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Mendelian Genetics of Drosophila | Lab Report

Joshua Park
Introduction Drosophila melanogaster, known generally as a fruit fly, is a cost-effective model organism with certain qualities that make it an ideal research tool in various fields of science. Drosophila melanogaster are preferred over vertebrate models as a research tool in genetic studies due to its basic food requirements, its ability to produce large numbers of progeny in a short period of time, and quick life cycle. Depending on diet and stress its life span ranges up to an average maximum of 120 days. They are also easy and inexpensive to culture in a laboratory environment, not to mention they take up very little space. In addition, they have diverse phenotypic traits that are easily observable. Its anatomical features like the compound eye allow easy access for phenotypic characterization. They also have abundant genetic variation in a relatively small genome. Approximately 13,600 protein-coding genes are located in only four chromosomes. Many heredity variations can be studied in these flies.An example of this is the gene that codes for eye color in Drosophila, which can result in red, sepia, white, or brown phenotypes. This unique ability to generate many different phenotypes provides D. melanogaster its practicality as an effective research tool for the study of the relationship between genotype and phenotype.
In this experiment, we wanted to understand which phenotypic results would result from a mating cross between D. melanogaster parents with certain wild type and mutant phenotypes for eye color and wing size. The wild type phenotype for eye color is red, while the mutant phenotypes are sepia and white. The wild type phenotype for wing size is long, while the mutant phenotype is apterous, or no wing. We also wanted to know the reason for such phenotypic ratios, which we hypothesized to be the Mendelian Law of Segregation in regards to the study of one trait. To do this, we mated D. melanogaster flies with similar and different phenotypes in order to see how well our results agreed with the Mendelian Law of Segregation. We created two crosses: a monohybrid cross where both male and female flies had the wild type phenotype (Bb and Bb), and a monohybrid cross where the male flies had the mutant phenotype (XwY) and the female flies had the wild type phenotype (X X ). For our first monohybrid cross, we wanted to know what the expected phenotypic ratio would be when crossing wildtype red-eyed flies, according to Mendel’s first law. We hypothesized that the expected phenotypic ratio for a cross with red-eyed flies would be 3:1 because according to the Mendelian Law of Segregation, the two alleles are separated from each other during gamete formation. For our other monohybrid cross, we wanted to know what the expected phenotypic ratio would be when crossing wildtype red-eyed flies with mutant white-eyed flies, once again according to Mendel’s first law. We hypothesized that the expected phenotypic ratio would be 1:1 due to the Mendelian Law of Segregation. Using the chi-square test, we will be prepared to reject or fail to reject our hypothesis according to our mating cross results.
Title: Mendelian Genetics of Drosophila Material and Methods Students in groups of two were given a number corresponding to a specific fly stock vial. Two culture vials pre-filled with food and cotton plugs were collected at the front of the classroom. The group number and initials were written on the vials. Next, the assigned stock vials were obtained and were lightly tapped on the lab bench to remove the flies from the cotton plug. An anesthesia wand was dipped into the FlyNap and carefully inserted through the cotton plugs into each stock vial. The stock vials were immediately placed horizontally on the lab bench after, preventing the flies from drowning in the food media. After waiting a few minutes for the flies to fall asleep, the wands were removed from the vials and the flies from each vial were dumped onto separate index cards. The flies were observed under a dissecting scope and found to be either male or female based on the body morphology. A lamp light was used for an easier inspection. Using a brush, 5 male and female flies from each cross set were transferred to the culture vials. Once again, the vials were made sure to be placed horizontally on the lab bench. Each culture vial was sealed with a cotton plug, and the leftover flies were returned to the stock vials. The vials were returned to the TA for storage. Once larva could be seen in the fly culture vials, the parent flies were put to sleep and removed from the vials to avoid backcrossing. Once it was evident that many F1 offspring had matured into adults, the flies were put to sleep and dumped onto notecards. The flies from the assigned cross set were observed under a dissecting microscope, and the eye color and wing presence was recorded for each fly. For cross set E, both the sex and phenotype for each fly was recorded. After every F1 generation fly was counted for each cross set, they were dumped into the morgue.
Results Cross Set: C
Table 1: F1 phenotypes for cross set C
Phenotype
Total number
Eye color
Wing size
Red
Long
39
Sepia
Long
9
The F1 data that was collected for cross set C is indicated in Table 1. The number of flies for each phenotype is shown.
Figure 1: Graphical representation of F1 data for cross set C

Figure 1 presents the data from Table 1 in the form of a bar graph. According to this data, there were more flies with wildtype red eyes (39) compared to flies with sepia eyes (9). There was a total of 48 flies for cross set C.
Cross Set: E
Table 2: F1 phenotypes and sex for cross set E
Phenotype
Number of Male Flies
Number of Female Flies
Total number
Eye color
Wing size
Red
Long
10
9
19
White
Long
10
13
23
The F1 data that was collected for cross set E is indicated in Table 2. The number of flies for each phenotype and sex is shown.
Figure 2: Graphical representation of F1 data for cross set E

Figure 2 presents the data from Table 2 in the form of a bar graph. According to this data, the number of male flies with wildtype red eyes (10) was equal to the number of male flies with white eyes (10). In addition, the number of female flies with wildtype red eyes (9) was very similar to the number of female flies with white eyes (13). There was a total of 42 flies for cross set E.
Phenotype
Observed Number (O)
Expected Number (E)
Deviation
(O – E)
(O – E)²
(O – E)²/E
Red eyes/long wings
39
36
3
9
0.25
Sepia eye/long wings
9
12
-3
9
0.75
Total
48
48
X² = 1.00
Chi-Square Analysis:
Table 3:Chi-square analysis for cross set C
Cross Set C: χ2 =
Degrees of freedom (df) = 1
p-value range = 0.1 < p < 0.9
Table 4: Chi-square analysis for cross set E
Phenotype
Observed Number (O)
Expected Number (E)
Deviation
(O – E)
(O – E)²
(O – E)²/E
Red eyes/long wings
19
21
-3
9
0.429
White eyes/long wings
23
21
2
4
0.190
Total
42
42
X² = 0.619
Cross Set E: χ2 =
Degrees of freedom (df) = 1
p-value range = 0.1 < p < 0.9
The chi-square analysis was completed for both crosses to compare the observed results with the expected results. The p-value for cross set C was between 0.1 and 0.9. The p-value for cross set E was also between 0.1 and 0.9. As a result, we fail to reject the null hypothesis of the chi-square test for both crosses.
Discussion According to the results of this experiment, our research hypothesis was supported in each mating cross. We hypothesized that the expected phenotypic ratio for a cross with red-eyed wildtype flies would be 3:1, because according to the Mendelian Law of Segregation, the two alleles are separated from each other during gamete formation. Cross set C produced about three times as many red-eyed wildtype flies compared to sepia-eyed flies, confirming our hypothesis. For our other monohybrid cross, we wanted to know what the expected phenotypic ratio would be when crossing wildtype red-eyed flies with mutant white-eyed flies. We hypothesized that the expected phenotypic ratio would be 1:1 due to the Mendelian Law of Segregation. Cross set E produced equal amounts of red-eyed flies and white-eyed flies, confirming our hypothesis. The ratios obtained for cross sets C and E accurately reflected the expected results for a monohybrid cross and test cross respectively, as claimed by Mendel’s law of segregation. The results of the chi-square analysis show that the observed counts were very close to the expected counts. The p-values for both cross sets C and E were between 0.1 and 0.9. Since these p-values were larger than 0.05, it was concluded that we should fail to reject the null hypothesis. Failing to reject the null hypothesis means that the observed data was not statistically different from the expected data.
The mode of inheritance for cross set C was determined to be complete dominance. In complete dominance, the dominant allele completely masks the effect of the recessive allele when both are present. Since both parents were found to be heterozygous for eye color (Bb), the dominant red allele masked the effect of the recessive sepia allele. The F1 data for cross set C demonstrates that wild type red eyes are dominant over sepia eyes, since the ratio of red-eyed flies (BB or Bb) to sepia-eyed flies (bb) was calculated to be 3:1, as expected for a monohybrid cross according to Mendel’s law of segregation.
The mode of inheritance for cross set E was determined to be X-linked. Genetic conditions with an X-linked recessive inheritance pattern usually affect only the males in a family. This is because they are caused by altered or mutant genes found on the X-chromosome. Females are born with two X-chromosomes, so the effects of a working gene on one can mask the effects of a mutant gene on the other, which is similar to complete dominance. They will normally be unaffected carriers of the condition. Males have only one X-chromosome, so if they inherit one with a mutant gene (XwY), they will be affected. The F1 data for cross set E demonstrates that the traits for red eyes and white eyes are located on the sex chromosome, since the ratio of red-eyed flies to white-eyed flies was determined to be 1:1. This indicated that the red-eyed female flies were heterozygous for eye color (X Xw), since the contribution of a mutant allele from both parent flies would result in offspring with red eyes and white eyes in equal proportions.
These results are comparable to the data from Thomas Hunt Morgan’s experiment with D. melanogaster. In his experiment, he discovered a mutant gene affecting eye color. This mutant gene was expressed as a white color in the eyes. Morgan proceeded to discover that the gene for eye color was inherited in different ways depending on the sex of the fly. This difference came from the fact that female flies have two X chromosomes, while male flies only have one X chromosome and a Y chromosome. Morgan found that this gene had a similar inheritance pattern to the X chromosome. Thus, Morgan concluded that this gene was X-linked.
A limitation of this experiment is the fact that we only counted progeny during one session. Multiple trials of counting offspring may have produced closer values to the expected ratios of Mendel’s law of segregation. Another limitation would be the difficulty of identifying male flies from female flies in a quick and efficient manner.
This research has demonstrated the practicality and versatility of D. melanogaster as a tool for research in the lab. Although these fruit flies may seem like very simple and insignificant organisms, they allow us to do research that may otherwise not have been possible. I plan to conduct further studies with D. melanogaster in the field of pharmacology. Since humans share key physiological processes with this organism, D. melanogaster would serve as an effective model for drug discovery. I aim to generate a new drug compound that would greatly aid in the treatment of type 2 diabetes. I hope that the research conducted with these flies will allow for greater discoveries in various fields of science that will contribute to the advancement of mankind.
References Jennings, B. H. (2011). Drosophila – a versatile model in biology

Critical Analysis of Ageing Biomarkers

Introduction Biomarkers have been used since 1980, in aging and age related diseases. The use of biomarkers helps increase the understanding of a disease and help in diagnosis. Biomarkers can investigate a disease from early manifestations to final stages and can characterise biological age. Aging results in the deterioration of functional capacity and exposes people to diseases over time. The biomarkers should be measurable in the blood, tissues or cells and should be easily obtained from blood or urine samples. Biomarkers of exposure and biomarkers of disease are the two key types of biomarkers used in clinical settings. The effect and rate of aging is reliant upon individuals (Strimbu and Tavel, 2010).
A reliable biomarker should be a predicator of life span not chronological age, should work on animals and humans and be frequently tested. There are three key types of biomarker; determine chronological age, predict life expectancy and disease predisposition. Biomarkers can enable the development of drugs to reverse or slow down the progression of a disease. Example of biomarkers of aging are cellular senescence, hormonal deregulation and oxidative stress (Sergievsky, 2004) and (Strimbu and Tavel, 2010)
Biomarkers Elevated C-reactive protein (CRP) levels have been associated with increased cardiovascular disease risk. C reactive protein (CRP) biomarker is an acute phase reactant produced in the liver, following tissue injury, inflammation and infection is released into the bloodstream. An indication of cardiovascular disease risk could be the elevations in CRP levels in the blood, people with a higher or lower cardiovascular disease risk can be identified by measuring CRP levels in the blood. Elevated CRP levels cause inflammation and oxidative stress regardless of metabolic syndrome (Horiuchi and Mogi, 2011) and (Abraham et al, 2007).
The data was analysed from 12 European countries and included patients over 50 years that had at least one cardiovascular risk factor with no history of cardiovascular disease. Diabetics were also analysed. Glycated haemoglobin levels were positively correlated with CRP levels and there was a negative correlation with high-density lipoprotein cholesterol levels. Women that had increased cardiovascular disease risk factor and more metabolic syndrome markers had elevated CRP levels. 30% of diabetic patients that were not receiving statin therapy had CRP levels ≥3mg/L and 50% CRP levels were ≥2mg/L, subjects with intermediate levels of cardiovascular disease risk.
Mean CRP levels were ≥4.2mg/L in the overall population, levels were similar to subjects with diabetes. Subjects were over 50 years however no association was found between elevated CRP levels and age. Almost 50% subjects regardless of cardiovascular disease risk factor had CRP levels of ≥2mg/L. Increasing CRP levels were associated with metabolic syndrome markers. There was an association between raised CRP levels and greater cardiovascular risk (Halcox et al, 2014).
This study looked at age related increase in compartments of visceral fat and the association with harmful changes in blood lipid profile and insulin sensitivity in non-obese women. Visceral fat has been suggested to be a predictor of variations plasma lipid levels, lipoprotein and plasma glucose-insulin concentrations. Abdominal adiposity increase helps identify age related decline in insulin sensitivity and plasma lipid levels.
178 women were categorized into four age groups, visceral and subcutaneous abdominal adipose tissue areas, body composition, blood lipid profile, glucose disposal and aerobic fitness were directly analysed. With age, there was an increase in abdominal adipose tissue. An age related proliferation was detected in total cholesterol (p<0.0003), triglycerides (p<0.0009), LDL cholesterol (p<0.027). Insulin sensitivity revealed a different age related pattern of change. Group 4 expressed reduced insulin sensitivity after visceral fat was statistically controlled, differences observed were weakened relative to other groups. Visceral fat expressed a stronger age related change in blood lipid profile. Age related changes in total cholesterol, triglycerides and LDL cholesterol were obliterated due to the differences in visceral fat and deep subcutaneous adipose tissue area. VO2 max or physical activity had no independent effects on the age related changes in blood lipid profile and insulin sensitivity. In-group 4 had the lowest insulin sensitivity expresses on an absolute basis of fat-free mass, no significant difference was observed between other groups (DeNino et al, 2001).
The study investigated age related fluctuations in cutaneous sensation, areas of the palm and dorsal surface of the hand and nerves in the hand were observed. In eight sites of the glabrous skin and two on hairy skin on both hands cutaneous perceptual threshold was tested. 70 subjects aged between 20-88 years were used. Three tests were used von Frey thresholds, two point stimulations and Texture discrimination.
The threshold for cutaneous sensation increased significantly with age (P<0.001); von Frey thresholds for 20s were 0.04g and 0.016 in 80s across 10 sites. Differences were observed between hands for older females (p=0.044) not for males. Differences were observed according to the site of the hand tested, cutaneous changes were smaller on the fingers as compared to the palms. With increasing age there was decline in two point discrimination however was observed between sex, handeness and skin mechanics.
Two point stimulation, showed increased threshold with age (P=0.046), lowest thresholds were observed in 20s (5mm) and in 60s had highest (7mm). Each area had increased loss of sensitivity with age. No significant increase was observed for threshold for texture discrimination, there was a stable surge until the 80s. From the 20s (0.27mm) up to 70s (0.44mm) an increase was observed however in the 80s (31mm) there was a decrease.No difference was seen between various sites of the hand, non-dominant/dominant and sexes. After the age of 60 males and 70 for females, there was an accelerated decline in cutaneous sensation (Bowden and McNutty, 2013).
The obvious sign of aging is the decrease in muscle mass, function and increase fatigability in old age, it is suggested that there is a decline in myosin heavy chain synthesis with sacropenia. The synthesis rate decline highlights functional consequences of a weakened remodelling process. Muscle mass is regulated by muscle protein synthesis and breakdown, a lower synthesis rate compared to breakdown may result in diminished muscle mass. The loss of lean mass and decreased performance highlights metabolic changes that occur with sacorpenia. Myosin heavy chain is involved in the hydrolysis ATP to ADP, it is vital for muscle contractile functions.
Myosin heavy chain synthesis rate was measured instantaneously with rates of mixed muscle and sarcoplasmic proteins. In young to middle aged people a decline in synthesis rate of mixed muscle protein (p<0.01) and whole body protein (p<0.01) was observed, a further alteration was not noted with progressing age. Myosin heavy chain synthesis rate declined with age (p<0.01), the deterioration was evident in young through to very old. The decline of myosin heavy chain synthesis with age was observed from young through old. No age related changed were observed in sarcoplasmic protein synthesis. Measures of muscle strength (P<0.05) correlated with the rate of myosin heavy chain synthesis. Elderly had significantly lower strength measurements. Middle age (P<0.05) and old subjects (P<0.01) had lower whole body and mixed muscle protein synthesis rates than young subjects, even when the values are corrected for fat free mass or body weight (Balagopal et al, 1997).
Age related bone loss in men and women is the result of decline in hormones such as oestrogen and estradiol. It is understood that tissue growth and metabolism is regulated by insulin like growth factor (IGF) and binding proteins. The growth endorsing regulatory system IGF is growth hormone dependant and independent, it is a complex system. Six IGF binding proteins included in the IGF system as well as IGF-I and IGF-II. Osteoblastic diversity and bone development is improved by IGF-I and IGF-II which are abundant growth factors in bone tissue.
These factors upsurge the production of type1 collagen fibres and apposition rates of bone matrix, degradation of bone collagen is reduced. The effects of IGF-I and IGF-II maybe potentiated or repressed by IGFBPs although they are anabolic. Metabolic activity and clearance of IGF-I and IGF-II is regulated by IGFBPs, IGF independent action that can inhibit or stimulate cellular function by four IGFBPs. Osteoblasts are able to synthesize all six IGFBPs.
The role of serum levels of IGF-I and IGF-II, and IGFBP-1, 2 and 3 on bone mineral density was examined on various skeletal sites, in an age stratified random sample of 344 males and females. IGF-I and IGFBP-3 levels declined with increasing age in males and females, IGFBP-2 levels increased with age. Associations between IGFBP-2 and lateral spine BMD were not observed however with age adjustment IGFBPs with BMD were significant for males and females. The most significant independent predictor of bone mineral density was IGFBP-2 amongst all the ones studies in males and females (Amin et al, 2004)
Alterations in brain tissue and grey matter can help in diagnosis and treatment of Alzheimer disease, multiple sclerosis, schizophrenia etc. Aging has a profound effect on the brain. Grey and white matter contrast functionally and anatomically as well as having different patterns in brain development. MRI imaging is an effective way of investigating brain morphometric in vivo enabling the production of accurate and reliable information. Investigations have looked at age specific effects on various brain regions findings from the analysis have revealed brain tissue loss with age may vary between the various brain regions and hemispheres.
55 healthy volunteers aged between 20 and 86 were separated into two groups (20-49 and 50-86). Current or existing neuropsychiatric illness and substance abuse was excluded by a neurologist in an interview. Evaluations were done using MRI imaging and 50 separate brain volume images were under review. Results revealed reduction in grey matter with increasing age in males and females, deterioration begins at 20 years of age. Increasing age results in significant loss grey matter (4.9%). Increase in white matter is observed however accelerated decline in instigated around age of 40. A significant difference was observed between the two age groups (p=0.38), older people had significantly lower grey and white matter in the intracranial space as compared to younger ones (p<0.0001 and P=0.02). No significant differences were observed between males and females. Alterations in grey and white matter contribute to the atrophy of the brain. Throughout life, there is a constant loss of grey matter (Robert et al, 2002).
Discussion Elevations of the biomarkers were helpful indicators in the development of disease and understanding age related changes in the body. Elevated CRP levels were observed in majority of patients regardless of cardiovascular risk. However, CRP is a non-specific inflammatory marker and elevations could be due to other biological processes. Amplified CRP levels is not the foremost casual factor for cardiovascular disease the levels (Halcox et al, 2014). Abdominal fat accumulation were seen to be an independent factor of age related change in plasma lipid levels and insulin sensitivity. Results revealed that abdominal visceral fat increased with age. The findings were consistent because age had an effect on insulin sensitivity (DeNino et al, 2001).
Cutaneous sensation deteriorates with age, differences are also observed between sexes and hands. The extent of deterioration may have been undervalued this could have affected the results. There was difficulty in the assessment of skin hydration and the role of skin mechanics was inadequate. The associations between fine motor control and cutaneous sensation could be an area to study (Bowden and McNutty, 2013).
There was an age related decline in myosin heavy chain synthesis but average synthesis rates were measured. Muscle mass decline was associated with incapacity for the skeletal muscle to remodel. Insulin resistance could have a role in declined myosin heavy chain synthesis due to its chronic effect. Differences between males and females were established. Data was normalized to whole body protein turnover to fat free mass because normalizations to body composition were fraught. Future research should investigate whether age related decline in synthesis rate could be retreated by use of replacement hormones (Balagopal et al, 1997).
Age related bone loss was evident in males and females; the predictor for bone density was IGFBP-2. The results were not generalizable to the cognitively impaired. Correlations were observed between serum levels of IGF-1 and tissue levels within bone but there was limited understanding of the action at a local level. Future research should be directed towards investigating the complex role of the IGF system influence on bone metabolism (Amin et al, 2004).
The exclusion of subjects with neurological conditions made comparisons difficult between young and old. An estimation of brain tissue loss can be done in healthy living subjects. The findings from the study were consistent with longitudinal studies (Robert et al, 2002).
References Anti aging team . (2014). Biomarkers of aging . Available: http://www.anti-aging-guide.com/62biomakers.php. Last accessed 28th Feb 2015.
Gertrude H. Sergievsky. (2004). Biomarkers: Potential Uses and Limitations. Taub Institute for Research on Alzheimer’s Disease and the Aging Brain,. 1 (2), 182-188.
Jacob Abraham, Catherine Y. Campbell, Aamir Cheema, Ty J. Gluckman, Roger S. Blumenthal, Peter Danyi,. (2007). C-Reactive Protein in Cardiovascular Risk Assessment:. The Journal of Cardiometabolic Syndrome. 2 (2), 119-123.
Jocelyn L. Bowden

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