Evolutionary study of thymic involution using comparative proteome data in mice

Design Brief

M. A. Japati and C. E. Liaw

Sinarmas World Academy, South Tangerang, Banten, Indonesia

Reviewed on 8 May 2021; Accepted on 28 June 2021; Published on 25 October 2021
With help from the 2021 BioTreks Production Team.

AbstractPDFCopyright
The atrophy of the thymus is found in most vertebrates, including humans. This process starts shrinking the thymus as early as the age of 1. This process is linked to the decline of free radical (molecules with unpaired electrons) defenses. Atrophy of the thymus happens more rapidly compared to other tissues and is considered as a sequence of stomal catalase deficiency in the context of a highly metabolic environment designed to support the demands of T-cell proliferation. Research led by Howard Petrie, senior study author of Scripps Research Institute, shows that thymic atrophy represents the process of accumulated cellular damage resulting from lifelong exposure to the oxidative byproducts of aerobic metabolism. Our team plans to analyse the mouse thymus genome set at different ages to see which distinct genomes are expressed differently and can lead us to potentially finding the specific cause of thymic involution. Through this,  we aim to expand the research on the phenomenon and provide more research focused on how to stop thymic involution.

Keywords: Thymic involution, atrophy, mice, humans, naive T-Cells, ageing

Authors are listed in alphabetical order. mentored the group. Please direct all correspondence to .

This is an Open Access article, which was copyrighted by the authors and published by BioTreks in 2021. It is distributed under the terms of the Creative Commons Attribution License, which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background 

Introduction to the problem

Our immune system is crucial to protect us from harmful substances that enter our bodies. Without it, the chances of death increase and a decrease in the standard of living occurs. The thymus is a small, irregularly shaped gland near the mediastinum. It is part of the lymphatic and endocrine systems. The thymus is responsible for producing progenitor cells that mature to become thymus-derived cells (T-Lymphocytes). The process starts when our hematopoietic stem cells produce lymphoid progenitors (thymocytes) which are then transferred to the thymus to undergo the maturation process to antigen-independent T-cells with their unique markers including TCR, CD3, or CD4. In the thymus, t-cell precursors enter the subcapsular cortical area to the thymic stroma and undergo a process of proliferation and start differentiating which allows them to mature into distinct types of T-cells in our body. T-cells are responsible for killing infected host cells, activating immune cells, and regulating immune responses to pathogens. Through time, our body goes through a process called thymic atrophy which results in the shrinking of the thymus. By the age of 65, the thymus becomes almost non-existent in humans which makes vaccines less effective, creating a higher risk of contracting infectious diseases and in fact, scientists have found a correlation between the higher risk of cancer and thymic atrophy (Palmer et al., 2018).

The atrophy of the thymus is found in most vertebrates, including humans. This process can be linked to the decline of free radical (molecules with unpaired electrons) defenses. Atrophy of the thymus happens more rapidly compared to other tissues and is considered as a sequence of stomal catalase deficiency in the context of a highly metabolic environment designed to support the demands of T-cell proliferation. Research led by Howard Petrie, senior study author of Scripps Research Institute, shows that thymic atrophy represents the process of accumulated cellular damage resulting from lifelong exposure to the oxidative byproducts of aerobic metabolism. 

The cause of thymic involution

Thymic involution is closely related to the loss of thymic epithelial space (TES) which is where thymopoiesis, the process where thymocytes turn into mature T-cells occurs, but there is, unfortunately, no universally accepted explanation as to why our thymus shrinks. One possibility suggests that this process may be an effect of antagonistic pleiotropy which is when we select genes whose benefits early in life outweigh costs late in life. Though the thymus is in charge of letting our body know which organisms are harmless and which are not, this can eventually lead to an easy target for any microbial parasites which can fool the immune system into thinking that they are harmless.

A new study has also identified a specific cell that may be involved in thymic involution (Sheridan et al., 2017). The research has found a cell named stromal progenitor which readily changes into fat cells and since the thymus increasingly turns into a mass of fat cells over time, there’s a possibility that the stromal progenitor is what causes the involution.

Other causes of thymic involution that have been suggested include physiological stress such as pregnancies, fighting off infections, proinflammatory cytokines, hormones, steroids as well as treatments for cancerous cells. This results in compromised host immunity and decreased levels of T-lymphocyte output. 

The process of thymic involution

When the thymus gland undergoes involution, thymic architecture becomes disorganized which results in reduced numbers of naive T-lymphocytes output. It can be observed using the Thymic Epithelial Space (TES) or the perivascular space (PVS). In humans specifically, the  TES decreases at the start of one year old by 3% until middle-age  (Steinmann et al., 1985). Other characteristics often observed during thymic involution include the declining of TEC-associated markers such as the major histocompatibility complex class-II (MHC-II) and also the altered rations of mTECs (medullary thymic epithelial cells) and cTECs (cortical thymic epithelial cells) which are both involved in different stages of thymocyte development. During thymus involution, fat cells build up to cover or replace lost thymic tissue. 

Impacts of thymic involution

Thymic involution results in a reduced ability to produce new T-lymphocytes. The decline of immune systems and the production of T-lymphocytes are closely related to the dramatic increase in risks of cancer and infectious disease. Datasets analysed and published in PNAS show that the declining T cell production leads to an increase in disease incidence and assumes that immunogenic cells arise with the same probability at any age (Smith et al., 2019). This also makes vaccine efficacy rates lower in those aged 65 and above compared to those aged 30 to 40. 

From an economical point of view, with more diseases and a sick population, people with a low financial status will not be able to look for treatments and therefore, increase the death rate of a country. The sick population is also directly related to the decrease in a country’s productivity as they will not be able to work and contribute to the country’s economy. The impacts of thymic involution go further than just causing a person to be more susceptible to diseases, it impacts our society economically and socially.

Exploring present research

A study published in the Journal of Immunology (Sutherland et al., 2005) described the effects of sex steroids and thymic involution through analysing prostate cancer patients who routinely underwent sex steroids ablation therapy called the LHRH-A treatment. They’ve found that after 4 months of treatment using the serum testosterone concentration, there is an increase in the total lymphocytes, T-cells (mostly CD4+), and NK cells. More detailed observations found that there is a drastic increase in the numbers of naive CD4+ T-cells with a p-value less than 0.05. This research shows evidence of sex steroids in the development of the thymus and a potential impact as the cause of thymic involutions.

Our approach and idea

The problem we are trying to solve is thymic involution in humans. We plan to do this by researching mice genes and their effect on thymic atrophy by using  “abnormal” T-Test p-values defined by p-values below 0.05. This data report will help support this process by identifying potential genes of interest that may hold significance in the research of thymic involution. 

Materials & Methods

Materials

We started this research by exploring the data sets available on the internet that are valuable and relevant to our research. Data sets from the AGEMAP data spreadsheet on thymus were collected and we chose this because of its completeness with mice ranging from different ages, the changes in every cell and it is specified on the thymus. Other than the datasets, we mainly use google sheets to create diagrams and perform our unigene analysis.

Basic T-tests

T-Tests were conducted on females months 1 and 6, females months 16 and 24, males months 1 and 6, and males months 16 and 24. A T-test value below 0.05 was considered as “significant” unigene type. T-tests are a type of inferential statistics which are used to determine if there is a significant difference between the mean of the 2 groups. It is a test often used for hypothesis testing. Through this, we eliminate cell changes expressed which are insignificant or that show no correlation to the possible involution of the thymus. The unigenes for each data set were then collected and a histogram was created to identify whether or not the unigene had recurred throughout the 4 data sets on t-Tests.

With these collected genes that fit the standard criteria for p-value, we monitored the changes in the unigene expression and further identified which of these unigenes had major variations.

Results

Out of the 1688 unigene types that fit the criteria of a T-test with the p-value below 0.05 (the standard) which means that there is a 95% chance that this occurrence is not caused by chance. However, this also means that there is a 5% chance that the specific genes aren’t relevant in the contribution of thymic involution. 

From all 4 data sets, 7 unigenes recurred thrice (0.4%) and 154 unigenes recurred twice (9.1%). No unigenes recurred four times. 

Key: Those in table recurred twice and bold recurred thrice.

This table only contains mouse unigenes and their corresponding gene names for those that have recurred twice. Full annotated data set can be downloaded here: 

https://drive.google.com/file/d/1izMlIEmv76y7myrEBv-MEap8JcDdf18z/view?usp=sharinghttps://drive.google.com/file/d/1izMlIEmv76y7myrEBv-MEap8JcDdf18z/view?usp=sharing 

 Table 1: Shows mouse unigenes and their corresponding gene names for those that have recurred twice

NoMouse UnigeneGene NameNoMouse UnigeneGene NameNoMouse UnigeneGene Name
1Mm.102278Scamp555Mm.249371500001M20Rik109Mm.30155Atp6v0c
2Mm.10665Tex29256Mm.252405Mgl1110Mm.314113Elovl6
3Mm.10946Ccdc6057Mm.25259Npr3111Mm.3154302610524G07Rik
4Mm.11934Zar158Mm.2534031810073N04Rik112Mm.320095830406J20Rik
5Mm.12459Ankrd1059Mm.255649Eif4enif1113Mm.320125730507H05Rik
6Mm.1262Cyp17a160Mm.256081110057K04Rik114Mm.320469Pan3
7Mm.131118Mm.13111861Mm.258969Unknown115Mm.321227Trim21
8Mm.133851Mrpl1262Mm.259197Rbm5116Mm.323997Rbpms
9Mm.1363Trh63Mm.25921AU015558117Mm.3283604631422O05Rik
10Mm.137134Tiam264Mm.259969BC003322118Mm.330803Gm1305
11Mm.139214Unknown65Mm.260103D030051D21119Mm.333594BC057627
12Mm.146984Psmf166Mm.260712A930025J12Rik120Mm.333597Ap2s1
13Mm.155583Il1rl267Mm.260786Rad54l121Mm.335866C330019G07Rik
14Mm.157442Hgd68Mm.264684632434I11Rik122Mm.339755Fliih
15Mm.158361Phactr469Mm.272551Cnot7123Mm.34108Rragd
16Mm.159681AW06020770Mm.272930Cog4124Mm.3417476330442E10Rik
17Mm.16925Thy2871Mm.2744922410002F23Rik125Mm.3440711700001M19Rik
18Mm.169673AI64203672Mm.274772010100O12Rik126Mm.349603B130036O03
19Mm.172634C7748873Mm.275583AA409802127Mm.353059Unknown
20Mm.182671Mm.18267174Mm.2755869430095K15Rik128Mm.353936Syncrip
21Mm.182737Smc1l275Mm.276389Hmox1129Mm.36885Leng4
22Mm.1838Vamp876Mm.279782Prdx5130Mm.371592Ubb
23Mm.193099Fn177Mm.280544Ddx52131Mm.371613D4Wsu43e
24Mm.1962905830411E10Rik78Mm.280559BC022145132Mm.375811700013G20Rik
25Mm.197518Laptm4b79Mm.28209Perp133Mm.379060C330027G06Rik
26Mm.201081200011D03Rik80Mm.2834062810433K01Rik134Mm.3792211700040I03Rik
27Mm.206206AU04032081Mm.284372700038L12Rik135Mm.379369Kctd3
28Mm.209300Pcyt1a82Mm.284770Lypla3136Mm.38055Es10
29Mm.20973Lgals783Mm.28484Tspan3137Mm.381181AI462493
30Mm.209941Mtl584Mm.28560Lyar138Mm.3815Sdc4
31Mm.2101979530026P05Rik85Mm.286061Tars139Mm.381662610203C20Rik
32Mm.210447LOC11460186Mm.288693D14Wsu89e140Mm.382329Mm.382329
33Mm.211654MGC635787Mm.288728Srpk2141Mm.383308BC038943
34Mm.212066Abi289Mm.288788Rev3l142Mm.384234Ube2i
35Mm.21414Ccs90Mm.289241810008A14Rik143Mm.386783Ikbke
36Mm.21482Clns1a91Mm.289583C920003I06144Mm.3996Chuk
37Mm.219684AA93992792Mm.289645Gtl2145Mm.41220Btbd9
38Mm.22239Ap1m293Mm.289914D1Ertd396e146Mm.417155330440M15Rik
39Mm.223508Glis394Mm.290026Cltb147Mm.42064432417N03Rik
40Mm.2279831110059P08Rik95Mm.290414Mfn1148Mm.42196Uhrf1
41Mm.22847Cobl96Mm.29071Nek9149Mm.43278Olfm1
42Mm.229532Ednrb97Mm.290876Sod2150Mm.43636Zfp289
43Mm.230853D330050I23Rik98Mm.291005Mm.291005151Mm.44202Yif1
44Mm.233799Igfbp499Mm.291192Tmem59152Mm.490410610006K04Rik
45Mm.2348231200015K23Rik100Mm.29133Bub1b153Mm.523125330438E18Rik
46Mm.23951Gmppa101Mm.294783Bclaf1154Mm.59152410012M04Rik
47Mm.2414895830416A07Rik102Mm.295767Unknown155Mm.60224Mphosph9
48Mm.245210Lrig1103Mm.297109Nf2156Mm.6390Grcc2f
49Mm.245340Atrnl1104Mm.297371Pou3f1157Mm.6710Rock1
50Mm.2462Psmc2105Mm.29824Psa158Mm.795Csf1
51Mm.2462418430410A17Rik106Mm.29845Suclg1159Mm.833Ap1s1
52Mm.246952Xrcc5107Mm.29997Rcn3160Mm.875134932417I16Rik
53Mm.247882310047M15Rik108Mm.30084Np15161Mm.9935Arhgap28
54Mm.2487794932434G09Rik
Figure 1: Scatter Plot Histogram of Recurrence of Mice Unigenes.

Further Reading: Vamp8; 5830416A07Rik; 8430410A17Rik; Ap2s1; 5330440M15Rik; Uhrf1; Yif1

Gene Analysis (based on available data on immune systems, ageing and mortality) 

Contrary to all the genes found on an online database, collected by Mouse Genome Informatics, that contained phenotypes and genetic marker data for mortality/ageing and the immune system and recurred thrice in our data collection (gene names highlighted in green), gene type Vamp8, gene type 8430410A17Rik (also known as Hmces) and gene type Ap2s1 were related to premature death, postnatal lethality and/or embryonic lethality (both complete and incomplete).

Vamp8 genes on the immune system are seen to have abnormal mast cells. There is also a significant reduction in beta-hexosaminidase release and serotonin secretion Cathepsin D secretion. Vamp8 genes in mice are also linked with kidney atrophy and increased blood osmolality as well as premature death. No mice in the Mouse Genome Informatics study survived more than 10 months. The 8430410A17Rik gene (also known as Hmces gene) is linked to abnormal epigenetic regulation of gene expression. It also comes with abnormal DNA methylation. Mice with this gene type come with low postnatal weight. It also comes with increased chances of embryonic lethality. The Ap2s1 gene was linked to increasing embryonic lethality. The Ap2s1 gene was also linked to gastrulation failure. It also comes with a decreased embryo size. This gene also results in an enlarged heart in Ap2s1 gene mice.  

Among the genes with gene ontology info given by the AGEMAP database, Vamp8, Ap2s1, Uhrf1, and Yif1 came with gene ontology information. The cellular component of the Vamp8 gene is the early endosome. Its cellular component is integral to the membrane. Its molecular function is protein binding and its biological process is vesicle-mediated transport. The cellular component of the Ap2s1 gene is an Assembly polypeptide-2 (AP-2) adaptor complex. Its biological process and molecular function are so far unknown. The cellular component of the Uhrf1 gene is having a nucleus. Its molecular function is DNA binding and transporter activity. Its biological process is cell proliferation and regulation of transcription and transport. The cellular component of the Yif1 gene is that this gene is integral to the membrane. 

Conclusion

To conclude our study, there were 1688 unigenes with a t-test p-value below 0.05. Among the 1688 genes that spanned across female mouse 1 and 6 months, female mouse 16 and 24 months, male mouse 1 and 6 months, and male mouse 16 and 24 months, 154 unigenes recurred twice and 7 unigenes recurred thrice. No unigenes recurred four times (refer to the scatter plot histogram, Figure 1). Data analysis was done on the unigenes that recurred thrice, which included genomes Vamp8, 5830416A07Rik, 8430410A17Rik, Ap2s1, 5330440M15Rik, Uhrf1, and Yif1. Further findings done on these unigenes/genomes showed that these genomes are also linked with premature death and/or embryonic lethality. However, no clear statement was given by the databases as to how these factors affect the rate of thymic involution. 

Discussions

Limitations

As mentioned above, our report is strictly based on the AGEMAP unigene data in mice presented by professors of Stanford University and scientists from the National Institute on Aging at the National Institutes of Health, Baltimore.  Although experiments have been done through rodents such as mice, it does not imply that these gene changes apply to humans as human genes are greatly more complex and hard to understand. 

Furthermore, experiments in mice were under scrutiny for a few years due to repeated failures of valid data and conclusions. However, in recent years, studies have shown that these data failures are not mainly caused by the mice themselves, but by improper data analysis (Justice & Dhillon, 2016). Therefore, using the unigenes of mice may affect the results, though there is a low chance of this happening. 

The purpose of the t-test is to find changes with a p-value less than 0.05 which indicates that there is at least a 95% chance of the changes found being relevant and significant but still a 5% chance of it being irrelevant and completely unrelated. Therefore, this unigene expression is not completely relevant to finding the true genes that may reduce/increase the rate of thymic involution, though it still has a high chance of it being a potential gene that affects the rate of thymic involution. 

In addition, the data sets we analysed consist of datasets of mice ranging from 1 month – 24 months. Although this could provide a conclusion, the sample size is still too small to provide a reliable study. To improve this for the next experiment, the data sample size should be increased to improve the reliability of this study. 

Benefits  

The datasets that were collected were from a reliable website and are collected by professors of Stanford University and scientists from the National Institute on Aging National Institutes of Health Baltimore. It is from a well-known and reliable source which improves the reliability of this report. 

Our report provides possible genes that contribute to the process of thymic involution which will, in the future, help further experiments in streamlining which unigenes should they look for. This will direct the attention of the scientific community into focusing on these genes and help us understand better the process of thymic involution and the causes of this phenomenon affecting most vertebrates.

Next Steps  

The unigenes and genomes of mice that have the potential due to its high recurrence (displays commonality between different mice genders and ages) of unigenes/genomes with T-Test p-values below 0.05 show gene “abnormality”. These unigenes and their respective genomes can be used in further research or scientific experiments to identify whether this “abnormality” affects thymic involution or the rate of the atrophy of the thymus.  

Potential experiments using synthetic biology in ways such as inserting this gene into a bacteria to see the way it affects microorganisms can be used to further explore what the specific gene does to our biological system. 

Author Contributions

 C.E.L. Contributed to data collection of potential gene mutations/variations in the thymus of mice. Contributed to the abstract, background and contributed to the methodology. M.A.J. Contributed to data tabulation of potential gene mutations/variations in the thymus of mice. Contributed to the abstract, background and contributed to the methodology. 

Acknowledgments

We would like to thank JenAge and CMGM Stanford Edu for providing AgeMap statistics on gene expressions of mice by unigene through spreadsheets which have enabled us to interpret our data in charts and graphs to search for standard-deviation abnormalities. 

We would like to thank our mentors Christopher Joseph Hayden and Patrick Holec, who have made this research possible by helping us understand methods of theoretically and hypothetically analysing data as well as complex concepts such as the functions of the thymus, immunity, and naive T-cells. 

References 

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Steinmann, G. G., Klaus, B., & MüllerHermelink, H. K. (1985). The involution of the ageing human thymic epithelium is independent of puberty: a morphometric study. Scandinavian Journal of Immunology, 22(5), 563-575. https://doi.org/10.1111/j.1365-3083.1985.tb01916.x

Justice, M. J., & Dhillon, P. (2016). Using the mouse to model human disease: Increasing validity and reproducibility, 9(2), 101–103.  https://doi.org/10.1242/dmm.024547

Thomas, R., & Su, D. M. (2019). Age-related thymic atrophy: Mechanisms and outcomes. Thymus. https://doi.org/10.5772/intechopen.86412

Palmer, S., Albergante, L., Blackburn, C. C., & Newman, T. J. (2018). Thymic involution and rising disease incidence with age. Proceedings of the National Academy of Sciences, 115(8), 1883-1888. https://doi.org/10.1073/pnas.1714478115

Bagalà, N. (2018, January 5). Understanding why the thymus shrinks with age. Lifespan.io. https://www.lifespan.io/news/understanding-why-the-thymus/

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