Metabolic syndrome
        With the successful conquest of many of the old infectious diseases in the world, non-communicable diseases (NCD) have become the major cause of morbidity and mortality not only in the developed world but also in the underdeveloped countries. Among all these NCD, metabolic syndrome had been the real scourge globally. We interviewed Professor Ji Gang, a Changjiang scholar, and conducted related research to further understand metabolic syndrome and its series of hazards. Metabolic syndrome is a pathological state in which various metabolic disorders aggregate within an individual, mainly manifested as insulin resistance, non-alcoholic fatty liver disease, obesity-type depression, etc. The metabolic syndrome originates primarily from an imbalance of calorie intake and energy expenditure.[1] No single remedy can be prescribed for its eradication or even curtailment.         We do not have global data on metabolic syndrome—which is harder to diagnose, but since MetS is about three times more common than diabetes, the global prevalence can be estimated to be about one quarter of the world population. In other words, over a billion people in the world are now affected with metabolic syndrome.[1] More importantly, MetS-related disorders account for two thirds of the NCDs deaths.
Diagnosis of metabolic syndrome
        Currently, diagnostic methods for metabolic syndrome are very limited. Most of them diagnose metabolic syndrome by measuring how obese people are. Regarded as the initiating factor of metabolic syndrome, obesity can greatly increase risks of metabolic syndrome and many people often equate obesity and metabolic syndrome. By measuring the waist-to-hip ratio, calculating BMI and other methods to determine whether a person is obese, and then doctors will infer whether the individual's metabolism is healthy. Obesity, however, is not always synonymous with MetS. There are so-called metabolically healthy obese (MHO) individuals who have high level of insulin sensitivity and do not have hypertension and hyperlipidemia and other features of MetS. Epidemiological survey suggests that MHO may account for a significant percentage of obese population [3]. In the process of communicating with Professor Ji Gang, we also learned that many non-obese people were diagnosed with metabolic syndrome in the clinical diagnosis. Therefore, it is unscientific to judge whether a person is metabolically healthy by obesity alone.
        At present, the diagnosis of metabolic syndrome is very complicated—patients need to go to hospital for blood tests regularly, which seems unpratical. In addition, according to NCEP ATP-III standard, patients can’t be diagnosed until they show symptoms like high blood glucose, hypertension or lipid disorders. Therefore, the early diagnosis of metabolic syndrome is very difficult, and many patients with metabolic syndrome often miss the best time for intervention. Patients are likely to further suffer from NAFLD, obesity depression, cardiovascular and cerebrovascular diseases, type 2 diabetes or even novel coronavirus pneumonia, and the final outcome is mostly lifelong medication control, causing huge physical and mental pressure and economic burden on society. Therefore, it is urgent to develop and popularize the early diagnosis technology of metabolic syndrome!
（Click here to learn more about our background research process）
Our solution：FAT test

General

        To compensate for the current deficiencies in the diagnosis of metabolic syndrome, we designed the FAT test (fast amplified trace-RNA test), which will enable home self-test. We also dedicate to make sure this test inexpensive, accurate, sensitive and rapid, and finally can be used as a new method for early diagnosis of metabolic syndrome.
        Our kit, utilizes the double-strand (ds) DNA cleavage activity of DSN enzymes to achieve linear amplification and trans-acting DNase activities of Cas12a to achieve exponential amplification. We pioneered the DIRENJIE system, which uses specially designed circular probes to connect the reaction system in series and uses colloidal gold particles (AuNPs) to convert specific microRNA content in serum into visible light signals for disease diagnosis.
        Finally, the results are analyzed through the APP upload and an easy-to-understand testing report is provided to achieve early diagnosis of diseases, home testing, and remote medical care, achieving testing, learning, diagnosis, and treatment at any time.

Biomarkers

Micro RNAs

        MicroRNAs (miRNAs) are a large family of post-transcriptional regulators of gene expression that are approximately 21 nucleotides in length and control many developmental and cellular processes in eukaryotic organisms[4]. miRNAs normally function as negative regulators of mRNA expression by binding complementary sequences in the 3'-UTR of target mRNAs and causing translational repression and/or target degradation[5], which are stable in serum and suitable as biomarkers.

Our biomarkers

        We ultimately selected a set of microRNA biomarkers associated with the metabolic syndrome rather than just a single one. This is because detecting a set of microRNAs gives us a more comprehensive report on the risk of multiple metabolic syndrome-related diseases and improves the predictive power of the test. The three final targets were hsa-miR-34a-5p, has-miR-155, and has-miR-1281.
（Click here to view the biomarker choice process）

Design of detection system

        We have created a diagnostic system for metabolic syndrome detection that uses microRNAs in circulating blood as biomarkers. We designed a single-stranded circular DNA-RNA chimeric molecule as the probe to identify the target microRNA. If the microRNA target is present, it hybridizes to the probe. And duplex-specific nuclease (DSN) will cleave DNA in DNA-RNA hybrid duplexes, re-releasing the microRNA to achieve linear signal amplification. At the same time, rest probe sequences (the RNA strand in thesingle-stranded circular DNA-RNA chimeric probe) will fold into sgRNA. After the completion of the first reaction, the reaction liquid enters the second reaction. SgRNA will form a ternary complex with Cas12a and assistant DNA, activating Cas12a's trans cleavage activity. Actived Cas12a will cleave single strand DNA in the probes to release new sgRNA and achieve exponential amplification of the signal.There are also aggregated colloidal gold particles (aggregated through ssDNA connections) in the reaction solution, which can also be cleaved by activated Cas12a to release the visible signal. （Click to view the detailed design）

Hardware

        Based on our detection system, we have designed a hardware facility.
The hardware part mainly consists of two parts, which are disposable peripheral blood collection needle and container. The container is divided into two parts A and B, and part A is the container with both upper and lower openings like a funnel. Part B is a cylindrical container with an open upper end.

The overall container is shown as follows:

Part A is as follows:

Part B is as follows:

        Part A has stoppers at both upper and lower openings. After blood collection with a disposable peripheral blood collection needle, drop the fingertip blood into container A and fasten the stopper on the upper part of A. The lower part of Part A is embedded with a honeycomb like structure, on which DSN enzymes attach. After the sample is dropped into container A, the first step of the reaction occurs to complete the identification of microRNA and linear amplification of the signal.
        After the first reaction is complete, the tester can use the spiral part of the upper part of A to separate the A and B parts, open the plug of the lower part of A, and connect the A device to the B part again. The liquid in A flows into part B for the next step of reaction.
        After the second reaction is complete, the tester can take photos and upload them with our APP to obtain the results and conduct preliminary analysis on the tester.
（Click to view the detailed design of the hardware）

Software

        Our kit determines whether a subject has metabolic syndrome by comparing the color difference between the reagent color added to the subject's blood and its initial color, and different range of color difference values will give different diagnosis and treatment recommendations. We built an APP that can identify color differences by analyzing https://static.igem.wiki/teams/4778/wiki. To a great extent, it avoids the subjectivity of the subject's interpretation of the result, improves the accuracy and reliability of the comparison results, and can define the color value and compare the color difference through specific numerical values. By using our kit and APP together, subjects can quickly and easily test themselves at home to see if they have metabolic syndrome or are at risk for it, and decide whether to seek medical treatment based on the APP's recommendations.
The user interfaces are shown below:
（Click to view the software development process）
Inspiration

Why metabolic syndrome diagnosis？

        As a team from the medical school, we’ve noticed that many people postpone going to hospital to find that they have serious diseases due to the lack of the consciousness of regular physical examinations. Crippling healthcare expenditure and tedious medical stream make many people prefer suffering the pain until the last minute, which leads to the progression of diseases. Wishing to improve this painful situation, we, as a team, did a lot of brainstormings and set up the same goal which is to create an instrumental diagnostic tool to help people previously and conveniently recognize whether they have diseases.
        Bearing this goal in mind, we’ve done a lot of human practices between students, workers, doctors, and teachers in our school to find out what diseases need to be tested at the preliminary stage for great physical state improvement and which diseases or health issues are crucial for human health. After several rounds of brainstorming, coming up with many good ideas, we finally agreed that metabolic syndrome seems to be a suitable target which not only has great potential to progress but is also relevant to almost everyone. Without early intervention, there is a high risk of causing many other potential diseases (such as non-alcoholic fatty liver disease(NAFLD), obesity depression, cardiovascular and cerebrovascular diseases, type 2 diabetes and even COVID-19. Today, the prevalence of metabolic syndrome and related diseases continues to increase steadily, becoming the "invisible killer" affecting the quality of life. Thus, we finally decided to develop a home self-test kit for metabolic syndrome.

During the iGEM 2023 competition we wanted to：

(1).   

Work on the realization of our concept and viable product.

(2).   

Study how our project would be implemented in real life by integrating input from stakeholders and investors in our business model.

(3).   

Integrate the help and advice from numerous experts and collaborators to complete our project.

(4).   

Provide a reliable method to monitor the metabolic health for everyone, especially the high-risk groups, such as overweight and sub-healthy people around the world, so that they can realize the measurement at home.

Future outlook
(1).   

In order to validate both our method and our biomarker selection for metabolic syndrome diagnosis, testing our diagnostic tool in patients' blood samples is essential. Complementary experiments such as RNA-seq to analyze the expression profile of hsa-miR-34a-5p, has-miR-155, has-miR-1281 and other micro RNAs will determine if these novel biomarkers can be readily used for detection. Cross-validating these results with the ones obtained from patients tested with the DIRENJIE system will prove the sensitivity and specificity of our method. However, a large number of patients is necessary to obtain high statistical power data, indicating that clinical trials are in order.

(2).   

We hope that in the next step, we can use colloidal gold particles to achieve stable visual signal output. So far, we have achieved the synthesis of colloidal gold aggregates, and we have developed an APP that can recognize the color change of colloidal gold solutions. Please refer to the results page and the software page for more information about the efforts we have made to achieve the goal. In the future, we hope to achieve the stability of colloidal gold into our detection system, instead of fluorescent signals, to achieve a more convenient home self-test.

(3).   

In addition to the detection of metabolic syndrome, we also hope that our detection system can be applied to detect other diseases. We hope to develop a more universal APP and accompanying test kit to detect other disease-related microRNAs, such as early detection of cancer, pathogen detection, and so on.

(4).   

In the future, we hope to cooperate with hospitals and companies to realize telemedicine based on our APP and test kits, and provide patients with more scientific and accurate personalized advice. Please refer to the Entrepreneurship page for more information about the efforts we have made to achieve the goal.

Reference
[1].   

Kassi E, Pervanidou P, Kaltsas G, Chrousos G. Metabolic syndrome: definitions and controversies. BMC Med. 2011 May 5;9:48.

[2].   

Wang Y, Mi J, Shan XY, Wang QJ, Ge KY. Is China facing an obesity epidemic and the consequences? The trends in obesity and chronic disease in China. Int J Obes (Lond). 2007 Jan;31(1):177-88.

[3].   

Wildman RP, Muntner P, Reynolds K, McGinn AP, Rajpathak S, Wylie-Rosett J, Sowers MR. The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering: prevalence and correlates of 2 phenotypes among the US population (NHANES 1999-2004). Arch Intern Med. 2008 Aug 11;168(15):1617-24.

[4].   

Krol J, Loedige I, Filipowicz W. The widespread regulation of microRNA biogenesis, function and decay. Nat Rev Genet. 2010 Sep;11(9):597-610.

[5].   

[5]Towler BP, Jones CI, Newbury SF. Mechanisms of regulation of mature miRNAs. Biochem Soc Trans. 2015 Dec;43(6):1208-14.

[6].   

[6]Natoli R, Fernando N. MicroRNA as Therapeutics for Age-Related Macular Degeneration. Adv Exp Med Biol. 2018;1074:37-43.