Epigenomic and Other Evidence for Cannabis-Induced Aging Contextualized in a Synthetic Epidemiologic Overview of Cannabinoid-Related Teratogenesis and Cannabinoid-Related Carcinogenesis
1Division of Psychiatry, University of Western Australia, Crawley, WA 6009, Australia
2School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA 6027, Australia
*Correspondence: stuart.reece@uwa.edu.auAbstract
Background: Twelve separate streams of empirical data make a strong case for cannabis-induced accelerated aging including hormonal, mitochondriopathic, cardiovascular, hepatotoxic, immunological, genotoxic, epigenotoxic, disruption of chromosomal physiology, congenital anomalies, cancers including inheritable tumorigenesis, telomerase inhibition and elevated mortality. Methods: Results from a recently published longitudinal epigenomic screen were analyzed with regard to the results of recent large epidemiological studies of the causal impacts of cannabis. We also integrate theoretical syntheses with prior studies into these combined epigenomic and epidemiological results. Results: Cannabis dependence not only recapitulates many of the key features of aging, but is characterized by both age-defining and age-generating illnesses including immunomodulation, hepatic inflammation, many psychiatric syndromes with a neuroinflammatory basis, genotoxicity and epigenotoxicity. DNA breaks, chromosomal breakage-fusion-bridge morphologies and likely cycles, and altered intergenerational DNA methylation and disruption of both the histone and tubulin codes in the context of increased clinical congenital anomalies, cancers and heritable tumors imply widespread disruption of the genome and epigenome. Modern epigenomic clocks indicate that, in cannabis-dependent patients, cannabis advances cellular DNA methylation age by 25–30% at age 30 years. Data have implications not only for somatic but also stem cell and germ line tissues including post-fertilization zygotes. This effect is likely increases with the square of chronological age. Conclusion: Recent epigenomic studies of cannabis exposure provide many explanations for the broad spectrum of cannabis-related teratogenicity and carcinogenicity and appear to account for many epidemiologically observed findings. Further research is indicated on the role of cannabinoids in the aging process both developmentally and longitudinally, from stem cell to germ cell to blastocystoids to embryoid bodies and beyond.
1. Introduction
Aging is the ubiquitous fate of biota and involves progressive loss of function [1]. Whilst the unkempt appearance and often poor physical and/or mental health of the patient chronically dependent on drugs including cannabis is widely appreciated, formal studies of cellular aging following chronic drug exposure are curiously absent from the literature. Major recent advances in various fields including epigenomics, epidemiology, stem-cell physiology and the mechanics of mitotic and meiotic cell division provide a unique opportunity to conduct an investigative review of the interaction of cannabis exposure and aging with a view to stimulating formal investigation of the field with epigenomic and other aging biomarkers.
Whilst teratology and cancerogenesis are well recognized aspects of genotoxicity and are now well documented in relation to cannabis-related genotoxicity, accelerated aging is the third well recognized aspect of genotoxicity generally [2], which presently lacks a detailed, coordinated and comprehensive review of its phenomenology and underlying theoretical and mechanistic bases with regard to cannabis and cannabinoids. The present paper addresses this gap.
Major hallmarks of biological aging include genomic instability, epigenomic alterations, telomere attrition, cellular senescence, mitochondrial dysfunction, altered intercellular communication, stem-cell exhaustion, difficulty with nutrient utilization and loss of proteostasis [1,3,4,5,6,7]. It is important to note that most of these pathways are now known to interact with the epigenome. Another frequently cited theory of aging is the free oxygen radical theory. Oxyradicals have also been shown to interact with epigenomic pathways via P16INK4A [8].
In 1942, Conrad Waddington hypothesized that epigenomic states constrained cell lineage differentiation to certain “valleys” so that cell specification within the major types was energetically constrained [9]. This profound insight had several implications including that differentiated cells do not readily transdifferentiate into a different cell type. Moreover, cells usually differentiate from a less differentiated progenitor state into a more highly differentiated state so that the biological age of cells in terms of numbers of cell divisions is encoded and recorded epigenetically, together with many other immune, metabolic and in neurological tissues, electrical, memories [10]. This usual process of differentiation from multipotent progenitors into progeny with increasingly restricted fate is known as canalization [11].
In 2006, Takahashi and Yamanaka screened 20 putative stem-cell factors to define the minimal signaling core group required to induce and maintain pluripotent stem cells. The four factors they defined were called OSKM factors (Oct3/4, Sox2, Klf4 and cMyc) or simply Yamanaka factors [12]. These authors used these factors to induce mouse fibroblasts to dedifferentiate back into embryonic stem cells thereby showing that the biological clock could be reversed. Elegant studies by other groups with Yamanaka factors or similar have since replicated these findings in other systems including recovery of aged rodent pancreatic islets and skeletal muscle crush [13], recovery of cardiac function and reversal of heart failure after rodent myocardial infarction [14], and recovery of vision after traumatic optic nerve crush injury, glaucoma, cataract and age-related blindness in old rats [15]. They were even able to restore and rejuvenate the aged cells of a mouse model of progeria [13]. Not only does this collection of studies generalize the Yamanaka findings relating to tissue and organismal age reversal, but, as observed by leading aging researchers, they also provide powerful evidence for the primacy of epigenomic regulation of the aging process overall [16]. In this context, the various hallmarks of aging mentioned above are now probably best understood from their relationship to the complex and multi-layered epigenomic regulatory pathways.
Cannabis dependence is defined as the state which exists when individuals become physically or mentally unwell after ceasing exposure to cannabis [17]. Cannabis withdrawal is characterized by a spectrum of symptoms including anxiety, irritability, dysphoria, craving, sleeping difficulties, abdominal cramps, muscle aches and diarrhea [17]. Chronic exposure may be defined as exposure which occurs during a period exceeding six months [17]. Daily cannabis exposure is operationally defined as being cannabis exposure on all or most days each month or twenty or more days per month [18].
Chronic cannabis dependence is characterized by many of the age defining hallmarks mentioned above with DNA breaks, fusions and bridges well described [19,20,21,22,23,24] and potentially including the breakage-fusion-bridge cycle (where chromosomal breaks lead to aberrant interchromosomal joinings and which causes chromosomal bridges to form when the chromosomes separate in anaphase which then leads to further breaks when the chromosomes are pulled apart in telophase so that the cycle repeats) [25]; major changes in DNA methylation [26,27] which have been shown to be transmissible to sperm and to a subsequent generation of offspring [26,27,28,29,30,31,32]; telomere attrition [33,34]; immunomodulation including heritable immunomodulation [35,36,37,38,39,40]; inhibition of mitochondrial function including increased free radical generation [41,42,43,44]; impairment of DNA, RNA and protein synthesis and cell growth [45,46,47,48,49] and thus stem-cell impairment and widespread negative trophic and functional effects in many tissues [47,48,50,51]. Chronic exposure is also associated with increased rates of many cancers [52,53,54,55,56,57,58,59,60,61,62,63,64,65,66]; a suppressed endocrine state [67,68,69,70,71,72,73] and impaired male and female fertility [67,71,74]. Thus, significant long-term exposure to cannabinoids recapitulates and accelerates many of the significant features of physiological aging.
The most prominent of the various biophysiological clocks which have been described to measure biological as compared to chronological aging are epigenomic clocks based on DNA methylation [75,76,77,78]. Cardiovascular [79,80,81,82], immunological [83], transcriptomic, microRNA, proteomic and metabolomic clocks have also been released [84,85,86,87,88,89,90].
1.1. Key Definitions
Genomic instability is a major mechanism in cancer, congenital anomalies, neurodevelopmental defects and aging. Genomic instability refers to cellular mutations and includes changes to the nucleic acid sequence, chromosomal rearrangements aneuploidy, copy number variations, circular DNA and microchromsomes [91,92,93,94,95,96,97,98,99,100,101,102].
“Canalization” refers to the process described in Waddington’s famous theory of cellular differentiation like a marble rolling down a landscape of hills and valleys and finding its energetically most favorable point, progressively becoming more terminally differentiated [9].
“Yamanaka factors” are those four cellular transcription factors originally described by Yamanaka and colleagues for potentiate the de-differentiation of terminally differentiated fibroblasts into induced pluripotential stem cells. The four factors identified were Oct3/4 (POU5F1, POU Class 5 Homeobox 1), Sox2 (SRY-Box transcription factor 2), c-Myc (MYC protooncogene, BHLH Transcription Factor), and Klf4 (KLF Transcription factor 4).
“Epigenomic regulatory pathways” refer to the many mechanisms of gene regulation including: DNA methylation, post-translational histone modifications, micro-RNAs, long non-coding RNAs, involvement in topologically defined domains and adjacency to transcription factories, closeness to the nuclear envelope (which suppresses gene transcription), involvement tin euchromatin or heterochromatin structure (the former promoting and the latter suppressing transcription), various post-transcriptional modifications of RNA including C6-adenosyl methylation, circular DNA structure and microchromsomes, amongst others.
1.2. Outline
The plan of this review is as follows. Firstly, twelve independent streams of empirical data for accelerated aging will be presented to make a strong case for accelerated biological aging associated with chronic cannabis exposure to set the context for the following discussion. Secondly, evidence for perturbation of some fundamental cellular machinery by cannabis exposure and withdrawal will be presented including alteration of the epigenomic machinery itself, modulation of various stem-cell factors and epigenomic interference with the chromosomal machinery of cell division. Epigenetic changes in brain and cardiovascular function are briefly considered as changes in these organs not only reflect but drive systemic aging, i.e., they are not only age-defining illnesses but also age-generating disorders. Thirdly, since cancer and congenital anomalies (birth defects) are both age-related disorders and are clinical reflections of genotoxicity and/or epigenotoxicity and are heightened after cannabis exposure [25,66,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118], contemporary USA and European epidemiological findings are reviewed and form the backdrop for a contextual exploration of the recent powerful longitudinal epigenomic data published by Schrott and Murphy and colleagues on changes in the DNA methylome of human sperm after cannabis exposure and withdrawal annotated for many benign and malignant conditions [27]. These datasets are augmented by other recent organ specific studies highlighting genes of particular interest which are then interrogated in the Schrott data. Consideration is also given to genotoxic effects of cannabinoids more broadly including cannabidiol and Δ8-tetrahydrocannabinol (Δ8THC).
These matters are set out in tabular form in Table 1.
It is concluded that these metrics collectively point towards cannabinoid-exposed tissues being of advanced biological age resulting in age related morbidity, and that this process is driven by cannabinoid-disruption of the human epigenome, with increasing global cannabis exposure to a much greater extent than is commonly realized [119], having far-reaching public health implications for the current and future generations
2. Methods
Literature Review. Evidence was overviewed from the authors prior knowledge of studies examining cannabis effects on mechanisms of ageing. A literature search was conducted of PubMed on 30 November 2022 using the two sets of search terms “cannabis AND aging” and “cannabinoids AND aging”. Identified articles were manually searched. In total, 48 and 108 articles were identified from the raw searches. However, these dealt generally with only specific organ systems of aging (such as Alzheimer’s disease or pancreatic aging) and not the whole field of the pathobiology of aging itself; or alternatively hypothesized about unproven aging preventative actions. Thus, it was not possible to identify any recent reviews of the impacts of cannabis or cannabinoids on the fundamental pathobiology of aging. This finding formally demonstrates the novelty of the present study.
The 12 streams of evidence referenced flow from cellular systems and mechanisms (Epigenomic Overview) through organ systems (Prefrontal Cortex and Brain), to health disorders including cancer (Carcinogenesis), to population impacts (on birth defects and cancer).
Data. Data on rates of congenital anomalies are taken from published reports in USA [103] and Europe [115,120]. Data on cancer rates are taken from published reports on USA [112,113,114,121] and Europe [121,122]. Epigenomic DNA methylation data were taken from the EWAS (Epigenome Wide Association Study) report of Schrott and colleagues relating to cannabis dependence and withdrawal in human sperm before and 11 weeks after a period of cannabis dependence [27]. Genes of interest were searched in the 359-page pdf document which comprises the supplementary Schrott database.
Analysis. Statistical processing of code to derive relevant descriptive statistics was performed in R Studio 1.4.1717 based on R version 4.1.1 and both data and code are available as supplementary files in the following Mendeley repository https://data.mendeley.com/datasets/sngdkpg8gy/1 (doi:10.17632/sngdkpg8gy.1) (accessed on 10 December 2022. Full address is: https://data.mendeley.com/datasets/sngdkpg8gy).
Ethics. Ethical approval for this study was provided from the Human Research Ethics Committee of the University of Western Australia number RA/4/20/4724 on 24 September 2021.
3. Results and Discussion
3.1. Streams of Evidence for Cannabinoid Acceleration of Aging
Twelve independent empirical data streams both independently and collectively indicate accelerated biological aging associated with chronic cannabis exposure.
3.1.1. Clinical Syndromes
Long-term cannabis dependence is characterized by a cluster of syndromes which are themselves age defining illnesses including: neuroinflammation from the many mental illnesses [123,124,125,126,127,128,129,130,131,132]; steatohepatitis and cirrhosis progression [133,134,135,136]; myocardial infarction, cerebrovascular disorders and cardiac arrythmia [17,137,138,139]; immunomodulation [35,36,37,38,39]; endocrine suppression [67,68,69,70,71,72,73]; impaired male and female fertility [67,71,74]; cancers [52,53,54,55,56,57,58,59,60,61,62,63,64,65,66]; congenital anomalies [66,103,108,109,110,111,115,116,118]; genotoxicity including DNA breaks, telomere loss and mitotic and meiotic errors [19,33,140]; epigenotoxicity including altered DNA methylation [26,27,28,29,30,31,32] and histone physiology [141,142].
3.1.2. Mitochondrial Inhibition
Mitochondrial inhibition is well described in lymphocytes, neurons, sperm, hepatocytes and oocytes following cannabis exposure [41,42,43,44,140,143,144]. Mitochondria carry all of the cannabinoid signal transduction machinery found in the plasmalemma [44,145,146,147]. Since mitochondria supply energy and epigenomic substrates to the nucleus and interact with it closely via mitohormetic and mitonuclear balance systems [148,149] metabolic inhibition implies epigenomic disruption. Mitochondrial inhibition is well established as one of the key hallmarks of aging [1,150,151,152,153,154,155,156,157,158] and implicated pathophysiological pathways include such novel mechanisms as the leakage of mitochondrial DNA into the cytosol and stimulation of innate γ-interferon-dependent immunity via the cGAS-STING pathway [156].
3.1.3. DNA Methylation
Many studies have documented extensive alteration of DNA methylation following cannabis administration in both rats and humans [26,27,28,29,30,31,32,159,160]. Moreover, an elegant study has proven not only that the epigenome controls the aging process but that reversion of epigenomic age can heal traumatic optic nerve injury, glaucoma and geriatric blindness as normally only seen in neonatal life [15]. Extensive reduction in histone synthesis has been demonstrated including reduced phosphorylated and acetylated isoforms [49,141].
3.1.4. Mental Illnesses
Cannabis is associated with many mental illnesses including depression, stress, anxiety, PTSD, other substance dependence, bipolar disorder, schizophrenia and suicide [123,124,125,126,127,128,129,130,131,132] all of which are characterized by neuroinflammation [161,162,163,164,165,166,167,168], which is one of the hallmarks of the aged and dementing brain [169,170,171,172]. Not only is neuroinflammation an age defining illness it is also an age causing illness as it induces systemic inflammation throughout the body (“inflamm-aging”) [4,173]. Cannabis exposure was recently shown to be causally related to all four indices of mental dysfunction (depressive symptoms, any mental illness, severe mental illness and suicidal thinking) tracked by the annual nationwide massive National Survey of Drug Use and Health in a space time and causal inferential analysis [106]. Cannabis exposure has also been linked with the development of autism-like and ADHD-like syndromes in children [117,174] in spacetime and causal inferential studies [175] and in epigenomic studies [26,159,176,177]. An extensive literature and many meta-analyses strongly connect cannabis use and the development of schizophrenia by many mechanisms [17,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196].
3.1.5. Cardiovascular Age
Biological age as cardiovascular physiological age has been measured directly biophysically in cannabis dependence and been found to be advanced above controls [81]. An effect size of 12% and a positive dose-response relationship (p < 0.002) were demonstrated.
3.1.6. Endocrine Suppression
Widespread suppression of many key endocrine systems including luteinizing hormone (in males and females), testosterone, prolactin (chronic effect), growth hormone, estradiol and progesterone, Graafian follicle maturation, vasopressin and pregnancy including reduced fertility have been demonstrated in association with chronic cannabis use [67,68,69,70,71,72,73]. It has also been demonstrated in combined opioid-cannabinoid-dependent patients that the reversal of the FSH/LH ratio, a key clinical biomarker of the perimenopause, happened 20 years earlier [197]. Ovarian failure has also been shown to invariably be due to DNA damage [198]. Hormonal signals are rapidly transduced by the epigenome [199]. Hormonal failure and reproductive senescence represent age-defining and age-generating illnesses [1,157,158,200].
3.1.7. Liver Inflammation
Liver inflammation, cirrhosis and cancer have also been linked with cannabinoid exposure [133,134,135,136]. In that hepatic inflammation causes systemic inflammation, insulin resistance and dysmetabolism [201], generally these are also age-defining and age-generating illnesses. Moreover, the complex multi-way interaction between dysmetabolic and immunopathic changes is increasingly being defined and emphasized [202].
3.1.8. Cancer
Clinical genotoxicity is expressed as heightened rates of many cancers including liver, breast, pancreas, diverse leukemias and lymphomas, oropharyngeal, thyroid, urinary, esophageal and testicular tumors [52,53,54,55,56,57,58,59,60,61,62,63,64,65,66]. Genotoxicity is also one of the well-established key hallmarks of cellular aging [1,157,158,203].
3.1.9. Inheritable Cancer
Several cannabis-related cancers occur in the pediatric age group and are therefore evidence of heritable carcinogenesis [204,205] and therefore combine both teratogenesis and malignancy in the one case. This has been found for acute myeloid and lymphoid leukemias and total pediatric cancer [65,66,104,105,206] and for rhabdomyosarcoma and neuroblastoma [207,208]. One recent survey of the cannabis-exposed DNA methylome showed 487 hits for various malignancies [27].
3.1.10. Congenital Anomalies
Clinical genotoxicity is also expressed as congenital anomalies. As a majority of congenital anomalies, particularly those affecting the heart and chromosomal systems, are known to be related to parental age [209,210] the congenital anomaly rate becomes a surrogate or biomarker for biological age. Dozens of congenital anomalies have been described following prenatal or community cannabis exposure in Hawaii, Colorado, Canada, Australia, USA and Europe affecting particularly limbs, central nervous, cardiovascular, gastrointestinal, uronephrological and chromosomal systems [66,103,108,109,110,111,115,116,118]. Hundreds of positive hits were recorded on a DNA methylome screen for all the organ systems involved including mitochondria, chromosomes, microtubules, body axis and embryonic growth [27].
3.1.11. Telomerase Inhibition
3.1.12. Elevated Mortality Rate
Mortality has been shown to be very elevated in cannabis users in several studies [212,213,214,215,216,217,218,219,220,221,222,223] at 30% over 30 years [223] and in another had a standardized mortality index of 14.61 (C.I. 9.21–23.19) over 14 years [222]. Whilst drug overdose, suicide and AIDS were the leading causes of death, cannabis itself predisposes to other drug use and mental illness [106,224,225,226]. Mortality is of course a hard end point for aging albeit in this context the pathway is complex.
“The sections that follow integrate cannabis ageing theories from eight pathogenetic fields”.
3.2. Pathogenetic Field of Interest
3.2.1. Epigenomic Overview
Longitudinal epigenomic data published by Schrott and colleagues on changes in the DNA methylome of human sperm after cannabis dependence and withdrawal [27] provide an explanation for the broad spectrum of cannabis-related teratogenicity and cancerogenicity mentioned above.
Table 2 presents a re-formatted extract of the Schrott data looking at the epigenomic modulation of the key epigenomic machinery itself [27]. As shown in the Table, most of these perturbations of DNA methylation occur in introns within genes but some are in upstream presumably promoter regions and some are in downstream enhancer regions.
DNA methyltransferases 1 (DNMT1) and 3A and 3B (DNMT3A, DNMT3B) are the main enzymes which are responsible for laying down the methylation signals on DNA both from conception and in response to many signals thereafter. TET1 (ten-eleven translocase) is the main enzyme responsible for removing the methylation signals. It oxidizes the methylcytosines of CpG dinucleotides and introduces a hydroxyl group which is then oxidized in subsequent steps with the effect of removing the methylation mark. Hence the first lines of this Table show that both writing and erasing the key DNA methylation marks are disturbed by cannabis dependence or withdrawal. Here it is important to note that most habitual cannabis users go through withdrawal daily which is one of the major motivations to repeat use and making withdrawal a major and defining feature of clinical cannabis dependence [227].
UHRF1 (ubiquitin-like containing PHD and RING finger domains 1) is a key enzyme which is involved with both DNA methylation and histone modifications [228]. It recruits both DNMT1 to write DNA methylation marks and histone deacetylases which control access by the transcription machinery [228]. Its tudor-like and PHD- domains recognize and bind histone 3 trimethylated at lysine 9 (H3K9me3) and unmethylated arginine-2 (H3R2me0) and recruits chromatin proteins. Hence this enzyme is regarded as a key epigenomic hub coordinating the activities of the DNA methylation and histone regulatory systems. It regulates both the retinoblastoma gene product and the P53 damage checkpoint. Its expression levels peak in late G1 and it controls the G1/S transition of the cell cycle. It plays a key role in the regulation of pericentric chromatin and thus kinetochore function and chromosomal segregation. It is also involved in DNA repair. It is a known oncogene and has been implicated in liver cancer amongst others [229]. Hence its perturbation can be predicted to have a major effect on epigenomic regulation.
DPPA3 (Developmental PluriPotency Associated protein 3) has been shown to protect the epigenome of the oocyte from methylation [230]. Whilst DPPA3 was not identified in the spermatocyte EWAS conducted by Schrott team DPPA2 was identified as indicated.
TERT (telomerase reverse transcriptase) is a key enzyme responsible for maintaining the length of telomeres and is key to maintaining pluripotency in stem cells and germ cells and is often highly induced in cancer cells. Telomeres are protective caps on the ends of chromosomes and because some length is lost with each cell replication event they usually shorten with age. Since telomere attrition is one of the key chromosomal hallmarks of aging the regulation of telomere length is a key metric for the cellular aging clock. This important finding of cannabinoid interference with this key cellular enzyme has also been reported by others [211].
The polycomb repressive complex (Table 2) is one of the main epigenomic complexes which silence heterochromatin long term. Therefore, interference with these activities can be expected to have long-term consequences for cellular health.
SMARCA2 and SMARCA4 are SWI/SNF (SWItch/Sucrose NonFermentable) ATP-dependent modifiers of chromatin which change nucleosome position in an energy-dependent manner and therefore rearrange the genome and make new sections available for transcription. Modulation of these epigenomic controllers was recently shown to have a very positive effect in advanced castrate resistant prostate cancer which was addicted to their activities [231]. Since the SWI/SNF system is a major rearranger of chromatin perturbation of this system carries major downstream implications for cellular health. SMARCA2 and SMARCA4 (also known as Brahma, BRM and Brahma-related Gene 1, BRG1) were also recently determined to be key determinants of differentiation and canalization of precursor mesodermal cells into a cardiac fate [11].
Not only is DNA methylated but so too are histone proteins. There were 161 hits in the Schrott database for histone methyltransferases which write this mark onto the histone code (some top hits shown in Supplementary Table S1) and 199 hits for the histone demethylases which remove this mark (of which an extract is shown in Supplementary Table S2).
Histone acetylation is a key mark on histone tails. By neutralizing the charge of histone tails histone acetylation opens up chromatin and makes it available for gene transcription. This key acetylation mark is written onto the histone code by histone acetyl transferases and removed by histone deacetylases. Eleven hits in the Schrott data for each of these which were noted in both cannabis dependence and withdrawal are detailed in Supplementary Tables S3 and S4 respectively.
Thus, this brief introductory overview provides good evidence of major changes not only of the DNA methylome but of the central machinery which writes and erases and coordinates the epigenetic code on both DNA and histones. Key chromosomal areas such as the telomeres and centromeres are also impacted which thereby directly impacts processes such as aging (via accelerated telomere loss) and cellular division (via disruptions of centromere/kinetochore function).
3.2.2. Stem-Cell Factors
Takahashi and Yamanaka published their seminal and ground-breaking paper on the use of four defined recombinant stem-cell factors to maintain and induce the pluripotential state of embryonic stem cells in 2006 [12]. Proof of the induced stem-cell concept was provided by their demonstration that they were able to revert mouse fibroblasts to embryonic stem cells by the use of their four defined factors OSKM. These induced embryonic stem (iPS) cells went on to contribute to viable mouse embryos after injection into blastocysts. Intermittent use of the OSKM factors, a technique known as partial reprogramming, was able both to rescue a mouse model of progeria and to dramatically accelerate injury recovery to skeletal muscle and pancreatic islets in aged mice [13] and was able to improve cardiac function after myocardial infarction in a mouse model [232]. Inducible expression of OSK in retinal ganglion cells was able to restore vision in a manner only seen in neonatal mouse pups after glaucoma, optic nerve crush and extreme age in old mice [15].
As shown in Table 3, there were 11 hits in the Schrott database for the Yamanaka stem-cell factors. The name of the Oct3/4 gene has since been changed to POU5F1. SOX2, KLF4 and MYC were positively identified but Nanog was not identified.
A modification of the Yamanaka protocol using slightly different stem-cell factors where Klf4 was replaced by Lin28 was also shown to induce iPS induction [14]. Stem-cell factors used by these researchers and also by Yamanaka were further investigated in the Schrott data with results shown in Supplementary Table S5. As there were 146 hits for Ras, 230 hits for Catenin and 185 hits for Kit in this database only a leading selection is shown in the Table. Hits for PAX7, one of the skeletal muscle master transcription factors and Lin28 are also shown at the bottom of the Table. Supplementary Table S6 provides an expanded list of some of the hits for Kit.
There is also a powerful and well documented multi-way link between immune activation, dysmetabolic changes and the aging process. For example, a recent study showed that much of the effect of calorie restriction, which has been well demonstrated to induce life extension in flies, worms and mice, when applied in humans was mediated by PLA2G7 (platelet activating factor acetyl hydrolase/phospholipase A2 group VII) [202]. PLA2G7 is found in cholesterol-rich low density lipoprotein particles and PLA2G7 oxidizes saturated lipids and activates vessel wall macrophages, lymphocytes and endothelial cells. It thereby stands at the intersection of immunity and metabolic processes.
A research group from Stanford developed a biological clock based on immune biomarkers and found that CXCR9 was the key chemokine which accounted for most of the variance they identified [83]. A sizeable literature exists around NAD (nicotinamide adenine dinucleotide) metabolism and the links between its normal dramatic age-dependent decline and the ageing process itself [233,234,235,236,237,238,239,240,241]. The key rate limiting enzyme in the NAD biosynthetic pathway is nicotinamide phosphoribosyl transferase (NAMPT) which acts as the gateway to this pathway [148]. It was therefore of interest to learn if these key immune and metabolic mediators were identified in the Schrott EWAS. The results of this investigation are shown in Supplementary Table S7. Both PLA2G7 and NAMPT were positively identified. CXCR9 was not identified but CXCR13 was found.
3.2.3. Chromosomal Mechanics
During the process of cell division at the beginning of prometaphase, the nuclear membrane breaks down and what has very properly been called the “mammoth” supramolecular mitotic and meiotic machine involving the mitotic spindle begins to form [242]. The process takes place on the large scale of the whole cell cytoplasm and each of its innumerable steps are tightly regulated, carefully choreographed and finely coordinated by elegant and sophisticated mechanisms. The implication of this vastness, complexity and sophistication is that the delicate process of cell division is open to perturbation and disruption at numerous steps.
Whilst the process of cell division is well known to students of biology the world over from watching time lapsed video micrographs, it is less well known that in the human oocyte the process is highly error prone with error rates of 60–90% being reported even when young oocytes are used [243,244,245,246,247,248,249] and this error rate is known to rise sharply with age [243,244,245,246,248,249]. The bipolar alignment of the mitotic spindle with two spindle poles is critical to directing the cell to divide into two daughter cells during the subsequent anaphase separation. Whilst most species have a pair of centrioles and pericentriolar material (called centrosomes) which direct this process this is absent from higher (non-rodent) mammalian species including humans. Such species organize their spindle poles using acentriolar microtubule organizing centers (aMTOC) organized by NUMA (nuclear mitotic apparatus protein) and the kinesin motor protein KIFCI (kinesin family member C1) to draw the microtubules together [248]. Supplementation of human oocytes with KIFC1 largely rescued the high mitotic error rate [248] and in mice its knockdown via degron mediated destruction increased the error rate of bovine and modified aMTOC-free mouse oocytes to be highly similar to that of the human oocyte [248]. In actual fact, the number of poles in human oocytes mitotic spindles oscillates dynamically during oocyte maturation over several hours from several poles to just one pole and most frequently settles at just two spindle poles [245]. This implies that NUMA and KIFC1 are key to the integrity and reliability of the inherently error-prone oogenesis mitotic process in humans [248].
In worms, a kinesin-12 protein (KLP-18, kinesin-like protein), dynein (and its binding partner dynactin) and a kinesin-5 member (BMK-1, Big Mitogen Activated Protein 1) are required to prevent spindle splaying [247,250].
The anaphase-promoting complex/cyclosome (APC/C) is known to be a key organizer of the mitotic spindle and to determine when all the paired chromosomes are aligned satisfactorily on the metaphase plate and thus licences and controls the chromosomal separation of anaphase [249,251]. In human-derived HEK293 cell lines it was shown that APC/C also localizes to the centrosome where its activity is controlled by Cep152 (Centrosomal Protein 152) in complex with Cep 57 and Cep 63 [249].
Tubulin is also subject to numerous post-translational modifications particularly acetylation, polyglutaminylation and tyrosinylation [252]. Acetylation is key to the formation of the tubulin polymers of the mitotic spindle and this is controlled by lysine (K) acetyltransferase and histone deacetylases (HDAC) particularly HDAC3, HDAC6 and HDAC11 and the sirtuin (SIRT) HDAC’s SIRT2 in meiosis I and SIRT1 in meiosis II [245]. The process is also sensitive to oxidative stress and ROS (reactive oxygen species) are known to play important roles in both folliculogenesis and oocyte maturation but excessive ROS levels have been linked to shrinkage of the width and length of the mitotic spindle, disruption of the spindle asters, chromosomal misalignment in metaphase II, chromosomal disassembly in meiosis I and II and increased aneuploidy rates [245]. Adducts of ROS including 4-hydroxynonenal form and co-localize with α-, β- and γ-tubulins [245]. Ovarian ROS production also rises with age [245].
Importantly, acetylation of lysine-40 on polymerized α-tubulin by α-tubulin acetyl transferase 1 (ATAT1) occurs on the inner surface of the microtubule and allows for running repairs to be undertaken on the polymer when the microtubules is stressed or bent thereby adding greatly to the structural strength and flexibility of the structure [253]. Without K-40 alpha-tubulin acetylation, the microtubules remain brittle and bending leads to microtubule fracture and chromosomal derailment, isolation, aneuploidy and micronucleus development during the anaphase disjunction. Unlike female meiosis, cell division in the fertilized zygote is organized around centriole-containing centrosomes which are derived from the paternal gamete as those associated with the female pronucleus are rudimentary [243,244]. It is therefore clear that interference with any of these structural, binding, signaling or motor proteins will lead to an elevated error rate of human female gametogenesis [248].
Supplementary Table S8 therefore presents the hits identified in the Schrott database for NUMA, CEP and kinesin- and dynein-dynactin motor proteins. It is noted that KIF14 is an alternate nomenclature for KIFC3 which was noted to be critical [248]. Hits in intron, exon and enhancer regions are noted. There were 218 hits for kinesin motors and these hits were some of the strongest hits identified in cannabis dependency in both Schrott’s Tables S1 and S4 [27]. Some of the top-scoring kinesin motor protein hits are detailed in Supplementary Table S9. It is noted that these results for the DNA methylome come from sperm so it remains to be determined how the detailed results from oocytes might compare.
When one considers tubulins in the database of Schrott and colleagues, 106 hits are obtained. Some of those for tubulin (not including the pseudogenes) and ATAT1 are shown in Supplementary Table S10. This Table also shows epigenomic hits identified for some of the key enzymes which write and modify the tubulin code including acetylation, tyrosinylation/detyrosinylation and acetylation. In total, 86 of the hits observed for tubulin are for TUBB6 (β-tubulin 6 class V) and these appear as the most significant of all of the functional annotations in the Schrott Table S4 for cannabis dependence as partially extracted in Supplementary Table S11. TUBB6 epimutations are also linked with many cancers [27].
3.2.4. Centromeres and Kinetochores
In addition to the poles, organization and microtubular rays of the mitotic and meiotic spindles the points of attachment of the chromosomes to the microtubules also form a key locus of control for the whole mitotic process and a key point of vulnerability at which xenotoxins may impact. Somewhat confusingly the combination of the central repetitive non-coding DNA at the center of the chromosome (the centromere) together with its accompanying histones and proteins is (also) referred to as the centrosome. The key marker for the development of the centromere is the substitution of histone 3 (H3) for its derivative CENPA (Centrosomal protein A) and the formation of neocentromeres can be induced by the forced expression of CENPA along chromosomal arms [254]. A multiprotein complex of 16 other centrosomal proteins called the kinetochore is then assembled on the centrosome at CENPA to form a large multimolecular complex which binds to the growing plus ends of 25–30 microtubules for each chromosome.
Detailed descriptions of the protein composition of the kinetochore have appeared [254,255,256]. When these proteins are run through the Schrott database 109 hits are obtained for the 19 proteins listed in Table 4. Interestingly, 86 of these hits are for CENPN which is the equal second protein to assemble alongside CENPA at the very commencement of kinetochore assembly. Some of the most significant hits for CENPN are shown in Supplementary Table S12 and are extracted from the Table S4 in Schrott’s dataset for cannabis dependence. They are notable for their very high levels of statistical significance along with their association with uniformly malignant disorders. With the exception of SPC24, all the hits identified were in cannabis dependence rather than cannabis withdrawal.
Table 4 also includes details on the addition of the Small Ubiquitin-like MOdifier (SUMO) protein to histones. Sumoylation is a key post-translational modification (PTM) of many proteins which has been shown to be critically involved in many key genomic functions such as DSB repair, DNA transcription and replication and chromosomal segregation and synapsis [257,258]. Sumoylation is a foundational post-translational modification on many proteins including RNA polymerase II which forms the basis for the addition of sometimes lengthy chains of PTM’s which control these key genomic activities [258]. Δ9THC acting via CB1Rs has been shown to directly modulate P53 (the “guardian of the genome”) and Mdm2 (murine double minute, one of its key controlling proteins) [259]. As documented in the lower segment of Table 4, it was demonstrated in the Schrott EWAS that SUMO1 itself, one of the key E3 SUMO ligases which attaches the PTM to proteins, ZNF451 (zinc finger 451) and two of the SUMO endopeptidase proteins (SENP6 and SENP7) which cleave the SUMO PTM’s are affected epigenomically by cannabis dependence and withdrawal.
Since centromeres form the site of attachment of the chromosomes to the mitotic spindle, it follows that centromeric stability is key to maintenance of genomic stability [2]. In fact, centromeres are intrinsically “stiffer” and more fragile than the rest of the chromosome and represent “hot spots” for double stranded break (DSB) occurrence and chromosomal rearrangements [2]. Accurate repair of these breaks by homologous recombination is therefore essential to genome stability. Homologous recombination is normally understood to be suppressed in the G1 phase of the cell cycle. However, it has recently been reported that CENPA together with its chaperone HJURP (Holliday Junction Recognition Protein) and dimethylation of H3 (H3K4me2) permit invasion of the double stranded DNA by the DNA-RNA hybrids (R-loops) and licences the assembly of the RAD51 (RAD51 Recombinase)—BRCA1 (BRCA1 DNA Repair Associated 1)—BRCA2 complex which is the core complex of the main high fidelity homologous recombination (HR) pathway. Inhibition of HR necessarily leads to activation of much lower fidelity pathways such as microhomology-mediated end joining mediated by RAD52 and compromises genomic stability [2]. These investigators were able to demonstrate that RAD51 inhibition greatly increased centromeric breaks and centromeric translocations in NIH3T3 cells (as immortalized embryonic fibroblast cell line). Inhibition of both RAD51 and RAD52 together, inhibited both major repair pathways and blocked the formation of chromosomal translocations [2].
These findings lend special significance then to the combined demonstration in Supplementary Table S13 of much greater epigenomic interference with RAD51 than RAD52 by cannabis dependence and withdrawal (9 hits vs. 1) in the Schrott data and the well documented increased rate of chromosomal translocations seen experimentally after cannabis exposure [19,20,21,22,23,24,260,261,262].
3.2.5. Prefrontal Cortex and Brain
It is of interest to consider the representation in the Schrott EWAS of some of the key genes and pathways which are believed to be central to brain development. DSCAM (Down syndrome cell adhesion molecule) is most highly expressed in the fetal brain and retina where it is involved in neuronal self-avoidance, axon growth cone guidance, amacrine and retinal ganglion cell dendrite arborisation, commissural midline crossing in the spinal cord, homophilic synapse development and congenital heart disease [263,264]. It is overexpressed in Down syndrome and this has been implicated in some of the development of intellectual impairment in that disorder [264]. Supplementary Table S14 sets out the 14 EWAS hits in the Schrott database for DSCAM.
DLGAP2 (DLG associated protein) is an autism associated candidate gene also implicated in schizophrenia which has previously been linked with paternal cannabis exposure in sperm EWAS Studies [27]. It was thus of interest to see if the present study confirmed these earlier results. Supplementary Table S15 shows that indeed these results were strongly confirmed by the present EWAS series.
It was shown in the last decade that one of the main reasons for the relatively very enlarged frontal lobes of the human brain is the increased activity of Robo (Roundabout) signaling in the frontal cortex which leads to a greatly expanded neurogenesis in the frontal lobes and hyperproliferation of dedicated neural progenitor cells which feed into the exuberant frontal lobar growth [265,266,267]. Slits 1–3 form the natural ligand for robo receptors. The system is involved in both nervous system development and patterning and axonal guidance and also in arterial pathfinding and steering [209]. It has also been shown that this activity is blocked by cannabinoids [268]. It was therefore fascinating to observe that SRGAP2C (SLIT-ROBO Rho GTPase Activating Protein 2C) was identified by genomic screens and comparative genetics across many species to be the gene responsible for the exuberant outgrowth of the human forebrain neocortex [269]. Indeed, inducible expression of the forebrain of mice increased the cortical neuronal density and the synaptic short and long range corticocortical and bidirectional thalamocortical connectivity of layer 2/3 pyramidal cortical cells, enhancing their computational power and the rodents’ ability to quickly learn complex sensory-discriminant tasks [269].
For these reasons, it was of interest to observe how this system performed in the Schrott EWAS. Supplementary Table S16 sets out five results for Slits, Supplementary Table S17 sets out 26 results for Robo and Supplementary Table S18 sets out the eight results for SRGAP2C and its natural antagonist and controller SRGAP2B.
Another system which has also been shown to induce the relative overgrowth of the enlarged human forebrain is retinoic acid (RA). It was recently shown that high concentrations of RA at the frontal pole decline to lower and more normal levels at the posterior of the prefrontal neocortex in the premotor cortex [270]. The enzyme at the anterior pole which is chiefly responsible for synthesizing the high levels of RA is ALDH1A1 (aldehyde dehydrogenase 1 family member 1), the RA signal is transduced by the retinoid receptors RXRG and RARB, and RA is catabolized near the premotor cortex by CYP26B1 which is part of the cytochrome P450 system [270].
It was thus of interest to examine how these systems were affected in the Schrott EWAS. Supplementary Table S19 lists 11 hits for ALDH1 including two hits for ALDH1A1 and cadherin and protocadherin (PCDH17) which also function in this pathway. Indeed there were 156 hits for protocadherin 17 from the very lowest p-vale of 7.73 × 10−20 [27]. The nine hits for retinoid receptors are disclosed in Supplementary Table S20. Although CYP26B was not identified in the Schrott screen there were twelve hits for CYP2 series cytochromes including CYP20A1, CYP27A1, CYP27C1 and CYP27C2; and CYP2B7P, CYP2C1, CYP2C18, CYP2C61P and CYP2W1.
3.2.6. Cardiovascular System
Aging of the cardiovascular system is known to be a critical determinant and driver of systemic aging [271,272,273,274,275,276]. Indeed, it is said that one is as “old as one’s arteries” [157,158,277,278,279]. This is true at both the macrovascular level, with myocardial infarction being a major cause of death in developed nations, and at the microvascular levels where capillaries and sinusoids often form critical elements of many stem-cell niches [157,278,279]. Moreover, a two-way crosstalk has recently been defined between major cardiovascular disorders (myocardial infarction, hypertension and atherosclerosis) and the bone marrow haemopoietic stem-cell niche where endothelial inflammation in one compartment directly signals to the stem-cell compartment of the other system [280,281]. For these reasons, consideration of the epigenomic findings in the Schrott cannabis exposure and withdrawal data of relevance to arterial health are central to any consideration of cannabinoid-related aging processes. A detailed consideration of the cardiovascular hits in the Schrott study is deferred until the later section on teratology (see Supplementary Table S25).
It is of interest to consider the genomic processes controlling arterial health. The key genes involved in generating arteries from embryonic angioblasts are listed as sonic hedgehog (shh), vascular endothelial growth factor (VEGF), notch and ephrin B2 [209]. These genes and pathways were therefore screened through the Schrott dataset and the hits identified in Table 5A,B were identified. PTCH1 is the main shh receptor. Gli3 (GLI family zinc finger 3) is one of the key transcription factors which mediates shh signaling in the nucleus [282]. Gli3 scored 185 hits in the Schrott EWAS data of which only a selection has been extracted for illustration. PSENEN (Presenilin enhancer, gamma secretase subunit) is a key plasmalemma bound enzyme which processes the shh ligand after receptor binding. SUFU (SUFU negative regulator of hedgehog signaling) inhibits shh [283].
Supplementary Table S21A,B list genes involved in the notch signaling pathway identified in the Schrott screen. JAG1 is a canonical notch ligand. Notch 1-3 are notch receptors. RBPJ (Recombination Signal Binding Protein for Immunoglobulin Kappa J Region) is an important transcriptional regulator of notch signaling. PSENEN also processes the notch ligand at the cell membrane [284].
Supplementary Table S22A,B list the six hits in the Schrott database relating to VEGF and EphrinB2 signaling. Both VEGF and EphrinB2 are key signaling and transduction factors involved in mediating numerous major morphogenic decisions and pathways [209,251].
In this regard, fascinating recent detailed studies have appeared on the profound impact of prenatal cannabinoid (as Δ9THC) exposure on cardiac development. Robinson and colleagues showed that prenatal exposure to Δ9THC led to cardiac wall thickening in three week old mice and thickening and hypertrophy of the semilunar valves and increased ventricular septal defects [285]. Myocardial cell proliferation was increased and cardiac function was reduced with lower ejection fraction, fractional shortening and cardiac output.
Lee and co-workers demonstrated rat fetal growth restriction following in utero exposure to Δ9THC, smaller hearts and reduced a heart to body weight ratio at birth [286]. By three weeks of post-natal life this has been reversed by post-natal catchup growth which resulted in larger but stiffer ventricular wall thickness and a corresponding reduction in cardiac output. This was linked with increased expression of collagens I and III, reduced matrix metalloproteinase 2 and increased glycogen synthase kinase 3β signaling all of which are linked with cardiac remodeling. This study is highly significant as it relates the smaller hearts at birth to subsequent cardiac stiffness and reduced cardiac output, all of which are age related changes [277]. These changes in early postnatal life are known to be causally related to increased incidence of adult heart disease in later life which is the leading cause of death globally [285,286,287].
Many congenital anomalies and cancers in USA and European epidemiological datasets have been shown to be heightened after cannabis exposure. The following sections on these cannabinoid-related teratogenic and carcinogenic findings are respectively reviewed using the epigenomic data on changes in the DNA methylome of human sperm after cannabis exposure and withdrawal with a focus on genotoxicity and/or epigenotoxicity.
3.3. Implications of Findings
From such a very broad array of objective reported results, basic cellular mechanisms and highly concordant epidemiological findings in both addiction medicine and aging science, it is necessary in discussing these results to highlight just a few key findings which are of particular importance to the overall flow of the main themes of this review and the major concepts presented. More detailed discussions have been presented in the references cited and other exhaustive and encyclopaedic sources [302,303,304,305,306,307]. The study is the first to combine and connect data from a broad range of genotoxic areas. Perhaps the most striking finding is the extraordinarily accurate predictive power of the epigenomic results to apparently explain the epidemiologically observed mutagenic and teratological phenomenology. This accuracy provides confirmation of the validity of the cannabis ageing mechanisms outlined in this paper. The epigenomic results of the Schrott group [27] not only predict with great accuracy such disparate findings as the high rates of atrial septal defect widely observed in Canada, Australia, Colorado, Hawaii, USA and Europe [103,107,108,111,114,115,116,118] and elevated rates of ventricular septal defect noted by the American Academy of Pediatrics and the American Heart Association and elsewhere [103,109,111,115,308], but also the mechanistically closely related pattern of congenital cardiac and renal anomalies which both share critical sensitivity to inhibition of the notch, sonic hedgehog and retinoic acid morphogenic pathways. Both atrial septal and ventricular septal defects feature prominently in the spectrum of cannabis teratological defects, and also in the multisyndromic VACTERL syndrome which formally relates renal, cardiac and limb anomalies (amongst others) and was the most strongly significantly cannabis-associated of all the European birth defects studied [114].
Findings also explain with extraordinary accuracy 20 cancers which are shared commonly between the epigenomic and epidemiological datasets featuring prominently liver, breast, pancreas, diverse leukemias and lymphomas, oropharyngeal, thyroid, urinary, esophageal and testicular tumors. These findings also accord closely with older published data which link cannabis to exposure of a range of tumors including lung, head and neck, larynx, brain, prostate, testis and urothelium [52,53,54,55,56,57,58,59,60,61,62].
The likely foundational importance of cannabis-induced epigenotoxicity implies that not only has the salience of epigenomic disturbances emerged as being pre-eminent from the mechanistic confusion surrounding the aging process itself [16] but in a similar way it appears that with time and further research the epigenomic perturbations induced by cannabis dependence and withdrawal are likely to be shown to be foundational in understanding the plethoric and protean manifestations of cannabinoid-induced mutagenesis, teratogenesis, carcinogenesis and indeed aging [2].
This foundational centrality of epigenotoxicity to the understanding of cannabinoid toxicity is highly reminiscent of the central understanding which the fundamentally epigenomic nature of fetal alcohol syndrome has been shown to display [309,310,311,312,313,314,315,316,317,318,319,320]. Indeed, fetal alcohol syndrome has been shown to be primarily mediated epigenomically via cannabinoid type 1 receptors (CB1Rs) [321,322,323,324,325,326,327,328,329,330,331]. It should come therefore as little surprise to note that cannabinoids can also act via CB1Rs with a unique spectrum of clinical manifestations.
One major corollary of the finding of the primacy of epigenomic toxicity is that at least some of these changes are likely to be epigenetically inheritable. Indeed, a heritable pediatric fetal cannabinoid syndrome, analogous to fetal alcohol syndrome has been previously proposed [321,322,323,327,328,329,330,332,333,334]. In the case of the pediatric cancers acute myeloid and lymphoid leukemia [65,66,206], this implies not only heritable teratogenicity but also heritable teratogenic carcinogenicity [204,205]. This finding likely also applies to other pediatric tumors previously linked with parental cannabis exposure such as rhabdomyosarcoma, neuroblastoma and astrocytoma [207,208,321,322,323,327,328,329,330,332,333,334].
It was noted that the ratios of the most significant p-values were inverted for the pediatric tumor ALL, and for AML of which some cases occur early in life. This suggests the intriguing possibility that it is the cannabis withdrawal state following birth which triggers and launches the leukemogenic gene cassettes of childhood.
Many other features of the above series of results stand out prominently. The high numbers and wide ranges of both congenital anomalies-45/62 in USA and 89/95 in Europe (Table 6)-and cancers-33/40 in Europe and 25/28 in USA (Table 10)-are striking both in their own right and by virtue of the range of tissues and organ systems affected. As these observations have been made previously [66,103,112,113,114,115,116,120,121,122], they do not form the primary focus of the present investigation. What is more important for the present discussion of cannabis-related aging and its mechanisms is the relationship of oncogenicity and teratogenesis to aging related processes.
The North Carolina group reported that the significance of the DMR’s in cannabis dependence was higher than cannabis withdrawal [27]. Hence most of the ratios for the gene numbers affected in Table 11 were expected. However, the higher gene numbers affected in ALL (primarily a pediatric cancer) and AML (occasionally a pediatric cancer) and the higher significance level in AML found in withdrawal were unexpected and raise the intriguing possibility that the cannabis withdrawal state following birth may trigger leukemogenic gene activation. Whether this holds true for the other pediatric cancers previously related to cannabis remains to be studied. Moreover, this topic was shown to be of much greater significance beyond the field of pediatric cancer by the recent finding that many adult haemopoietic tumors have been shown to commence in fetal life [335], a finding which these latter investigators note may also apply more widely to the field of solid organ tumorigenesis.
One of the prominent findings to emerge from the above epidemiological overview was the salience of chromosomal disorders in both the congenital anomaly and the cancer datasets. Trisomies or monosomies of chromosomes 13, 18, 21 and X (including syndromes described by Turner and Klinefelter) were observed directly [66,103,115,120]. Moreover, strong signals were detected for acute lymphoid leukemia (which has been shown to often involve translocations between chromosomes 4, 9, 10, 11 and 22) [105,336] and testicular cancer [105,112,113,114,121,122] (which has been shown to implicate chromosomes 1, 7, 8, 11, 12, 13, 18, 21, X and Y) [337]. The total length of these chromosomes together comprehends 1754 megabases of the 3000 megabases, or 59%, of the whole human genome directly impacted by cannabinoid-related genotoxicity/epigenotoxicity. Deletions of chromosome 22 in USA and microdeletions in Europe were also significantly cannabis-associated [66,103,115]. These data make the issue of chromosomal non-segregation, non-disjunction, aneuploidy, chromosomal breaks and translocations and subsequent teratogenic malignancy a very prominent feature of cannabis related genotoxicity. As described in considerable detail in the pathophysiological review, multiple direct and epigenomic pathways exist which comfortably explain and may account for these prominent and important clinical findings of hundred megabase scale epi/genotoxic activities.
Given so much powerful evidence for cannabinoid-related epigenotoxicity, the possibility that these epigenomic changes are potentially reflected as pro-ageing effects effectively accelerating natural aging warrants particularly careful consideration. On this issue, three tissues are of particular and pivotal importance namely: spermatocytes, oocytes and zygotes.
3.4. Spermatocytes
Classic photomicrographs of cannabis exposed sperm featuring multiple (up to four) heads, multiple tails, obviously deformed heads and tails on a background of proteinaceous and inflamed tissue [24] along with gross chromosomal translocations and ring and chain formation [23,260] give an obviously degenerate genotoxic appearance. Multiple cannabinoids are known to induce adverse mitochondrial effects, reduced energy charge and increased free radial flux [33,140] which are all changes that are well established as being age related. It has been shown that cannabinoid signaling via CB1R has a deleterious effect on sperm chromatin which increases along the epididymis, altered histone-protamine substitution via inhibition of transition protein 2 (TNP2) and leads to genome DNA fragmentation with compromise of male fertility [34]. Moreover, the above demonstration of cannabinoid-related gross changes to the tubulin code and meiotic apparatus (Supplementary Tables S9–S13) implies that not only are the microtubules of the sperm flagellum disrupted but so also are those comprising the sperm centrioles and first and second meiotic spindles. Since all of these various changes are age-defining and age-causing disorders, this implies that the age of cannabinoid-exposed sperm is advanced.
3.5. Oocytes
Diminished ovarian reserve was noted in the epigenomic dataset of Schrott (Supplementary Table S27; Schrott [27] Page 349) and is both a defining feature of female aging [1] and an important cause thereof [200]. Gross and severe morphological changes were noted in cannabis exposed oocytes induced to divide including chromosomal nucleoplasmic bridges, non-disjunctions, tripolar, quadripolar and pentapolar cell divisions along with an extremely high (20%) rate of oocyte death with just a single cell division. Moreover, oocyte depletion has been attributed primarily to failure of DNA damage repair [230]. As noted above, cannabis has been shown to suppress pituitary FSH secretion thereby interfering with the normal female hormonal cycle. All of these are clearly age-related and age-inducing changes.
3.6. Zygotes
Since both sperm and oocytes bear many chromosomal, genetic and epigenetic features of aging, it seems clear that these changes would persist in the pronuclei of the fertilized zygote and carry important influences into the first few rounds of zygotic cell division which are epigenetically controlled from the time of fertilization. These deleterious changes would be compounded by aberrant histone and protamine changes in sperm and by the disrupted tubulin code known to be borne by sperm. Together, these changes indicate that not only are the gametes themselves aged, but so too must the fertilized zygote be aged from—and actually even prior to—conception. It is noted again that the fragile process of human female meiosis is highly error prone ordinarily [248] which suggests that the tolerance for error under the influence of external xenobiotic genotoxic agents is very narrow indeed. These considerations raise the intriguing and very concerning possibility that the zygote itself may manifest advanced epigenomic age from even before fertilization and conception. It is noted that the newly described method of analysis of blastocystoid bodies derived from induced human pluripotential embryonic stem cells (iPS) might provide an ideal and ethical laboratory method to formally assess these issues [338].
It was recently shown that a key part in sperm maturation is played by the addition of mRNA exosomes (as epididymosomes) in the tail of the epididymis during sperm maturation. These extracellular packages of mRNA play a key part in early embryonic development during the initial divisions of the fertilized zygote and are under close control at several points by CB1R-mediated cannabinoid control [339]. Interference with this normal mechanism led to profound perturbation of sperm maturation, fertility and function. In this regard, the human system closely mirrors that seen in mice.
3.7. Cannabidiol and Δ8THC
At the time of writing, cannabidiol and Δ8THC have been allowed to freely penetrate culture without restriction in many places and have been made available in cookies, sauces, lollies, candies, crackers and in solid translucent blocks often being marketed as “legal weed”.
In such a context, it is important to note that it was found long ago that the genotoxic moiety of cannabinoids lies primarily in their central olevitol nucleus, an activity which is little modified by their various side chains [340,341]. This important finding implicates most cannabinoids in genotoxic effects.
Cannabidiol has an experimental [24,342,343,344,345] and an epidemiological literature describing its genotoxic effects in both cancer [112,113,114] and congenital anomalies [103]. Cannabidiol is also genotoxic by virtue of its involvement in signaling via the nuclear receptor—transcription factor PPARγ (Peroxisome proliferator receptor gamma) [346,347,348,349,350,351,352,353], by its inhibition of mitochondrial respiration which forms the energetic and co-factor substrate basis for the epigenomic machinery [41,42,354,355,356,357,358,359,360,361], and by its interaction at higher doses [362,363,364,365,366,367,368,369,370] with the cannabinoid type 1 receptors present on mitochondria themselves [44,145,146,147,371,372,373,374]. Importantly, the PPARγ nuclear signal is transduced by binding to retinoic acid receptors (RXR) which together then bind the genome [375]. Similarly, Δ8THC has been epidemiologically implicated in both cancer [376] and birth defects [377].
A recent very concerning paper demonstrated not only that many cannabinoids (including Δ9THC, Δ8THC and cannabidiol) could freely pass into the milk of dairy cattle fed legal hemp (with nominally less that 0.3% THC content) but that the cannabinoid concentration in milk could rise to a level where the total recommended daily dose of Δ9THC was exceeded [119]. Moreover, the cows themselves became obviously ataxic and “stoned” and stood motionless for extended period, not moving and not eating, apparently “stoned”. They were also ataxic and had difficulty walking. After cessation of the hemp/cannabinoid feed, these changes abruptly declined. Most concerningly, the levels of cannabinoid found in the feed when analyzed by state-of-the-art tandem liquid chromatography/gas chromatography—mass spectrometry (LCGC-MS) techniques were more than ten times those found with the standard legally prescribed tests for cannabinoid, a finding which necessarily impugns and indicts so called legally safe “low-THC” hemp products and imperils public heath and safety.
Moreover, such findings dramatically and eloquently illustrate the florid manner in which such grossly affected animals in the food chain might pass on the severe genotoxic cannabinoid-mediated damage (which includes limblessness) as has been chronicled in recent reports from France and Germany [378,379,380,381].
4. Conclusions
Many metrics, including hormonal, mitochondriopathic, cardiovascular, hepatotoxic, immunological, genotoxic, epigenotoxic, disruption of chromosomal physiology, congenital anomalies, cancers including inheritable tumorigenesis, telomerase inhibition and elevated mortality point towards cannabinoid-exposed tissues being of advanced biological age. Evidence from many studies indicates extensive perturbation of the human epigenome by exposure to many cannabinoids. Since the epigenome has emerged as the key and central mediator of the panorganismal aging process [13,14,15,16,202,382], it becomes of primary importance to investigate its likely implication in aging processes directly by the application of late-generation epigenomic clocks [383,384,385,386,387,388,389]. The likely involvement of spermatogonia, oocyte and fertilized zygote in this accelerated aging process increases the importance of this enquiry for the health of subsequent generations, an enquiry which is heightened and intensified by the transgenerational transmission of cannabinoid-related epigenotoxicity in human sperm [26,27], to subsequent rodent generations [28,29,30,31,32,390], for pediatric brain function and development including autistic-like disorders [117,174,288,290,292,295,296,297,298,299,391] and through the heritable passage of many birth defects [103,108,109,110,111,115,118,120] including several pediatric cancers [63,64,65,66,105,121]. Inversion of the ratio of the minimum p-values between dependence and withdrawal for ALL and AML may imply that it is the activation of leukemogenic gene cassettes by the withdrawal state occasioned by birth which gives rise to these pediatric cancers. The genotoxic, epigenotoxic, mutagenic and teratological issues raised are clearly very serious and have been shown several times to greatly outweigh those attributable to tobacco and alcohol [103,112,113,114,115]. These changes carry such far-reaching public health implications that they are worthy of investigation by the most advanced multiomics techniques including multichannel single cell epigenomic and 3D chromosomal topological techniques with appropriate resourcing to exhaustively perform these investigations in a translational multigenerational context.
Acknowledgments
All authors had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Appendix Group
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijerph192416721/s1. Table S1: Histone (Lysine) Methyltransferases; Table S2: Histone (L:ysine) Demethylases; Table S3: Histone Acetyltransferases; Table S4: Histone Deacetylases; Table S5: Other Stem Cell Factors; Table S6: Funtional Annotations of Kit; Table S7: Age-Related Immunometabolic changes; Table S8: Oocyte-Centrosome DNA Methylation Alterations; Table S9: Funtional Annotations of Kinesins; Table S10: Funtional Annotations of Tubulins; Table S11: Funtional Annotations of Tubulins-2; Table S12: Funtional Annotations of CENPN; Table S13: Funtional Annotations of RAD51/52; Table S14: Funtional Annotations of DSCAM; Table S15: Funtional Annotations of DGALP2; Table S16: Funtional Annotations of Slit; Table S17: Funtional Annotations of Robo; Table S18: Funtional Annotations of SRGAP2; Table S19: Funtional Annotations of Alcohol Dehydrogenase (ALDH); Table S20: Funtional Annotations of Retinoid Receptors; Table S21: Funtional Annotations of Notch Receptors; Table S22: Funtional Annotations of VEGF, EFNB2; Table S23: p-Values of Teratological Significance from Schrott; Table S24: Annotations from the Schrott Database for Central Nervous System Abnormalities; Table S25: Annotations from the Schrott Database for Cardiovascular System Abnormalities; Table S26: Annotations from the Schrott Database for Orofacial Abnormalities; Table S27: Annotations from the Schrott Database for General Abnormalities; Table S28: Annotations from the Schrott Database for Limb Abnormalities; Table S29: Annotations from the Schrott Database for Gastrointestinal System Abnormalities; Table S30: Annotations from the Schrott Database for Chromomsomal Abnormalities; Table S31: Annotations from the Schrott Database for Uronephrological System Abnormalities; Table S32: Annotations from the Schrott Database for Body Wall Abnormalities; Table S33: Summary of Teratological Findings of Schrott Database by Organ System & Target; Table S34: Summary of Significance of Schrott DNA Methylation hits by Target; Table S35: Significance Levels for Cancers from Schrott Dataset; Table S36: Summary of Significance Levels for Overall Cancers from Schrott Dataset—Ordered by Minimum p-Value; Table S37: Summary of Significance Levels for Overall Cancers from Schrott Dataset—Ordered by Median p-Value; Table S38: Summary of Significance Levels for Cancers in Cannabis Dependency from Schrott Dataset-Ordered by Minimum p-Value; Table S39: Summary of Significance Levels for Cancers in Cannabis Dependency from Schrott Dataset—Ordered by Median p-Value; Table S40: Summary of Significance Levels for Cancers in Cannabis Withdrawal from Schrott Dataset-Ordered by Minimum p-Value; Table S41: Summary of Significance Levels for Cancers in Cannabis Withdrawal from Schrott Dataset-Ordered by Median p-Value; Table S42:Contrast Between Gene Numebrs and Significance Levels in Withdrawal and Dependency Ordered by Gene Number Ratio. Figure S1: Overall results-negative logarithm of p-Values for congenital anomalies from Schrott Database by organ system. Figure S2: Overall results-negative logarithm of p-Values for congenital anomalies from Schrott Database by organ target. Figure S3: Overall results–Boxplot of negative logarithm of grouped p-Values for congenital anomalies from Schrott Database comparing cannabis dependency with cannabis withdrawal. Figure S4: Number of genes annotated in the Schrott database for target organs by dependency status in (A) cannabis dependence and (B) withdrawal. Figure S5: (A) Numbers of gene annotations, (B) numbers of genes affected and (C) negative logarithm of p-value by cancer type-overall Schrott data. Figure S6: Direct comparison between p-values for cannabis cancer relationships between (A) cannabis dependence and (B) cannabis withdrawal, Schrott data.
Institutional Review Board Statement
Ethics Approval and Consent to Participate. The Human Research Ethics Committee of the University of Western Australia provided ethical approval for the study to be undertaken 24 September 2021 (No. RA/4/20/4724).
Informed Consent Statement
Patient consent was not applicable.
Data Availability Statement
All data generated or analyzed during this study are included in this published article and its supplementary information files. Data along with the relevant R code have been made publicly available on the Mendeley Database Repository and can be accessed from this URL https://data.mendeley.com/datasets/sngdkpg8gy/1 (doi:10.17632/sngdkpg8gy.1) (accessed on 10 December 2022).
Conflicts of Interest
The authors declare that they have no competing interests.
| No. | Streams of Evidence | Focus of the Discussion |
|---|---|---|
| Section 3.1.1 | Clinical syndromes | Clinical phenomenology |
| Section 3.1.2 | Mitochondrial inhibition | Cellular systems and mechanisms |
| Section 3.1.3 | DNA Methylation | Cellular systems and mechanisms |
| Section 3.1.4 | Mental illnesses | Organ systems |
| Section 3.1.5 | Cardiovascular age | Organ systems |
| Section 3.1.6 | Endocrine suppression | Organ systems |
| Section 3.1.7 | Liver inflammation | Organ systems |
| Section 3.1.8 | Cancer | Heath disorders and Population impacts |
| Section 3.1.9 | Inheritable cancer | Heath disorders and Population impacts |
| Section 3.1.10 | Congenital Anomalies | Heath disorders and Population impacts |
| Section 3.1.11 | Telomerase inhibition | Cellular systems and mechanisms |
| Section 3.1.12 | Elevated Mortality rate | Epidemiological Studies |
| Pathogenetic Field of Interest | ||
| Section 3.2.1 | Epigenomic Overview | Cellular systems and mechanisms |
| Section 3.2.2 | Stem-Cell Factors | Cellular systems and mechanisms |
| Section 3.2.3 | Chromosomal Mechanics | Cellular systems and mechanisms |
| Section 3.2.4 | Centromeres and Kinetochores | Cellular systems and mechanisms |
| Section 3.2.5 | Prefrontal cortex and Brain | Organ systems |
| Section 3.2.6 | Cardiovascular System | Organ systems |
| Section 3.2.7 | Teratogenesis | Analysis DNA Methylation data and epidemiological impacts |
| Section 3.2.8 | Carcinogenesis | Analysis DNA Methylation data and epidemiological impacts |
| Nearest Gene Name | Chromosome Number | Nearest Gene Number | Dependency Status | Functional Annotation | Page | Distance from Nearest Gene | Relative Position | p-Value | Bonferroni Adjusted p-Value |
|---|---|---|---|---|---|---|---|---|---|
| DNA Methyltransferases | |||||||||
| DNMT1 | 19 | ENSG00000130816 | Withdrawal | Maintenance DNA methyltransferase | 156 | 0 | Intron | 1.89 × 10−6 | 0.010563 |
| DNMT1 | 19 | ENSG00000130816 | Withdrawal | Maintenance DNA methyltransferase | 179 | 0 | Intron | 4.81 × 10−6 | 0.016176 |
| DNMT3B | 20 | ENSG00000088305 | Dependence | de novo DNA methyltransferase | 109 | 0 | Intron | 1.22 × 10−5 | 0.023205 |
| DNMT3B | 20 | ENSG00000088305 | Withdrawal | de novo DNA methyltransferase | 125 | 1067 | Upstream | 2.08 × 10−8 | 0.001062 |
| DNMT3A | 2 | ENSG00000119772 | Withdrawal | de novo DNA methyltransferase | 194 | 0 | Intron | 7.57 × 10−6 | 0.020149 |
| DNA Demethylases | |||||||||
| TET1 | 10 | ENSG00000138336 | Dependence | Ten-Eleven translocase | 107 | 0 | Intron | 1.18 × 10−5 | 0.022782 |
| TET1P1 | 13 | ENSG00000232204 | Dependence | Pseudogene for TET | 63 | 36,150 | Downstream | 4.14 × 10−6 | 0.013905 |
| TET1P1 | 13 | ENSG00000232204 | Dependence | Pseudogene for TET | 85 | 47,940 | Upstream | 7.47 × 10−6 | 0.018443 |
| TET1P1 | 13 | ENSG00000232204 | Dependence | Pseudogene for TET | 98 | 9930 | Downstream | 9.97 × 10−6 | 0.021086 |
| TET1P1 | 13 | ENSG00000232204 | Dependence | Pseudogene for TET | 98 | 55,192 | Upstream | 6.32 × 10−6 | 0.018533 |
| Others | |||||||||
| UHRF1 | 19 | ENSG00000276043 | Withdrawal | Integrator of epigenetic information | 128 | 0 | Intron | 5.74 × 10−8 | 0.001782 |
| UHRF1BP1L | 12 | ENSG00000111647 | Withdrawal | Regulator of UHRF1 | 155 | 0 | Intron | 1.79 × 10−6 | 0.010239 |
| UHRF1BP1L | 12 | ENSG00000111647 | Withdrawal | Regulator of UHRF1 | 233 | 0 | Intron | 1.67 × 10−5 | 0.028881 |
| DPPA2 | 3 | ENSG00000163530 | Dependence | Developmental Pluripotency Associated 2 | 40 | 15,599 | Downstream | 1.66 × 10−6 | 0.009001 |
| DPPA2 | 3 | ENSG00000163530 | Dependence | Developmental Pluripotency Associated 2 | 133 | 6894 | Downstream | 1.90 × 10−7 | 0.003298 |
| DPPA2P1 | Y | ENSG00000223915 | Withdrawal | Pseudogene for DPPA2A | 135 | 26,055 | Upstream | 2.78 × 10−7 | 0.004034 |
| Telomerase | |||||||||
| TERT | 5 | ENSG00000223915 | Dependence | Telomerase | 44 | 4227 | Upstream | 2.82 × 10−6 | 0.012582 |
| Polycomb Repressors | |||||||||
| PCGF6 in PRC1 | 10 | ENSG00000156374 | Dependence | Polycomb Repressive Complex 1 | 65 | 0 | Intron | 4.37 × 10−6 | 0.014300 |
| PCGF6 in PRC1 | 10 | ENSG00000156374 | Withdrawal | Polycomb Repressive Complex 1 | 137 | 0 | Intron | 4.03 × 10−7 | 0.004978 |
| EZH2 in PRC2 | 7 | ENSG00000180628 | Dependence | Polycomb Repressive Complex 2 | 94 | 0 | Intron | 9.22 × 10−6 | 0.020342 |
| Chromatin Remodellers | |||||||||
| SMARCA2 | 9 | ENSG00000080503 | Dependence | SWI/SNF Matrix, Actin Chromatin Regulator 2 | 6 | 0 | Intron | 5.27 × 10−9 | 0.000438 |
| SMARCA2 | 9 | ENSG00000080503 | Dependence | SWI/SNF Matrix, Actin Chromatin Regulator 2 | 62 | 3071 | Downstream | 4.00 × 10−6 | 0.013641 |
| SMARCA2 | 9 | ENSG00000080503 | Dependence | SWI/SNF Matrix, Actin Chromatin Regulator 2 | 114 | 0 | Intron | 1.34 × 10−5 | 0.024371 |
| SMARCA4 | 19 | ENSG00000127616 | Withdrawal | SWI/SNF Matrix, Actin Chromatin Regulator 4 | 145 | 9567 | Upstream | 8.86 × 10−7 | 0.007300 |
| SMARCA4 | 19 | ENSG00000127616 | Withdrawal | SWI/SNF Matrix, Actin Chromatin Regulator 4 | 199 | 9258 | Upstream | 8.54 × 10−6 | 0.021311 |
| Nearest Gene Name | Chromosome Number | Nearest Gene Number | Dependency Status | Functional Annotation | Page | Distance from Nearest Gene | Relative Position | p-Value | Bonferroni Adjusted p-Value |
|---|---|---|---|---|---|---|---|---|---|
| POU5F1P2 | 8 | ENSG00000253382 | Dependence | Oct3/4 Pseudogene | 5 | 2871 | Downstream | 1.49 × 10−9 | 0.000216 |
| SOX2-OT | 3 | ENSG00000242808 | Dependence | Sox2 Overlapping Transcript | 6 | 0 | Intron | 5.25 × 10−9 | 0.000438 |
| SOX2-OT | 3 | ENSG00000242808 | Dependence | Sox2 Overlapping Transcript | 48 | 0 | Intron | 2.38 × 10−6 | 0.017245 |
| SOX2-OT | 3 | ENSG00000242808 | Dependence | Sox2 Overlapping Transcript | 88 | 0 | Intron | 8.12 × 10−6 | 0.019185 |
| SOX2-OT | 3 | ENSG00000242808 | Withdrawal | Sox2 Overlapping Transcript | 116 | 0 | Intron | 1.40 × 10−5 | 0.024849 |
| SOX2-OT | 3 | ENSG00000242808 | Withdrawal | Sox2 Overlapping Transcript | 146 | 0 | Intron | 9.74 × 10−7 | 0.007679 |
| SOX2-OT | 3 | ENSG00000242808 | Withdrawal | Sox2 Overlapping Transcript | 211 | 0 | Intron | 1.11 × 10−5 | 0.023974 |
| Klf4 | 9 | ENSG00000136826 | Dependence | Kruppel-like factor 4 | 117 | 12,186 | Upstream | 1.41 × 10−5 | 0.024968 |
| MycBP2 | 13 | ENSG00000005810 | Dependence | Myc Binding Protein 2 | 49 | 0 | Intron | 2.50 × 10−6 | 0.010960 |
| MycBP2 | 13 | ENSG00000005810 | Withdrawal | Myc Binding Protein 2 | 153 | 0 | Intron | 1.58 × 10−6 | 0.009647 |
| Myc | 8 | ENSG00000136826 | Withdrawal | Myc proto-oncogene | 227 | 23,489 | Downstream | 1.49 × 10−5 | 0.027466 |
| Nearest Gene Name | Nearest Gene Number | Chromosome Number | Relative Location | Distance to Nearest Gene (Bases) | Number of Annotations | p-Value | Bonferroni-Adjusted p-Value |
|---|---|---|---|---|---|---|---|
| Centrosomal Proteins | |||||||
| CENPIP1 | ENSG00000224778 | 13 | Upstream | 1100 | 1 | 2.38 × 10−9 | 0.000279 |
| CENPF | ENSG00000117724 | 1 | Downstream | 72,569 | 3 | 2.98 × 10−8 | 0.001109 |
| CNEPVL3 | ENSG00000224109 | X | Downstream | 2146 | 1 | 2.80 × 10−6 | 0.001153 |
| CENPK | ENSG00000123219 | 5 | Intron | 0 | 1 | 8.01 × 10−6 | 0.019098 |
| CNEPP | ENSG00000188312 | 9 | Intron | 0 | 2 | 8.26 × 10−6 | 0.019330 |
| CNEPJ | ENSG00000151849 | 13 | Exon | 0 | 1 | 4.66 × 10−7 | 0.005279 |
| CNEPUP1 | ENSG00000255075 | 11 | Upstream | 8401 | 1 | 2.81 × 10−6 | 0.012567 |
| INCENP | ENSG00000149503 | 11 | Intron | 0 | 1 | 3.07 × 10−6 | 0.013077 |
| CNEPO | ENSG00000138092 | 2 | Exon | 0 | 1 | 6.25 × 10−6 | 0.018393 |
| CNEPI | ENSG00000102384 | X | Intron | 0 | 2 | 7.54 × 10−6 | 0.020123 |
| CNEPL | ENSG00000120334 | 1 | Intron | 0 | 1 | 8.22 × 10−6 | 0.020943 |
| CNEPX | ENSG00000169689 | 17 | Exon | 0 | 1 | 9.35 × 10−6 | 0.022176 |
| CNEPC | ENSG00000145241 | 4 | Intron | 0 | 1 | 9.60 × 10−6 | 0.002248 |
| CENPV | ENSG00000166582 | 17 | Upstream | 13,237 | 2 | 1.63 × 10−5 | 0.002861 |
| CENPN | ENSG00000166451 | 16 | 86 | 7.73 × 10−20 | |||
| Others | |||||||
| KNL1 | ENSG00000137812 | 15 | 3UTR | 0 | 1 | 7.71 × 10−7 | 0.006173 |
| ZWINT | ENSG00000122952 | 10 | Downstream | 58,081 | 1 | 6.00 × 10−6 | 0.016644 |
| NUF2 | ENSG00000143228 | 1 | Intron | 0 | 1 | 1.12 × 10−6 | 0.007421 |
| SPC24 | ENSG00000161888 | 19 | 3UTR | 0 | 1 | 1.61 × 10−6 | 0.009713 |
| Sumoylation | |||||||
| SUMO1 | ENSG00000112701 | 2 | Intron | 0 | 1 | 1.25 × 10−5 | 0.023445 |
| ZNF451 | ENSG00000226803 | 6 | Intron | 0 | 1 | 2.22 × 10−6 | 0.011398 |
| SENP6 | ENSG00000112701 | 6 | Intron | 0 | 1 | 3.12 × 10−6 | 0.013217 |
| SENP7 | ENSG00000138468 | 3 | Intron | 0 | 1 | 4.73 × 10−6 | 0.014903 |
| SENP7 | ENSG00000138468 | 3 | Intron | 0 | 1 | 1.16 × 10−5 | 0.024458 |
| No. | Europe | USA | ||||||
|---|---|---|---|---|---|---|---|---|
| Congenital Anomaly | Term | Model | p-Value | Congenital Anomaly | Term | Model | p-Value | |
| 1 | Abdominal Wall Defects | pm.Resin.Daily | Categorical | 3.01 × 10−120 | ||||
| 2 | All Anomalies | Daily_Use | Categorical | <2.2 × 10−320 | ||||
| 3 | Amniotic band | pm.Resin.Daily | Categorical | 1.09 × 10−47 | ||||
| 4 | Anencephalus and similar | Resin_THC | Categorical | 1.53 × 10−212 | ||||
| 5 | Annular Pancreas | Daily_Use | Categorical | 1.52 × 10−13 | ||||
| 6 | Anophthalmos | Daily_Use | Categorical | 1.06 × 10−6 | ||||
| 7 | Ano-rectal atresia and stenosis | pm.Resin.Daily | Categorical | 4.03 × 10−39 | Large intestinal and Rectal atresia/stenosis | Cannabidiol_Estimates | Continuous | 0.0040 |
| 8 | Anotia | Herb_THC | Categorical | 4.63 × 10−13 | Anotia/microtia | LM_Cannabis | Continuous | 7.57 × 10−4 |
| 9 | Aortic atresia/interrupted aortic arch | LM.Cann_Resin_THC | Categorical | 5.71 × 10−25 | Interrupted aortic arch | LM_Cannabis | Continuous | 3.40 × 10−6 |
| 10 | Aortic Valve stenosis/atresia | Herb_THC | Categorical | 7.14 × 10−13 | Aortic valve stenosis | LM_Cannabis | Continuous | 0.0019 |
| 11 | Arhinencephaly/holoprosencephaly | LM_Herb.Daily | Continuous | 0.0052 | ||||
| 12 | Arterial Truncus | pm.Herb.Daily | Categorical | 9.92 × 10−7 | ||||
| 13 | Atrial septal defect (ASD) | Herb_THC | Categorical | <2.2 × 10−320 | Atrial septal defect (ASD) | LM_Cannabis | Continuous | 0.0378 |
| 14 | Atrioventricular septal defect (AVSD) | pm.Resin.Daily | Categorical | 1.65 × 10−101 | Atrioventricular septal defect (AVSD) | LM_Cannabis | Categorical | 0.0470 |
| 15 | Bilateral renal agenesis including Potter syndrome | Herb_THC | Categorical | 1.08 × 10−47 | Renal agenesis/hypoplasia | LM_Cannabis | Continuous | 7.34 × 10−4 |
| 16 | Bile duct atresia | Daily_Use | Categorical | 1.00 × 10−40 | Biliary atresia | Cannabidiol_Estimates | Continuous | 2.43 × 10−4 |
| 17 | Bladder Extrophy/Epispadias | pm.Resin.Daily | Categorical | 1.56 × 10−18 | Bladder extrophy | LM_Cannabis | Continuous | 0.0170 |
| 18 | Choanal Atresia | Herb_THC | Categorical | 7.34 × 10−94 | Choanal atresia | Δ9THC_Estimates | Continuous | 0.0033 |
| 19 | Chromosomal | Daily_Use | Categorical | <2.2 × 10−320 | Chromosomal | LM_Cannabis | Mixed Effects | 9.38 × 10−30 |
| 20 | Cleft lip with or without palate | Herb_THC | Categorical | 1.80 × 10−101 | Cleft lip with and without cleft palate | Cannabidiol_Estimates | Categorical | 0.0159 |
| 21 | Cleft palate | Herb_THC | Categorical | 1.79 × 10−34 | Cleft palate alone | LM_Cannabis | Continuous | 0.0014 |
| 22 | Cloacal exstrophy | LM_Cannabis | Categorical | 2.13 × 10−86 | ||||
| 23 | Club foot-talipes equinovarus | Daily_Use | Categorical | 4.23 × 10−292 | Clubfoot | LM_Cannabis | Continuous | 3.16 × 10−5 |
| 24 | Coarctation Aorta | Daily_Use | Categorical | 5.78 × 10−33 | Coarctation of the aorta | LM_Cannabis | Categorical | 9.74 × 10−45 |
| 25 | Congenital cataract | Daily_Use | Categorical | 4.88 × 10−66 | Congenital cataract | LM_Cannabis | Continuous | 0.0479 |
| 26 | Congenital glaucoma | Daily_Use | Categorical | 1.52 × 10−43 | ||||
| 27 | Congenital Heart | pm.Herb.Daily | Categorical | <2.2 × 10−320 | ||||
| 28 | Conjoined twins | Daily_Use | Categorical | 8.62 × 10−14 | ||||
| 29 | Craniosynostosis | Daily_Use | Categorical | 5.72 × 10−155 | ||||
| 30 | Cystic adenomatous malformation of lung | Daily_Use | Categorical | 4.05 × 10−80 | ||||
| 31 | Diaphragmatic Hernia | Daily_Use | Categorical | 8.77 × 10−57 | Diaphragmatic hernia | LM_Cannabis | Categorical | 2.11 × 10−8 |
| 32 | Digestive system | pm.Herb.Daily | Categorical | 1.61 × 10−264 | ||||
| 33 | Double outlet right ventricle | pm.Herb.Daily | Categorical | 1.28 × 10−46 | Double outlet right ventricle | LM_Cannabis | Categorical | 7.31 × 10−4 |
| 34 | Down Syndrome | Daily_Use | Categorical | <2.2 × 10−320 | Trisomy 21 (Down syndrome) | LM_Cannabis | Categorical | 4.02 × 10−26 |
| 35 | Duodenal stenosis/atresia | Herb_THC | Categorical | 1.50 × 10−10 | ||||
| 36 | Ear, face and neck | Daily_Use | Categorical | 3.38 × 10−44 | ||||
| 37 | Ebstein’s Anomaly | pm.Resin.Daily | Categorical | 3.23 × 10−17 | ||||
| 38 | Edward syndrome/Trisomy 18 | Daily_Use | Categorical | <2.2 × 10−320 | Edward syndrome/Trisomy 18 | LM_Cannabis | Categorical | 1.06 × 10−61 |
| 39 | Encephalocele | pm.Resin.Daily | Categorical | 4.76 × 10−21 | Encephalocele | LM_Cannabis | Continuous | 0.0013 |
| 40 | Epispadias | LM_Cannabis | Continuous | 0.0111 | ||||
| 41 | Eye | Daily_Use | Categorical | 2.27 × 10−175 | ||||
| 42 | Fetal alcohol syndrome | pm.Resin.Daily | Categorical | 5.88 × 10−57 | ||||
| 43 | Gastroschisis | Herb_THC | Categorical | 6.55 × 10−39 | ||||
| 44 | Genetic syndromes + microdeletions | pm.Herb.Daily | Categorical | 1.38 × 10−228 | Deletion 22q11.2 | LM_Cannabis | Continuous | 0.0024 |
| 45 | Genital | pm.Herb.Daily | Categorical | 2.55 × 10−243 | ||||
| 46 | Hip dislocation and/or dysplasia | Daily_Use | Categorical | <2.2 × 10−320 | Congenital hip dislocation | LM_Cannabis | Categorical | 7.27 × 10−70 |
| 47 | Hirschsprung’s disease | Daily_Use | Categorical | 2.54 × 10−88 | Hirschsprung disease (congenital megacolon) | LM_Cannabis | Categorical | 6.69 × 10−6 |
| 48 | Holoprosencephaly/Arhinencephaly | LM_Cannabis | Categorical | 1.22 × 10−72 | Holoprosencephaly | LM_Cannabis | Categorical | 2.90 × 10−12 |
| 49 | Hydrocephalus | pm.Herb.Daily | Categorical | 1.76 × 10−110 | ||||
| 50 | Hydronephrosis | Herb_THC | Categorical | <2.2 × 10−320 | ||||
| 51 | Hypoplastic Left Heart | Daily_Use | Categorical | 3.37 × 10−61 | Hypoplastic left heart syndrome | LM_Cannabis | Continuous | 0.0047 |
| 52 | Hypoplastic right heart | Resin_THC | Categorical | 2.85 × 10−59 | ||||
| 53 | Hypospadias | pm.Herb.Daily | Categorical | 2.92 × 10−177 | Hypospadias | LM_Cannabis | Continuous | 1.16 × 10−5 |
| 54 | Klinefelter syndrome | Daily_Use | Categorical | 1.75 × 10−41 | ||||
| 55 | Large intestinal and Rectal atresia/stenosis | Cannabidiol_Estimates | Continuous | 0.0040 | ||||
| 56 | Lateral anomalies | LM.Cann_Herb_THC | Categorical | 2.36 × 10−48 | ||||
| 57 | Limb anomalies | pm.Herb.Daily | Categorical | <2.2 × 10−320 | ||||
| 58 | Limb reductions | Daily_Use | Categorical | 8.20 × 10−65 | Limb deficiencies (reduction defects) | LM_Cannabis | Continuous | 0.0134 |
| 59 | Lower limb Reduction deformity | LM_Cannabis | Continuous | 0.0420 | ||||
| 60 | Maternal infections resulting in malformations | Daily_Use | Categorical | 4.15 × 10−87 | ||||
| 61 | Microphthalmos/Anophthalmos | Daily_Use | Categorical | 1.25 × 10−55 | Microphthalmos/Anophthalmos | Δ9THC_Estimates | Continuous | 0.0045 |
| 62 | Mitral valve anomalies | pm.Herb.Daily | Categorical | 8.99 × 10−58 | ||||
| 63 | Multicystic renal dysplasia | pm.Resin.Daily | Categorical | 6.70 × 10−251 | ||||
| 64 | Nervous system | pm.Herb.Daily | Categorical | <2.2 × 10−320 | ||||
| 65 | Neural Tube Defects | Resin_THC | Categorical | 9.97 × 10−269 | ||||
| 66 | Obstructive genitourinary defect | Cannabidiol_Estimates | Categorical | 2.22 × 10−15 | ||||
| 67 | Oesophageal stenosis/atresia | Daily_Use | Categorical | 3.49 × 10−44 | Oesophageal atresia/tracheoesophageal fistula | LM_Cannabis | Continuous | 4.83 × 10−6 |
| 68 | Omphalocele | pm.Resin.Daily | Categorical | 4.94 × 10−131 | Omphalocele | LM_Cannabis | Continuous | 0.0025 |
| 69 | Oro-facial clefts | Herb_THC | Categorical | 3.99 × 10−133 | ||||
| 70 | Patau syndrome/trisomy 13 | Daily_Use | Categorical | 1.08 × 10−144 | Patau syndrome/trisomy 13 | LM_Cannabis | Continuous | 2.08 × 10−7 |
| 71 | PDA as only CHD in term infants (>=37 weeks) | pm.Herb.Daily | Categorical | 2.14 × 10−20 | ||||
| 72 | Polydactyly | pm.Resin.Daily | Categorical | 1.46 × 10−292 | ||||
| 73 | Posterior urethral valve and/or prune belly | pm.Resin.Daily | Categorical | 1.28 × 10−42 | Congenital posterior urethral valves | LM_Cannabis | Continuous | 1.18 × 10−4 |
| 74 | Pulmonary valve atresia | Daily_Use | Categorical | 1.42 × 10−27 | Pulmonary valve atresia | Cannabidiol_Estimates | Categorical | 1.02 × 10−5 |
| 75 | Pulmonary valve stenosis | Daily_Use | Categorical | 2.09 × 10−95 | ||||
| 76 | Respiratory | pm.Herb.Daily | Categorical | 2.57 × 10−203 | ||||
| 77 | Severe CHD | Herb_THC | Categorical | 1.81 × 10-317 | ||||
| 78 | Severe microcephaly | pm.Herb.Daily | Categorical | 3.17 × 10−148 | ||||
| 79 | Single ventricle | Daily_Use | Categorical | 1.03 × 10−25 | Single ventricle | LM_Cannabis | Categorical | 0.0060 |
| 80 | Situs inversus | Daily_Use | Categorical | 1.42 × 10−44 | ||||
| 81 | Skeletal dysplasias | Daily_Use | Categorical | 5.12 × 10−74 | ||||
| 82 | Small Intestine stenosis/atresia | pm.Herb.Daily | Categorical | 8.23 × 10−31 | Small intestinal atresia/stenosis | Cannabidiol_Estimates | Continuous | 3.39 × 10−6 |
| 83 | Spina Bifida | Resin_THC | Categorical | 3.93 × 10−84 | Spina bifida without anencephalus | Δ9THC_Estimates | Continuous | 0.0008 |
| 84 | Syndactyly | pm.Resin.Daily | Categorical | 3.47 × 10−16 | ||||
| 85 | Teratogenic syndromes with malformations | Daily_Use | Categorical | 1.42 × 10−139 | ||||
| 86 | Tetralogy of Fallot | Daily_Use | Categorical | 3.12 × 10−47 | Tetralogy of Fallot | LM_Cannabis | Continuous | 0.0168 |
| 87 | Total Anomalous Pulmonary Venous Return | Herb_THC | Categorical | 4.07 × 10−09 | Total anomalous pulmonary venous connection | LM_Cannabis | Continuous | 0.0299 |
| 88 | Transposition of great vessels | Resin_THC | Categorical | 9.96 × 10−33 | Transposition of great arteries | Cannabidiol_Estimates | Continuous | 0.0479 |
| 89 | Turner syndrome | Daily_Use | Categorical | 1.10 × 10−146 | Turner syndrome | LM_Cannabis | Categorical | 7.69 × 10−49 |
| 90 | Tricuspid valve stenosis/atresia | Daily_Use | Categorical | 6.86 × 10−24 | ||||
| 91 | Urinary | pm.Resin.Daily | Categorical | <2.2 × 10−320 | ||||
| 92 | Valproate syndrome | Daily_Use | Categorical | 1.57 × 10−7 | ||||
| 93 | Vascular disruption anomalies | Herb_THC | Categorical | 3.46 × 10−101 | ||||
| 94 | VATER/VACTERL | pm.Herb.Daily | Categorical | 2.43 × 10−36 | ||||
| 95 | Ventricular septal defect (VSD) | pm.Resin.Daily | Categorical | <2.2 × 10−320 | Ventricular septal defect | LM_Cannabis | Continuous | 0.0021 |
| Gene Acronym | Gene Name | Gene Number | Functional Annotation | Status | Page Number | Number of Genes Annotated | p-Value |
|---|---|---|---|---|---|---|---|
| Meis1 | Meis Homeobox 1 | ENSG00000143995 | Withdrawal | 194 | 37 | 7.55 × 10−6 | |
| Meis1 | Meis Homeobox 1 | ENSG00000143995 | Cancer growth | Withdrawal | 325 | 149 | 7.17 × 10−6 |
| Meis1 | Meis Homeobox 1 | ENSG00000143995 | Sensory organ development | Withdrawal | 327 | 18 | 1.30 × 10−5 |
| Meis1 | Meis Homeobox 1 | ENSG00000143995 | Eye formation | Withdrawal | 328 | 15 | 2.81 × 10−5 |
| Meis1 | Meis Homeobox 1 | ENSG00000143995 | Cancer | Withdrawal | 329 | 151 | 4.32 × 10−5 |
| Meis1 | Meis Homeobox 1 | ENSG00000143995 | Lens formation | Withdrawal | 333 | 4 | 9.17 × 10−5 |
| Meis1 | Meis Homeobox 1 | ENSG00000143995 | Cancer | Withdrawal | 334 | 88 | 1.22 × 10−4 |
| Meis1 | Meis Homeobox 1 | ENSG00000143995 | Eye formation | Withdrawal | 334 | 11 | 1.23 × 10−4 |
| Meis2 | Meis Homeobox 2 | ENSG00000134138 | Withdrawal | 134 | 97 | 2.36 × 10−7 | |
| Meis2 | Meis Homeobox 2 | ENSG00000134138 | Withdrawal | 181 | 1 | 0.016676 | |
| Meis2 | Meis Homeobox 2 | ENSG00000134138 | Withdrawal | 209 | 1 | 0.023289 | |
| Meis2 | Meis Homeobox 2 | ENSG00000134138 | Upper Aerodigestive SCC | Withdrawal | 325 | 40 | 1.28 × 10−6 |
| Meis2 | Meis Homeobox 2 | ENSG00000134138 | Upper Aerodigestive SCC | Withdrawal | 325 | 53 | 3.59 × 10−6 |
| Meis2 | Meis Homeobox 2 | ENSG00000134138 | Cranial nerve abnormality | Withdrawal | 325 | 7 | 6.34 × 10−6 |
| Meis2 | Meis Homeobox 2 | ENSG00000134138 | Cancer | Withdrawal | 325 | 149 | 7.17 × 10−6 |
| FGFs | Fibroblast Growth Factor | Withdrawal | 175 | ||||
| FGFR1OP | FGF Receptor 1 Oncogene Partner | ENSG00000213066 | Withdrawal | 13 | 1 | 0.002226 | |
| FGF5 | Fibroblast Growth Factor 5 | ENSG00000138675 | Withdrawal | 21 | 1 | 0.004362 | |
| FGF14 | Fibroblast Growth Factor 14 | ENSG00000102466 | Withdrawal | 25 | 1 | 0.005329 | |
| FGFR2 | Fibroblast Growth Factor Receptor 2 | ENSG00000066468 | Withdrawal | 28 | 1 | 0.005981 | |
| FGF14 | Fibroblast Growth Factor 14 | ENSG00000102466 | Dependence | 30 | 1 | 8.68 × 10−7 | |
| FGF12 | Fibroblast Growth Factor 12 | ENSG00000114279 | Dependence | 41 | 1 | 0.009199 | |
| FGF12 | Fibroblast Growth Factor 12 | ENSG00000114279 | Dependence | 54 | 1 | 0.001187 | |
| FGF3 | Fibroblast Growth Factor 3 | ENSG00000186895 | Dependence | 81 | 1 | 0.017663 | |
| FGFRL1 | FGF Receptor Like 3 | ENSG00000127418 | Dependence | 86 | 1 | 0.018855 | |
| FGF14 | Fibroblast Growth Factor 14 | ENSG00000102466 | Dependence | 106 | 1 | 0.002259 | |
| FGF4 | Fibroblast Growth Factor 4 | ENSG00000122642 | Dependence | 17 | 7 | 2.34 × 10−7 | |
| FGF4 | Fibroblast Growth Factor 4 | ENSG00000122642 | KEGG: Rap1 signaling | 236 | 41 | 0.000353 | |
| FGF4 | Fibroblast Growth Factor 4 | ENSG00000122642 | KEGG: actin cytoskeleton | 237 | 37 | 0.004586 | |
| FGF4 | Fibroblast Growth Factor 4 | ENSG00000122642 | KEGG: melanoma | 237 | 15 | 0.021590 | |
| FGF4 | Fibroblast Growth Factor 4 | ENSG00000122642 | KEGG: MAP kinase pathway | 237 | 39 | 0.029222 | |
| FGF4 | Fibroblast Growth Factor 4 | ENSG00000122642 | KEGG: Cancer pathways | 238 | 54 | 0.067770 | |
| FGF4 | Fibroblast Growth Factor 4 | ENSG00000122642 | KEGG: Ras signaling | 328 | 38 | 0.008745 | |
| RXRA | Retinoid X Receptor Alpha | ENSG00000186350 | Withdrawal | 125 | 1 | 1.48 × 10−8 | |
| RXRG | Retinoid X Receptor Gamma | ENSG00000143171 | Withdrawal | 136 | 1 | 3.40 × 10−7 | |
| RXRA | Retinoid X Receptor Alpha | ENSG00000186350 | Withdrawal | 144 | 1 | 8.40 × 10−7 | |
| RARA | Retinoic Acid Receptor Alpha | ENSG00000131759 | Dependence | 44 | 1 | 1.95 × 10−6 | |
| RARB | Retinoic Acid Receptor Beta | ENSG00000077092 | Dependence | 73 | 1 | 5.54 × 10−6 | |
| RARB | Retinoic Acid Receptor Beta | ENSG00000077092 | Withdrawal | 124 | 1 | 7.94 × 10−9 | |
| RARB | Retinoic Acid Receptor Beta | ENSG00000077092 | Withdrawal | 168 | 1 | 3.25 × 10−6 | |
| RARB | Retinoic Acid Receptor Beta | ENSG00000077092 | Withdrawal | 190 | 1 | 6.89 × 10−6 | |
| RARB | Retinoic Acid Receptor Beta | ENSG00000077092 | Withdrawal | 215 | 1 | 1.20 × 10−5 | |
| RARA | Retinoic Acid Receptor Alpha | ENSG00000131759 | KEGG: Cancer pathways | 238 | 54 | 0.067777 | |
| WNT’s | Wnt’s | Withdrawal | 203 | ||||
| WNT7B | Wnt family member 7B | ENSG00000188064 | Dependence | 74 | 1 | 5.78 × 10−6 | |
| WNT7A | Wnt family member 7A | ENSG00000154764 | Dependence | 119 | 1 | 1.47 × 10−0 | |
| WNT7A | Wnt family member 7A | ENSG00000154764 | Dependence | 123 | 1 | 4.13 × 10−9 | |
| WNT3A | Wnt family member 3A | ENSG00000154342 | Head and neck cancer | Withdrawal | 239 | 356 | 7.73 × 10−20 |
| WNT8B | Wnt family member 8B | ENSG00000075290 | Head and neck cancer | Withdrawal | 239 | 342 | 7.74 × 10−20 |
| TBX4 | T-Box transcription factor 4 | ENSG00000121075 | Dependence | 52 | 1 | 2.72 × 10−6 | |
| TBX4 | T-Box transcription factor 4 | ENSG00000121075 | Withdrawal | 235 | 1 | 1.71 × 10−5 | |
| TBX5-AS1 | T-Box transcription factor 5 Antisense 1 | ENSG00000255399 | Withdrawal | 202 | 1 | 9.18 × 10−6 | |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | Dependence | 37 | 124 | 1.37 × 10−6 | |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | Upper aerodigestive SCC | Withdrawal | 325 | 40 | 1.28 × 10−6 |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | Upper aerodigestive SCC | Withdrawal | 325 | 115 | 1.65 × 10−6 |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | Skin lesion | Withdrawal | 325 | 53 | 3.59 × 10−6 |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | Skin cancer | Withdrawal | 325 | 113 | 4.79 × 10−6 |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | Cancer | Withdrawal | 325 | 149 | 7.17 × 10−6 |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | Large bowel adenocarcinoma | Withdrawal | 326 | 120 | 7.45 × 10−6 |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | Cutaneous melanoma | Withdrawal | 326 | 110 | 7.71 × 10−6 |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | High grade astocytoma | Withdrawal | 326 | 82 | 8.42 × 10−6 |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | Abdominal adenocarcinoma | Withdrawal | 326 | 135 | 8.46 × 10−6 |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | Solid organ cancer | Withdrawal | 327 | 150 | 9.16 × 10−6 |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | Head and neck cancer | Withdrawal | 327 | 137 | 9.54 × 10−6 |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | Sensory organ development | Withdrawal | 327 | 18 | 1.30 × 10−5 |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | Carcinoma | Withdrawal | 327 | 148 | 1.38 × 10−5 |
| CHD7 | Chromodomain Helicase DNA Binding Protein 7 | ENSG00000171316 | Upper aerodigestive SCC | Withdrawal | 327 | 44 | 1.60 × 10−43 |
| MEGF8 | Multiple EGF-like domains 8 | ENSG00000105429 | Skin lesion | Withdrawal | 325 | 105 | 1.65 × 10−6 |
| MEGF8 | Multiple EGF-like domains 8 | ENSG00000105429 | Skin cancer | Withdrawal | 325 | 113 | 4.79 × 10−6 |
| MEGF8 | Multiple EGF-like domains 8 | ENSG00000105429 | Cranial nerve abnormality | Withdrawal | 325 | 7 | 6.34 × 10−6 |
| MEGF8 | Multiple EGF-like domains 8 | ENSG00000105429 | Cancer | Withdrawal | 325 | 149 | 7.17 × 10−6 |
| MEGF8 | Multiple EGF-like domains 8 | ENSG00000105429 | Large bowel adenocarcinoma | Withdrawal | 326 | 120 | 7.45 × 10−6 |
| MEGF8 | Multiple EGF-like domains 8 | ENSG00000105429 | Cutaneous melanoma | Withdrawal | 326 | 110 | 7.71 × 10−6 |
| MEGF8 | Multiple EGF-like domains 8 | ENSG00000105429 | High grade astocytoma | Withdrawal | 326 | 82 | 8.42 × 10−6 |
| MEGF8 | Multiple EGF-like domains 8 | ENSG00000105429 | Abdominal adenocarcinoma | Withdrawal | 326 | 135 | 8.46 × 10−6 |
| MEGF8 | Multiple EGF-like domains 8 | ENSG00000105429 | Solid organ cancer | Withdrawal | 327 | 150 | 9.16 × 10−6 |
| MEGF8 | Multiple EGF-like domains 8 | ENSG00000105429 | Head and neck cancer | Withdrawal | 327 | 137 | 9.54 × 10−6 |
| MEGF8 | Multiple EGF-like domains 8 | ENSG00000105429 | Carcinoma | Withdrawal | 327 | 148 | 1.38 × 10−5 |
| MEGF8 | Multiple EGF-like domains 8 | ENSG00000105429 | Carcinoma | Withdrawal | 329 | 151 | 4.32 × 10−5 |
| MEGF8 | Multiple EGF-like domains 8 | ENSG00000105429 | Squamous cell tumor | Withdrawal | 332 | 65 | 7.59 × 10−5 |
| MEGF8 | Multiple EGF-like domains 8 | ENSG00000105429 | Preaxial polydactyly | Withdrawal | 333 | 3 | 9.19 × 10−5 |
| TMEM107 | Transmembrane protein 107 | ENSG00000179029 | Upper aerodigestive SCC | Withdrawal | 325 | 22 | 1.28 × 10−6 |
| TMEM107 | Transmembrane protein 107 | ENSG00000179029 | Cancer | Withdrawal | 325 | 149 | 7.17 × 10−6 |
| TMEM107 | Transmembrane protein 107 | ENSG00000179029 | Solid organ cancer | Withdrawal | 327 | 150 | 9.16 × 10−6 |
| TMEM107 | Transmembrane protein 107 | ENSG00000179029 | Head and neck cancer | Withdrawal | 327 | 137 | 9.54 × 10−6 |
| TMEM107 | Transmembrane protein 107 | ENSG00000179029 | Carcinoma | Withdrawal | 327 | 148 | 1.38 × 10−5 |
| TMEM107 | Transmembrane protein 107 | ENSG00000179029 | Carcinoma | Withdrawal | 329 | 151 | 4.32 × 10−5 |
| TMEM107 | Transmembrane protein 107 | ENSG00000179029 | Squamous cell tumor | Withdrawal | 331 | 65 | 7.59 × 10−5 |
| TMEM107 | Transmembrane protein 107 | ENSG00000179029 | Preaxial polydactyly | Withdrawal | 333 | 3 | 9.19 × 10−5 |
| TMEM107 | Transmembrane protein 107 | ENSG00000179029 | Squamous cell tumor | Withdrawal | 334 | 64 | 1.45 × 10−4 |
| TMEM107 | Transmembrane protein 107 | ENSG00000179029 | Head and neck cancer | Withdrawal | 335 | 127 | 1.75 × 10−4 |
| TMEM107 | Transmembrane protein 107 | ENSG00000179029 | Cancer | Withdrawal | 337 | 79 | 2.83 × 10−4 |
| TMEM107 | Transmembrane protein 107 | ENSG00000179029 | Head abnormalities | Withdrawal | 338 | 21 | 3.27 × 10−4 |
| TMEM107 | Transmembrane protein 107 | ENSG00000179029 | Haemopoietic stimulation | Withdrawal | 338 | 23 | 3.51 × 10−4 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | Upper aerodigestive SCC | Withdrawal | 325 | 166 | 1.28 × 10−6 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | Upper aerodigestive SCC | Withdrawal | 325 | 115 | 1.65 × 10−6 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | Cranial nerve abnormality | Withdrawal | 325 | 7 | 6.34 × 10−6 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | Cancer | Withdrawal | 325 | 149 | 7.17 × 10−6 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | Large bowel adenocarcinoma | Withdrawal | 326 | 120 | 7.45 × 10−6 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | Abdominal adenocarcinoma | Withdrawal | 326 | 135 | 8.46 × 10−6 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | Solid organ cancer | Withdrawal | 327 | 150 | 9.16 × 10-=6 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | Head and neck cancer | Withdrawal | 327 | 137 | 9.54 × 10−6 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | Sensory organ development | Withdrawal | 327 | 18 | 1.30 × 10−5 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | Carcinoma | Withdrawal | 327 | 148 | 1.38 × 10−5 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | Upper aerodigestive SCC | Withdrawal | 327 | 44 | 1.60 × 10−5 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | Carcinoma | Withdrawal | 328 | 119 | 2.47 × 10−5 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | Eye formation | Withdrawal | 328 | 15 | 2.81 × 10−5 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | Upper GIT carcinoma | Withdrawal | 328 | 75 | 3.42 × 10−5 |
| BMP4 | Bone morphogenetic protein 4 | ENSG00000125378 | GIT adenocarcinoma | Withdrawal | 328 | 121 | 3.56 × 10−5 |
| GREM1 | GREM1, DAN family BMP antagonist | ENSG00000126873 | Withdrawal | 171 | 1 | 3.61 × 10−6 | |
| GREM2 | GREM2, DAN family BMP antagonist | ENSG00000180875 | Withdrawal | 85 | 1 | 9.90 × 10−6 | |
| GLI3 | GLI zinc finger family 3 | ENSG00000106571 | Skin lesion | Withdrawal | 325 | 183 | 1.28 × 10−6 |
| GLI3 | GLI zinc finger family 3 | ENSG00000106571 | Head and neck squamous carcinoma | Withdrawal | 325 | 53 | 1.65 × 10−6 |
| GLI3 | GLI zinc finger family 3 | ENSG00000106571 | Skin cancer | Withdrawal | 325 | 113 | 3.59 × 10−6 |
| GLI3 | GLI zinc finger family 3 | ENSG00000106571 | Lung adenocarcinoma | Withdrawal | 325 | 42 | 4.79 × 10−6 |
| GLI3 | GLI zinc finger family 3 | ENSG00000106571 | Cancer | Withdrawal | 325 | 149 | 7.17 × 10−6 |
| GLI3 | GLI zinc finger family 3 | ENSG00000106571 | Large bowel adenocarcinoma | Withdrawal | 326 | 120 | 7.45 × 10−6 |
| GLI3 | GLI zinc finger family 3 | ENSG00000106571 | Cutaneous melanoma | Withdrawal | 326 | 110 | 7.71 × 10−6 |
| GLI3 | GLI zinc finger family 3 | ENSG00000106571 | High grade astocytoma | Withdrawal | 326 | 82 | 8.42 × 10−6 |
| GLI3 | GLI zinc finger family 3 | ENSG00000106571 | Abdominal adenocarcinoma | Withdrawal | 326 | 135 | 8.46 × 10−6 |
| GLI3 | GLI zinc finger family 3 | ENSG00000106571 | Solid organ cancer | Withdrawal | 327 | 150 | 9.16 × 10−6 |
| GLI3 | GLI zinc finger family 3 | ENSG00000106571 | Head and neck cancer | Withdrawal | 327 | 137 | 9.54 × 10−6 |
| GLI3 | GLI zinc finger family 3 | ENSG00000106571 | Sensory organ development | Withdrawal | 327 | 18 | 1.30 × 10−5 |
| GLI3 | GLI zinc finger family 3 | ENSG00000106571 | Carcinoma | Withdrawal | 327 | 148 | 1.38 × 10−5 |
| GLI3 | GLI zinc finger family 3 | ENSG00000106571 | Upper aerodigestive SCC | Withdrawal | 327 | 44 | 1.60 × 10−43 |
| System | Mean p-Value | Median p-Value |
|---|---|---|
| Gastrointestinal | 0.0011 | 7.45 × 10−6 |
| Chromosomes | 0.0018 | 1.31 × 10−4 |
| Neurological | 0.0035 | 6.15 × 10−4 |
| Cardiovascular | 0.0011 | 0.0011 |
| Face | 0.0021 | 0.0014 |
| Body Wall | 0.0018 | 0.0016 |
| General | 0.0026 | 0.0017 |
| Uronephrology | 0.0021 | 0.0022 |
| Limb | 0.0036 | 0.0037 |
| Target | Cannabis Dependence | Cannabis Withdrawal | ||||||
|---|---|---|---|---|---|---|---|---|
| Number of Annotations | Cumulative Genes | Minimum p-Value | Median p-Value | Number of Annotations | Cumulative Genes | Minimum p-Value | Median p-Value | |
| Gastrointestinal | 8 | 2561 | 4.60 × 10−16 | 1.13 × 10−15 | - | - | - | - |
| Large Intestine | 5 | 1240 | 7.65 × 10−15 | 6.40 × 10−14 | 3 | 363 | 7.45 × 10−6 | 6.80 × 10−5 |
| Esophagus | 4 | 393 | 3.15 × 10−13 | 9.40 × 10−4 | 3 | 69 | 0.0020 | 0.0028 |
| Neurological | 8 | 710 | 5.33 × 10−8 | 4.45 × 10−4 | 1 | 2 | 7.20 × 10−4 | 7.20 × 10−4 |
| Heart | 5 | 53 | 8.83 × 10−8 | 1.57 × 10−4 | - | - | - | - |
| Liver | 2 | 404 | 1.28 × 10−7 | 1.79 × 10−7 | - | - | - | - |
| Brain | 6 | 750 | 1.39 × 10−7 | 1.86 × 10−5 | 1 | 3 | 1.16 × 10−4 | 1.16 × 10−4 |
| Pancreas | 8 | 769 | 9.10 × 10−7 | 1.25 × 10−5 | 4 | 112 | 0.0052 | 0.0061 |
| Embryo | 9 | 285 | 8.20 × 10−6 | 3.61 × 10−4 | - | - | - | - |
| Atrioventricular valves | 3 | 13 | 9.04 × 10−6 | 4.00 × 10−5 | - | - | - | - |
| Neurons | 14 | 336 | 9.27 × 10−6 | 1.88 × 10−4 | 3 | 11 | 0.0020 | 0.0031 |
| DNA | 12 | 373 | 1.50 × 10−5 | 0.0011 | 5 | 33 | 3.58 × 10−4 | 0.0070 |
| Chromosomes | 3 | 16 | 1.60 × 10−5 | 7.90 × 10−5 | 1 | 1 | 0.0070 | 0.0070 |
| Cardiovascular | 4 | 85 | 2.10 × 10−5 | 0.0019 | - | - | - | - |
| Synapse | 15 | 308 | 3.12 × 10−5 | 0.0018 | 7 | 36 | 1.43 × 10−4 | 0.0013 |
| Microtubules | 1 | 58 | 3.30 × 10−5 | 3.30 × 10−5 | 1 | 24 | 0.0045 | 0.0045 |
| Embryo | 6 | 93 | 3.60 × 10−5 | 0.0018 | 2 | 8 | 0.0023 | 0.0046 |
| Ventricle | 4 | 23 | 5.10 × 10−5 | 6.09 × 10−4 | - | - | - | - |
| Body | 5 | 132 | 7.80 × 10−5 | 0.0016 | 2 | 51 | 1.93 × 10−4 | 3.74 × 10−4 |
| Eye | 6 | 65 | 7.90 × 10−5 | 0.0010 | 13 | 73 | 2.80 × 10−5 | 6.89 × 10−4 |
| Cerebrum | 2 | 153 | 1.20 × 10−4 | 7.35 × 10−4 | 4 | 22 | 7.41 × 10−4 | 0.0020 |
| Head | 1 | 47 | 1.20 × 10−4 | 1.20 × 10−4 | - | - | - | - |
| Bone | 7 | 50 | 1.40 × 10−4 | 0.0018 | 14 | 48 | 1.93 × 10−4 | 0.0070 |
| Sensory | 1 | 29 | 1.64 × 10−4 | 1.64 × 10−4 | - | - | - | - |
| Body Axis | 1 | 1 | 1.93 × 10−4 | 1.93 × 10−4 | - | - | - | - |
| Urinary system | 1 | 17 | 2.20 × 10−4 | 2.20 × 10−4 | 1 | 8 | 0.0044 | 0.0044 |
| Kidney | 5 | 84 | 4.29 × 10−4 | 0.0022 | 1 | 4 | 0.0042 | 0.0042 |
| Breast | 1 | 3 | 5.73 × 10−4 | 5.73 × 10−4 | 2 | 9 | 0.0021 | 0.0023 |
| Granulocytes | 1 | 3 | 5.73 × 10−4 | 5.73 × 10−4 | - | - | - | - |
| Ear | 6 | 36 | 7.20 × 10−4 | 0.0021 | 5 | 21 | 1.65 × 10−4 | 8.04 × 10−4 |
| Atria | 4 | 15 | 8.55 × 10−4 | 0.0017 | - | - | - | - |
| Body trunk | 1 | 50 | 0.0015 | 0.0015 | - | - | - | - |
| Myogenesis | 2 | 4 | 0.0018 | 0.0018 | - | - | - | - |
| Vertebra | 1 | 3 | 0.0049 | 0.0049 | - | - | - | - |
| Limb | - | - | - | - | 6 | 18 | 9.20 × 10−5 | 0.0037 |
| Nose | - | - | - | - | 1 | 3 | 0.0011 | 0.0011 |
| Ovarian reserve | - | - | - | - | 1 | 2 | 0.0031 | 0.0031 |
| Mitochondria | - | - | - | - | 1 | 1 | 0.0070 | 0.0070 |
| Palate | - | - | - | - | 1 | 1 | 0.0070 | 0.0070 |
| No. | Europe | USA | |||||
|---|---|---|---|---|---|---|---|
| Model | Cancer | Minimum p-Value | Model | Correlate | Cancer | Minimum p-Value | |
| 1 | Categorical | Acute Lymphoid Leukemia | 8.70 × 10−24 | Categorical | Δ9THC | Acute Lymphoid Leukemia | 7.65 × 10−25 |
| 2 | Continuous | Acute Myeloid Leukemia | 2.11 × 10−4 | Categorical | Δ9THC | Acute Myeloid Leukemia | 3.11 × 10−110 |
| 3 | Categorical | Cannabidiol | All_Cancer | <2.2 × 10−320 | |||
| 4 | Categorical | Anus | 6.71 × 10−35 | ||||
| 5 | Categorical | Bladder | <2.2 × 10−320 | Categorical | Cannabidiol | Bladder | <2.2 × 10−320 |
| 6 | Continuous | Brain.Medulloblastoma | 5.64 × 10−42 | Categorical | Cannabidiol | Brain | 5.67 × 10−33 |
| 7 | Categorical | Breast | 4.03 × 10−17 | Categorical | Δ9THC | Breast | 8.06 × 10−146 |
| 8 | Continuous | Chronic Lymphoid Leukemia | 1.20 × 10−34 | Categorical | Cannabidiol | Chronic Lymphoid Leukemia | 2.98 × 10−12 |
| 9 | Continuous | Chronic Myeloid Leukemia | 1.32 × 10−32 | Categorical | Δ9THC | Chronic Myeloid Leukemia | 1.52 × 10−12 |
| 10 | Categorical | Colorectum | 6.14 × 10−242 | Categorical | Cannabidiol | Colorectum | <2.2 × 10−320 |
| 11 | Categorical | Corpus uteri | 2.28 × 10−4 | ||||
| 12 | Categorical | Esophagus | 1.12 × 10−110 | Categorical | Cannabidiol | Esophagus | 2.31 × 10−43 |
| 13 | Categorical | Gallbladder | 2.24 × 10−4 | ||||
| 14 | Continuous | Hepatocellular Cancer | 2.29 × 10−42 | ||||
| 15 | Categorical | Hodgkin lymphoma | 1.80 × 10−8 | Categorical | Cannabidiol | Hodgkins | 1.22 × 10−30 |
| 16 | Categorical | Kaposi sarcoma | 1.16 × 10−7 | Categorical | Cannabidiol | Kaposi | 4.75 × 10−29 |
| 17 | Categorical | Kidney | 7.46 × 10−5 | Continuous | Cannabinol | Kidney | 0.0067 |
| 18 | Categorical | Larynx | <2.2 × 10−320 | ||||
| 19 | Categorical | Liver | <2.2 × 10−320 | Categorical | Δ9THC | Liver | <2.2 × 10−320 |
| 20 | Categorical | Lung | 1.45 × 10−8 | Categorical | Cannabidiol | Lung | 6.87 × 10−194 |
| 21 | Categorical | Melanoma of skin | <2.2 × 10−320 | Categorical | Cannabidiol | Melanoma | <2.2 × 10−320 |
| 22 | Categorical | Mesothelioma | 3.37 × 10−111 | ||||
| 23 | Categorical | Multiple myeloma | 6.92 × 10−8 | Categorical | Δ9THC | Multiple myeloma | 1.73 × 10−30 |
| 24 | Categorical | Non-Hodgkin lymphoma | 1.60 × 10−44 | Categorical | Cannabidiol | Non-Hodgkin lymphoma | 3.15 × 10−145 |
| 25 | Continuous | Oropharynx | 7.02 × 10−21 | Continuous | Δ9THC | Oropharynx | 3.21 × 10−6 |
| 26 | Categorical | Ovary.Germ Cell Tumor | 1.07 × 10−38 | Categorical | Cannabidiol | Ovary | 2.49 × 10−312 |
| 27 | Categorical | Pancreas | 4.09 × 10−9 | Categorical | Δ9THC | Pancreas | 4.57 × 10−166 |
| 28 | Categorical | Penis | 1.64 × 10−19 | ||||
| 29 | Categorical | Prostate | <2.2 × 10−320 | Categorical | Cannabidiol | Prostate | <2.2 × 10−320 |
| 30 | Categorical | Cannabidiol | Stomach | 2.30 × 10−192 | |||
| 31 | Categorical | Testis | 3.83 × 10−81 | Continuous | Cannabinol | Testis | 1.47 × 10−5 |
| 32 | Continuous | Testis.Non-Seminoma Germ | 1.25 × 10−75 | ||||
| 33 | Categorical | Testis.Seminoma | 5.14 × 10−58 | ||||
| 34 | Categorical | Thyroid | <2.2 × 10−320 | Categorical | Δ9THC | Thyroid | <2.2 × 10−320 |
| 35 | Continuous | Vulva | 8.88 × 10−44 | ||||
| Cancer | Minimum p-Value Dependence | Minimum p-Value Withdrawal | p-Value Ratio Dependence/Withdrawal | Total Gene Number Dependence | Total Gene Number Withdrawal | Gene Number Ratio Dependence/Withdrawal |
|---|---|---|---|---|---|---|
| Thyroid | 1.21 × 10−17 | 0.0014 | 1.17 × 1014 | 637 | 115 | 5.54 |
| Melanoma | 3.70 × 10−15 | 7.71 × 10−6 | 2.08 × 109 | 579 | 225 | 2.57 |
| Urinary | 2.54 × 10−14 | 2.16 × 10−4 | 8.50 × 109 | 1191 | 679 | 1.75 |
| Esophagus | 3.15 × 10−13 | 6.80 × 10−5 | 2.16 × 108 | 465 | 117 | 3.97 |
| Stomach | 3.15 × 10−13 | 6.80 × 10−5 | 2.16 × 108 | 443 | 102 | 4.34 |
| Colorectal | 7.27 × 10−13 | 6.17 × 10−4 | 8.49 × 108 | 1734 | 452 | 3.84 |
| Testis | 1.14 × 10−8 | 6.75 × 10−4 | 5.92 × 104 | 304 | 60 | 5.07 |
| Liver | 1.17 × 10−8 | NA | NA | 890 | NA | NA |
| Prostate | 2.88 × 10−8 | 5.33 × 10−4 | 1.85 × 104 | 399 | 158 | 2.53 |
| Breast | 3.25 × 10−8 | 0.0013 | 3.91 × 104 | 674 | 177 | 3.81 |
| Brain | 5.33 × 10−8 | 8.42 × 10−6 | 157.97 | 2779 | 947 | 2.93 |
| Oropharynx | 1.25 × 10−7 | 1.60 × 10−5 | 128.00 | 195 | 44 | 4.43 |
| Pancreas | 9.10 × 10−7 | 0.0052 | 5.73 × 103 | 769 | 112 | 6.87 |
| ALL | 4.08 × 10−5 | 6.01 × 10−4 | 14.73 | 23 | 118 | 0.19 |
| NHL | 4.08 × 10−5 | 6.11 × 10−4 | 14.98 | 322 | 43 | 7.49 |
| Ovary | 1.16 × 10−4 | 0.0070 | 60.43 | 529 | 1 | 529.00 |
| CML | 2.13 × 10−4 | 0.0021 | 9.95 | 11 | 11 | 1.00 |
| AML | 8.96 × 10−4 | 6.26 × 10−4 | 0.70 | 11 | 36 | 0.31 |
| Kidney | 0.00101 | NA | NA | 89 | NA | NA |
| Myeloma | NA | 0.0016 | NA | NA | 10 | NA |