartel, Moussa A. Zouache, Burt T. Richards, and Gregory S. Hageman, which published July 22, 2021; 10.1073/pnas.2103617118 (Proc. Natl. Acad. Sci. U.S.A. 118, e2103617118).

The authors note that Sven Heinz and Michael G. B. Hayes should be added to the author list between Jacquelin R. Eve and Paul L. Bartel. These two authors developed and trained the other coauthors in the techniques used to perform the epigenetic profiling of the chromatin upstream of the HTRA1 gene. The chromatin data were invaluable to the paper and could not have been performed without the training and assistance from Michael Hayes and Sven Heinz. Both Sven Heinz and Michael G. B. Hayes should be credited with Contributed new reagents/analytic tools to this research. The corrected author line, affiliation line, and author contributions appear below. The online version has been corrected.

Brandi L. Williamsa,1, Nathan A. Seagera, Jamie D. Gardinera, Chris M. Pappasa, Monica C. Cronina,2, Cristina Amat di San Filippoa,3, Robert A. Anstadta, Jin Liua, Marc A. Tosoa, Lisa Nicholsa, Timothy J. Parnellb, Jacqueline R. Evea,4, Sven Heinzc, Michael G. B. Hayesc, Paul L. Bartela,6, Moussa A. Zouachea, Burt T. Richardsa, and Gregory S. Hagemana,1

aSteele Center for Translational Medicine, John A. Moran Eye Center, University of Utah, Salt Lake City, UT 84132; bBioinformatics Analysis, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84132; and cDepartment of Medicine, University of California San Diego, La Jolla, CA 92093

Author contributions: B.L.W., P.L.B., B.T.R., and G.S.H. designed research; N.A.S., J.D.G., C.M.P., M.C.C., C.A.d.S.F., R.A.A., J.L., M.A.T., and J.R.E. performed research; L.N., J.R.E., S.H., M.G.B.H., and M.A.Z. contributed new reagents/analytic tools; B.L.W., T.J.P., and M.A.Z. analyzed data; B.L.W. wrote the paper; L.N. coordinated donor eye collection and processing; and G.S.H. provided donor eye tissue, graded AMD status, critically reviewed work, and provided all funding.

Excerpt from:
Correction for Williams et al., Chromosome 10q26driven age-related macular degeneration is associated with reduced levels of HTRA1 in human retinal...

Denmark, like other Nordic countries (14), has maintained for more than half a century population-wide demographic, health care, and socioeconomic registers that provide detailed information on the interaction between all residents and the extensive national social services system (5, 6), including familial information via parental links (7, 8). This has allowed population-based studies of the causes and consequences of disease at an unprecedented scale and detail (9).

Several studies in the Nordic countries have leveraged diagnostic information from cross-referenced civil and health care registers on pairs of close relatives for quantitative genetic studiesthat is, co-occurrence and familial coaggregation, heritability and genetic correlation, and nonrandom mating (1014). However, the dynamics of a population (e.g., changes in mating patterns and family structure, health care provision, clinical practice, and diagnostic systems) may compromise such initiatives and bias quantitative genetic estimates and inference on human behavior. Thus, realizing the potential of Nordic population and health care registers depends on insights into the structure and network properties of the entire genealogy and accounting for underlying changes in the frequencies of human traits, notably in population demographics and disease frequencies.

Here, we reconstruct the Danish genealogy using the population-wide Danish Civil Registration System that holds information on family relationships for all individuals with at least 1 d of legal residence in Denmark since 1968 (7, 8). We describe the size, structure, and network properties of the genealogy along 116 y. We leverage the cross-reference to the nationwide, public, and health care registers to estimate occurrence, heritability, and genetic correlations for 10 broad categories of medical conditions across eight consecutive demographic cohorts.

The Danish Civil Registration System (7, 8) has been registering all people legally residing in Denmark since 1968, and it includes information about sex, date of birth, parental links, and life events (e.g., migration or death). By April 2017 (time of data freeze for this report), 9,851,330 individuals were registered in the Danish Civil Registration System. The system is linked via anonymized identification numbers to the Danish National Patient Register (6) and the Danish Psychiatric Central Research Register (5) that include all diagnostic information regarding general medical conditions and specific psychiatric conditions, respectively, including all inpatient and outpatient contacts.

By use of the parental links, we reconstructed the Danish genealogy, which we then leveraged together with the diagnostic information from the Danish National Patient Register to study the genetic architecture of medical conditions as defined in the 8th and 10th Revisions of the International Classification of Diseases (ICD-8 and ICD-10, respectively). Inspired by the extensive comorbidity (15) between disorders affecting the same organ or characterized by the same pathology, we analyzed 10 broad diagnostic categories of medical conditionsthat is, nine somatic and one mental (SI Appendix, Table 1).

To study changes in genetic architecture in time and identify possible epidemiological biases such as truncation and censoring, we carried out the analyses in eight consecutive demographic cohorts with characteristic cultural, political, and economic features of Western societies (SI Appendix, Table 2; https://www.careerplanner.com/Career-Articles/Generations.cfm). The eight cohorts are the Interbellum Generation (birth year: 1901 to 1913), the Greatest Generation (1910 to 1924), the Silent Generation (1925 to 1945), the Baby Boomers (1946 to 1964), Generation X (1965 to 1979), Millennials (1980 to 1994), Generation Z (1995 to 2012), and Generation Alpha (2013 to 2025). We note that there is a 4-y overlap between the Interbellum and Greatest Generation. Individuals born outside the eight cohorts were not considered in the analyses. In addition, we discarded individuals that died before January 1, 1977 (date on which the Danish National Patient Register was established).

To inform downstream heritability and genetic correlation analyses, we initially determined the size and structure of the Danish genealogy by examining its network properties (Fig. 1).

Size and structure of the Danish genealogy. (A) Membership of the 9,851,330 registered participants in the identified network components. The vast majority of the participants (69.04%) had at least one known relative (pink, blue, and green). (B) Frequency of network components ordered by pedigree size. One component with size n = 5,396,661 (pink) includes 79.35% of the connected individuals. (C) Frequency of number of an individuals relatives. The Danish genealogy is dominated by individuals with few relatives. (D) Frequency of familial relationships and relative position to Self. Color-coding corresponds to degree of relationship. FS, full siblings; HS, half siblings; 1C, first cousins, etc.; PO, parentoffspring; 1G, grandparentgrandchild, etc.; Av, avuncular; 1GAv, grandavuncular, etc.; 1R, once removed, etc. The structure is enriched for close familial relationships (outlined by the dotted line).

Of the 9,851,330 registered individuals, 6,801,107 (69.04%) had at least one registered relative, while 3,050,223 (30.96%) were unconnected singletons and were therefore excluded from further analysis (Fig. 1A). The single largest pedigree includes 5,396,661 individualsthat is, 54.78% of all registered individuals and 79.35% of the individuals with at least one relative (Fig. 1A). The genealogy also includes 251,513 smaller unconnected pedigrees (n = 1,404,446), among which there are 100,400 trios and 58,804 quartets (Fig. 1 A and B).

The 6,801,107 connected individuals span only six generations and include 2,377,043 foundersthat is, individuals with no parental links. It is expected that some of the founders are closely related (e.g., siblings or cousins), but, in the lack of parental links or genetic information, we are unable to consider this in our analyses. The narrow generation span combined with the high number of founders has implications in the ascertainment of distant relative pairs (Fig. 1C). As a result, 29,739,188 out of 41,798,152 annotated relative pairs (71.15%) are concentrated within a radius of three meioses, encompassing parentoffspring, full siblings, half siblings, grandparentgrandchild, avuncular, half avuncular, and great grandparentgreat grandchild pairs (Fig. 1D).

The two oldest (Interbellum and Greatest) and the one youngest (Generation Alpha) demographic cohort had considerably fewer individuals (N45,103 to 325,066) and were therefore expected to be less informative than the five larger cohorts (N1,020,953 to 1,489,329) (SI Appendix, Table 2).

Disease prevalence for all 10 diagnostic categories peaks in the Greatest and the Silent Generation and declines to a minimum in Generation Alpha (SI Appendix, Table 3). Circulatory conditions constitute the most frequent category, affecting 61.9% of the individuals in the Greatest Generation, whereas hematological and musculoskeletal conditions are the least frequent categories, affecting at their peak 12.11 and 12.71% of individuals in the Greatest and Silent Generations, respectively (SI Appendix, Table 3 and Fig. 1).

While the decline in relative frequency is very similar across diagnostic categories, consistent with a uniform age-dependent effect on the age of onset of disease, mental and pulmonary conditions are characterized by distinct profiles, remaining at elevated frequency until the two youngest cohortsthat is, Generation Z and Generation Alpha (SI Appendix, Fig. 2).

We estimated heritability (h2) of the 10 diagnostic categories (15) by applying the latent correlation of relative pairs to Falconers method (16). In our analysis, we considered all family relations within a radius of three meioses (i.e., all up to second degree and great grandparents/great grandchildren) because these were abundant enough to yield accurate estimates.

For all 10 diagnostic categories, heritability increases across demographic cohorts and peaks in Generation Z. Due to truncation, censoring, and data scarcity that characterize the oldest and youngest generations, we consider estimates from the four midmost and largest cohortsthat is, Silent Generation, Baby Boomers, Generation X, and Millennialsto be a priori more reliable (Figs. 2 and 3).

Heritability estimates of 10 broad diagnostic categories by demographic cohort. Most estimates correspond to average values from all available relative pairs weighted by sampling variance. Least-squares estimates are reported for the hematological category in Generation Z and Generation Alpha. Both tile size and shade intensity are proportional to heritability values. All estimates were significantly different from zero.

Heritability estimates (and 95% CIs) of 10 broad diagnostic categories by demographic cohort. Estimates were derived from parentoffspring pairs alone (empty circles), averages from all available relative pairs weighted by sampling variance (filled squares), or least-squares regression (filled circles). The gray line corresponds to cross-category weighted average estimates.

Estimates of heritability varied notably between diagnostic categories (Figs. 2 and 3) and consistently across demographic cohorts as reflected in their cross-cohort weighted average estimates (Table 1). Heritability estimates for mental disorders were consistently the highest across demographic cohorts (average h2 = 0.406, 95% CI = [0.403, 0.408]), whereas estimates for cancers and neurological conditions were the lowest (average h2 = 0.130, 95% CI = [0.125, 0.134] and average h2 = 0.154, 95% CI = [0.151, 0.157], respectively).

Heritability of 10 broad diagnostic categories across eight demographic cohorts

Heritability could not be estimated for some diagnostic categories in the two oldest and the two youngest demographic cohorts due to data scarcity (Figs. 2 and 3 and Table 1). In addition, most heritability estimates were similar when analyses were restricted to full sib pairs only, although the consideration of multiple sib pairs from the same family resulted in wider CIs (SI Appendix, Figs. 3 and 4 and Table 4). A similar trend was observed when analyses were restricted only to individuals born in Denmark (n = 6,017,195) as reflected in the high correlation between measures (r = 0.94; SI Appendix, Fig. 5).

Finally, we note that with the notable exception of cancers and conditions of the hematological system, no single disease seems to dominate the 10 diagnostic categories under study (SI Appendix, Fig. 6)and consequently, the corresponding heritability estimates.

To understand the mutual relationships between the 10 broad diagnostic categories (15), we estimated their genetic correlations (rg) by combining within- and between-category estimates of the latent correlation into Falconers method (16). We considered all family relations within a radius of three meioses and restricted the analyses to the four most data-rich demographic cohorts mentioned in Genealogy Network Structure (Fig. 4 and Dataset S1).

Genetic correlations of each of 10 broad diagnostic categories with the remaining nine by demographic cohort. Only the four most data-rich cohortsSilent Generation, Baby Boomers, Generation X, and Millennialswere considered. Estimates were based on averages from all available relative pairs within a radius of three meioses weighted by sampling variance. Blank cells correspond to correlations not significantly different from zero.

All rg except two were positive, and all of them except one were also significantly different from zero. Overall, rg were highly consistent between consecutive cohorts, thus further boosting confidence in the estimates (SI Appendix, Fig. 7). This trend was more marked for certain diagnostic categories such as mental, pulmonary, and neurological than others. In all 10 diagnostic categories, younger cohorts showed lower rg than older generations, whereas the opposite trend was observed for heritability that consistently increased in younger cohorts (Fig. 4 and SI Appendix, Dataset S1). The average rg of each of the 10 diagnostic categories with the other nine categories was highest for gastrointestinal conditions (0.567; SE = 0.0005) and lowest for urogenital conditions (0.386; SE = 0.0008).

We further used 45 cross-generation weighted average genetic correlations to hierarchically cluster the 10 diagnostic categories (Fig. 5). We observed three major clusters in the dendrogram: one including mental, pulmonary, gastrointestinal, and neurological conditions; another one involving musculoskeletal conditions and cancers; and a third one involving urogenital, hematological, circulatory, and endocrine conditions.

Dendrogram of 10 broad diagnostic categories based on cross-cohort weighted average genetic correlations. Only the four most data-rich cohortsSilent Generation, Baby Boomers, Generation X, and Millennialswere considered. Estimates were based on averages from all available relative pairs within a radius of three meioses weighted by sampling variance.

Finally, genetic correlation estimates based on full sib pairs alone, in which most pairings are not intergenerational, are shown in SI Appendix, Figs. 810 as well as Dataset S2 and were generally consistent with analyses based on all family relations.

In this study, we present the Danish genealogy constructed from the Danish Civil Registration System, which holds information on all individuals born or with residence in Denmark since 1968. The genealogy extends back up to six generations, with the oldest connected individuals being born in 1872 and the youngest in 2017. We partitioned 6,691,426 Danish citizens into eight demographic cohorts based on year of birth. Notably, by cross-linking the Danish Civil Registration System with hospital discharge diagnoses from the public and egalitarian Danish health care system, we were able estimate heritability and genetic correlations for 10 broad diagnostic categories encompassing all major organ systems and most ICD-8/ICD-10 codes while describing the epidemiological biases of truncation and right censoring in the oldest and youngest demographic cohorts, respectively.

The heritability of single diseases and genetic correlations between them have been studied extensively not only in family data but also thanks to the development and application of genome-wide association studies to thousands of human traits (17). In a few instances (e.g., for mental disorders), genetic risk variants shared across diagnoses with clearly distinct clinical characteristics and age of onset have been identified (18). However, neither the heritability nor the genetic correlations have been systematically studied for organ-defined disease categories as grouped by 10 chapters of ICD-10. In addition, such studies have never been carried out within a single population such as the Danish, serviced and monitored uniformly for decades by an egalitarian health care system.

We estimate the heritability to be high for several of the 10 disease categories. This is consistent with high genetic correlation between individual diagnoses within each category as reported for mental disorders (18) and more broadly for brain disorders (19) as well as with the broader notion that genetic liability is generally organ specific. For mental conditions in particular, heritability point estimates reach 0.7, which is higher than reported for the common and less heritable mental disorders such as depression (0.4) (20) and anxiety (0.30.4) (21) and similar to those for highly heritable, rare illness, such as schizophrenia (0.81) (22) and bipolar disorder (0.60.8) (23).

Moreover, the lower heritability estimates in older cohorts and the higher heritability estimates in younger cohorts might be because disease risk is generally plateauing in the former, whereas accumulation of diagnoses in the latter is an ongoing process, interrupted by right censoring. Younger cohorts are therefore enriched for younger ages of onset, which in many instances go along with stronger genetic signals and higher heritability estimates as known for mental disorders in which early onset disorders such as autism and attention-deficit/hyperactivity disorder are commonplace. It could also be posited that the accumulation of environmental exposures throughout life renders nongenetic factors more important in aging-related conditions, thus resulting in overall lower heritability estimates in older cohorts. On the other hand, stronger genetic correlations in older cohorts might be due to the accumulation of comorbidities in older cohorts compared to younger cohorts.

The fact that genetic correlations were almost exclusively positive across all cohorts probably reflects how diseases, at least in the broad composite definitions we use in this work, are problems of the normal functioning of organs and systems, whereby the disorganization of one or more of them should be detrimental for others, ultimately resulting in further pathology. The positive genetic correlations match comorbidity observations in the clinical domain.

Notably, we observe that the ranking of heritability and average genetic correlation estimates compare for most of the 10 diagnostic groups, although there are also marked exceptions. Mental, gastrointestinal, and circulatory conditions rank high both for heritability and average genetic correlation, whereas neurological conditions, despite showing the lowest heritability estimates, are genetically highly correlated with the other diagnostic groups, implicating broadly the etiology of disease affecting the nervous system in disorders of most other organ systems. Contrary, other low-heritability groups, such as cancer and musculoskeletal conditions, have low genetic correlations suggestive of their etiologies being dominated by disease-specific, environmental exposures and somatic mutations for the former and accidents for the latter. Similarly, endocrine conditions, dominated by type 2 diabetes, have relatively low heritability, possibly reflecting behavioral causes.

While circulatory and gastrointestinal conditions are the most heritable and genetically correlated diagnostic categories, their patterns of genetic correlation with other diagnostic categories are nonetheless highly diverse. In fact, gastrointestinal conditions were clustered with neurological and mental disorders, and while the clustering of the two latter disease categories of the nervous system could be anticipated and possibly reflects organ-specific components of their heritability, their proximity to gastrointestinal conditions is notable and may stem from the extensive innervation that underlies the gutbrain axis and the proposed relation between gut microbiota for brain functioning and mental health (24). Contrary to the proximal clustering of brain and gut disorders reflecting shared organ specificity or functionalities, that of endocrine with circulatory conditions as well as that of cancers with hematological illnesses more likely reflects sequelae in which one illness is a consequence or complication of a prior and otherwise, unrelated condition, in case, diabetes leading to circulatory complications and cancers to anemia because of bleeding from internal organs.

Although the reconstructed Danish genealogy is limited to six generations and thus dates back in time considerably less than the genealogy of Iceland (25), we note that most diagnostic categories include between a quarter- and a-half-million individuals, making this genealogy dataset highly apt for studies of heritability, genetic correlations, and the impact of behavioral and environmental changes over time. Also in comparison with Iceland, the relative shallowness of the reconstructed Danish genealogy, compared to, for instance, the much deeper Icelandic pedigree dating back to the 11th century (25), renders linking distant relatives a challenging task and supports the use of classical relative pair-based methods rather than linear mixed models. Furthermore, truncation and censoring biases in the oldest and youngest cohorts, as well as changes in the environment and clinical practices over time, favor the use of horizontal over vertical familial relationships and justify the stratification of the analysis by demographic cohort rather as opposed to a single analysis across the entire genealogy.

While this dataset is ideally poised for quantitative genetic analyses, it also presents limitations. As already discussed, truncation in the older demographic cohorts and right censoring in the younger ones can introduce bias to heritability and genetic correlation estimates. In order to explore the effects and biases of time, we split the available data into eight demographic cohorts and show that the four midmost cohortsthat is, the ones least affected by truncation and censoringyield consistent estimates.

In addition, given the lack of genetic data, we have no means to safeguard our analysis from false paternities and adoptions. As a result, a small portion of the ascertained familial relationships may be overstated, affecting our heritability and genetic correlation estimates. Nevertheless, given the high abundance of relative pairs, we believe that the effect of these biases is limited. Similarly, the lack of parental links before the timeframe of the registries will lead to understating distant familial relationships, which could bias heritability estimates based on frameworks that utilize the entire relationship matrix such as linear mixed models. However, because our estimates are based on known family pairs, we believe that issues coming from an underestimation of familial relationships are limited in our analysis.

Furthermore, modifications in the diagnostic classification system, which changed from ICD-8 to ICD-10 in 1995, and the registration of outpatient contacts that began in the same year (9) may complicate precise tracking across demographic cohorts, although the focus on broad diagnostic categories in this study is expected to reduce this bias.

Finally, our analyses make no attempt to distinguish a priori between genetic correlation resulting from pleiotropy and co-occurrence of disease in relatives because of sequelae as discussed in the seventh paragraph of Discussion for cancers and anemia.

For mental disorders, the relatively high frequency in the younger cohorts coincides with the introduction of novel child and adolescent disorders in ICD-10that is, attention-deficit/hyperactivity disorder and autism. Similarly, pulmonary conditions show increasing frequencies in younger generations consistent with increasing worldwide prevalence of smoking and asthma in young people (26). While potentially biasing our findings, changes in disease frequency across time also constitutes an entirely novel research field opening for the identification of nongenetic factors independently or through gene-environment interactions influencing risk of disease. In fact, as the habit of smoking spreads and increases during the middle of the 20th century (26) and the prevalence of pulmonary and circulatory conditions increases correspondingly, the heritability is expected to decrease; thus, modeling a shared environment in households will allow for studies seeking to identify nongenetic factors that impact disease. Such analyses can be empowered by the knowledge of geographical (co)location of the residence of Danish citizens from cradle to grave as a proxy for shared environment.

In conclusion, here we presented the Danish genealogy as a resource that, in combination with the National Health Registers, allows whole-population, quantitative genetic analysis with applications to health sciences. The presented resource and analytical framework will contribute to the advancement of precision medicine, allowing the systematic mapping of heritabilities and genetic correlations of comorbidity patterns and sub-diagnostic traits such as age of onset and treatment response and to inform on clinically relevant phenomena such as assortative mating, nonadditive genetics, and shared environment. While this and similar genealogies from the Nordic countries represent unique resources (14), the changes in biases, environment, and clinical practices necessitate the integration of time-dependent and survival analysis frameworks. Explicit modeling of biases is warranted to fully exploit the oldest and youngest generations.

The Danish Civil Registration System was established in 1968, registering all people alive and living in Denmark since then (7, 8). The Danish Civil Registration System includes personal identification number, sex, date of birth, and continuously updated information on vital status (e.g., migration or death). The personal identification number is virtually immutable, thus enabling accurate links across different registers. As of April 2017, the system contained 9,851,330 individuals born between January 1, 1858, and April 21, 2017.

The Danish National Patient Register (6) includes the medical records of all patients treated in Danish general hospital inpatient departments since January 1, 1977, as well as in outpatient clinics since 1 January 1994 (or occasionally since 1995). Since 2002, the Register also includes Danish patients treated in hospitals outside Denmark and activities in specialist medical practices not paid by the health insurance agreement. As of April 2017, the register contained 287,593,154 records with diagnostic information for the 135,070,194 patient contacts available in the dataset.

The Danish Psychiatric Central Research Register (5) was first computerized in 1969 and includes admissions to psychiatric inpatient facilities up to and including 1994. Since 1995, the Register also contains outpatient contacts to psychiatric departments. As of April 2017, the register contains 7,298,910 records with diagnostic information for the 4,826,984 psychiatric hospital contacts.

This study was approved by the Danish Health Data Authority (project no. FSEID-00003339) and the Danish Data Protection Agency. By Danish law, informed consent is not required for register-based studies.

The most important requirement for accurately establishing pairwise familial relationships is that any given individual has either no register links to their parentsthat is, they are a founderor both register links to their parents. This is to guarantee that familial relationships are not underestimated (e.g., incorrectly ascertaining half siblings instead of full siblings). Bearing this in mind, the 2017 Danish Civil Registration System data freeze includes 1) 198,892 individuals with only one parental link, 2) five individuals with two identical parental personal identification numbers, 3) 880 individuals that are adopted, 4) 3,000 individuals with same-sex parents, and 5) 123,331 individuals belonging to twin pairs/multiple births. There is overlap in the above five categories. In order to yield as many pairwise relationships as possible, instead of eliminating the aforementioned individuals, we converted them into foundersthat is, we eliminated their parental links. Thus, if said individuals have descendants that meet our two-parent criterion, we can include their pairwise familial relationships in our analyses.

Genealogies can be analyzed as graphsthat is, a set of nodes (individuals) that are joined by edges representing parentoffspring relationships (27). Bearing this in mind, we used the networkx module in Python (28) to explore network connectivity in our data.

After eliminating invalid parental links, we converted the data into an edge list and loaded it as an undirected graph. Each edge in the graph represents a parentoffspring relationship between two nodes. If the parents of an individual are known, then two edges are added to the list (one for each parent). If no parental information is availablethat is, in the case of foundersno edge is added to the list. Individuals can be entirely unconnected (singletons)that is, they present no parental or offspring links.

The list consisted of 8,848,128 edges involving 6,801,107 individualsthat is, 69.04% of all available individuals in the Register. The remaining 3,050,223 individuals (30.96%) were singletons. A bit over half of those singletons (1,753,057 or 57.47%) were born in Denmark or Greenland, whereas the rest were born elsewhere. The distribution of the singletons by demographic cohort is shown in SI Appendix, Fig. 11. Overall, singletons born in Denmark belong to older demographic cohorts and represent childless individuals with no parental links, whereas singletons born outside of Denmark belong to younger demographic cohorts and represent immigrants without familial links in Denmark.

networkx computes the number and size of componentsthat is, the network subsets that are completely unconnected from all other subsets. This process returned one large component (n = 5,396,661) and 251,513 significantly smaller ones (n = 1,404,446), among which there were 100,400 trios and 58,804 quartets (Fig. 1 A and B). The single largest network component includes 54.78% of all registered individuals and 79.35% of the individuals with at least one relative (Fig. 1A). The overwhelming majority of the connected individuals (88.47%) were born in Denmark or Greenland. The distribution of the connected individuals by demographic cohort is shown in SI Appendix, Fig. 11.

Graph topology also indicated that the 6,801,107 connected individuals span only six generations; of those individuals, 2,377,043 (34.95%) are foundersthat is, they have no parental links. The narrow generation span combined with the high number of founders has implications in the ascertainment of distant relative pairs.

We used the pydigree module in Python (29) in order to estimate all nonzero pairwise coefficients of expected relatedness ^ for the 6,801,107 connected individuals. pydigree reads a file in pedigree (PED) format as a directed acyclic graph and enumerates all legitimate paths connecting a given pair of individuals. From any given starting point, only paths toward previous generations are allowed as well as one change of direction at most. The lengths gG of the paths connecting a pair of individuals are used to estimate their kinship coefficient (30, 31):=gG12g+1.

We note that ^ is twice the kinship coefficient .

To avoid looping over unconnected individuals, we applied the procedure only to each of the 2,377,043 founders with their corresponding descendants (easily identified with pydigree). Because different founders can share descendants, we removed duplicate estimates with a Python script. Kinship coefficients for unreported pairs were assumed to be 0.

As a result of the above procedure, we obtained 44,099,130 pairs of familial relationships from the large pedigree and 4,522,710 from the rest of the smaller pedigrees, totaling 48,621,840.

Apart from the value of ^ for any given pair of individuals, we registered the number of all possible connecting paths and their corresponding length as well as node depth of each individual in the path. Combined with ^, this additional topological information allowed us to annotate the familial relationships with great accuracy (SI Appendix, Table 5).

The distribution of number of an individuals relatives is heavily right skewed with a long tail (mean = 12.3; median = 9; mode = 6; Fig. 1C). Moreover, the distribution of number of meioses between connected individuals, considering the shortest path per pair, is also right skewed with mean = 2.7, median = 3, and mode = 2. This implies that the ascertained relative pairs in the Danish genealogy are dominated by close familial relationships.

Only a negligible fraction (0.03%) of the annotated familial relationships were connected by more than two paths, consistent with very few consanguineous relationships or marriage loops in the population, and these pairs were discarded from further analyses.

In this work, we focused on 10 broad diagnostic categories that correspond to the definitions used in a recent publication (15). These were conditions of the 1) circulatory system, 2) endocrine system, 3) pulmonary system including allergies, 4) gastrointestinal system, 5) urogenital system, 6) musculoskeletal system, 7) hematological system, and 8) neurological system as well as 9) cancers and 10) mental conditions. Each of these broad diagnostic categories is a composite measure of presence or absence of any disease falling within the specific diagnostic category (SI Appendix, Table 1).

In general, if an individual has an in- or outpatient hospital admission or contact for one of the above medical conditions in the Danish National Patient Register and/or the Danish Psychiatric Central Research Register, we ascertain said individual as a case for said condition, with no respect to contact frequency or comorbiditiesthat is, diagnostic categories were not mutually exclusive. We considered both ICD-8 and ICD-10 codes for the ascertainment of a given phenotype, even though it is important to note that there is not always a 1-to-1 correspondence between the two coding systems. Only diagnoses coded as main or auxiliary were considered for the phenotyping (as opposed to basic, referral, temporary, and complication).

In general, this study considered all diagnoses assigned in relation to an in- or outpatient hospital admission or contact as recorded systematically in the Danish National Patient Register and/or the Danish Psychiatric Central Research Register.

Individuals with no entries for a given condition were treated as controls for said condition. However, this strategy is vulnerable to truncation and censoring biases because health records are not quantitatively or qualitatively homogeneous across demographic cohorts. To minimize the risk of including too many false controls in the control group, we only studied individuals who were alive and living in Denmark after January 1, 1977 (date on which the Danish National Patient Register was established) or born in the interval (January 1, 1977, to January 1, 2017). As a result, we ended up with a subset of 6,691,426 individuals for all our quantitative analyses.

We used a classical approach for the estimation of total narrow-sense heritability and genetic correlations (16). For phenotype xand given a familial relationship R (e.g., parentoffspring, full siblings, etc.)if rx1,x2 is the correlation coefficient between two paired variables (x1, x2) holding the phenotypic observations for pairs of related individuals, the corresponding heritability is:hx2=rx1,x22R.

Similarly, the genetic correlation between phenotypes x and y, for a given familial relationship R, is:rg,xy=rx1,y2+ry1,x22rx1,x2ry1,y2.

Because disease phenotypes are binarythat is, case controlwe applied the latent correlation coefficient (also known as tetrachoric correlation coefficient), which measures agreement between two raters. In its simplest form, latent trait modeling assumes that the observed binary variables result from the discretization (at a given threshold) of unobserved (latent) variables that are normally distributed. The correspondence to the liability threshold model (32, 33) is apparent. In the case-control context, raters are vectors of binary phenotypes corresponding either to within- [(x1, x2) and (y1, y2)] or between-phenotype [(x1, y2) and (y1, x2)] paired data. We note that one rater corresponds to the genealogically older member of a familial relationship (e.g., father), whereas the other rater corresponds to the genealogically younger one (e.g., daughter). In the case of genealogically contemporary relationships such as siblings or cousins, relatives in the raters are sorted by age.

For the estimation of latent correlation coefficients, we used a standard maximum likelihood procedure from the polycor package in R.

In the case of heritability, valid estimates were those 1) with a positive value and 2) significantly different from zero. Moreover, when heritability estimates from multiple familial relationships were available, we combined them by computing their weighted average and weighted SE.

We computed average heritability values (h2) and SE (s) weighted by sampling variance:h2=i=1nhi2si2i=1n1si2,s=1i=1n1si2.

We also used weighted least squares to estimate the slope (corresponding to h2) of the modelR=+2+,where R is a vector of correlation coefficients, is a vector of kinship coefficients, is the intercept vector, and is the error vector with 2() = W1. W is a diagonal matrix of weights used in the regression.

We carried out the analysis for all available pairs with no regard to sex. For estimates from horizontal familial relationshipsthat is, siblings and cousinsboth individuals had to be from the same generation. For estimates from the rest of the relationships, only relatives from previous generations were considered. We did not consider heritability estimates when the correlation coefficient was negative or when the CIs fell outside [0, 1].

In the case of genetic correlations, valid estimates were those whose 95% CIs were contained within [1, 1]. When genetic correlation estimates from multiple familial relationships were available, we combined them by computing their weighted average and weighted SE as above.

We note that estimates of heritability and genetic correlations depend on the definitions of the traits under study and that heritability of broadly defined traits will also reflect genetic correlations between the narrowly defined traits included in each broad trait category.

Read more from the original source:
A comprehensive map of genetic relationships among diagnostic categories based on 48.6 million relative pairs from the Danish genealogy - pnas.org

I get up in the morning and I read the obits. If Im not in them, I have breakfast.Carl Reiner

When were young, we say hello to our friends and share our daily highs and lows. When were old(er), we read the obits to say goodbye and remember.

So recently, before my breakfast, I saw a familiar name in the paper. The wife of a friend had died. After more than 50 years of marriage, there would be no more Hi honey, Im home or How was your day? No more conversations, deep or frivolous. No more shared worries about the state of our planet, economy, or children. No more date nights on the couch with a bowl of popcorn and a movie on demand. No more hugs and kisses. No more. No more. Such is the stuff of heart-rending grief.

As it so happens, a few years earlier the same friend described his wife with the following: She is my heart and soul. She has vastly broadened my world view, changed me for the better, and I am indebted to her in endless ways. Without her, I am nothing.

The combination of his loss and his tribute got me thinking about the notion of immortality. Carl Reiner hit on one level of immortality: The ability to eat breakfast cheats death for at least for one more day. On the other end of the spectrum, billions of people around the world believe that when their time for breakfast runs out, they will be united for all eternity with their family and friends, and their God, in heaven. Bookends to the notion of immortality. But what else lies on the shelf of our lives between those bookends?

After thinking about it, there are in fact other forms of immortality between breakfast and the belief of billions. The pharaohs have their pyramids, Shakespeare his plays, and every family their scrapbooks (or, today, iPhones) filled with hundreds if not thousands of photos of us full color immortality.

On a more serious note, psychologists debate how much of who we are is due to nature (genetics) or nurture (family, cultural, and environmental factors). While they debate the percentages of those two factors, the real point lies in the continuation (or immortality) of their effects.

So I went back to my friends tribute to his wife: She has vastly broadened my world view, changed me for the better. On the nurture side of the argument, we enter relationships as blind dates, looking for commonalities, ways we can connect, future roads we might travel together. After any long-term relationship, each partner has transformed, contributed to, and changed each other in ways both significant and subtle. And those changes endure even after the death of a spouse or friend. We are not human etch-a-sketches instantly reverting to blank slates upon the loss of the artist. In this way, the best traits of the people important to us, the traits that change us for the better, are immortal.

On the nature side of the equation, half (more or less) of our childrens DNA is me; the other half, my wife. I look at our grandchildren and realize that the half in each parent is now a quarter in them, and yet to be an eighth in a great-grandchild. Just as I am half of each of my parents and an eighth of each great-grandparent. This genetic immortality is something we share only with our immediate family, something of a seed bank to continue the lineage of a family.

The quest for immortality is a strictly human pursuit. We search for the fountain of youth, have our bodies frozen to near absolute zero waiting for a cure to the disease that put us on ice, have countless amazing life-extending surgeries, and take billions of dollars of life-extending medications. We resist death at all costs, and continually look forward to a tomorrow. Its the human condition.

So when someone changes us for the better, changes how we behave because of their influence be they spouse, child, parent, friend, mentor, or personal hero do they not live on in that sense? And when we inherit the DNA of our parents, and their parents, and pass that on to our children, and grandchildren, do they, and we, not also live on in that way? After all, without these influences, none of us would be who we are.

Without her, I am nothing. No. You were you until you met her and then the two of you created personalities entwined like two strands of psychological DNA, built around the molecular DNA you each inherited, and which, along with psychological traits, you pass on to your children. Is that not a way in which partners find immortality in others lives, and in the lives of all who will follow them?

Mexicos annual celebration of Die de los Muertos (captured so beautifully in the movie Coco), reminds people to remember their ancestors and to thank them for their lives and contributions. In reality, they live on not for just one day, but every day through the genetic and psychological contributions each of us exhibits every day of the year.

More than breakfast, less than heaven; not as enduring as a pyramid nor as fleeting as a digital photograph. Immortality lies in the play we write with each other with a debt to the past and hope for the future. I am enjoying writing that play and looking forward to fewer goodbyes and a lot more breakfasts.

Its the human condition.

See the article here:
On immortality and the human condition - Wednesday Journal

A recent study conducted by researchers at Baylor College of Medicine and Texas Childrens Hospital is the first to identify an underlying mechanism and possible drug targets for a new severe neurological disorder in children called NEDAMSS.

The disorder, called NEDAMSS for neurodevelopmental disorder with regression, abnormal movements, loss of speech and seizures, is caused by spontaneous mutations in the Interferon Regulatory Factor 2 Binding Protein Like (IRF2BPL) gene. In this study, the team led by Drs. Hugo Bellen, Paul Marcogliese and Debdeep Dutta at Baylor and the Jan and Dan Duncan Neurological Research Institute (Duncan NRI) at Texas Childrens employed fruit flies to dissect the biological function of IRF2BPL.

In 2018, Marcogliese and Bellen, along with a team of researchers affiliated with the Undiagnosed Diseases Network a National Institutes of Health-funded network whose goal is to find genetic variants responsible for previously unidentified disorders discovered the NEDAMSS disorder. In collaboration with Drs. Shinya Yamamoto, Michael Wangler and others at the Duncan NRI and Baylor, as well as Drs. Loren Pena and Vandana Shashi at the UDNs Duke clinical site, they identified mutations in IRF2BPL as the cause of severe steady regression of motor and language skills in the initial cohort of five patients.

Today, there are 31 patients identified with mutations in IRF2BPL, said Marcogliese, co-corresponding author of the study and postdoctoral associate in the Bellen lab. IRF2BPL has been named as one of the top 100 autism candidate genes and its variants have been implicated in early-onset Parkinsonism. However, until now, very little was known about the biological function of this gene and how its loss results in neuronal dysfunction.

Interestingly, overexpression of Pits (the fly version of IRF2BPL) or human IRF2BPL in flies resulted in serrated wings and bristle loss, both of which are common when the Wnt/Wingless signaling pathway is perturbed during development. Reducing Pits in neurons, however, led to an age-dependent neurological defect in flies, which the team later found was associated with an increase in the activity of the Wnt/Wingless pathway.

In collaboration with Dr. Nan Cher Yeo at the University of Alabama Birmingham and Dr. Kathrin Meyer at Nationwide Childrens Hospital, they confirmed these findings in zebrafish and in patient-derived cells as well.

Numerous experimental observations led the researchers to conclude that Pits and Wnt/Wg act in an antagonistic manner. In agreement with these observations in animal models, they found that Wnt signaling is increased in NEDAMSS patients, whose cells are known to have reduced levels of IRF2BPL.

To understand how Pits/IRF2BPL regulates Wg/Wnt pathway, the researchers conducted biochemical and mass spectrometry assays, which revealed a Wnt antagonist, Casein kinaseI (CkI), as one of the top binding partners of Pits. Further, they also confirmed genetic interactions between IRF2BPL and CkI.

In summary, the study shows an antagonistic relationship between IRF2BPL/Pits and the Wnt/Wg pathway in the fruit fly model. It also shows that under normal circumstances, IRF2BPL/Pits regulates neural function by inhibiting the Wnt/Wg pathway.

In NEDAMSS patients, loss of IRF2BPL is accompanied by increased levels of Wnt in astrocytes a prominent type of cell in the brain. The Wnt pathway, the human counterpart of the Wingless pathway in flies, is known to be important for the proper development and function of neurons and other organs, and its disruption results in several types of cancers. The researchers also showed that they can suppress characteristics associated with the loss of IRF2BPL/Pits using Wnt inhibitors, which have proven to be safe and effective in treating many cancers.

It used to take decades from the initial discovery of a novel gene to dissecting its biological mechanism to finding a therapy, said Bellen, Distinguished Service Professor of Molecular and Human Genetics at Baylor and the corresponding author of the work. However, with the UDNs unique approach that fosters close collaborations between clinicians and researchers working on various model systems, we were able to move from the initial identification of this mutation to a potential therapy in just three years.

Furthermore, we were able to progress rapidly thanks to the continued support from the iDREAM For a Cure, as well as supplemental support from the NIH for which we are truly grateful. We hope to translate these promising discoveries into a viable therapy in the future.

The researchers report their findings in the journal Science Advances.

Other researchers involved in the study include Shrestha Sinha, Nghi Dang, Zhongyuan Zuo, Yuchun Wang, Di Lu, Fatima Fazal, Thomas Ravenscroft, Hyunglok Chung, Oguz Kanca, JiJuWan, Emilie Douine, Stanley Nelson, Matthew Might, Kathrin Meyer and Nan Cher Yeo. They are affiliated with one or more of the following institutions: Baylor College of Medicine, Texas Childrens Hospital, the Research Institute at Nationwide Childrens Hospital, University of Alabama, David Geffen School of Medicine, Cincinnati Childrens Hospital and Ohio State University.

Read more.

Continued here:
Wnt signaling is identified as a target in NEDAMSS disorder - Baylor College of Medicine News

GAINESVILLE, Fla.--(BUSINESS WIRE)--Cyclo Therapeutics, Inc. (Nasdaq: CYTH) (Cyclo Therapeutics or the Company), a clinical stage biotechnology company dedicated to developing life-changing medicines through science and innovation for patients and families living with diseases, today announced the formation of a Global Steering Committee (GSC) to guide the pivotal Phase 3 global clinical development program of Trappsol Cyclo for the treatment of Niemann-Pick Disease Type C (NPC). As the Global Principal Investigator for the TransportNPC study, Caroline Hastings, MD serves as the senior scientific and clinical expert for the trial and will also chair the GSC.

Dr. Caroline Hastings, global principal investigator for TransportNPC and chair of the GSC, has been instrumental in assembling this high caliber Global Steering Committee with representation of renowned Key Opinion Leaders and clinical experts in NPC. It is another testimony of our commitment to serve the NPC community and deliver on the unmet medical needs. I feel humbled and privileged to be working with this outstanding group of professionals who are committed to advance science and clinical trials that can bring hope and treatment benefits to so many patients and their families, commented Lise Kjems, MD, PhD, Chief Medical Officer of Cyclo Therapeutics.

The Companys ongoing pivotal Phase 3 study, TransportNPC, is a randomized, double-blind, placebo-controlled, parallel group, multicenter study designed to evaluate the safety, tolerability, and efficacy of 2,000 mg/kg doses of Trappsol Cyclo administered intravenously and standard of care (SOC), compared to placebo administered intravenously and SOC alone, in patients with NPC1. The Phase 3 study intends to enroll at least 93 pediatric (age 3 years and older) and adult patients with NPC1 in at least 23 study centers in 9 countries. Eligible patients will be randomized 2:1 to receive either Trappsol Cyclo or a placebo. Randomization will not be constrained based on patient age, nor will patient enrollment be gated by patient age. The study duration is 96 weeks and includes an interim analysis at 48 weeks.

Dr. Hastings, Global Principal Investigator for the TransportNPC trial and member of Cyclo Therapeutics Scientific Advisory Board added, I am very grateful by the overwhelmingly positive responses as I reached out to fellow scientists and physicians to invite them to join the Global Steering Committee. I am honored to be working alongside these wonderful colleagues with outstanding knowledge and expertise and who represent the excellent investigators taking part in the TransportNPC trial. Together, we have a very unique opportunity to further refine the scientific strategy for Trappsol Cyclo and help drive this important program toward potential approval.

NPC is a devastating neurodegenerative disease that needs more effective therapies. Given the clinical course and progressive nature of this disease, novel therapeutic strategies with the potential for disease modifying effects are necessary. The TransportNPC trial is unique as it is designed to demonstrate the long-term clinical benefits and potential for disease modification, commented Professor Roberto Giugliani, MD, PhD.

I have been caring for patients with NPC for more than 25 years. These patients urgently need better treatment options that will better halt the cruel, neurodegenerative course that this disease takes. In this study with cyclodextrin intravenously, I see an opportunity to improve the therapeutic offer, added Dr. Eugen Mengel.

The members of the TransportNPC Global Steering Committee are:

For more information about the Companys TransportNPC pivotal Phase 3 study, visit http://www.ClinicalTrials.gov and reference identifier NCT04860960.

Cyclo Therapeutics received Orphan Drug Designation for Trappsol Cyclo to treat NPC1 in both the U.S. and EU and Fast Track and Rare Pediatric Disease Designations in the U.S. The Rare Pediatric Disease Designation is one of the chief requirements for sponsors to receive a Priority Review Voucher in the U.S. upon marketing authorization.

About Cyclo Therapeutics

Cyclo Therapeutics, Inc. is a clinical-stage biotechnology company dedicated to developing life-changing medicines through science and innovation for patients and families suffering from disease. The Companys Trappsol Cyclo, an orphan drug designated product in the United States and Europe, is the subject of four formal clinical trials for Niemann-Pick Disease Type C, a rare and fatal genetic disease, (www.ClinicalTrials.gov NCT02939547, NCT02912793, NCT03893071 and NCT04860960). The Company is planning an early phase clinical trial using Trappsol Cyclo intravenously in Alzheimers Disease based on encouraging data from an Expanded Access program for late-onset Alzheimers Disease (NCT03624842). Additional indications for the active ingredient in Trappsol Cyclo are in development. For additional information, visit the Companys website: http://www.cyclotherapeutics.com.

Safe Harbor Statement

This press release contains forward-looking statements about the companys current expectations about future results, performance, prospects and opportunities, including, without limitation, statements regarding the satisfaction of closing conditions relating to the offering and the anticipated use of proceeds from the offering. Statements that are not historical facts, such as anticipates, believes and expects or similar expressions, are forward-looking statements. These statements are subject to a number of risks, uncertainties and other factors that could cause actual results in future periods to differ materially from what is expressed in, or implied by, these statements. The factors which may influence the companys future performance include the companys ability to obtain additional capital to expand operations as planned, success in achieving regulatory approval for clinical protocols, enrollment of adequate numbers of patients in clinical trials, unforeseen difficulties in showing efficacy of the companys biopharmaceutical products, success in attracting additional customers and profitable contracts, and regulatory risks associated with producing pharmaceutical grade and food products. These and other risk factors are described from time to time in the companys filings with the Securities and Exchange Commission, including, but not limited to, the companys reports on Forms 10-K and 10-Q. Unless required by law, the company assumes no obligation to update or revise any forward-looking statements as a result of new information or future events.

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Cyclo Therapeutics Announces Formation of Global Steering Committee Comprised of Leading Experts to Advise on the Global Phase 3 Clinical Development...

Pfizer: William Pao

William Pao, M.D., Ph.D., will join Pfizer as executive vice president and chief development officer as of March 21.

Dr. Pao will become a member of Pfizer's executive leadership team, reporting to chairman and CEO Albert Bourla. Hesucceeds Rod MacKenzie, who is retiring after 35 years at Pfizer.

At Pfizer, Dr. Pao will oversee the Companys Global Product Development organization, which is responsible for the clinical development and advancement of Pfizers pipeline of innovative medicines in inflammation and immunology, internal medicine, hospital, oncology and rare disease, as well as regulatory affairs in support of Pfizers R&D pipeline and portfolio of marketed therapies.

Dr. Pao joins Pfizer from Roche,where he most recently served as the Head of Pharma Research and Early Development (pRED) and oversaw the discovery and early development of a portfolio of new molecular entities to treat diseases related to cancer, neuroscience, ophthalmology, rare diseases, immunology, infectious diseases, and rare blood disorders, across seven global sites.

Before joining Roche, Dr. Pao simultaneously held key positions as Professor of Medicine and Director of the Division of Hematology/Oncology at Vanderbilt University, and Director of Personalized Cancer Medicine at Vanderbilt-Ingram Cancer Center.

During this time, he was co-corresponding author on the first paper to describe osimertinib (Tagrisso), a medication used to treat non-small-cell lung carcinomas with specific mutations. He also co-founded MyCancerGenome, a pioneering cancer medicine knowledge resource for physicians, patients, caregivers, and researchers.

GlaxoSmithKline plc has appointed Tony Wood as Chief Scientific Officer (CSO) designate: who will assume full accountability for R&D across GSKs portfolio and pipeline as of August.

One of the worlds pre-eminent chemists, Wood has more than 30 years of experience working across diverse disciplines of R&D to deliver innovative medicines. He joined GSK from Pfizer in 2017, as Senior Vice President, Medicinal Science and Technology, and is responsible for all science and technology platforms supporting the discovery, clinical development and delivery of new medicines across GSK.

Over his career, Wood has led large-scale global organisations in drug discovery and development in multiple therapeutic areas, including immunology, oncology and infectious diseases. He has been involved in the launch of many new medicines at GSK, includingNucala,Blenrep,Jemperli,Cabenuvaand most recentlyXevudy. Wood has also been integral to delivering the recent improvements in GSKs R&D productivity and central to developing its R&D approach focusing on science of the immune system, human genetics and advanced technologies, notably building capabilities in functional genomics, artificial intelligence and machine learning.

In his earlier career at Pfizer, Wood created and led its first global Medicinal Chemistry organisation, supporting all small molecule discovery output from Pfizers research units. Among many achievements, this group designed the antiviral molecules that led to the development of the SARS-CoV-2 medicine Paxlovid.

Wood also created and led Pfizers first Medicinal Sciences organisation. In this role he was accountable for the design and development of medicines including the JAK1 inhibitor abrocitinib, JAK3 inhibitor ritlecitinib, and tofacitinib follow-on medicines. He was also responsible for the structure-based design of the Pfizer RSV vaccine, which is currently in phase III development. Prior to this, Tony co-led Pfizers research for the antiviral therapeutic area. He invented maraviroc, a CCR5 antagonist for the treatment of HIV and Pfizers first successful drug derived from high-throughput screening.

Wood will take over from current CSO, Dr Hal Barron. After this, Dr Barron will remain a member of GSKs Board transitioning to serve as a Non-Executive Director and a member of the Boards Science Committee for an initial period of three years. In addition to his Non-Executive responsibilities, Barron will also provide advice and support on scientific and asset development matters and will attend key R&D executive investment and advisory committees. He will also continue to engage with the scientific community, R&D partners and other companies, as required, in support of R&D and on behalf of GSK.

Barron will assume the position of CEO and Board Co-Chair of Altos Labs effective 1 August 2022. Altos Labs is a new, private biotechnology company based in the San Francisco Bay Area, with multiple global sites, and is focused on the biology of cellular rejuvenation programming with the goal of reversing disease.

Biopharma CDMO AGC Biologics has appointed Regina Choi-Rivera as the new General Manager of the companys large-scale biopharmaceutical mammalian production facility in Boulder, Colorado.

AGC Biologics acquired the Boulder Facility in June 2020, giving the company additional capacity and a significantly larger production scale for mammalian-based projects in the US. The Boulder site houses two 20,000-liter stainless steel cell bioreactors and has more than twenty acres of undeveloped land, creating opportunities for future expansion, including space for up to four more 20,000-liter bioreactors. The facilitys automation and cost-effective capabilities make it well-suited for high volume commercial production and high titer antibody processes.

In her new role, Choi-Tivera assumes executive oversight and leadership and will manage strategic development and facility operations. She brings more than 25 years of experience in the biotech industry with her and joins AGC Biologics after working for Samsung Biologics for eight years. While at Samsung Biologics she most recently served as vice president, Head of Drug Product Business Unit. Prior to that Ms. Choi-Rivera was vice president, Head of the Drug Substance Contract Manufacturing Business unit. Before her time at Samsung, she spent nearly a decade with Janssen Pharmaceuticals research and development division, supporting pilot plant operations and managing outsourcing activities.

CurevacsChief Technology Officer, Dr. Mariola Fotin-Mleczek, will resign from CureVac at the end of this month: after nearly 16 years of scientific leadership at the company. She leaves to pursue a family business outside the biotech industry in her home country of Poland.

Fotin-Mleczek joined CureVac in May 2006 and became a member of the management board in 2013, first as Chief Scientific Officer and as Chief Technology Officer in 2018. As a scientist trained in immunology and cell biology, Mariola was responsible for the development and preclinical testing of CureVacs mRNA technology platform across the therapeutic areas of prophylactic vaccines, oncology and molecular therapy. She is co-inventor of multiple key mRNA technology-related patents and has authored more than 30 scientific publications with a focus on mRNA technology.

Further development of CureVacs mRNA technology platform will be led by Dr. Igor Splawski, Chief Scientific Officer of CureVac, and spearheaded by Dr. Patrick Baumhof, Senior Vice President Technology, who has a 15-year scientific tenure with the company. The consolidated scientific frontend will seamlessly integrate with the subsequent clinical development of new mRNA-based vaccines and therapeutics.

Ajinomoto Bio-Pharma Services, a global provider of bio-pharmaceutical contract development and manufacturing services, has appointed Tony ONeill as vice president of compliance, US Operations.

ONeill brings extensive experience leading quality, manufacturing, and operational excellence teams in the pharmaceutical and bio-pharmaceutical industry. He joins Aji Bio-Pharma after 25 years at Allergan, where he held a number of quality and operational leadership positions in biologics manufacturing and development with responsibility both in Ireland and US operations. His most recent roles include Executive Director Quality Operations and Executive Director Risk Management and Compliance, where he was responsible for leading a team in developing standard policies and processes for data management and controls across a network of 14 sites.

As we continue to expand our capacity and service offerings, including the addition of our new multi-purpose fill/finish suite, Tonys expertise will be integral in ensuring the required sterility and regulatory standards are met for both cGMP clinical and commercial manufacturing, said Nobu Shimba, President and CEO of Aji Bio-Pharma, US

Genezen, Inc., a cell and gene therapy CDMO focused on early-phase process development, GMP vector production and analytical testing services, has appointed Laura Jacanin as Senior Director of Business Development.

Previouslywith similar roles at Wuxi, Lonza, and Cytovance, Laura has over 20 years experience in the life science sector and has extensive cell and gene therapies (C>s) and biologics expertise.

Jacanin will be responsible for developing and expanding Genezens offering oflentiviral and retroviral vectors to meet the growing demands of the C> market and supporting the development and production of therapies.

Jacanin is the latest in a series of appointments at Genezen which has included: Natasha Rivas as Vice President of Quality Assurance and Regulatory Affairs; Raymond Kaczmarek as CEO; and Brok Weichbrodt as Vice President of Operations.

The appointments have been made to help drive the business growth alongside a new 75,000+ square foot cGMP-compliant lentiviral and retroviral vector production facility. The first phase, a process development and analytical lab, officially opened in late 2021. The next phase, with cGMP production suites, is currently underway and due to complete in early 2022.

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New appointments in the biopharma industry - BioPharma-Reporter.com

The research report Monoclonal Antibodies Market includes qualitative and quantitative insights into the major drivers, restraints, opportunities, and challenges impacting worldwide market growth. The analysis contains detailed statistical market data on the major companies, as well as revenue forecasts. The Monoclonal Antibodies market study also includes information on the sales growth of many regional and country-level markets, as well as the competitive landscape and specific company analysis for the forecast period. The Market Report examines future growth factors as well as the existing status of market share, penetration of various types, technologies, applications, and geographies through 2027.

In accordance with the Monoclonal Antibodies market is set to grow at a CAGR of 10.2% over a forecast period (2022-2027).

Sample Report:

https://marketintelligencedata.com/reports/1537656/global-monoclonal-antibodies-market-growth-2022-2028/inquiry?Mode=Vaishnavi

Top Players Analysed in the Report are:

, AbbVie, Roche, Johnson & Johnson, Amgen, Merck, BMS, Eli Lilly, Formation Biologics, Genmab, GlaxoSmithKline, Human Genome Sciences, mmunogen, MedImmune, Novartis, Pfizer, Seattle Genetics, Stemcentrx, Synthon Biopharmaceuticals, Takeda, Teva,

Monoclonal Antibodies Market Segmentation, By Type:

Cancer

Autoimmune Diseases

Infection

Hematological Diseases

Others

Monoclonal Antibodies Market Segmentation, By Application:

Cancer

Autoimmune Diseases

Infection

Hematological Diseases

Others

Segmentation by application: breakdown data from 2016 to 2021, in Section 2.4; and forecast to 2026 in section 11.8.

Oncology

Autoimmune and inflammatory diseases

Respiratory diseases

Ophthalmology

Regional Analysis:

Global Monoclonal Antibodies Market is further classified on the basis of region as follows:

North America (USA, Canada, Mexico)

Europe (Great Britain, France, Germany, Spain, Italy, Central and Eastern Europe, CIS)

Asia Pacific (China, Japan, South Korea, ASEAN, India, rest of Asia Pacific)

Latin America (Brazil, rest of LA)

Middle East and Africa (Turkey, CCG, rest of the Middle East)

Report Link:

https://marketintelligencedata.com/reports/1537656/global-monoclonal-antibodies-market-growth-2022-2028?Mode=Vaishnavi

Table of Contents Monoclonal Antibodies Market:

Chapter 1: Overview of Monoclonal Antibodies Market

Chapter 2: Global Market Status and Forecast by Regions and Typed

Chapter 3: Company Profiles, recent developments, and investments

Chapter 4: Market Competition Status by Major Manufacturers

Chapter 5: Major Manufacturers Introduction and Market Data

Chapter 6: Upstream and Downstream Market Analysis

Chapter 7: Cost and Gross Margin Analysis

Chapter 8: Marketing Status Analysis

Chapter 9: Market Report Conclusion

Chapter 10: Research Methodology and Reference.

Monoclonal Antibodies Market Key Points:

Define, describe, and forecast the market for Monoclonal Antibodies products by type, application, end user, and region.

Execute enterprise external environment and PEST analysis.

Develop plans for the organisation to deal with the effects of COVID-19.

Provide market dynamics analysis, such as market driving forces and market growth restrictions.

Provide market entrance strategy study for new or existing businesses, including market segment definition, client analysis, distribution model, product messaging and positioning, and price strategy analysis.

Stay track of worldwide market trends and give a study of the impact of the COVID-19 outbreak on key global areas.

Analyse participants market opportunities and give industry leaders with competitive landscape insights.

Customization:

The Global Monoclonal Antibodies Market report may be modified to meet your specific business needs. Because we understand what our clients want, we provide 25% customization for any of our Market Intelligence Data reports at no additional cost to all of our clients.

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Monoclonal Antibodies Market Profitability and Leading Players To 2027 | North America, Europe, Asia and Pacific The Grundy Register - The Grundy...

Food that is organic or not organic? Grown from genetically modified (engineered) seeds or not?

How about neither genetically modifiednor organic?What aboutboth organic and genetically modified??

The first two are pretty common questions, while the latter two are almost never asked. Some people believe that within thecontinuum of macro and micro phenomenaof the natural world, deliberate and precise crop modifications at the genomic level should be prohibited, must be linked to pesticide use, and are distinctly different than the relatively random modifications achieved via traditional breeding techniques. They are virulently anti-biotechnology. A whole industry developed around these beliefs.

This limited thinking comes at a cost: We spend a greater proportion of income on food; we omit some foods out of the diet due to cost, possibly reducing nutrient quality intake. In Canada, where I live, we risk becoming a laggard nation by signaling to the pubic and businesses that we are not willing to keep up with technology; and inadvertently or note we perpetuate fear mongering agendas to result in an ignorant population.

Heres a question Ive struggled with for a long time: why are those that are pro-organic and concerned around pesticide use also not embracing GMO technology, which in some cases dramatically reduces the amount and toxicity of crop chemicals?

I believe organic GMOs should be not only accepted but actively pursued and especially by those who embrace the goal of organics to reduce the environmental impact of farming.

I know that assertion seems absurd to most people but Im going to try to explain my perspective. Keep in mind there are many layers to this topic, some outside the scope here, but Im going to try to clearly break it down step-by-step.

Almost all of the plant food you eat was processed from commercial crop varieties. Of the dozens of varieties registered and available in any given year, a farmer must then assess performance of that variety (either from past experience, or from accumulated field trial data provided by researchers), consider if the data is relevant for the farms geography/soil/latitude, as well as seed availability, production economics, and other production implications, while staying aware of what market demands are while usually trying to predict what prices will be in the coming year. For example (and assuming prices arent contractually locked-in with a buyer prior to producing it), does the buyer of his grain wantcertified organicgrain or not? This question in particular is relevant since production practices of the crop in the field are largely determined by those intended markets, especially in terms of the land being used as well as pesticides used.

Think about the chronological order of everything described so far. The way the crop is grown in the field determines whether or not the food made from it can be labelled as organic or not. Non-GMO is dependent upon the way the genetics came together prior to variety registration, years before the variety is grown in the field. Yet, the terms organic and non-GMO are used interchangeably and inseparably.

Why?

Heres an analogy. Lets say youre shopping for a new sports car, and your only must-have is that it must be powered by a BMW engine. Logically, it would make sense to start looking at BMW cars, but actually you can get other brands with BMW engines: Morgan (brand) out of the UK is a distinct brand but actually uses BMW engines:

Morgans also happen to be mostly hand-built, use wooden body panels, and their factories are mostly old brick buildings. BMW uses much automation/robotics and their factories are more modern and constructed differently. If your only must-have is the BMW engine, do you really care how the car around it was produced? Probably not.

This analogy can help us understand the controversy over growing GMO seeds organically. A widely-held but inaccurate perception of agricultural practices / food production is that they must be either one or the other of these two categories:

But this is a more accurate way of viewing it:

In other words and in most cases the method of production (growing the crop) used in the field is independent of the method used to establish the genetics of that plant (from which the food or ingredients are sourced). What is more, the way that the genetics were assembled in the crop must always precede production of the crop commodity that later becomes food.

Because the genome all genes of the plant is the blueprint for the plant, genetic changes can influence a huge diversity of traits, including (but not strictly or limited to) things like herbicide (such as glyphosate) tolerance. Conversely, not all herbicide tolerance is the result of genetic modification techniques either.

One disclaimer. I have worked for one of the big companies and I have also worked in government alongside incredibly smart people that were not secretly funded by an agricultural biotechnology company contrary to what some camps ceaselessly believe. Neither of those groups have any interest in talking about this topic. Guess why? Because theres no money in it. Thats only half an answer though, because profit in a capitalist market is the by-product of supplying a demand with a desired and affordable producta product that consumers you and me are willing to pay for.

So to ask the question again: Why dont private companies or government have an interest in talking about organic GMOs? Its because theres zero demand. But why is there zero demand? I think its because consumers are completely unaware that its even a theoretical option and thus nobody is asking for such products. In my opinion, organic GMOs have massive potential: to improve nutrition, minimize pesticide use, quickly mitigate production challenges related to climate change, increase carbon sequestration and minimize environmental damage of climate change, reduce costs along the value chain (thus reducing cost of groceries), open up an entire new branch of research, and employ highly-skilled people.

Im going to use a weird-looking square version of a Venn diagram to avoid awkward gaps between boundaries of overlapping circular borders of a normal Venn. Im going to start broad, then increasingly narrow down the details, adding layer upon layer in the diagram. There will be 3 major layers to this process as well:

Unlessan entire genome has been synthetically assembledde novo(by manually connecting millions of independent nucleotides and inserting it into an empty cell resulting in a viable life form) or, by manually editing codons of an existing genomewith alternative sequences with the same function or, unless a living organism has somehow spontaneously manifested itself out of the aether, any organism plant or animal, organic or not must be derived from existing genetics. In other words, in plants, parent genetics have to come together and produce the next generation. For the sake of this post, Im going to represent this starting pool of genetics using a giant grey square:

This is the first major layer to this discussion. So again, this grey square blob contains all the compatible genetics in nature that could possibly be combined and recombined to produce the baby plants of the next generation. (The label at the top refers to two differing and naturally-occurring plant reproductive systems, but regardless, this square still represents all available genetics). In the case of GMOs, cisgenic sources would be grouped into this pool. I didnt show it here because this illustration will become really busy as it is already, but transgenic would be a small, different shade of grey square off to the side (since those genetic modifications do become compatible but technically come from a non-compatible source organism, and make up a very small proportion of all genetic material available.

Next, lets look at the 2nd major layer to this process (As we move inwards towards the centre of this diagram, were moving further along the breeding process, step by step, until we reach the new plant variety. (The letters A, B, C and D refer to various routes to get to that end point, which Ill summarize at the end, but for now just ignore them). Note that sizes of any category are not necessarily proportional to the real amount of material established through either method, its just to illustrate the relationships.):

Since the grey represents any and all possible genetic resources from which the genetics of a new plant variety can be established, any technique, whether itstraditional breeding, or GMO, must be derived from those resources. Those genetic resources used, regardless of the methods, are thus a sub-set of that whole pool of resources available. During this process, tools to enhance the breeding process, such as MAS described earlier, may be used. (Another example might the general category of gene-editing, including techniques such as CRISPR, which are not consistently considered GMO across all regulatory jurisdictions at this time.)

Even though a plant may be the recipient of novel genes, or of engineered changes within its existing genome, these still make up a very small percentage of the entire genome of the plant. Therefore, selection must still be carried out in the field as it would be with traditional breeding. A GMO is not a completely unnatural, 100% fabricated or synthetic thing; it is actually a mostly-the-same variant of the non-GMO counterpart.

Lets look atBrassica napus, for example. This oilseed crop has848,200,303 total nucleotide base pairs(the subunit of genes, and the level at which modifications can be engineered) and a large proportion of varieties in North America are GMO. Now lets compare a GMO version ofBrassica napus,anInVigor variety: one transgene enables it to be unaffected by a herbicide called glufosinate, and a second one that influences fertility in the plant. The former is comprised of171 base pairs, the latter of90 base pairs. In total, these GMO alterations relative to the wild type progenitor account for 0.00003077% difference. Id argue the proteins expressed by the genes should be the focus of any opponent to such technologies, but even those products of the modifications are a minuscule proportion oftotal expression in the plant.

The lighter shading here represents the breeder selection for traits, such as that glufosinate tolerance, or something as straightforward as the height of the plant. Since any breeding program has multiple generations of populations at any given time, theres a lot of back and forth as far as activities go, but this depicts the progress further towards the end goal of establishing a variety:

These activities will be pretty much the same regardless of whether or not GMO techniques have been used, since as mentioned, GMO is not an entirely new thing, it is mostly the same as a non-GMO variant and must still be grown in the field like any other plant. Aside from the new trait(s) of a GMO that result from novel genes introduced, the GMO variant will respond to environmental pressures the same as any other version of it will. A few common traits selected for are highlighted below (the dotted outlines represent particulars that may be regulated differently in different jurisdictions, can cant be considered definitely a traditional or a GMO tool/process, technically speaking):

This part is super interesting, because herbicide tolerance (HT) traits are commonly assumed to always be GMO, but actually they can be established in plants via either traditional breeding, or applying GMO techniques (genetic engineering). HT gives the crop the ability to go unaffected after being sprayed by a specific herbicide which would otherwise kill it.

Ill use canola as an example again here. There are three main categories of HT canola: glufosinate-resistant (GF), glyphosate-resistant (GP), andimidazolinone-resistant(IMI). All are HT, all have functional differences relative to the wild type progenitor, however GF and GP are GMO while IMI isnotGMO since it was established via traditional breeding. In other words, a couple transgenes were deliberately introduced into parent plants chosen from the starting pool (the grey square) giving the GF and GP varieties this HT ability; in contrast, mutagenesis was used for the starting material in preceding generations for the IMI non-GMO varieties. Mutagenesis involves exposing the population to a mutagen that deliberately induces random mutations (genetic disruptions, resulting in new or different traits) in individuals of the population, from which those with ideal traits (including HT) are selected and further generations are derived from those ones. The mutagen agent is of course not carried forward in subsequent generations, but the resulting mutations (and thus resulting traits) are.

This next part is the third major phase of this concept: field production. This is the part where you see farm equipment operating in the field each year. There are two production methods that farmers can choose: conventional, or organic (but the decision will have been made months before planting and/or before acquiring seed). Conventional is a general category referring to the most modern technology (in crop production, or with field equipment), whereas organic is a technical term described in theOrganic Products Regulationswithin the Canada Agricultural Products Act.

Now keep in mind this is technically speaking: put viable seeds in the ground, and they will grow; the field conditions dont really care about whether generations previous to that seed were bred using traditional or genetic modification techniques. This is why the green representing field production practices overlaps with both breeding approaches. Heres that section outlined in the line-dash:

But, because this is regulation (i.e. power of law), this is the part where the value chain becomes somewhat incongruent with technical realities. Remember the chronological order in which these events must all happen. Up to this point, everything follows a logical flow, but suddenly one of them (the dark green, outlined shaded area) is backwards.

Why isorganic a distinct and regulated term.

In Canada, section 1.4 of Organic production systems: General principles and management standardsclearly prohibits materials or techniques in organic production and preparationall products of and materials from genetic engineering (GE), as defined in this standard, and as specified in 4.1.3, 5.1.2 and 6.2.1 of CAN/CGSB-32.311;. This is currentas of March 2021.It defines genetic engineering to produce GMOs as artificial manipulation of living cells for the purpose of altering its genome constitutes genetic engineering and refers to a set of techniques from modern biotechnology by which the genetic material of an organism is changed in a way that does not occur other than through traditional breeding by multiplication or natural recombination. The genome is considered an indivisible entity; artificial technical/physical insertions, deletions, or rearrangements of elementsof the genome constitute genetic engineering.

Lastly, lets address pesticides. This is the final square overlay.

Neither contemporary systems are perfect in this context, since there are a plethora of pesticides approved for bothconventionalandorganic systems. Of course, toxicology will differ for each but regardless, for registration and use in Canada, they are all subject to the same extensiveregulatoryprocessof the Pest Management Regulatory Agency (PMRA) of Health Canada.

Now, look at the four letters labelling the pathways around the sides (A, B, C & D). All three, A, B, and D are technically possible, and allowed. But look at path C: plants are bred from the starting pool using genetic engineering techniques, and could very well be grown in the field with organic production methods, since production occurs after the genetics are established in a variety. Sure, you could argue there would be no point to doing that, if the assumption was that all GMO traits are herbicide tolerance, and thus to benefit from the modifications, the appropriate herbicide must be applied. However, HT is only one of many real and theoretical traits that can be conferred to an oRgaNiSm.

One last layer: A sub-category of conventionally-produced crops are those with a herbicide tolerant (HT) trait and thus a specific herbicide is to be used in the field with them.

This concept was already introduced earlier at the trait selection step in the 2nd phase of this process. To date, there are only HT varieties compatible with conventional production of GMO varieties + application of conventional herbicides (path D), and conventional production of non-GMO varieties + application of conventional herbicides (path A). Technically, path B could be possibleandpermitted under current regulation, assuming there were an organically-approved herbicide to which the crop variety were tolerant, but Im unaware of any. And HT under path C of course can never be reached within current regulation.

Its a bit tricky to depict in an illustration. to select for a trait that confers herbicide tolerance, the plants at that step of the breeding process have to be exposed to a specific herbicide (when the herbicide to be used is already defined) or to be exposed to a spectrum of different herbicides (different chemistries)

Lets put it all together. Heres the full illustration with all the details added:

And guess what: the fundamental genetic building blocks are identical whether the plant is GMO or not. Whether youre eating the plants carbohydrates, proteins or fats, the body therefore isnt concerned about how they were assembled. Either the material is digested / broken down and used in the body, or dissembled and excreted and eventually becomes fertilizer for more things to grow, thus adding to the

Considering the information presented on the Canada Organic Trade Association (COTA), the national association which protects, promotes and builds information on Canadas organic sector, it isvehemently opposedto anything GMO, stating that Genetically engineered products (GMOs) are prohibited in organic production. This means an organic farmer cant plant GMO seeds Actually, I can get on board with most ofwhat they actively promote, lets take a look:

While these are mostly noble goals, the opposition to GMOs is over-reaching and not well founded, as it rejects genetically engineered solutions with identical goals. I suppose it makes sense for an organics association to dictate what/what type of pesticides can be used to establish a specific production environment, but for the same association to take any stance on GMOs is inappropriate.I am not saying safety/practicality/feasibility of genetic engineering should not be assessed; rather, I am saying that should be an independent association/effort rather than distorting interest via the lens of a specific production system.

Genetic engineering is already used to significantly reduce the use of toxic and persistent synthetic pesticides and fertilizers while encouraging biodiversity. Consider Bt crops In this case, one gene from a bacteria (Bacillus thuringiensis, Bt for short originally registered in the USA60 years agoas a biopesticide) that is naturally found in soil was genetically engineered into a corn varietys genome (and subsequently into other crops including soybeans, cotton and eggplant). Why? That gene enabled the bacteria to produce a protein that was naturally (and selectively) an insecticide to a particular insect that happens to be a pest to the corn. Instead of spending time, money, and fuel to spray a synthetic insecticide across entire fields, this modification gave the corn inherent ability to repel that insect.Bt crops eliminate the need to spray to kill insect pests, minimize pest-related crop losses and reduce the financial risksall by using tools already in that environment.

Bt corn was the first insect resistant crop rolled out, in 1995 in the United States. The seeds are now grown in many countries around the world.

Bt cropswhether corn grown in the Canada or South Africa or Brazil, or Bt eggplants grown in Bangladesh have led to a dramatic drop in the use of chemical pesticides.

Organic GMOs will become increasingly relevant in the next few years, not only to comply withglobal effortsto reduce pesticide use, but for reasons unrelated to pesticides.

Here are a few intriguing non-human-nutrition-focused benefits that GMOs could offer, assuming their progress isnt further impeded. Over the past couple decades, they havehelped to reduce pesticide (herbicide and insecticide) and indirectly reduced greenhouse gas emissions. Recent efforts are investigating the ability tomodify plants(trees, maybe field crops soon) to reduce carbon emissions by increasing their capacity to store carbon in their root systems. They hint at other modifications to improve their amenability to processing for biofuel use as well, further adding to efforts to reduce GHG emissions.Other groupsbelieve GMOs are essential to quickly adapting commercial crop varieties for extreme environmental changes, such as drought, or even to produce no-carbon fuel. Check out SingaporesGardens by the Bay, one of many futures that agriculture could take.

Such technologies to combat field pests isnt perfect; some populations of the target pest can eventuallyovercomethe crops inherent insecticidal ability note that this is not unique to insects nor to genetic engineering solutions to pests. There are greater implications beyond the scope here, but briefly, implies how crucial crop rotations are (in other words, removing continuous availability of hosts), as well as taking an integrated approach to production (switching up the tools used to combat pests, and/or using 2 or more tools at once).

Another valid concern raised is that crops modified to endogenously produce pesticides means that it is dispersed throughout the plant & thus the parts that are processed for food. If prohibition of this area of research were to end, I would anticipate that the end-products would be subject to the same extensive Health Canadaregulationsas food additives currently are.

To reiterate: Im not saying this is an all-or-nothing scenario, or that GMOs offer all the answers. But these technologies and systems should be more integrated and be given more consideration as complementary rather than viewed as competition. The rapidity with which environments have changed in recent history I would think is even more reason to put all options on the table. When has prohibition of anything ever been effective?

The purpose of this article is not to declare anything as right or wrong, good or bad, best or worst; rather, it is to help non-specialists understand technical nuances and offer some new information to ponder, or cause some progressive discussion among industry. Hopefully in some way, it might help guide a more progressive, rational, sustainable and/or profitable industry. Nothing here is sponsored; it is simply my own view on current and future directions that agriculture may take.

A version of this article was originally posted at the Former Farmboy and is reposted here with permission.

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Viewpoint: Will there ever be organic GMOs? Here's the science behind why it it's a good idea - Genetic Literacy Project

Deer graze along the dunes at Robert Moses State Park in Babylon, NY.Photo: Thomas A. Ferrara/Newsday RM (Getty Images)

The Omicron variant of the coronavirus has found its way into white-tailed deer living in New York, new research released this week has found. The results are the latest to show that deer in the U.S. have become frequent carriers of SARS-CoV-2a phenomenon that could have important implications for the future of the virus and our vulnerability to new variants.

Numerous studies have found that deer can readily contract the coronavirus. Last November, for instance, researchers from Penn State University and elsewhere reported that up to a third of free-living and captive deer in Iowa carried traces of the virus from late 2020 to early 2021. Some of these same researchers from Penn State and others, including those with the New York City Department of Parks & Recreation, have released their latest findings this week on the preprint website bioRxiv.

The team tested blood and nasal samples from wild deer living on Staten Island that were temporarily captured as part of a sterilization program to keep the population in check. The samples were collected between December 2021 and January 2022, and the scientists performed antibody and RNA tests on them.

Overall, 14.5% of 131 deer that had their blood taken tested positive for antibodies to the coronavirus, indicating a prior infection. About 10% of the 68 deer that had nasal swabs taken tested positive for an acute infection. And when the researchers sequenced the genetics of these positive samples, they found that some had caught the Omicron variant, the most transmissible version of the coronavirus to emerge yet.

The Omicron found in these deer bore a close genetic resemblance to Omicron strains found in human residents of the city, all but confirming that humans had somehow been the source of the deer infections. Its unclear how this is happening, but direct contact via hand-feeding or through exposure to contaminated wastewater or trash are possibilities. Interestingly, at least one infected deer had both an active infection and very high antibody levels, possibly indicating that it had been reinfected.

This work, the researchers say in their paper, clearly shows that Omicron can infect white-tailed deer and highlights an urgent need for comprehensive surveillance of susceptible animal species to identify ecological transmission networks and better assess the potential risks of spillback to humans.

Deer, at least in the lab with older strains of the virus, dont appear to experience much if any illness from their infections, unlike other animals such as minks. But the widespread transmission of the virus seen in these animals doesnt bode well for several reasons. The virus could mutate to become a serious health problem for deer in the U.S., which would only add to the list of infectious diseases circulating in these animals. The virus could also mutate in unpredictable ways or recombine with other coronaviruses in deer that would allow it to become more immune-evading or virulent once its transmitted back to humans.

None of this is certain, of course, and theres plenty of coronavirus already circulating and mutating in humans. But one reason why diseases like influenza are considered a pandemic threat is that flu viruses are constantly being spread back and forth between different species. Every once in a while, the genetic shuffling that this process produces can spit out a version of the flu thats both highly contagious in humans and much more deadly than the typical seasonal flu. So if the same thing can happen with SARS-CoV-2, its a risk that we have to keep an eye on as much as possible, the researcherssay.

The circulation of the virus in deer provides opportunities for it to adapt and evolve, study author Vivek Kapur, a veterinary microbiologist at Penn State University, told the New York Times. And its likely to come back and haunt us in the future.

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Deer in New York Test Positive for Omicron, Researchers Warn of Future 'Spillback to Humans' - Gizmodo