OPTIMIZATION OF THE TWO-TAILED RT QPCR METHOD WITH SYNTHETIC MIR- 16 AND MIR-210 IN HUMAN PLASMA FOR FUTURE DIAGNOSTICS OF SEPSIS

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OPTIMIZATION OF THE TWO-TAILED RT

QPCR METHOD WITH SYNTHETIC

MIR-16 AND MIR-210 IN HUMAN PLASMA

FOR FUTURE DIAGNOSTICS OF SEPSIS

Bachelor Project in Bioscience, BV506G, 30

credits

2020-02-09 – 2020-06-07

Version 2

Michelle von Ehr

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Abstract

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Abbreviations

ABI system Applied biosystem

AKI Acute kidney injury

cDNA complementary DNA

Cq quantification cycle

CRP C-reactive protein

DNA Deoxyribonucleic acid

DNase I deoxyribonuclease I

IL-6 Interleukin-6

miRNA MicroRNA

mRNA messenger RNA

NTC No template control

PCR Polymerase chain reaction

PCT1 Procalcitonin

Pre-miRNA precursor microRNA

Pri-miRNA primary microRNA

qPCR quantitative polymerase chain reaction

RNA Ribonucleic acid

RT reverse transcription

no-RT no reverse transcriptase

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Introduction

“Putrefaction” – the definition of this word according to the Oxford English Dictionary means “make rotten”. The term originated from Greek, sepsis, and is nowadays known as the medical condition sepsis. Explaining this word, it refers to an infection that occurred in a body, together with the response to it.

Every pathogen that enters the human body triggers a response. Pathogens as underlying cause for an infection to arise range from viruses and bacteria to fungi or other parasites (Martin, 2012). Once an infection arises, the body is determined to fight it, which can in some cases lead to an overreaction. Sepsis is a medical condition that occurs when the body excessively reacts to such pathogens (Singer et al., 2016).

Sepsis definition

Not only is the diagnostic part for sepsis still under development, but also the definition of what it exactly is. The first definition of sepsis was based on an ongoing process, ranging from sepsis to severe sepsis and then to septic shock (Gül et al., 2017). In 1991, this definition of sepsis became linked to parameters that describe the systemic inflammatory response syndrome [SIRS] (Bone et al., 1992). Respectively seen, sepsis itself occurs when the suspected patient has two or more of the criteria that describe SIRS together with an underlying infection. The SIRS criteria include tachycardia, tachypnea, fever, and leucocytosis (Surviving Sepsis Guidelines, 2012). Severe sepsis was described as sepsis with acute organ dysfunction, and septic shock was described as sepsis with hypotension or hyperlactatemia. While this gave a standardized method to define sepsis, it often interfered with the precise diagnosis as it was also explaining other inflammatory responses that are very similar to sepsis but originate through other, non-infectious, ways (Evans, 2018). More recently, in 2001, the term severe sepsis was fused with sepsis, while septic shock describes sepsis together with the drastic decrease in blood pressure with organ failure. (Gül et a., 2017). Since 2016, the newest definition of sepsis, now also called Sepsis-3, has been established. Explanations for that are the low sensitivity to describe sepsis with SIRS symptoms with the lack of specificity. The updated definition is based on the sequential organ failure assessment, which allows improved detection of organ dysfunction in sepsis (Gül et al., 2017). This describes sepsis as a dysregulated response from the host to an infection which leads to life-threatening organ dysfunction. Septic shock, in these terms, describes sepsis together with profound abnormalities in circulatory or metabolic mechanisms which increase the mortality rate compared to sepsis itself (Marik & Taeb, 2017).

Sepsis and sepsis diagnostics today

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Page: 2 gold-standard for the diagnostics of sepsis to date is based on blood culturing, in which the results take up to three days to be validated without a clear detection possibility (Panday et al., 2019).

MicroRNAs

Within recent years, research on microRNAs [miRNAs] has indicated to be useful as minimally-invasive biomarkers for many diseases of different origins (Correia et al., 2017). miRNAs are approximately 18-22bp long, non-coding molecules of RNA. miRNAs are of endogenous origin, and the production starts in the nucleus. Primary miRNA [pri-miRNA] is transcribed by RNA polymerases II and III before it gets further processes to precursor miRNA [pre-miRNA] (Condrat et al., 2020). Once transported to the cytoplasm by Exportin-5, it becomes a mature miRNA that is found in both cells and body fluids, including peripheral blood, serum, and plasma (Androvic et al., 2017).

It has been studied that miRNAs are involved in the regulation of gene expression at a post-transcriptional level, meaning the mediation of messenger RNA [mRNA] degradation (MacFarlane & Murphy, 2010). Furthermore, miRNAs are involved in intercellular signaling as circulating miRNAs within bodily fluids. Of these circulating miRNAs, 90% are secreted in complexes with proteins (Vickers et al., 2011). Additionally, miRNAs are involved in the regulation of stem cells, which are capable of self-renewal. The differential process has many control points, of which miRNAs happen to be key-regulators (Gangaraju & Lin, 2009). Alteration of the miRNA expression may change the physiological or pathological outcome and thus, cause the emergence of diseases (Androvic et al., 2017). Based on previous studies, circulating miRNAs with changed levels of occurrence are considered as potential biomarkers to diagnose and prognoses diseases, including sepsis (Szilágyi et al., 2019).

Many miRNAs have previously been studied to be involved in infections and also in correlation with sepsis. Two of which are miR-16 and miR-210.

MiR-16

MiR-16 is a relevant miRNA to be used in this study as it has previously indicated to be differentially expressed in patients with sepsis. Especially, it was associated with death from sepsis (Wang et al., 2012). A different study has shown that miR-16 was upregulated during sepsis research and could therefore be applied for further investigations for qPCR amplification and were accounted as possible biomarkers for sepsis (Wu et al., 2013). Furthermore, miR-16 is one of the miRNAs that is identified to be responsible as tumor suppressors. This has been seen by the formation of tumors once miR-16 was downregulated. Upregulation, on the other side, was found in patients with suspected sepsis or SIRS (Szilágyi et al., 2019). Another study from Wu et al. (2013) also confirmed the upregulation of miR-16 in septic patients compared to healthy individuals, and therefore, it is assumed that miR-16 may potentially be used as biomarker of sepsis.

MiR-210

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Future diagnostics of sepsis with miRNA

Due to their features and availability, working with miRNAs has become increasingly important in medical research. There are several techniques for the detection of miRNA expression, but limitations to it often follow (Androvic et al., 2017). A recent study at TATAA Biocenter has developed a new, more sensitive method to make the work, including extraction and amplification, with miRNAs more specific. This method is used to elongate the short miRNAs by connecting them with two-tailed RT primers made up of hemiprobes, in order to improve the RT- reaction. The advantages of this method include a high specificity and the reduction of mismatching as it prevents the binding to any pre-mature miRNA, like pre- or pri-miRNA. Only one sequence primer is required for the formed cDNA to be amplified. This method applies to all types of small RNAs, like miRNAs or sRNAs, etc. (Androvic et al., 2017).

Two-tailed RT-qPCR

Quantitative polymerase chain reaction [qPCR] is a method previously applied for amplification of RNA and DNA. For RNA, reverse transcription [RT] to complementary cDNA has to be performed, then called RT-qPCR (Deepak et al., 2008). This method also promises high accuracy and is especially useful when either or both the sample size and the amount of material available is little. For the detections of very small DNA and RNA molecules like miRNAs with precision, a new primer has been developed which is called the two-tailed RT primer (Androvic et al., 2017) (Figure 1). This specific primer is modified to detect only mature miRNA and not any precursors such as pri- or pre-miRNAs. Each two-tailed RT-primer is composed of an oligonucleotide tether that is folded into a hairpin as well as two hemiprobes which are complementary to different regions of the miRNA target, binding limitedly to these regions. This method is used to elongate the short miRNAs once they link together and form a stable complex. For each miRNA elongation, only one specific primer is required as it binds to both ends of the target (Androvic et al., 2017).

Figure 1. Copyrighted by Androvic et al. The two-tailed RT- primer as described by Androvic et al. [A] shows the two hemiprobes and the folded tether that comprise this primer, with the 5’ hemiprobe being longer than the 3’ hemiprobe. [B] indicates how the two hemiprobes bind to the miRNA. [C] demonstrates how the RT Enzyme replaces the 5’ hemiprobe of the primer, opens up and synthesizes the cDNA. The 3’ end extends by reverse transcriptase. [D] illustrates the amplification of cDNA during qPCR by applying two target-specific primers (forward and reverse).

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Page: 4 standard curves to determine amplification efficiency and linearity of the data points, and aids to calculate unknown quantities within tested samples (Boulter et al., 2016; Seo et al., 2017).

Problem formulation

While extracting RNA is nowadays not a challenge, it is important to keep in mind that only a small part of total RNA consists of miRNA (Binderup et al., 2018). Therefore, using kits that are specified in extraction of total RNA show limitations when the desired target is miRNA. Previously, the two-tailed RT-qPCR has proved to be able to detect miRNAs from cells and bolidly fluids like plasma or serum. To especially target miRNAs, the developed two-tailed primers show promising results. The study has been performed at TATAA Biocenter in Gothenburg with usage of machines, and now an optimization of this method by hand is necessary to test for eventual application of this method for detection on sepsis. By spiking with synthetic miRNA, it is ensured that the target miRNA is contained in the plasma which then aids to evaluate whether the primers are functioning. If the spiked miRNAs can be amplified, the two-tailed primers are working and the results can be combined into a multimarker panel for relevant miRNAs which can eventually lead to reduced diagnostic time.

Combining this information, it remains essential to look for possible miRNAs that lead to the diagnosis of sepsis as part of the future diagnostics of sepsis as well as developing target specific tools. The method should as well be optimized for it to be used in conventional labs. The aim of this study is thus, to perform the two-tailed RT-qPCR method manually and reduce the amount of body fluids, here plasma, to a minimum of 100 µl. The underlying research question is to find out whether the two-tailed primers are sensitive enough to detect the target miRNAs from as little as 100 µl of plasma. This is done by performing the objectives of extracting blood from healthy donors and centrifuging it to separate the plasma and whole blood. Continuing with spiking the plasma with two synthetic miRNAs, miR-16 and miR-210, at different time-points, thus, before and after total RNA extraction, to ensure presence of the target miRNA as well as identifying the functioning of the kit used for extraction. Both purity and concentration of the elutes should be measured before proceeding to cDNA synthesis and RT-qPCR. Together with melt curve and standard curve analysis, optimization will be performed do determine the functioning of the two-tailed primers and the results will be statistically analysed.

Materials and Methods

Ethical considerations

For this thesis project, the ethical considerations are limited to the blood from healthy donors used during this study. All donors are voluntary, have agreed to give blood for this or other studies performed at the University of Skövde. No genome sequencing or other identity revealing tests were included. No further ethical considerations had to be taken into account.

Plasma preparation

All samples used are from the same person. Whole blood was drawn in 6 ml K2EDTA Vacuette Tubes (Greiner Bio-One) from self-assessed healthy volunteers and then centrifuged at 16,000 x g at 4°C for 15 minutes in a ScanSpeed 1580R centrifuge (Labogene) to separate the plasma from the whole blood. The plasma was carefully transferred and equally aliquoted to 1.5 ml microcentrifuge tubes with each tube containing 500 µl of plasma. All plasma samples were stored at -80°C.

miRNA extraction

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Page: 5 was carried out for 20 samples, each of which had 100 µl of plasma as starting volume according to the manufacturer’s protocol except for the following modifications.

Frozen plasma samples were thawed on ice for 15 minutes and transferred to room temperature (22°C) until completed according to the Plasma/Serum Exosome Purification and RNA Isolation Kit (Norgen Biotek).

Spike-in’s with two synthetic miRNA, miR-16 and miR-210 provided by the supervisor, were performed either before or after extraction. That is, either replacing the optional step c) in section 1, or added to the resulting elutes after step c) in section 4, respectively. The miRNAs were added as 107_{and 10}6_{copies in a volume of 2.5 µl for the resulting 50 µl elute to contain 10}6_{and 10}5

copies, respectively. In total, the 20 samples were separated to five samples for each spike-in option.

The optional step after step d) in section 2 was always performed, disregarding step 7. A QBT2 Block heater (Grant) was used for incubation. For maximum RNA recovery, a separate second elution was always performed as advised in the protocol. All samples were stored at -20°C. After the extraction, the quantity and purity of RNA were measured with the Qubit ® microRNA Assay (Life Technologies) and Nanodrop™ spectrophotometer (Life Technologies), respectively.

cDNA synthesis with two-tailed primer

RNA was synthesized to cDNA by performing reverse transcription (RT). The RT-reactions were performed with the TATAA GrandScript cDNA FreePrime Kit (TATAA Biocenter) according to the Two-tailed RT-qPCR protocol (TATAA Biocenter). The total reaction volume was 10 µl/RT reaction. The two-tailed RT primers provided had a start concentration of 100 µM, therefore 0.5 µl of primer were diluted in 249.5 µl of nuclease free water for the final concentration to be 0.2 µM. Then, 4 µl of miRNA were added to the reaction mix as shown in Table 1. Additionally, no template control (NTC) and no RT-controls (-RT) were included. For NTC, nuclease free water was used instead of sample. For –RT, the RT-enzyme was replaced by nuclease free water. For quantification of the extracted samples, a standard curve was created as described in the section “standard curve” below. The reverse transcription was then run in a thermocycler for 45 min at 42 °C, followed by 5 min at 85 °C and finally stored at 4 °C. Afterwards, the cDNA was diluted five times by adding 40 µl of nuclease free water.

Table 1. Components of RT reaction for extracted samples.

Component Volume (µl)

GrandScript FreePrime Reaction Mix (5x) 2

GPS enhancer (10x) 1 GrandScript RT Enzyme 0.5 Two-tailed primer (0.2 µM) 2.5 RNA sample 4 Total 10

Quantitative PCR

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Page: 6 system, wherefore 25 µM/µl Rox (25.000 nM) (BioRad) dye was added as a volume of 0.2 µl for the final concentration to be 500 nM.

Table 2.Components of qPCR reaction for one triplicate.

Component Volume (µl)

TATAA SYBR® GrandMaster® Mix (2x) 5 Two-tailed PCR forward primer (10 µM) 0.2 Two-tailed PCR reverse primer (10 µM) 0.2

cDNA 2

Nuclease Free Water

Rox (500 nM) 2.6 0.2

Total 10.2

*ROX dye additionally added

Every sample was loaded in triplicates on a 96-well plate (Applied Biosystems), vortexed and centrifuged for 1 minute at 350 x g in a universal 320 centrifuge (Hettich). Thereafter, the plate was placed in a 7300 Real-Time PCR system (Applied Biosystems). The cycling protocol was adjusted with the following modifications: 95 °C for 30 s, 40 cycles were performed consisting of 5 s at 95 °C, 15 s at 60 °C and 30 s at 72 °C. Data collection was obtained during the last step of the second cycle and a melt curve was added. Both melt- and standard curve analyses were performed afterwards.

qPCR optimization

Melt curve analysis

SYBR® Green is a specific binding dye that exclusively binds to double stranded DNA or cDNA which was created during the RT reaction. To determine whether the primers used functioned, a melt curve analysis assists. Melting temperature (Tm) determines the point at which cDNA

denatures and the fluorescence decreases. Standard curve analysis

As template, the original synthetic miRNAs (TATAA Biocenter) were prepared as a 10-fold series. The standards contained 105_-1012_{copies of template and a standard curve was created by plotting}

the logarithmic initial copy numbers (N0) against the determined quantification cycles (Cq values)

in a scatter chart. A trendline was applied and the amount of unknown samples were quantified using the created standard curve. The amplification efficiency (AE) was determined by using the equation of the linear regression line. The efficiency can be calculated with the formula

𝐸 = 10 −1

𝑠𝑙𝑜𝑝𝑒 , which then can be converted to percentage with 𝐸% = (𝐸 − 1) ∗ 100.

Absolute quantification was applied to determine the quantity in copy numbers of the extracted samples. The equation for linear regression [𝑦 = 𝑚𝑥 + 𝑏], obtained from the standard curve can be used to calculate the quantity of unknown sample as followed:

𝑁𝑛= 10(

𝑛−𝑏

𝑚), where n = Cq, m = slope  𝑄𝑢𝑎𝑛𝑡𝑖𝑡𝑦 = 10( 𝐶𝑞−𝑏

𝑚 ) For accuracy of the calculations, standard deviation is applied on the mean of Cq values.

Individual reaction triplicates were analysed and optimized in a matter, to improve the correlation coefficient (R2) and the amplification efficiency (AE) determined from the standard

curve.

Statistical Analysis

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Page: 7 applied to compare the difference between the first and second elute. Independent T-tests were applied for all other comparisons. That is, comparing the samples spiked before and after, as well as comparing the different copynumbers used for spiking. After qPCR optimization, standard curves have been created both manually with Microsoft Excel (2016) and through GenEx™ professional (MultiD analysis) to determine linearity and amplification efficiency.

Results

Quality and Quantity of the RNA after extraction

The quantity of miRNA and other small RNA present within the extracted samples measured with the qubit fluorometer resulted in many out of range detections as the concentration was too low. Quantity could not be determined in any of the samples from miR-16, no matter when the samples were spiked nor which elute or which copy number was applied (not shown). However, plasma spiked with miR-210 106_{copies had some results within the range of detection (Table 3a and 3b).}

If detected, the values were just higher than 0.500 ng/µl. It was irrelevant whether the time point of spiking was before or after extractions, but most of the possible quantities could be determined within the first elute.

Table 3. Qubit results from elutions performed on plasma spiked with miR-210 106 copies/µl

with (A) spiked before and (B) after extraction. (A)

Sample (E), Elute (E) ng/µl Sample (E), Elute (E) ng/µl

S1, E1 - S1, E2 - S2, E1 0.66 S2, E2 - S3, E1 0.72 S3, E2 - S4, E1 0.50 S4, E2 - S5, E1 0.58 S5, E2 0.60 (B)

Sample (E), Elute (E) ng/µl Sample (E), Elute (E) ng/µl

S1, E1 0.56 S1, E2 -

S2, E1 - S2, E2 -

S3, E1 - S3, E2 -

S4, E1 0.52 S4, E2 -

S5, E1 0.60 S5, E2 -

*_{Values out of range are indicated by “-“. S shows the sample; E shows the elute.}

Bold samples represents the one used for qPCR.

The raw data of the results from the Nanodrop™ spectrophotometer can be found in the appendix (Appendix 2, Table I). Based on that, the average for each sample was calculated (Table 4). The desired absorbance at the wavelength for RNA is around 2, showing the first elute of miR-210 being closest to that value, followed by elute one of miR-16, elute two of miR-16 and elute two of miR-210.

Table 4. The average absorbance at the 260/280 ratio results from Nanodrop for both miRNAs. Avrg* Absorbance (260/280) A 106 _{B 10}6 _{A 10}5 _{B 10}5 _{Avrg total}

MiR-210 E1 1.47 1.86 1.97 1.19 1.62

MiR-210 E2 1.07 1.23 -0,08 0.99 0.81

MiR-16 E1 2.16 1.62 1.48 1.11 1.59

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*Avrg = average, A = spiked after extraction, B = Spiked before extraction, number shows copy number Last column shows the average per elute.

Two-tailed RT-qPCR

Amplification was detected for all spiked samples. For miR-16, both NTC and –RT control remained undetected, as desired. For miR-210, two wells with –RT controls detected amplification for two separate samples, the remaining controls all resulted undetected as seen in the raw data (Appendix D, Table III).

Two-tailed RT-qPCR validation by melt curve analysis

As the primers used in this experiment have not been validated before, the melt curve analysis is an interesting step. Both, two-tailed RT primers as well as the forward and reverse primers used during cDNA synthesis and RT-qPCR were to be analysed. With the single peak of each sample within the melt curve analysis (Figure 2a and 2b), a clear amplification can be seen which indicates that the primers functioned and the amplification occurred as desired.

(A)

(B)

Figure 2. Melt curve analysis for the different methods for (A) miR-210 and a melting temperature of Tm = 60 °C and (B) miR-16 and a melting temperature of Tm = 60 °C.

Two-tailed RT-qPCR optimization by Standard Curve analysis

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Page: 9 Together with the trendline, the Pearson correlation coefficient of determination (R2) is an applied

evaluation coefficient used to illustrate qPCR assay optimization and shows the linearity of the data points. The applied trendline and its belonging equation for linear regression were used to calculate an amplification efficiency of 77% for both miRNAs (Table 5).

Table 5. Amplification efficiency*_.

miR-210 miR-16

Slope (m) -4,0048 -4,0139

In equation for E E= 1,777 E = 1,774

In equation for E% E% = 77% E% = 77%

*_{calculated with 𝐸 = 10}−1𝑚 and converted to percentage with 𝐸% = (𝐸 − 1) ∗ 100.

Figure 3. Optimized standard curve for miR-210 where 1012_{has been considered as an outlier to improve}

the correlation efficient, R2_{to 0.9887 (left). Optimized standard curve for miR-16 with exclusion of 10}5 _and

1012_{to archive an R}2_{value of 0.9867 (right). With N(0) showing the logarithm of the original copy number.} To determine the quantity of miRNA copy numbers for the extracted samples the equation for linear regression, 𝑄𝑢𝑎𝑛𝑡𝑖𝑡𝑦 = 10(𝐶𝑞−𝑏

𝑚 ) was applied (Table 6) and absolute quantification was performed.

Table 6

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Absolute quantification of (A) miR-210 and (B) miR-16 for copy number determination.

(A)

Sample Average Cq ± Stdev Copy number ± Stdev Log Concentration

1B6 29.92 ± 0.32 41003.65 ± 7855.29 104.56 2B6 30.35 ± 0.26 33799.36 ± 3813.03 104.52 1B5 35.16 ± 0.57 2048.37 ± 614.40 103.23 2B5* - - - 1A6 24.93 ± 0.46 612079.2 ± 2488.44 105.78 2A6 25.60 ± 0.37 491344.1 ± 104245.10 105.69 1A5 26.87 ± 0.11 233937 ± 14436.49 105.23 2A5 27.52 ± 0.46 187251.3 ± 761.28 104.27 (B)

Sample Average Cq ± Stdev Copy number ± STDEV Log Concentration

1B6 32.86 ± 0.83 272259.4 ± 27562.18 105.43

2B6 34.91 ± 0.45 83904.77 ± 6460.18 104.92

1B5 31.53 ± 0.26 586261.8 ± 85885.91 105.76

2B5* - - -

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2A6** (32.40 ± 0.86) (381706.9 ± 186577.60) -

1A5** (33.74 ± 1.24) (184722 ± 121708.80) -

2A5** (32.69 ± 1.34) (353467.2 ± 219077.20) -

*no determined values

** Outliers due to high variance (<0.5 apart) within determined Cq values

One and two determine elute number, B and A timepoint of spiking before or after, respectively, five and six copy number (105_{, 10}6_{). For average determination, one of three triplicates was removed.}

The standard curves were also analyzed using GenEx (MultiD analysis version 7) (Figure 4). The confidence interval was set by default to 95 % and shows which data points lay inside the interval. For miR-210, GenEx expressed an R2_{value of 0.98 and an AE of 0.779. For miR-16, the values are}

0.98 and 0.775, respectively which matches the manually calculated values.

Figure 4. Standard curves created with GenEx with for MiR-210 (left) and MiR-16 (right). The trendline is shown in blue, the working-hotelling line illustrated by the red, dashes band shows the 95% confidence interval. The colored circles show the different samples.

The absolute quantities for the different samples are shown in Figure 5. Generally, miR-16 seems to be expressed in a higher quantity, but a complete conclusion cannot be made as the determined Cq values were not usable for all samples as seen in Table 6.

Figure 5. The log of the quantities as seen in Table 6 for both miRNAs. For miR-16, three results had to be exluded due to high variation in Cq values. Solid bars show the samples spiked before extraction and striped bars indicated samples that were spiked after extraction. The first number shows the elute (1 = Elute 1, 2 = Elute 2), the letter shows the timepoint of spiking (B = Before extraction, A = after extraction) and the second number shows the copy number that was used for the spiking (5 = 105_{, 6 = 10}6_{). The black double line shows}

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Statistical analysis

Statistical tests were applied to see which of the samples had better results. It was first of all in interest whether the second elution step was necessary to be performed or if the results were higher in quantity in the first elute. The Cq values were used as parameter for these tests.

The applied paired T-test to determine significance between the two different elutes showed only significance when comparing before and after within the samples spiked with miR-210 106_copies,

with a p-value of 0.035. All other comparisons for both miRNAs were not significantly different. That shows that both elutes had about equal values and it is worth to perform the additional elution step for maximum RNA recovery.

As the spike-ins were added to separate samples, dependency was excluded and independent T-tests were performed to compare between the copy number difference and the time point of spiking. For all comparisons performed, the hypotheses are as followed:

H0 = There is no significant difference (p > α)

H1 = There is a significant difference (p < α)

For miR-210, the comparison between spiked before and after resulted in a significant difference (p = 0.015) while there was no significant difference between the copy numbers (p = 0.692). There was no significant difference between any comparisons when applying the test on miR-16 with p = 0.584 and p= 0.281, respectively.

When analyzing the mean Cq values of each sample for miR-210 spiked before and after, it is clearly indicating that the samples spiked before extraction required more cycles to reach the threshold as the start quantity was significantly lower (Table 7). The mean of the two groups shows a difference of ~5 cycles.

Table 7. N shows the number of samples for each group, with before having one less to compare as it the qPCR results were undetermined for that sample.

Timepoint of spiking N Mean Std. Deviation Std. Error Mean

Before 3 31.78 2.83 1.69

After 4 26.27 0.95 0.41

Discussion

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Page: 12 The total RNA purification kit (Norgen Biotek corporation) is mentioned by the manufacturer as a reliable kit that can isolate all kind of RNA with low time consumption and high RNA recovery, without the use of additional protocols (Norgen Biotek, n.d.). Both of these aspects were approved after performing the isolation procedure. Additionally, studies shown that this kit result in an increased yield of isolated miRNAs compared to other kits, although it is to mention that previous research often implied overestimation of quantity extracted (Brown et al., 2018) as discussed below. When performing the optional step in the total RNA purification kit, deoxyribonuclease I (DNase I) is applied to improve the quality of extracted RNA by removing contaminants (Norhazlin et al., 2015). DNases are enzymes that hydrolyse DNA (Liao, 1997) and when applied on RNA samples, it has shown to decrease the amount of DNA contaminants present (Norhazlin et al., 2015; Tavares et al., 2011).

The quality and quantity of the extracted samples were measured with a Qubit® and a Nanodrop, respectively. As previously stated, many detections by Qubit were out of range indicating a quantity too low to be detected (Table 3a and 3b). The Qubit® reagents involve fluorescent dyes that specifically detect small RNA, aiming for an accurate quantification. However, it is not limited to miRNA detection. Each of the elutes from the extractions had a volume of 50 µl, and a low starting quantity of miRNA allows to conclude that detection out of range does not indicate false results, but generally shows limitations of the machine (Garcia-Elias et al., 2017). For improvement, the dilutions could have been tested when using the elution limit of 20 µl instead. From the manufacturer’s website, the Qubit Fluorometer measures sample concentrations as low 0.05 ng/µl. However, the obtained samples could only be detected above 0.5 ng/µl wherefore it is recommended to repeat the experiment with a higher sample amount. To ensure accurate measurements, an additional test supplementing the qubit working solution with nuclease free water was performed to validate whether the synthetic miRNA could be detected or not. Still, it again resulted in out of range data which approves the limitation.

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Page: 13 When performing the two-tailed RT-qPCR Protocol (TATAA Biocenter), it is important to understand the contents for the qPCR reaction together with the required components for the used ABI system. These experiments have been carried out multiple times. During the first test run, no amplification could be detected. That was due to a lack of a reference dye in the master mix for the qPCR reactions. ROX (BioRad) is a passive reference dye used for normalized sample fluorescence when qPCR is performed and is required in addition to the reporter dye, SYBR® Green (TATAA Biocenter), for the ABI-system used. The reference dye does not get influenced by the PCR reaction and its fluorescence signal does not change over time during the amplification and allows for normalization between the wells (BioRad). A recommended amount of ROX dye to be added lays between 50 and 500 µM (Lumiprobe). According to Biotium, the 7300 real-time qPCR system (Applied Biosystems) involved in this experiment requires a high concentration of ROX, wherefore 500 µM/µl have been added to the reactions (Table 2).

To troubleshoot for the underlying cause why the first experiment did not show any amplification detection in the samples, the created cDNA was run on gel electrophoresis to see whether the cDNA synthesis was successful or not. For this, 1% agarose gel was prepared, and the cDNA was separated by fragment size, indicating successful cDNA synthesis (Lee et al., 2012). After the results were obtained, it was to conclude that the complication was attributed to the qPCR reaction. After ROX was added, no further amplification detection problems occurred.

The RT-qPCR was performed out with multiple optimizations, including melt curve analysis and creation of a standard curve. The melting curve analysis assists for the optimization of this experiment. Melting curve analyses are usually used to assess the essential characteristics of types of DNA during heating. The SYBR® green dye is very specifically binding to only double-stranded DNA which proves that the cDNA synthesis with the two-tailed primer was successful. As cDNA starts to denature, fluorescence is decreasing, and a melting temperature (Tm) is determined (Figure 2a and 2b) (Ririe, Rasmussen & Wittwer, 1997). Single peaks are known as indicators for specific amplification by the primers, while multiple peaks may be due primer-dimer formation or non-sepcific primers and thus inaccurate amplification. The single peaks obtained during this experiment illustrate that the primers bound the product and amplification occurred specifically, allowing to conclude that the two-tailed primers targeted the synthetic miRNA wanted only (Ahmed et al., 2017).

The standard curves created were used for the determination of quantification within the extracted samples with unknown copy numbers (Bustin, 2000; Larionov, Krause & Miller, 2005). Standard curves were created (Figure 3; Figure 4) and together with absolute quantification, the copy numbers of the extracted samples could be determined (Table 6a and b), which proves that the two-tailed RT qPCR method worked. As the starting copy numbers added were known, it was to expect that the extracted samples should be near that value although taking into account that RNA reduces during the extraction process. Although the purity of the samples of miR-16 seemed better, as discussed above (Figure 5), the qPCR performed resulted in more reliable results for miR-210. In regards of the standard curve itself, evenly distributed values of the triplicates are aimed for, as it is dependent on the doubling in each amplification cycle within the ABI system. With n being the number of cycles, 2n_{is the dilution factor. In a 10-fold dilution, hence 2}n_{=10, the}

Cq values of a 10-fold dilution should show a separation of n = 3.32 cycles for a 100% efficient reaction (Taylor et al., 2015).

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Page: 14 synthetic miRNA amplification with two-tailed primers and archived high amplification efficiencies. For future research, the standard curve dilutions should be prepared with high care to ensure precise 10-fold dilution series with 3.32 cycles apart after amplification. The low efficiency was therefore also taken into account when analyzing the qPCR results. For the standard curve of miR-210, the difference between each copy number ranged from 2.68 to 6.24 cycles, when excluding the standard 1012_{. For miR-16, these values ranged from 5.25 to 6.91 cycles after}

excluding the standards 105_{, 10}7_{and 10}11_{(Appendix C, Table I).}

After determining the copy numbers within the extracted samples, it can be seen that the samples came close to their expected values (Figure 5). It is visualized that all samples spiked with 106

copies have a higher copy number than those spiked with 105_{copies. However, for miR-16, many}

Cq values could not be used to determine the starting quantity.

The statistical analyses applied were not relevant to determine whether the two-tailed RT-qPCR method manually performed worked, but helped toward the optimization process (Table 7). Seeing that there is no significant difference between the first and second elutes, shows equal amounts of miRNA in the samples (Norgen Biotek, n.d). Comparing the copy numbers, 106_and

105_{, added to the samples and not finding a significant difference helps to understand the accuracy}

of primer amplification and the efficiency of the qPCR method. Since both copy numbers could be quantified, a study performed on even lower concentration could be compared to these results to detect the lowest amplification limit for this method. Furthermore, there was a significant difference observed when comparing the samples spiked before and after for miR-210. A separation of around five cycles indicates that the samples spiked before extraction had a lower quantity of starting cDNA present compared to the samples spiked after (Table 7). However, the reason of spiking at different time-points was to show if the kit used for RNA extraction was suitable. It was to assume that the synthetic miRNA would be present in the samples spiked after extraction, as the process of extraction was already finished. For the samples spiked before extraction, the main goal was to be able to see if the two-tailed priming can be applied on samples extracted with the total RNA purification kit (Norgen Biotek). With this said, it can be concluded that the extraction functioned well, and miRNA could be synthetized as well as amplified.

Although this study has been carried out on healthy donor blood, the synthetic miRNAs used have been selected as they previously showed correlation with sepsis diagnostics. Before applying tests on approved cases, using synthetic miRNA on healthy blood is a safe and accurate way for trials. Synthetic miRNAs were included in many studies related to botanical research. For instance, researchers applied synthetic miRNA to control gene expression (Eamens, McHale & Waterhouse, 2013). Also in medical research, the use of synthetic miRNA has shown to improve research qualities by correctly aiming for the target miRNAs (Dhas, Dirisala & Bhat, 2018). Furthermore, the study on which this project is based on also first developed the two-tailed primers to be performed with synthetic miRNAs (Androvic et al., 2017).

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Acknowledgements

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References

Ahmed, F. E., Gouda, M. M., Hussein, L. A., Ahmed, N. C., Vos, P. W., & Mohammad, M. A. (2017). Role of Melt Curve Analysis in Interpretation of Nutrigenomics' MicroRNA Expression Data. Cancer genomics & proteomics, 14(6), 469–481. https://doi.org/10.21873/cgp.20057

Androvic, P., Valihrach, L., Elling, J., Sjoback, R., & Kubista, M. (2017). Two-tailed RT-qPCR: a novel method for highly accurate miRNA quantification. Nucleic Acids Research, 45(15). doi: 10.1093/nar/gkx588

Binderup, H. G., Madsen, J. S., Heegaard, N. H. H., Houlind, K., Andersen, R. F., & Brasen, C. L. (2018). Quantification of microRNA levels in plasma – Impact of preanalytical and analytical conditions. Plos One, 13(7). doi: 10.1371/journal.pone.0201069

BioRad (n.d.) Normalization of Real-Time PCR Flourescent Data with ROX Passive Reference Dye. Retrieved at: https://www.bio-rad.com/en-se/applications-technologies/normalization-real-time-pcr-fluorescence-data-with-rox-passive-reference-dye?ID=MW472W15

Bone, R. C., Balk, R. A., Cerra, F. B., Dellinger, R. P., Fein, A. M., Knaus, W. A., … Sibbald, W. J. (1992). Definitions for Sepsis and Organ Failure and Guidelines for the Use of Innovative Therapies in Sepsis. Chest, 101(6), 1644–1655. doi: 10.1378/chest.101.6.1644

Boulter, N., Suarez, F. G., Schibeci, S., Sunderland, T., Tolhurst, O., Hunter, T., … Duggan, K. (2016). A simple, accurate and universal method for quantification of PCR. BMC Biotechnology, 16(1). doi: 10.1186/s12896-016-0256-y

Brown, R., Epis, M. R., Horsham, J. L., Kabir, T. D., Richardson, K. L., & Leedman, P. J. (2018). Total RNA extraction from tissues for microRNA and target gene expression analysis: not all kits are created equal. BMC biotechnology, 18(1), 16. https://doi.org/10.1186/s12896-018-0421-6 Bustin S. A. (2000). Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays. Journal of molecular endocrinology, 25(2), 169–193. https://doi.org/10.1677/jme.0.0250169

Cheng, A. M., Byrom, M. W., Shelton, J., & Ford, L. P. (2005). Antisense inhibition of human miRNAs and indications for an involvement of miRNA in cell growth and apoptosis. Nucleic Acids Research, 33(4), 1290–1297. doi: 10.1093/nar/gki200

Condrat, Thompson, Barbu, Bugnar, Boboc, Cretoiu, … Voinea. (2020). miRNAs as Biomarkers in Disease: Latest Findings Regarding Their Role in Diagnosis and Prognosis. Cells, 9(2), 276. doi: 10.3390/cells9020276

Correia, C. N., Nalpas, N. C., Mcloughlin, K. E., Browne, J. A., Gordon, S. V., Machugh, D. E., & Shaughnessy, R. G. (2017). Circulating microRNAs as Potential Biomarkers of Infectious Disease. Frontiers in Immunology, 8. doi: 10.3389/fimmu.2017.00118

Deepak, S., Kottapalli, K., Rakwal, R., Oros, G., Rangappa, K., Iwahashi, H., Masuo, Y., & Agrawal, G. (2007). Real-Time PCR: Revolutionizing Detection and Expression Analysis of Genes. Current genomics, 8(4), 234–251. https://doi.org/10.2174/138920207781386960

Dhas, B. B., Dirisala, V. R., & Bhat, B. V. (2018). Expression Levels of Candidate Circulating microRNAs in Early-Onset Neonatal Sepsis Compared With Healthy Newborns. Genomics Insights, 11, 117863101879707. doi: 10.1177/1178631018797079

(22)

Page: 18 Acute Lymphoblastic Leukemia. International Journal of Molecular Sciences, 19(10), 2858. doi: 10.3390/ijms19102858

Eamens, A. L., Mchale, M., & Waterhouse, P. M. (2013). The Use of Artificial MicroRNA Technology to Control Gene Expression in Arabidopsis thaliana. Methods in Molecular Biology Arabidopsis Protocols, 211–224. doi: 10.1007/978-1-62703-580-4_11

El Bali, L., Diman, A., Bernard, A., Roosens, N. H., & De Keersmaecker, S. C. (2014). Comparative study of seven commercial kits for human DNA extraction from urine samples suitable for DNA biomarker-based public health studies. Journal of biomolecular techniques: JBT, 25(4), 96–110. https://doi.org/10.7171/jbt.14-2504-002

Evans T. (2018). Diagnosis and management of sepsis. Clinical medicine (London, England), 18(2), 146–149. https://doi.org/10.7861/clinmedicine.18-2-146

Fasanaro, P., Greco, S., Lorenzi, M., Pescatori, M., Brioschi, M., Kulshreshtha, R., Banfi, C., Stubbs, A., Calin, G. A., Ivan, M., Capogrossi, M. C., & Martelli, F. (2009). An integrated approach for experimental target identification of hypoxia-induced miR-210. The Journal of biological chemistry, 284(50), 35134–35143. https://doi.org/10.1074/jbc.M109.052779

Fleischmann, C., Scherag, A., Adhikari, N. K. J., Hartog, C. S., Tsaganos, T., Schlattmann, P., … Reinhart, K. (2016). Assessment of Global Incidence and Mortality of Hospital-treated Sepsis. Current Estimates and Limitations. American Journal of Respiratory and Critical Care Medicine, 193(3), 259–272. doi: 10.1164/rccm.201504-0781oc

Gangaraju, V. K., & Lin, H. (2009). MicroRNAs: key regulators of stem cells. Nature Reviews Molecular Cell Biology, 10(2), 116–125. doi: 10.1038/nrm2621

Garcia-Elias, A., Alloza, L., Puigdecanet, E. et al. Defining quantification methods and optimizing protocols for microarray hybridization of circulating microRNAs. Sci Rep 7, 7725 (2017). https://doi.org/10.1038/s41598-017-08134-3

Gül, F., Arslantaş, M. K., Cinel, İ., & Kumar, A. (2017). Changing Definitions of Sepsis. Turkish journal of anaesthesiology and reanimation, 45(3), 129–138. https://doi.org/10.5152/TJAR.2017.93753 Huang, J., Sun, Z., Yan, W., Zhu, Y., Lin, Y., Chen, J., … Wang, J. (2014). Identification of MicroRNA as Sepsis Biomarker Based on miRNAs Regulatory Network Analysis. BioMed Research International, 2014, 1–12. doi: 10.1155/2014/594350

Kang, K., Zhang, X., Liu, H., Wang, Z., Zhong, J., Huang, Z., … Gou, D. (2012). A Novel Real-Time PCR Assay of microRNAs Using S-Poly(T), a Specific Oligo(dT) Reverse Transcription Primer with Excellent Sensitivity and Specificity. PLoS ONE, 7(11). doi: 10.1371/journal.pone.0048536 Larionov, A., Krause, A., & Miller, W. (2005). A standard curve based method for relative real time PCR data processing. BMC bioinformatics, 6, 62. https://doi.org/10.1186/1471-2105-6-62 Lee, P. Y., Costumbrado, J., Hsu, C.-Y., & Kim, Y. H. (2012). Agarose Gel Electrophoresis for the Separation of DNA Fragments. Journal of Visualized Experiments, (62). doi: 10.3791/3923

Liao T. H. (1997). Deoxyribonuclease I and its clinical applications. Journal of the Formosan

Medical Association = Taiwan yi zhi, 96(7), 481–487.

(23)

Page: 19 Lumiprobe (n.d.) ROX reference dye for qPCR. Life science solutions. Retrieved at: https://www.lumiprobe.com/p/rox-reference-dye

Macfarlane, L.-A., & Murphy, P. R. (2010). MicroRNA: Biogenesis, Function and Role in Cancer. Current Genomics, 11(7), 537–561. doi: 10.2174/138920210793175895

Marik, P. E., & Taeb, A. M. (2017). SIRS, qSOFA and new sepsis definition. Journal of thoracic disease, 9(4), 943–945. https://doi.org/10.21037/jtd.2017.03.125

Martin G. S. (2012). Sepsis, severe sepsis and septic shock: changes in incidence, pathogens and outcomes. Expert review of anti-infective therapy, 10(6), 701–706. https://doi.org/10.1586/eri.12.50

Mayeux R. (2004). Biomarkers: potential uses and limitations. NeuroRx : the journal of the American Society for Experimental NeuroTherapeutics, 1(2), 182–188. https://doi.org/10.1602/neurorx.1.2.182

Norhazlin, J., Nor-Ashikin, M., Hoh, B., Kadir, S. S. A., Norita, S., Mohd-Fazirul, M., … Abdullah, B. (2015). Effect of DNase treatment on RNA extraction from preimplantation murine embryos. Genetics and Molecular Research, 14(3), 10172–10184. doi: 10.4238/2015.august.28.1 Panday, R. S. N., Lammers, E. M. J., Alam, N., & Nanayakkara, P. W. B. (2019). An overview of positive cultures and clinical outcomes in septic patients: a sub-analysis of the Prehospital Antibiotics Against Sepsis (PHANTASi) trial. Critical Care, 23(1). doi: 10.1186/s13054-019-2431-8

Ririe, K. M., Rasmussen, R. P., & Wittwer, C. T. (1997). Product Differentiation by Analysis of DNA Melting Curves during the Polymerase Chain Reaction. Analytical Biochemistry, 245(2), 154–160. doi: 10.1006/abio.1996.9916

Sankar, V., & Webster, N. R. (2012). Clinical application of sepsis biomarkers. Journal of Anesthesia, 27(2), 269–283. doi: 10.1007/s00540-012-1502-7

Seo, J.-W., Moon, H., Kim, S.-Y., Moon, J.-Y., Jeong, K. H., Lee, Y.-H., … Lee, S. H. (2017). Both absolute and relative quantification of urinary mRNA are useful for non-invasive diagnosis of acute kidney allograft rejection. PloS One, 12(6). doi: 10.1371/journal.pone.0180045

Szilágyi, B., Fejes, Z., Pócsi, M., Kappelmayer, J., & Nagy, B. (2019, June 24). Role of sepsis modulated circulating microRNAs

Tavares, L., Alves, P. M., Ferreira, R. B., & Santos, C. N. (2011). Comparison of different methods for DNA-free RNA isolation from SK-N-MC neuroblastoma. BMC research notes, 4, 3. https://doi.org/10.1186/1756-0500-4-3

Taylor, S., Wakem, M., Dijkman, G., Alsarraj, M., & Nguyen, M. (2015). A Practical Approach to RT-qPCR – Publishing Data that Conform to the MIQE Guidelines. Tech note, 5859

Vickers, K.C.; Palmisano, B.T.; Shoucri, B.M.; Shamburek, R.D.; Remaley, A.T. MicroRNAs are transported in plasma and delivered to recipient cells by high-density lipoproteins. Nat. Cell. Biol. 2011, 13, 423–433.

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Appendices

Appendix A

Figure I. qPCR Layout planned for miR-16. Orange marks the samples spiked before extraction, green the samples spiked after extraction. The blue rows show the standard curve included. The same was performed for miR-210.

Appendix B

Table I. Nanodrop™ results for all five extracted samples (A) MiR-210 106_{(B) MiR-210 10}5_(C)

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Page: 23 (D) Sample 260/280 260/230 ng/µg After 1-1 0,93 -0,03 4,5 2-1 1,9 0,08 -14,7 3-1 1,57 0,07 -10,6 4-1 1,61 0,08 -14,2 5-1 1,41 -0,43 41,1 1-2 1,63 0,07 -13,3 2-2 1,58 0,12 -25 3-2 1,65 0,11 -21,2 4-2 1,66 0,1 -19,6 5-2 1,5 0,09 -17,9 Before 1-1 1,75 -0,27 -9,2 2-1 -0,78 0,05 1,8 3-1 1,23 0,09 3,6 4-1 1,93 0,27 18,3 5-1 1,41 0,18 16 1-2 3,3 0,08 1,8 2-2 1,08 0,16 2 3-2 1,55 0,6 44 4-2 -0,26 0,03 0,3 5-2 0,69 0,15 6,6

*The first number counts the spiked samples, the second number illustrates the elute.

Appendix C

Table II. qPCR triplicates used for standard curve creation for miR-210 (A) and miR-16 (B). (A)

Sample 1st_triplicate ₂nd_triplicate ₃rd_triplicate _{Avrg ± Stdev}

SC 5 27.87 27.53 27.30 29.56 ± 0.28 SC 6 24.13 24.29 24.47 24.29 ± 0.16 SC 7 21.67 21.94 21.23 21.61 ± 0.36 SC 8 17.01 17.23 17.09 17.11 ± 0.12 SC 9 10.78 10.75 11.07 10.87 ± 0.18 SC 10 8.75 8.54 8.77 8.69 ± 0.13 SC 11 4.19 4.19 4.15 4.18 ± 0.02 SC 12* 4.55 4.43 5.12 4.70 ± 0.40 (B)

Sample 1st triplicate 2nd triplicate 3rd triplicate Avrg ± Stdev

SC 5* - - - - SC 6 29.76 29.58 29.53 29.62 ± 0.12 SC 7* 29.61 29.58 (29.09) 29.56 ± 0.02 SC 8 24.34 24.28 24.41 24.34 ± 0.06 SC 9 18.21 18.34 18.09 18.21 ± 0.12 SC 10 14.12 (15.11) 14.51 14.32 ± 0.25 SC 11* 13.28 13.12 (12.17) 13.12 ±0.28 SC 12 6.28 6.14 6.20 6.20 ± 0.07

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Appendix D

Table III. qPCR raw data according to layout in Appendix A. (A) for miR-210 and (B) for miR-16. (A) Cq 1 Cq 2 Cq 3 Cq 1 Cq 2 Cq 3 Cq 1 Cq 2 Cq 3 Cq 1 Cq 2 Cq 3 Sample 30.13 29.55 30.07 35.21 35.21 34.62 25.2 25.19 24.39 26.91 26.75 26.95 Sample 30.03 30.53 30.19 - - 34.25 25.97 25.61 25.23 27.26 27.25 28.05 -RT - - - 38.20 - 38.23 -RT - - - 36.30 - - - - 36.00 - - - NTC - - - SC 27.87 27.53 27.30 24.13 24.47 24.29 21.67 21.94 21.23 17.23 17.01 17.09 SC 10.79 10.75 11.07 8.75 8.54 8.77 4.19 4.19 4.15 4.55 4.43 5.12 (B) Cq 1 Cq 2 Cq 3 Cq 1 Cq 2 Cq 3 Cq 1 Cq 2 Cq 3 Cq 1 Cq 2 Cq 3 Sample 32.73 31.43 32.98 31.42 31.33 31.83 - 34.62 32.86 29.69 29.67 28.93 Sample 34.81 35.00 34.14 39.13 - - 33.25 32.44 31.52 34.18 31.58 32.32 -RT - - - - -RT - - - - NTC - - - SC - - - 29.76 29.58 29.53 29.61 29.58 29.09 24.34 24.28 24.41 SC 18.21 18-43 18.09 14.12 - 14.51 13.28 13.12 12.71 6.28 6.14 6.2

OPTIMIZATION OF THE TWO-TAILED RT QPCR METHOD WITH SYNTHETIC MIR- 16 AND MIR-210 IN HUMAN PLASMA FOR FUTURE DIAGNOSTICS OF SEPSIS