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Find The relationship between the rabbit and fox stock illustrations and royalty free photos in HD. Explore millions of stock photos, images, illustrations, and. Table 2: The relationships of Alzheimer's disease susceptibility genes with vitamin The Fox, the Rabbits, Gene, and Environmental Variables. By coincidence, reports about the cycles in the rabbit and fox marriage and how their relationship unfolded, his actions took on new meaning.
All are less prevalent in convents, where nuns appear less susceptible to the ravages of ageing. These agents are treatable by diet and drugs, vitamin supplementation, pathogen detection and elimination, and autoantibody removal, although again, the beneficial effects of individual treatments may be tempered by genes and environment.
Introduction If there is one factor common to complex polygenic diseases it is the heterogeneity in both gene and risk factor association studies. Although these have discovered key genes and risk factors, the results for most are invariably confounded by conflicting data [ 1 ].
In the genetic arena, the clear familial component of many diseases has driven the search for major genes using genome-wide association studies GWAS with large numbers of patients pooled from different regions [ 2 ]. Such studies have been able to discover rare variants that play a major role in a small percentage of patients, for example VIPR2 in schizophrenia [ 3 ].
GWAS studies have, however, been more successful in uncovering larger numbers of genes of greater effect for simpler traits such as lipid levels [ 7 ]. Viruses and other pathogens have been implicated as risk factors in many diseases, although again, conflicting evidence leads to scepticism in many areas. For example, the involvement of the Epstein-Barr virus in multiple sclerosis is hotly contested [ 8 — 10 ]. Gene-gene and gene-environment interactions may play an important role in such inconsistency.
Relationship Between Rabbit Fox Stock Illustration - Shutterstock
For example, the risk promoting effects of genes can be better explained when using pathway analysis or combining the effects of genes with common function, rather than by studying single genes in isolation [ 1112 ]. Genes and risk factors can also act together, and in certain cases genes can be linked to environmental variables.
Environment-environment interactions are also apparent. For example, the effects of vitamin E on lifespan, or on resistance to various infections can be null, deleterious, or protective, depending on confounding factors such as age, exercise, smoking, and vitamin C consumption [ 15 — 17 ]. Complex diseases are also composed of many endophenotypes or underlying pathologies, and different genes or risk factors may contribute to any of these.
The efficiency of each of these subprocesses is controlled by genes, many of which have been implicated in association studies see Table 1. The number of genes in each pathway is shown in brackets see http: In genetic association studies, the drive has been to increase statistical power by increasing the numbers of subjects enrolled.
This has resulted in the discovery of important genes and rare genetic variants, but has not delivered genes that confer a high degree of risk in the majority of patients. However, as illustrated below, more could perhaps be gained by a reanalysis of existing data in relation to other genetic and risk factor variables that could result in elucidation of the causes rather than the risks. KEGG pathway analysis of over associated genes was performed [ 20 ], and the results of the exercise were posted at http: Other gene-risk factor relationships were identified by literature survey.
B cell and T cell epitopes within the beta-amyloid peptide were identified using the immune epitope database server http: Sequence comparisons of the beta-amyloid peptide versus selected bacterial, fungal, or viral proteomes was performed using the NCBI blast server [ 2223 ].
The hunter and the hunted
Results and Discussion 3. Glutathione pathways were also present. Glutathione has potent viricidal and bactericidal properties and is often depleted by infections [ 27 — 30 ]. A number of pathogen entry pathways are also concerned, and although C. Neoformans pathways are not specifically represented, many of these pathways can be considered as generic pathways relevant to many bacteria and other pathogens. Pylori heat shock protein, HSP60 [ 32 ]. Similarly, there is no specific HSV-1 viral entry pathway in the KEGG database, but the virus uses actin pathways, endocytosis, protein processing, and DNA repair pathways during its life cycle, which are heavily represented [ 33 ].
Vitamin-A-Related Genes These were identified by literature survey and the most directly relevant are shown in Table 2. APOE4 is the isoform least able to bind to retinyl palmitate. ABCA1 is also involved in cholesterol and retinol transport. Retinoids modulate APP processing, via regulation of beta and gamma secretases while the RIG-1 pathway is crucial in viral defence.
A large number of genes are also regulated by retinoids or retinoid receptors. It has served to illustrate the concept of natural selection where, over time, dark genes become more common in polluted areas, an effect that could eventually lead to speciation [ 34 ].
On two islands one covered in snow and the other in black volcanic ash live an equal number of black and white rabbits and a family of foxes, who will find it easier to trap the black rabbits on the snowy island and the white rabbits on the island covered in black ash.
Gene association studies would correctly identify the black and white genes as being protective or risk promoting depending upon the environment. The snow, the ash, or the fox, being equally present on each island, regardless of the toll of dead rabbits, could not be considered as risk factors. Genetic meta-analysis or pooled GWAS data would also rule out any genetic involvement, leaving no susceptibility genes, no risk factors, and no cause.
However, a GWAS study, apportioning the genetic data in relation to ash, snow, and fox would be able to correctly surmise that the white gene is a risk factor on the ash-covered island, and the black gene a risk factor on the snowy island, as would have D. Again the fox is undetectable, being present in all compartments. On other similar islands, live further populations of black and white rabbits with no fox, an equal number of deaths due to old age, and no reason to investigate either genes or risk factors.
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However, it is only by including this island, again partitioning GWAS data in relation to all variables, that the genes, the risk factors, and the cause can be correctly allotted their respective roles. In this example, the genes and environmental variable are risk or protective factors for the cause as well as for the deaths, depending on circumstance.
The genes or risk factors are not killing the rabbits, but are allowing the cause to do so. Nonstratified association studies would thus seem to be ill-suited to find important genes, risk factors, or causes, and the pursuit of greater statistical power may well be futile, although such strategies can find rare variants that may cause disease in a minority of patients, which is evidently useful.
However, for the majority of cases, much could perhaps be gained from a reappraisal of existing data and by partitioning GWAS data in relation to the many known risk factors in each disease.
The situation is evidently more complex in polygenic diseases, where hundreds of interacting genes, many risk factors, and probably many causes are present. This is already appreciated, and several groups have analysed the statistical problems involved due to the mass of genes and risk factors [ 35 — 37 ]. However, it is likely that an appropriate selection of genes and risk factors could markedly affect the degree of risk. For example, the odds ratio for APOE4 was shown to be 1.
The population genetics example, and the discussion below, suggests that certain susceptibility genes are restricted to risk factor subsets. From the above, it would appear that a cause can be present in equal proportion in control and disease populations but should be able to produce the pathological features of the disease, and the disease incidence should be reduced where the causes are few. The genes and risk factors are, however, both able to influence the cause.
Pneumoniae  or Helicobacter pylori infection , mild hypercholesterolaemia [ ], but declining cholesterol levels from midlife to late life [ ], atherosclerosis of the carotid arteries, leptomeningeal arteries, or the circle of Willis, and stroke, leading to cerebral hypoperfusion [ — ], hyperhomocysteinaemia [ ], type 2 diabetes and modified insulin metabolism [ ], age-related loss of sex steroid hormones in both women and men [ ], but high total oestradiol levels , and vitamin A deficiency [ ].
Each risk factor can act independently of any gene or other risk factor variant, in animal models—as with the fox. Anoxia, ischaemia, hypoglycaemia, hypercholesterolaemia, and vitamin A deficiency are all able to kill neurones, in some cases including cholinergic neurones, without the aid of beta amyloid. Their vitamin A levels and general health are sustained by a healthy diet, regular fish on Fridays, and exercise. The TNF antagonist, etanercept, has also been reported to produce a striking remedial effect on symptomatology, following perispinal application [ ].
However, the use of TNF antagonists is also associated with an increased incidence of opportunistic bacterial, fungal, and viral infestations, including cytomegalovirus, and cryptococcal infections [ ], perhaps a contraindication for their prolonged use. These protective factors are in most cases the obverse of the risk factors and include diets rich in fish or polyunsaturated fatty acids , the Mediterranean diet [ ] and the use of statins [ ], which are counter to the effects of high cholesterol.
A diet rich in fruit and vegetables is associated with reduced dementia incidence [ ] and is able to sustain Vitamin A levels and reduce homocysteine levels in the elderly population [ ]. High folate intake, which reduces homocysteine levels [ ], and the use of nonsteroidal anti-inflammatories have also been reported to reduce risk [ ].
Again these are related to the risk factors and to the genes, which may condition their success cf. The close relationships between these genes and herpes simplex infection have been the subject of a previous article [ ]. APOE4 also favours the binding of C. Pneumoniae elementary bodies to host cells [ ].
The hunter and the hunted
It is also a risk factor for hypercholesterolaemia, per se [ ], and for carotid artery atherosclerosis in men with diabetes [ ]. APOE4 is also the isoform least able to promote lipid efflux from neuronal cells [ ], a factor that may enhance the cholesterol dependent cleavage of beta-amyloid by beta and gamma secretase [ ]. It is also the least able isoform binding the vitamin A precursor retinyl palmitate [ ] see below. Complement receptor 1 is a pathogen receptor for both herpes simplex [ ], and C.
Neoformans [ ] and also for the atherogenic pathogen, P.All scenes of Courage saving Bunny from Mad Dog
Both Helicobacter pylori and C. Pneumoniae [ ] use the mannosephosphate IGF2 receptor inter alia for entry. PICALM phosphatidylinositol binding clathrin assembly proteinas its name implies, is involved in clathrin-mediated endocytosis [ ], a process used by C. We can add the concept of a carrying capacity K to the exponential growth model by adjusting the differential equation to become: This equation is called the logistic model, and although finding its solution is more complicated than the exponential model, we can still get an idea of how it behaves by examining the relationship between N and K.
The exact behaviour of the model depends on the growth rate r, and for certain values the population level becomes chaotic, meaning it rapidly changes in a way that seems almost random. Unleash the foxes The logistic model is a good description of a single animal population, but in real life the rabbits are unlikely to have a whole island to themselves.
When even small areas of land can contain hundreds of different animals, modelling the interaction of an entire ecosystem is no easy task. Imagine a small population of foxes is introduced to Rabbit Island.
The situation is described by the following equations, with x and y representing the rabbit and fox populations: If there are no foxes around i. The additional term -Bxy represents rabbits being eaten by foxes, reducing the overall rabbit population.
The second equation is a sort of mirror image of the first. Cxy represents the growth in fox numbers due to eating rabbits, and the negative growth rate -Dy means that without rabbits to eat, the foxes will quickly die off. Instead, ecologists use computers to find numerical solutions, and plot graphs like the ones below: As you may remember from studying science at GCSE, predator-prey populations rise and fall in cycles.
As the rabbit population increases there is more food for the foxes, so their population also increase.