Similar hypothyroid and sepsis circulating mRNA expression could be useful as a biomarker in nonthyroidal illness syndrome: a pilot study

ABSTRACT

Objective:

Based on hypothetical hypothyroidism and nonthyroidal illness syndrome (NTIS) gene expression similarities, we decided to compare the patterns of expression of both as models of NTIS. The concordant profile between them may enlighten new biomarkers for NTIS challenging scenarios.

Materials and methods:

We used Ion Proton System next-generation sequencing to build the hypothyroidism transcriptome. We selected two databanks in GEO2 platform datasets to find the differentially expressed genes (DEGs) in adults and children with sepsis. The ROC curve was constructed to calculate the area under the curve (AUC). The AUC, chi-square, sensitivity, specificity, accuracy, kappa and likelihood were calculated. We performed Cox regression and Kaplan-Meier analyses for the survival analysis.

Results:

Concerning hypothyroidism DEGs, 70.42% were shared with sepsis survivors and 61.94% with sepsis nonsurvivors. Some of them were mitochondrial gene types (mitGenes), and 95 and 88 were related to sepsis survivors and nonsurvivors, respectively. BLOC1S1, ROMO1, SLIRP and TIMM8B mitGenes showed the capability to distinguish sepsis survivors and nonsurvivors.

Conclusion:

We matched our hypothyroidism DEGs with those in adults and children with sepsis. Additionally, we observed different patterns of hypothyroid-related genes among sepsis survivors and nonsurvivors. Finally, we demonstrated that ROMO1, SLIRP and TIMM8B could be predictive biomarkers in children's sepsis.

Keywords
Transcriptome; RNA; sepsis; thyroid; nonthyroidal illness syndrome

INTRODUCTION

The nonthyroidal illness syndrome (NTIS) occurs when an extrathyroidal disease affects thyroid hormone concentration without the appropriate hypothalamic-pituitary-thyroid (HPT) axis response (¹). NTIS is the leading cause of thyroid hormone metabolism disturbed in hospitalized patients and could be a critical step in increasing their survival. A critically ill patient's thyroid function is affected by diseases (thyroid-originated or not) and drugs (such as amiodarone, dopamine or heparin), which could affect thyroid metabolism or result in interferences in laboratory measurements (²,³). Therefore, considering all the clinical and laboratory interferences in critically ill patients, thyroid function evaluation and NTIS diagnosis represent a challenge to physicians.

Although we do not fully understand the physiopathology of NTIS, we already know an essential part of its mechanism (⁴). NTIS is mainly related to decreased deiodinase type 1 (DIO1) activity, abnormal deiodinase type 3 (DIO3) production and thyroid axis suppression with an inappropriately normal thyroid-stimulating hormone (TSH) (⁴-⁷). These alterations promote tissue and systemic triiodothyronine (T3) drops associated with an increase in reverse T3 (rT3) in the presence of TSH value in the reference range and with normal or low concentrations of thyroxine (T4) (⁸). Even though thyroid hormone concentrations during healthy childhood and adulthood are different, the thyroid axis changes caused by NTIS in the newborn, child and adult are the same (³,⁹). The debate persists about whether NTIS involves an adaptative response or real hypothyroidism at the tissue level (¹⁰). The NTIS-related tissue decrease in T3 probably leads to indistinguishable gene expression repercussions similar to those observed in hypothyroid patients.

The unfavorable prognosis observed in low thyroid hormone concentrations is found in different clinical settings and study designs (¹¹-¹⁸). Septic shock is a significant cause of death in intensive care units (ICUs) and is associated with NTIS in newborns, children, and adults (⁴,¹⁹,²⁰).

Castro and cols. in an experimental model of septic shock, demonstrated systemic and tissue decreases in T4 and T3 (²¹). Taşcı and cols. also showed that sepsis progression was less severe in the hyperthyroid group and more severe in the hypothyroid group (²²). The prevalence of NTIS in critically ill patients may vary from 27.5% to 38.7%, but it is even higher in cases of sepsis (¹⁹,²³,²⁴). In addition, this prevalence is probably underestimated. There are laboratorial difficulties in measuring thyroid hormones in such clinical scenarios, as medications cause interference, and the neuroendocrine response to stress dynamic evolution is also a confounding factor (²⁵). Therefore, thyroid hormone concentrations with or without reverse T3 (rT3) measures might only lead us to the suspicion of NTIS (¹).

We hypothesized that the circulating RNA measurement alterations might directly evaluate the hormonal action from the blood cells or could be an indirect reflection of the tissue thyroid hormone repercussion, which can be obtained in a less invasive form. In the blood, these RNA alterations can result from the canonical and noncanonical thyroid hormone action leading to changes in gene expression (²⁶). On the other hand, the tissue microRNA (miR) expression can be transported through vesicles to circulation and affect mRNA production (²⁷). To emphasize this point of view, the Translational Safety Biomarker Pipeline (TransBioLine) published a Letter of Intent (LOI) in 2020, which was accepted by the Food & Drug Administration (FDA), establishing that circulating miR can be used as a non-invasive tool for tissue and mechanism-specific diagnosis.

The adipose tissue is the main source of miR in the circulation, and we have long known that thyroid hormones affect the RNA expression in this tissue (²⁷,²⁸). Additionally, thyroid hormones, primarily T3, participate directly in metabolism by mediating the transcription of mitochondrial proteins (²⁹,³⁰).

Our study aims to identify differentially expressed genes (DEGs), focusing on mitochondrial genes in hypothyroid patients without NTIS and correlating with sepsis and septic shock patients. The concordant profile between hypothyroid and septic shock patients may provide new biomarkers for challenging NTIS scenarios.

MATERIALS AND METHODS

Population

This study was conducted in accordance with the ethical principles of the Declaration of Helsinki and was approved by the Institutional Review Board (number 665,331; CAAE: 30746814.4.0000.5511). The transcriptome hypothyroidism study participants attended the university outpatient clinic and signed informed consent forms.

Blood samples, biochemical analysis, RNA extraction and cDNA synthesis from transcriptome hypothyroidism study

Venous blood samples were used for biochemical and RNA analyses. The blood for the total RNA analysis was collected and preserved with PAXgene blood RNA (Qiagen, NL, DE). TSH, free thyroxine (FT4), antithyroglobulin antibody (TgAb) and anti-thyroperoxidase antibody (TPOAb) analyses were performed with an Elecsys 2010 (Roche Diagnostics, IN, USA), following specific automated protocols for each test. The TSH reference values were 0.270-4.50 mU/L. The FT4 reference values were 0.93 to 1.70 ng/dL. The TgAb negative reference value was less than 115 IU/mL, and the TPOAb negative reference value was less than 34 IU/mL.

Total RNA was obtained from peripheral blood and extracted using the PAXgene Blood RNA Kit (Qiagen, NL, DE). The quantification of total RNA was performed on a Qubit Fluorometer 2.0 with its respective kit (Thermo Fisher Scientific, MA, USA). cDNA synthesis was performed using the SuperScript VILO Mastermix kit (Thermo Fisher Scientific, MA, USA) following the recommended protocol.

Hypothyroidism transcriptome libraries

The libraries were constructed with four individuals for the healthy euthyroid control group (CTL) and four patients for the hypothyroid group (HT). HT patients have never been treated with levothyroxine. The CTL individuals have a stable and reference range TSH. In contrast, the HT group also had stable TSH above 10 mU/L. The eight libraries used in this study are available on GEO (https://www.ncbi.nlm.nih.gov/geo/, accession number: GSE176153).

The transcriptome libraries were constructed using Ion Proton System next-generation sequencing (Thermo Fisher Scientific, MA, USA) with Ion AmpliSeq Transcriptome Human Gene Expression Kit protocols.

Bioinformatics workflow for transcriptome analysis

Transcriptome data analysis was performed using R Software version 2021.09.2 build 382 (³¹). The data were normalized using the trimmed mean of M-values (TMM), which uses the stable internal genes to establish the dispersion (³²). The NOISeq package (version 2.38.0) was used to call the DEG (³³). The analysis pipeline is available in Supplementary File 1. To produce the intersection data, we use the tool InteractiVenn (³⁴).

The characterization of mitochondrial RNAs was performed by the Human MitoCarta 3.0 database (³⁵). The Reactome database was used to analyze gene pathways in FunRich software version 3.1.3 (³⁶,³⁷).

Critical illness GEO Datasets

Based on the high prevalence of NTIS on sepsis, we searched for NTIS databanks in GEO Datasets (https://www.ncbi.nlm.nih.gov/gds) and selected two datasets for analysis. One is in the adult scenario (GSE54514), and the other is in the children scenario (GSE26440). In GSE54514, we separated the analyses into two blocks: sepsis survivors versus control and sepsis nonsurvivors versus control. The control group comprised thirty-six individuals, the sepsis survivor group comprised ninety-six, and thirty-one individuals formed the sepsis group's nonsurvivors (³⁸).

In GSE26440, we divided the analyses into two blocks: sepsis survivors versus control and sepsis nonsurvivors versus control. The control group consisted of thirty-two children, the sepsis survivor group comprised eighty-one children and seventeen children in the sepsis group's nonsurvivors (³⁹).

Bioinformatics workflow for microarray

All analyses were performed in R Software (³¹). The Limma package (version 3.50.0) was used to identify the differentially expressed genes in the microarrays. GEOquery (version 2.62.2) connected the chosen database with the software, and UMAP (version 0.2.8.8) was used to construct the array according to the selected datasets. The microarray analysis pipelines are available in Supplementary File 1. We considered differentially expressed transcripts with an FDR < 0.05. To produce the intersection data, we used the tool InteractiVenn (³⁴).

The characterization of mitochondrial RNAs was performed by the Human MitoCarta 3.0 database (³⁵). The Reactome database was used to analyze gene pathways in FunRich software version 3.1.3 (³⁶,³⁷).

Statistical analysis

Data are mainly presented as median, percentiles, and maximum and minimum values. The Mann-Whitney test was used to perform the two-group analysis of the continuous variables. We used the ROC curve to calculate the area under the curve (AUC) and established the cutoff point by Youden's method. The categorical variables were analyzed by the chi-square test (χ2) with Fisher's exact test when necessary. Sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) were calculated based on the Galen and Gambino formula. We also estimated the prevalence (pretest probability) and accuracy to weigh the biomarker values in each dataset. Cohen's kappa was used to avoid errors induced by missing data, and positive and negative likelihood ratios were calculated because prevalence does not influence them. Cox regression and the Kaplan-Meier method were used for the survival analysis. A p-value < 0.05 was considered significant. IBM SPSS Statistics for Windows, Version 26.0, from IBM Corp., released in 2019 (Armonk, NY, USA), was used to analyze the data.

RESULTS

Transcriptome analysis

The hypothyroidism scenario was identified on the GSE176153 dataset, which compared healthy participants and hypothyroidism patients. We included at least one male in each group to avoid a strong sex influence. The clinical and laboratory parameters are shown in Table 1. The analysis revealed 1,369 DEGs regulated by thyroid hormones in peripheral blood, represented in the first box from Figure 1A, B.

Table 1
Clinical and laboratory parameters from the hypothyroidism transcriptome (GSE176153)

	Control	Hypothyroidism	p value
Sex (male/female)	1/3	1/3	1.00
Age (years)	38 (36-40)	44.5 (34-52)	0.56
TSH (μUI/mL)	2.35 (1.3-4.2)	82.3 (58.6->100)	0.02
FT4 (ng/dL)	1.22 (1.1-1.3)	0.2 (0.1-0.5)	0.02
TgAb (UI/mL)	<10	766 (<10-1195)	0.09
TPOAb (UI/mL)	9.3 (<5-32.4)	198 (<5-515)	0.24

Figure 1
Shared genes between hypothyroidism and adult and pediatric sepsis survivors and nonsurvivors. (A) Differentially expressed gene (DEG) workflow between hypothyroidism and sepsis survivors in adults and children. (B) DEG workflow between hypothyroidism and sepsis nonsurvivors in both scenarios. (C) The mitGenes when comparing shared genes between hypothyroidism and sepsis survivors. (D) The mitGenes when comparing hypothyroidism and sepsis nonsurvivor shared genes.

Microarray analysis

The analysis of the GSE54514 dataset formed by the sepsis survivor and control (adults) groups revealed 3,072 DEGs in peripheral blood, represented in the second box in Figure 1A. The analysis of the sepsis nonsurvivor and control (adults) groups revealed 3,227 DEGs in peripheral blood, as described in the second box in Figure 1B.

The analysis of the GSE26440 dataset formed by the sepsis survivor and control (children) groups revealed 11,769 DEGs in peripheral blood, represented in the third box in Figure 1A. The analysis of the sepsis nonsurvivor and control groups (children) revealed 8,276 DEGs in peripheral blood, as described in the third box in Figure 1B.

Comparison of the DEGs in sepsis

Hypothyroidism versus sepsis survivors

Comparing hypothyroidism (GSE176153) versus sepsis survivors in GSE54514 and GSE26440, we found 964 shared DEGs, as shown in Supplementary List 1 and Figure 1A.

Hypothyroidism versus sepsis nonsurvivors

Comparing hypothyroidism (GSE176153) versus sepsis nonsurvivors (GSE54514 and GSE26440), we found 848 shared DEGs, as shown in Supplementary List 1 and Figure 1B.

Mitochondrial genes

Intersecting the 964 DEGs present in hypothyroidism and sepsis survivors with the list of 1136 human mitochondrial genes (mitGenes) in MitoCarta 3.0, we found 95 mitGenes (10%) in this scenario, as shown in Supplementary List 2 and Figure 1C.

Intersecting the 848 DEGs present in hypothyroidism and sepsis nonsurvivors with the 1136 human mitochondrial genes list (mitGenes) MitoCarta 3.0, we found 88 mitGenes (10%) in this scenario, as shown in Supplementary List 2 and Figure 1D.

Agreement in the increased or decreased expression levels of mitGenes in the analyzed scenarios

We looked at the mitGenes concordant logarithmic fold change (logFC) between hypothyroidism, sepsis survivors and sepsis nonsurvivors. From 964 shared genes between hypothyroidism and sepsis survivors, we found 95 mitGenes. All 95 mitGenes were present in hypothyroidism; 92 (97%) were overexpressed, and only 3 (3%) were underexpressed. In the adult sepsis survivor group, we found 26 of the 95 mitGenes (27%); 11 of them were overexpressed (42%) and 15 mitGenes were underexpressed (58%). In the child sepsis survivor group, we found 85 of the 95 mitGenes (90%); 13 (15%) were overexpressed and 72 (85%) were underexpressed.

From 848 shared genes between hypothyroidism and sepsis nonsurvivors, we found 88 mitGenes. They all appeared in hypothyroidism, with 85 (97%) mitGenes with increased expression and 3 (3%) mitGenes with decreased expression. In the adult sepsis nonsurvivor group, we found 48 of the 85 mitGenes; 43 (90%) were overexpressed and 5 (10%) were underexpressed. In the child septic nonsurvivor group, we found 64 of the 85 mitGenes; 25 (39%) were overexpressed and 39 (61%) were underexpressed.

We observed concordant increased expression of the BLOC1S1 and ROMO1 mitGenes in the hypothyroidism and sepsis survivor scenarios (adult and child). However, we did not find underexpressed mitGenes between these three scenarios. In addition, COX6A1, COX7B, DBI, MRPL22, MRPL51, MRPS18C, NDUFA4, POLQ, SLIRP, TIMM8B, UQCRQ, and also BLOC1S1 and ROMO1 mitGenes had concordant gains of expression in the hypothyroidism and nonsurvivor sepsis scenarios. However, we did not find any mitGenes with loss of expression between these three scenarios.

As we were interested in genes related to the ATP production mechanism, we considered the biological processes of BLOC1S1, ROMO1, SLIRP and TIMM8B mitGenes shared between all scenarios (hypothyroidism, sepsis survivors and nonsurvivors) and constructed a plot, as shown in Figure 2.

Figure 2
Biological pathways identified between the groups. Shared hypothyroidism mitochondrial genes and biological processes present in sepsis survivors and nonsurvivors are shown. The percentage of genes was calculated from the number of genes available in the database.

We evaluated the expression levels of four mitochondrial nonsurvivor genes that appeared in the three datasets and exhibited concordant expression. ROMO1 and TIMM8B showed higher AUCs, specificities, and accuracies in adults. ROMO1 and SLIRP showed higher AUCs, sensitivities, specificities, and accuracies in children (Table 2). Unfortunately, we could only construct children's survival curves and hazard ratios (GSE26440) (Figure 3 and Table 3). The follow-up time was tracked for 28 days after admission (40). Moreover, the selected mitochondrial genes showed the ability to distinguish sepsis survivors from nonsurvivors, as illustrated in Figure 3.

Table 2
Analysis summary of the 4 differentially expressed mitochondrial genes from sepsis nonsurvivors. The area under the curve (AUC), chi-square, chi-square p value, sensibility, specificity, positive and negative predictive values, accuracy, positive and negative likelihood ratios, Cohen's kappa and the kappa p value of each gene were calculated based on the gene expression from sepsis survivors and nonsurvivors in dataset GSE54514 from adults and GSE26440 from children. GSE54514 showed a prevalence of mortality of 24.41%, and GSE26440 showed a prevalence of 14.65%. A p value < 0.05 was considered significant

GSE54514 Adult Dataset
Gene symbol	BLOC1S1	ROMO1	SLIRP	TIMM8B
AUC	0.62	0.71	0.64	0.80
Chi-square	6.39	22.03	8.81	32.95
Chi-square (p value)	0.01	<0.01	0.003	<0.01
Sensibility	0.65	0.65	0.77	0.74
Specificity	0.61	0.8	0.53	0.81
Positive predictive value	0.35	0.51	0.35	0.56
Negative predictive value	0.84	0.88	0.88	0.91
Accuracy	0.62	0.76	0.59	0.8
Cohen's kappa	0.20	0.41	0.22	0.50
Cohen's kappa (p value)	0.01	<0.01	<0.01	<0.01
Positive likelihood ratio	2.23	3.70	2.15	4.43
Negative likelihood ratio	0.25	0.16	0.20	0.12
GSE26440 Pediatric Dataset
Gene symbol	BLOC1S1	ROMO1	SLIRP	TIMM8B
AUC	0.66	0.80	0.72	0.70
Chi-square	8.00	17.28	9.59	7.47
Chi-square (p value)	0.01	<0.01	<0.01	<0.01
Sensibility	0.59	0.76	0.71	0.71
Specificity	0.13	0.75	0.69	0.65
Positive predictive value	0.1	0.34	0.28	0.26
Negative predictive value	0.65	0.95	0.93	0.93
Accuracy	0.2	0.75	0.69	0.66
Cohen's kappa	0.26	0.34	0.24	0.20
Cohen's kappa (p value)	<0.01	<0.01	<0.01	<0.01
Positive likelihood ratio	0.81	3.79	3.00	2.69
Negative likelihood ratio	1.77	0.07	0.10	0.11

Figure 3
Survival curves of children with sepsis. The selected genes were shared between hypothyroidism, sepsis survivors and nonsurvivors scenarios and show the capability to distinguish sepsis survivors and nonsurvivors in children. The follow-up time was tracked for 28 days after admission by Wong et al. A- BLOC1S1; B- ROMO1; C- SLIRP and D- TIMM8B.

Table 3
Hazard ratios of nonsurvival for concordant expression genes between hypothyroidism and sepsis in children. Nonsurvival ratios with 95% confidence intervals (CIs) of BLOC1S1, ROMO1, SLIRP and TIMM8B expression in septic children (GSE26440)

Gene	Hazard Ratio	95% CI	p value
BLOC1S1	1.36	0.96-1.91	0.08
ROMO1	2.75	1.56-4.72	<0.01
SLIRP	1.65	1.21-2.25	<0.01
TIMM8B	1.85	1.15-2.98	0.01

DISCUSSION

NTIS is still considered an adaptative response in critically ill patients. To date, treatment with levothyroxine or liothyronine is not recommended in NTIS (⁸). Nevertheless, NTIS occurred in 62.9% of critically ill children and was an independent predictor of mortality (⁴¹). It has long been known that a low serum T4 is associated with an increased probability of death (⁴²). In critically ill patients and those with liver failure, NTIS was observed in 67.12% and was associated with a higher mortality rate than in those without the syndrome (⁴³). In sepsis, NTIS was associated with mortality, and a low total T3, free T3, or the combination of low T3 with low T4 are predictors of mortality (⁴⁴).

Bedside evaluation represents a real challenge for critically ill patients, especially those with a severe infectious disease such as sepsis, because multiple direct and indirect dysfunctions occur at the molecular and cellular levels (³⁸). As Todd and cols. reported, part of this complexity may be related to NTIS (⁴⁵). We identified a similar DEG in the blood pattern between hypothyroidism and septic patients, even with different datasets constructed with different methodologies. Our study identified that 964 (70.42%) and 848 (61.94%) of our hypothyroidism DEGs were shared with sepsis survivors and nonsurvivors, respectively.

Based on the thyroid hormone actions, as expected, some of our identified genes were mitochondrial types. Mitochondrial genes are primarily responsible for the production of ATP. In our study, 90% of mitochondrial genes are overexpressed in adult sepsis nonsurvivors. On the other hand, only 39% were overexpressed in children nonsurvivors.

Although critical care survival runs differently among adult and child populations, our main intention was to identify the similarities in the NTIS mechanism in those two populations (⁴⁶). However, the challenge in comparing those two populations was many because they have distinct backgrounds. These differences, for example, are the epidemiological profile, the previously undiagnosed illnesses, the disease that progresses to sepsis and the different clinical responses (⁴⁷).

Although we are hunting for similarities, not differences, the difference between adult and children populations deserves more consideration. This apparent disagreement could have two explanations, one physiological and another analytical. The physiological explanation is that some genes involved in children's growth and development could be already turned on (⁴⁸). Consequently, these growth and development genes may affect our analytic strategy. It happens because we used the TMM normalization strategy, and the stable genes through the samples are used to calculate the normalization factor (³²). So different populations with different genes composing the baseline alter the normalization factor and influence the significance of some genes.

Furthermore, some mitGenes with a concordant expression gain related to ATP production mechanism were shared between hypothyroidism and sepsis: SLIRP and TIMM8B appeared in sepsis nonsurvivor scenarios; ROMO1 and BLOC1S1 were present in both survivor and nonsurvivor scenarios. These specific mitGenes can represent NTIS biomarkers in nonsurvivors (SLIRP and TIMM8B).

Mitochondrial functions are necessary for ATP production and control of apoptosis mechanisms (⁴⁹). Long-term mitochondrial function and genes are associated with thyroid hormone influence (⁵⁰). Thyroid hormones regulate critical biological processes, such as energy consumption, thermogenesis, cell development and growth (⁵¹). Sepsis can also interfere with mitochondrial functions and cause damage to the mitochondrial electron transport chain (⁴⁹). Due to the inflammatory response, the increase in reactive oxygen species (ROS) leads to a change in mitochondria, causing a drop in ATP levels (⁵²,⁵³).

In hyperglycemic animal cardiomyocyte hypertrophy cells, TIMM8B was overexpressed in the colonic mucosa and myocardium (⁵⁴,⁵⁵). In addition, hyperglycemia is seen in patients with sepsis (⁵⁶). In accordance with our results, TIMM8B was up-regulated in hypothyroidism and only in nonsurvivors patients. Also, the overexpression showed an increased risk of death outcome, and TIMM8B could distinguish who survivor or not in children.

The mitGene SLIRP encodes a protein with a stabilizing function of ribosomal mRNA strands, which protects them from degradation, prevents abnormalities in the translation process, and plays a role in mitochondrial quality control between untranslated transcripts (⁵⁷). The action of the mitGene SLIRP guarantees a fundamental role in the maintenance of translations of transcripts that encode the subunits of proteins linked to oxidative phosphorylation, the primary cellular pathway for obtaining ATP (⁵⁸). In our study, SLIRP was up-regulated in hypothyroidism and only in nonsurvivors, possibly demonstrating the attempt to stabilize the mitochondria by maintaining adequate protein synthesis levels. In this case, principally, proteins are linked to the production of ATP, which will be essential in mitochondrial, cellular and tissue homeostasis in hypothyroidism and the fight against sepsis. This gene was also able to identify who survived, and the gene overexpression showed an increased risk of death in children with sepsis.

The MitGene BLOC1S1, also known as GCN5L1, has a critical protein-coding role with a homologous function of the acetyltransferase enzyme. This protein participates in acetyl-CoA binding, modulating the acetylation of electron transport chain proteins, whose final impact is directly linked to mitochondrial oxygen consumption and ATP levels (⁵⁹). The revealed increase in its expression in hypothyroidism and sepsis contributes to energy maintenance in these diseases.

ROMO1 encodes a protein present in the mitochondrial membrane and is responsible for the increase in the production of reactive oxygen species. This same protein has antimicrobial activity against several bacterial species. This gene is already a potential biomarker in diagnosing and predicting many diseases, including prostate and lung cancers, inflammation and oxidative stress in chronic obstructive pulmonary disease (⁶⁰,⁶¹). The antimicrobial action already justifies the increase in expression in patients with sepsis. The oxidative stress produced during sepsis, resulting from the increase in reactive oxygen species, also reinforces the increase in the expression of mitGene. This possible condition is strengthened when we see an increase in the expression of the mitGene ROMO1 in hypothyroidism. In our study, BLOC1S1 and also ROMO1 were up-regulated in all the scenarios: in hypothyroidism, survivors and non-survivors. Despite that, BLOC1S1 and ROMO1 could distinguish the children who survived or not. However, only ROMO1 showed an increased risk of death outcome in children with sepsis.

The decrease in metabolic expenditure would be favorable for preserving life in a critical care situation. However, reducing muscle strength, especially the respiratory or cardiac muscle, contributes to poor patient prognosis. Additionally, diaphragm weakness increases the mortality rate in critically ill patients (⁶²). An experimental sepsis model with NTIS showed that decreased thyroid hormones led to severe changes in mitochondrial physiology in the diaphragm (⁶³). In sepsis, skeletal musculature deiodinase activity can improve muscle repair, injury or muscular atrophy (⁵¹).

In adult sepsis, BLOC1S1, ROMO1, SLIRP and TIMM8B showed excellent ability to identify nonsurvivor samples. We observed the same results in children, except for the mitGene BLOC1S1. ROMO1, SLIRP and TIMM8B led to an elevated risk of nonsurvivor outcomes in children.

Our study has some drawbacks, as the datasets used were not designed to look for NTIS, and the thyroid hormone concentrations are unavailable. In addition, the different RNA detection methodologies and bioinformatic strategies represent another fragility, especially for correctly defining lost or gained expression. Furthermore, circulating RNA is mainly influenced by thyroid receptor alpha; in other words, this RNA profile reflects only a part of the whole scenario (⁶⁴). Also, sepsis databases were used in different populations, adults and children, and unfortunately, the information about adults’ follow-up time was unavailable. However, although the pattern of adults and children with sepsis is not precisely the same, we noticed that some genes found in our study are common in both. As a final point, we found a similar pattern between hypothyroid patients and those with sepsis, which could be the molecular fingerprint of NTIS. We also identified potential candidate genes for a biomarker panel of nonsurvivors patients.

Additionally, some genes could distinguish sepsis survivors and nonsurvivors and showed an increased risk of developing death outcomes in children. Therefore, we theorize that, in this scenario, after identifying the nonsurvivors’ expression pattern, the treatment with levothyroxine in the correctly selected group could improve survival. However, more research is needed to evaluate these genes in a well-designed study to control for confounders.

• Sponsorship: this study was supported by a research grant from the São Paulo State Research Foundation (FAPESP), number 2014/04193-0 to C P C.
• Data availability: the hypothyroidism transcriptome libraries are available in GEO (https://www.ncbi.nlm.nih.gov/geo/, accession number: GSE176153). The R code used to conduct the hypothyroidism transcriptome analysis is available on request from the corresponding author.

Acknowledgments:

we express our special thanks to Dr. Hector R. Wong for the GSE26440 follow-up information, which allowed us to construct the survivor curves. We also thank Coordination for the Improvement of Higher Education Personnel (Coordenação de Aperfeiçoamento Pessoal de Nível Superior – Capes) for their financial support to the Post Graduation Programs and the scholarship for AHLH (number: 88887.483598/2020-00).

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Supplementary Pipeline 1.

Pipeline analysis of databases: GSE176153, GSE54514 and GSE26440

GSE176153 - Hypothyroidism

if(!require("BiocManager",quietly=TRUE))

install.packages("BiocManager")

BiocManager::install("NOISeq")

BiocManager::install("org.Hs.eg.db")

BiocManager::install("edgeR")

BiocManager::install("clusterProfiler")

setwd("Select your directory")

require(NOISeq)

require(org.Hs.eg.db)

require(edgeR)

require(clusterProfiler)

df<-read.table("GSE176153.txt",header=TRUE,row.names=1)

design<-as.factor(rep(c("1","2"),each=4))

depth<-list(sum(df$CTL1),sum(df$CTL2),sum(df$CTL3),

sum(df$CTL4),sum(df$HT1),sum(df$HT2),sum(df$HT3),sum(df$HT4))

bol<-filterByExpr(df,design)

df<-df[bol,]

rm(bol)

df<-tmm(df)

myfactors<-data.frame(Thyroid = c("1","1","1","1","2","2",

"2","2"),ThyroidRun=c("1_1","1_1","1_1","1_1","2_2","2_2",

"2_2","2_2"),Run=c(rep("R2",4),rep("R2",4)))

mydata<-readData(data=df,factors=myfactors)

mynoiseq<-noiseqbio(mydata,norm="n",factor="Thyroid",

filter=3,a0per=0.9,depth=c(depth[[1]],depth[[2]],

depth[[3]],depth[[4]],depth[[5]],depth[[6]],depth[[7]],

depth[[8]]))

mynoiseq.deg<-degenes(mynoiseq,q=.95)

mynoiseq.deg<-mynoiseq.deg %>% filter(log2FC> 0.5|log2FC< -0.5)

mynoiseq.deg$symbol=mapIds(org.Hs.eg.db,keys=row.names

(mynoiseq.deg),column="SYMBOL",keytype="REFSEQ",multiVals="first")

write.csv(mynoiseq.deg,file="Output DGE GSE176153.csv")

GSE54514 – Sepsis survivor adults

if (!require("BiocManager", quietly = TRUE))

install.packages("BiocManager")

BiocManager::install("GEOquery")

BiocManager::install("limma")

install.packages("umap")

setwd("Select your directory")

require(GEOquery)

require(limma)

require(umap)

gset <- getGEO("GSE54514", GSEMatrix =TRUE, AnnotGPL=TRUE)

if (length(gset) > 1) idx <- grep("GPL6947", attr(gset, "names")) else idx <- 1

gset <- gset[[idx]]

fvarLabels(gset) <- make.names(fvarLabels(gset))

gsms <- paste0("000000000000000000XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX1",

"11111111111111111111111111111111111111111111111111",

"11111111111111111111111111111111111111111111100000",

"0000000000000")

sml <- strsplit(gsms, split="")[[1]]

sel <- which(sml != "X")

sml <- sml[sel]

gset <- gset[ ,sel]

ex <- exprs(gset)

qx <- as.numeric(quantile(ex, c(0., 0.25, 0.5, 0.75, 0.99, 1.0), na.rm=T))

LogC <- (qx[5] > 100) ||

(qx[6]-qx[1] > 50 && qx[2] > 0)

if (LogC) { ex[which(ex <= 0)] <- NaN

exprs(gset) <- log2(ex) }

gs <- factor(sml)

groups <- make.names(c("Sepsis survivor","Control"))

levels(gs) <- groups

gset$group <- gs

design <- model.matrix(~group + 0, gset)

colnames(design) <- levels(gs)

fit <- lmFit(gset, design)

cts <- paste(groups[1], groups[2], sep="-")

cont.matrix <- makeContrasts(contrasts=cts, levels=design)

fit2 <- contrasts.fit(fit, cont.matrix)

fit2 <- eBayes(fit2, 0.01)

tT <- topTable(fit2, adjust="fdr", sort.by="B", number=Inf)

tT <- subset(tT, select=c("ID","adj.P.Val","P.Value","t","B","logFC","Gene.symbol","Gene.title"))

write.table(tT, file=stdout(), row.names=F, sep="\t")

tT=subset(tT, adj.P.Val<0.05)

write.csv(tT,file="Output DGE GSE54514 Sepsis survivor.csv")

GSE54514 – Sepsis nonsurvivor adults

setwd("Select your directory")

require(GEOquery)

require(limma)

require(umap)

gset <- getGEO("GSE54514", GSEMatrix =TRUE, AnnotGPL=TRUE)

if (length(gset) > 1) idx <- grep("GPL6947", attr(gset, "names")) else idx <- 1

gset <- gset[[idx]]

fvarLabels(gset) <- make.names(fvarLabels(gset))

gsms <- paste0("0000000000000000001111111111111111111111111111111X",

"XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX",

"XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX00000",

"0000000000000")

sml <- strsplit(gsms, split="")[[1]]

sel <- which(sml != "X")

sml <- sml[sel]

gset <- gset[ ,sel]

ex <- exprs(gset)

qx <- as.numeric(quantile(ex, c(0., 0.25, 0.5, 0.75, 0.99, 1.0), na.rm=T))

LogC <- (qx[5] > 100) ||

(qx[6]-qx[1] > 50 && qx[2] > 0)

if (LogC) { ex[which(ex <= 0)] <- NaN

exprs(gset) <- log2(ex) }

gs <- factor(sml)

groups <- make.names(c("Sepsis nonsurvivor","Control"))

levels(gs) <- groups

gset$group <- gs

design <- model.matrix(~group + 0, gset)

colnames(design) <- levels(gs)

fit <- lmFit(gset, design)

cts <- paste(groups[1], groups[2], sep="-")

cont.matrix <- makeContrasts(contrasts=cts, levels=design)

fit2 <- contrasts.fit(fit, cont.matrix)

fit2 <- eBayes(fit2, 0.01)

tT <- topTable(fit2, adjust="fdr", sort.by="B", number=Inf)

tT <- subset(tT, select=c("ID","adj.P.Val","P.Value","t","B","logFC","Gene.symbol","Gene.title"))

write.table(tT, file=stdout(), row.names=F, sep="\t")

tT=subset(tT, adj.P.Val<0.05)

write.csv(tT,file="Output DGE GSE54514 Sepsis nonsurvivor.csv")

GSE26440 – Sepsis survivor children

setwd("Select your directory")

require(GEOquery)

require(limma)

require(umap)

gset <- getGEO("GSE26440", GSEMatrix =TRUE, AnnotGPL=TRUE)

if (length(gset) > 1) idx <- grep("GPL570", attr(gset, "names")) else idx <- 1

gset <- gset[[idx]]

gsms <- paste0("X111111111111111111111111X11X1111111X1XX00011XX000",

"00111X1111111X11111XX11111110010000X00001011110000",

"00000111X000101X11X111111111X1")

sml <- strsplit(gsms, split="")[[1]]

sel <- which(sml != "X")

sml <- sml[sel]

gset <- gset[ ,sel]

ex <- exprs(gset)

qx <- as.numeric(quantile(ex, c(0., 0.25, 0.5, 0.75, 0.99, 1.0), na.rm=T))

LogC <- (qx[5] > 100) ||

(qx[6]-qx[1] > 50 && qx[2] > 0)

if (LogC) { ex[which(ex <= 0)] <- NaN

exprs(gset) <- log2(ex) }

gs <- factor(sml)

groups <- make.names(c("Sepsis survivor","Control"))

levels(gs) <- groups

gset$group <- gs

design <- model.matrix(~group + 0, gset)

colnames(design) <- levels(gs)

fit <- lmFit(gset, design)

cts <- paste(groups[1], groups[2], sep="-")

cont.matrix <- makeContrasts(contrasts=cts, levels=design)

fit2 <- contrasts.fit(fit, cont.matrix)

fit2 <- eBayes(fit2, 0.01)

tT <- topTable(fit2, adjust="fdr", sort.by="B", number=Inf)

tT <- subset(tT, select=c("ID","adj.P.Val","P.Value","t","B","logFC","Gene.symbol","Gene.title"))

write.table(tT, file=stdout(), row.names=F, sep="\t")

tT=subset(tT, adj.P.Val<0.05)

write.csv(tT,file="Output DGE GSE26440 Children sepsis survivor.csv")

GSE26440 – Sepsis nonsurvivor children

setwd("Select your directory")

require(GEOquery)

require(limma)

require(umap)

gset <- getGEO("GSE26440", GSEMatrix =TRUE, AnnotGPL=TRUE)

if (length(gset) > 1) idx <- grep("GPL570", attr(gset, "names")) else idx <- 1

gset <- gset[[idx]]

fvarLabels(gset) <- make.names(fvarLabels(gset))

gsms <- paste0("1XXXXXXXXXXXXXXXXXXXXXXXX1XX1XXXXXXX1X11000XX11000",

"00XXX1XXXXXXX1XXXXX11XXXXXXX00X000010000X0XXXX0000",

"00000XXX1000X0X1XX1XXXXXXXXX1X")

sml <- strsplit(gsms, split="")[[1]]

sel <- which(sml != "X")

sml <- sml[sel]

gset <- gset[ ,sel]

ex <- exprs(gset)

qx <- as.numeric(quantile(ex, c(0., 0.25, 0.5, 0.75, 0.99, 1.0), na.rm=T))

LogC <- (qx[5] > 100) ||

(qx[6]-qx[1] > 50 && qx[2] > 0)

if (LogC) { ex[which(ex <= 0)] <- NaN

exprs(gset) <- log2(ex) }

gs <- factor(sml)

groups <- make.names(c("Sepsis nonsurvivor","Control"))

levels(gs) <- groups

gset$group <- gs

design <- model.matrix(~group + 0, gset)

colnames(design) <- levels(gs)

fit <- lmFit(gset, design)

cts <- paste(groups[1], groups[2], sep="-")

cont.matrix <- makeContrasts(contrasts=cts, levels=design)

fit2 <- contrasts.fit(fit, cont.matrix)

fit2 <- eBayes(fit2, 0.01)

tT <- topTable(fit2, adjust="fdr", sort.by="B", number=Inf)

tT <- subset(tT, select=c("ID","adj.P.Val","P.Value","t","B","logFC","Gene.symbol","Gene.title"))

write.table(tT, file=stdout(), row.names=F, sep="\t")

tT=subset(tT, adj.P.Val<0.05)

write.csv(tT,file="Output DGE GSE26440 Children sepsis nonsurvivor.csv")

Supplementary list 1.

Differentially expressed genes (DEGs) between comparisons of hypothyroidism, sepsis survivors and sepsis nonsurvivors groups

DEG in hypothyroidism and sepsis survivor, n = 964 DEG in hypothyroidism and sepsis nonsurvivor, n = 848 ABCA5 ABCA5 ABCB1 ABCB1 ABCB7 ABCB7 ABCC2 ABI1 ABI1 ACOT11 ABRACL ACP1 ACAT2 ACP6 ACOT11 ACTN1 ACP1 ACTN4 ACP6 ACTR10 ACTN1 ADAMTS5 ACTN4 ADAP1 ADAMTS5 ADD1 ADAP1 ADGRA2 ADH5 ADH5 ADHFE1 ADHFE1 ADM ADM ADRB2 ADRB2 AIM2 AFDN AKAP7 AIM2 AKT3 AKAP7 ALG6 AKT3 ALOX15 ALOX15 ANAPC4 AMPD2 ANKMY2 ANK1 ANKRA2 ANKMY2 ANKRD12 ANKRA2 ANKRD42 ANKRD12 ANKS1A ANKS1A ANXA1 ANXA1 ANXA11 ANXA11 AP3S1 AP3S1 APLF APLP2 APLP2 APOBEC3G APOBEC3G APOBR APOBR ARF3 ARAP2 ARF4 ARF3 ARGLU1 ARF4 ARHGAP24 ARGLU1 ARHGAP4 ARHGAP24 ARL1 ARID5B ARMC1 ARL1 ARPC1B ARMC1 ARSB ARMC8 ARV1 ARPC1B ASB3 ARSB ASCC2 ARV1 ASTE1 ASB3 ATAD2 ASCC2 ATG12 ASTE1 ATG4C ATAD2 ATP10D ATG12 ATP11B ATG4C ATP11C ATP10D ATP6V0A1 ATP11B ATP6V0C ATP11C ATP6V0D1 ATP23 ATP6V1C1 ATP6V0A1 ATP6V1D ATP6V0C ATP6V1E1 ATP6V0D1 ATP6V1G1 ATP6V1C1 ATP8A1 ATP6V1D B3GNT2 ATP6V1E1 BACE2 ATP6V1G1 BANK1 ATP8A1 BAZ2A ATPAF1 BBIP1 B3GNT2 BBS9 BACE2 BBX BANK1 BCAP31 BAZ2A BCCIP BBIP1 BDP1 BBS9 BEND4 BBX BET1 BCAP29 BICD2 BCCIP BLK BDH2 BLOC1S1 BDP1 BLOC1S2 BEND4 BOLA2 BET1 BOLA3 BICD2 BRI3BP BLK BTAF1 BLOC1S1 BTF3 BOLA2 BTLA BOLA3 BTN3A2 BORA BTN3A3 BRI3BP C12orf57 BTAF1 C12orf75 BTF3 C15orf39 BTLA C1GALT1C1 BTN3A2 C1orf21 BTN3A3 C21orf91 C12orf57 CAMKK2 C12orf75 CAMP C15orf39 CANT1 C18orf21 CAPN2 C1GALT1 CAPN7 C1GALT1C1 CAPNS2 C1orf21 CARD16 C21orf91 CARNMT1 CA1 CASK CAMKK2 CASP3 CAMP CBFA2T3 CANT1 CBR4 CAPN2 CCDC112 CAPN7 CCDC134 CAPNS2 CCDC141 CARD16 CCDC146 CARNMT1 CCDC15 CASK CCDC66 CBR4 CCDC7 CBX3 CCDC82 CCDC134 CCDC90B CCDC141 CCDC91 CCDC146 CCL5 CCDC15 CCND3 CCDC66 CCNJL CCDC7 CCSER2 CCDC82 CD14 CCDC90B CD160 CCDC91 CD180 CCL5 CD19 CCNC CD1C CCND3 CD200 CCNJL CD244 CCNL1 CD300LB CCSER2 CD3D CD14 CD3G CD160 CD47 CD180 CD52 CD19 CD53 CD1C CD79A CD200 CD79B CD226 CD84 CD244 CD96 CD33 CDC42 CD3D CDC5L CD3G CDK17 CD47 CDK18 CD52 CDKN2C CD53 CDKN3 CD69 CEBPE CD79A CEP120 CD79B CEP78 CD84 CETN2 CD96 CFAP44 CDC42 CHD3 CDC5L CHI3L2 CDK17 CHML CDK18 CKS2 CDKAL1 CLDND1 CDKN2C CLEC2B CDKN3 CLEC2D CEBPE CLTC CENPP CMC2 CEP120 CMPK2 CEP78 CNGA1 CETN2 CNOT7 CFAP44 CNTLN CHD3 COA1 CHI3L2 COBLL1 CHML COCH CHORDC1 COL4A3 CKS2 COPS2 CLCN3 COPS4 CLDND1 COPS8 CLEC1A COQ8A CLEC2B COX16 CLEC2D COX17 CLIC2 COX6A1 CLK1 COX6B1 CLTC COX7B CMC1 COX7C CMSS1 CPSF7 CNGA1 CPVL CNOT7 CR2 CNOT9 CREBRF CNTLN CREBZF COA1 CRTAM COBLL1 CSK COL7A1 CSNK1G2 COMMD3 CTNNA1 COPS4 CTSA COPS8 CTSW COQ8A CUZD1 CORO1A CWF19L2 COX10 CXorf65 COX16 CYFIP2 COX17 DBI COX6A1 DCAF12 COX6B1 DCAF13 COX7B DCP2 CPVL DDA1 CR2 DEPDC1 CRADD DGAT2 CREBRF DGKA CREBZF DHFR2 CRTAM DHX34 CRYGS DLGAP5 CSNK1G2 DMTF1 CTBP1 DMTN CTNNA1 DNA2 CTNNBL1 DNAJC15 CTSA DNM2 CTSW DPH3 CWF19L2 DPY19L3 CXCR6 DYNC1I2 CXorf65 DZIP3 CYFIP2 E2F5 DCAF13 EAF2 DCP2 ECE1 DDA1 EEF1A1 DENND4A EEF1B2 DEPDC1 EGLN2 DGAT2 EIF2AK3 DGCR2 EIF3E DGKA EIF4E DHX34 ELAC1 DMTF1 ELP6 DNAAF2 EMC2 DNAJC15 EMG1 DNAJC19 ENSA DNM2 EPB41L4A DPH3 EPM2AIP1 DPM1 ERCC8 DPY19L3 ERF DYNC1I2 ERGIC2 DYNC1LI2 ERI2 DYNLT3 ERMARD DZIP3 EXOSC10 E2F5 FAM126A ECE1 FAM149B1 EEF1A1 FAM169A EEF1B2 FAM204A EEF1E1 FAM3C EFCAB7 FANCB EGLN2 FARP2 EIF2AK3 FASTKD1 EIF3E FASTKD3 EIF4E FAU ELAC1 FBXO8 ELP6 FBXW11 EMG1 FBXW4 ENPP4 FCER1A ENSA FCER2 EPB41L4A FCGR2B EPM2AIP1 FCGR2C EPS15L1 FCGRT ERCC8 FCRL2 ERF FCRLA ERGIC2 FFAR2 ERI2 FGD4 ERMARD FGFBP2 ESRRA FGGY EVI2A FGR EXOSC10 FKBP3 EXOSC8 FLI1 EXOSC9 FLII FAM126A FLOT2 FAM149B1 FNTA FAM169A FOSL2 FAM204A FRA10AC1 FAM3C FRG1 FANCB FRY FANCL FXR1 FARP2 GCC2 FASTKD1 GCHFR FAU GCNT2 FBXO8 GEN1 FBXW11 GGH FBXW4 GIMAP7 FCER1A GLOD4 FCER2 GLS FCGR2B GNL3 FCGR2C GPATCH2 FCRL2 GPD2 FCRLA GPM6A FFAR2 GPR141 FGD4 GPR174 FGFBP2 GPR55 FGFR1OP2 GPR89A FGGY GPRC5B FGR GPSM3 FKBP3 GRHL1 FLI1 GTF2H3 FLII GUK1 FLOT2 GYPA FMNL1 GZMA FNTA GZMH FOSL2 GZMK FRA10AC1 HACD3 FRG1 HACD4 FRY HAT1 FXR1 HBS1L GATAD2A HCK GBP6 HDAC4 GCC2 HEATR5B GCHFR HENMT1 GEMIN2 HERPUD2 GEN1 HIGD1A GFM2 HIKESHI GGH HINT1 GIMAP7 HLA-DOB GLMN HLA-DQA1 GLOD4 HMGB2 GLS HMGN3 GNAI1 HSF5 GNL3 HSH2D GPATCH2 HSP90AA1 GPCPD1 HSPA1A GPD2 HSPB11 GPM6A HSPBAP1 GPR141 IAH1 GPR174 IFI44L GPR21 IFIT1 GPR55 IFIT3 GPR89A IFNGR1 GPRC5B IFT20 GPSM3 IL15 GRK2 IL18RAP GSAP IL1RN GTF2H3 IL7 GTSF1 ING3 GYPA INTS6L GZMA IQCB1 GZMH IQCE GZMK ISG15 HABP2 ITGA4 HACD3 ITGB2 HACD4 ITM2A HACE1 ITPR2 HAUS1 KDM3A HBS1L KDM6B HCK KDM7A HDAC4 KIAA0232 HEATR5B KIAA0825 HERPUD2 KIAA0930 HIGD1A KIAA1109 HINT1 KIF15 HK1 KIF19 HLA-DOB KIZ HLA-DQA1 KLF13 HMGB2 KLHDC2 HMGN3 KLRB1 HSF5 KLRC3 HSH2D KLRC4 HSPA14 KLRD1 HSPB11 KLRF1 IAH1 KLRK1 IFIT1 KMO IFIT3 KRBOX4 IFNG KRCC1 IFNGR1 KRR1 IFT20 LAMTOR5 IL18RAP LANCL1 IL1RN LAX1 IL5RA LCN2 IL7 LDAH IMMP2L LDHB ING3 LGALS8 INTS6L LGALS9 IQCB1 LILRA6 IQCE LLPH IRF2BPL LONRF3 ISG15 LPXN ITGA4 LRFN1 ITGAX LRP10 ITGB2 LSM1 ITM2A LSM8 ITPR2 LSP1 KDM3A LTBR KDM6B LTN1 KDM7A LTV1 KIAA0232 LUC7L3 KIAA0825 LY6E KIAA0930 LYN KIAA1109 LYRM2 KIAA1586 LYST KIF18A LYVE1 KIF19 LZTFL1 KIF5C MAGOHB KIZ MAP2K3 KLHDC2 MAP3K3 KLHL9 MAP4K3 KLRB1 MAP7D3 KLRC3 MAPKAPK2 KLRC4 MAPKAPK5 KLRD1 MAPRE2 KLRF1 MBIP KMO MBNL3 KRBOX4 MBOAT7 KRCC1 MCOLN2 KRR1 MCPH1 LAMTOR5 MCUB LANCL1 MED13 LAX1 MED14 LCN2 MELK LDAH MERTK LDHB METTL14 LGALS8 METTL18 LGALS9 METTL4 LILRA6 MIB1 LONRF3 MICU3 LPXN MIDN LRFN1 MIER1 LRP10 MIPEP LSM8 MLLT6 LTBR MMAA LTN1 MMP25 LTV1 MRPL11 LUC7L3 MRPL19 LYN MRPL22 LYRM2 MRPL24 LYST MRPL35 LYVE1 MRPL36 LZTFL1 MRPL40 MAP3K3 MRPL43 MAP4K3 MRPL46 MAP7D3 MRPL47 MAPKAPK2 MRPL51 MAPKAPK5 MRPS11 MAPRE2 MRPS17 MBIP MRPS18C MBNL3 MS4A3 MBOAT7 MSN MCAM MTERF1 MCOLN2 MTERF4 MCPH1 MTHFD2 MCUB MTM1 MDH1 MVP MED13 MX1 MED14 MYBL1 MELK MYC MEMO1 MYL12B MERTK MYNN METTL14 MYO1F METTL15 MYO9A METTL18 N4BP2L2 METTL2A NAA38 METTL4 NAA50 METTL5 NADK MIB1 NAP1L4 MICU3 NBEAL1 MIDN NCK1 MIER1 NCOA3 MIPEP NDC80 MITD1 NDUFA1 MLLT6 NDUFA4 MMAA NDUFA7 MMP25 NDUFAF4 MORF4L2 NDUFB1 MRPL11 NDUFB2 MRPL15 NDUFB8 MRPL19 NDUFS3 MRPL24 NDUFS4 MRPL32 NEDD4 MRPL35 NEDD8 MRPL39 NEIL3 MRPL43 NEK11 MRPL46 NFKBIA MRPL50 NFU1 MRPL58 NFXL1 MRPL9 NFYB MRPS11 NIFK MRPS18C NIPAL2 MS4A3 NIPSNAP3B MS4A6A NLRP1 MSN NME8 MTCL1 NOC3L MTERF1 NOL11 MTERF4 NOP10 MTHFD2 NOTCH1 MTIF3 NPM1 MVP NPRL3 MYBL1 NSMCE4A MYC NSUN6 MYL12B NUDT7 MYNN NUP107 MYO1F NUP54 MYO1G NUSAP1 MYO9A OAS2 N4BP2L2 OASL NAA38 OCIAD2 NAA50 ODF2L NADK OLA1 NAP1L4 OLIG1 NBEAL1 OLIG2 NCF1 OLR1 NCK1 OMA1 NDUFA7 OR2W3 NDUFAF4 ORC2 NDUFB2 ORC3 NDUFB8 OSBPL9 NEDD4 P2RY10 NEK11 PANK3 NFKBIA PARP15 NFXL1 PAXBP1 NFYB PBX2 NIFK PCM1 NINJ1 PCMTD1 NIPSNAP3B PDCD10 NIT2 PDE4D NLRP1 PDE6D NME8 PGAM1 NOC3L PGBD2 NOD1 PHF20L1 NOL11 PHTF2 NOP10 PIAS1 NOTCH1 PIGP NPM1 PIK3CB NPRL3 PKN1 NSA2 PLAA NSMCE4A PLB1 NSUN3 PLEKHO2 NSUN6 PMS1 NUCB2 PNISR NUDT7 PNRC2 NUP107 POC5 NUP54 POLA1 NUSAP1 POLQ OASL POLR2F OCIAD2 PPA2 ODF2L PPAT ODF3B PPIA OLA1 PPIB OLIG1 PPIL3 OLIG2 PPP4C OMA1 PPP6R1 ORC2 PPWD1 ORC3 PREX1 OXER1 PRIMPOL OXR1 PRKDC OXSM PRPF18 P2RY10 PRPF40A PANK3 PRPS1 PARP15 PRR3 PAXBP1 PRRC2C PBDC1 PSMA6 PBX2 PSMC6 PCM1 PTCD2 PCMTD1 PTCD3 PDCD10 PTGDR2 PDCL PTGES3 PDE4D PTOV1 PDE6D PTPN12 PDPR PTPN4 PDS5A PTRHD1 PDZD11 PUS3 PFKL PXN PGAM1 PXYLP1 PGBD2 RAB12 PHF20L1 RAB20 PHF6 RAB28 PHTF2 RAB35 PIAS1 RAB3D PIGC RAB3IP PIGP RAB7A PIK3CB RABEP1 PIK3CD RABGAP1L PINK1 RABIF PKN1 RABL3 PLAA RAC2 PLB1 RAD17 PLEKHO2 RAD51C PLRG1 RALGAPA1 PMS1 RARA PNISR RARS2 PNRC2 RASA3 POC5 RASGEF1B POLA1 RASSF6 POLQ RBM25 POLR3F RBM27 POU5F2 RBM4B PPA2 RBM7 PPAT RBX1 PPIA RETN PPIL3 RFESD PPP4C RGS19 PPP4R3A RHOBTB3 PPP6R1 RMDN1 PPWD1 RNASE2 PRELID3B RNASE3 PREX1 RNF19A PRIMPOL RNF213 PRKDC ROMO1 PROSER2 RPA4 PRPF18 RPL10A PRPF40A RPL12 PRPS1 RPL15 PRRC2C RPL22 PSAP RPL26L1 PSMC6 RPL29 PSMD10 RPL30 PTCD2 RPL34 PTCD3 RPL35 PTGDR2 RPL5 PTGES3 RPRD1A PTPN12 RPS15A PTPN22 RPS21 PTPN4 RPS23 PTPRC RPS27 PXN RPS27A RAB11B RPS29 RAB12 RPS5 RAB20 RPS6 RAB28 RPS6KA1 RAB35 RRAS2 RAB3D RUFY3 RAB3IP RWDD2A RAB7A S100A12 RABEP1 S100Z RABGAP1L SAMD3 RABIF SBDS RABL3 SBF2 RAD17 SCCPDH RAD51C SCOC RAD51D SDR39U1 RALGAPA1 SECC RAN SEL1L RANBP2 SENP6 RARA SEPSECS RARS2 SETDB2 RASA2 SETX RASA3 SF3B1 RASSF6 SF3B5 RBL1 SF3B6 RBM25 SGK1 RBM27 SH2B3 RBM4B SH2D1A RBM7 SHISA5 RERE SHKBP1 RETN SIGLEC6 RGS19 SIRPA RHOBTB3 SKA2 RNASE2 SKA3 RNASE3 SLA RNF10 SLAMF6 RNF135 SLC11A2 RNF19A SLC25A26 RNF213 SLC25A29 RNFT1 SLC25A36 ROMO1 SLC30A4 RPA4 SLC38A11 RPE SLC5A3 RPF1 SLCO4C1 RPL10A SLIRP RPL12 SMAP2 RPL15 SMC5 RPL22 SMC6 RPL24 SMCHD1 RPL27A SMDT1 RPL29 SNRPA1 RPL30 SNRPD2 RPL32 SNRPE RPL34 SNX13 RPL35 SNX14 RPL36A SNX5 RPL37 SOAT1 RPL5 SOX6 RPL6 SPAG7 RPL7 SPECC1 RPL9 SPIDR RPRD1A SPIRE2 RPS15A SRP14 RPS21 SRSF4 RPS23 SRSF7 RPS25 SS18 RPS27 SSR2 RPS27A ST3GAL2 RPS29 ST6GALNAC3 RPS3A STAMBP RPS5 STAP1 RPS6 STARD3NL RPS6KA1 STAT3 RRAGB STAT4 RRAS2 STK26 RSL24D1 STRBP RSPH14 STXBP2 RSRC2 SUB1 RUFY3 SUCLG1 RWDD1 SUMO1 RWDD2A SUPT3H RWDD3 SVIP S100A12 SYCP2 S100Z SYNE2 SAMD12 TAF12 SAMD3 TAF2 SBDS TANK SBF2 TAOK2 SCCPDH TARP SCOC TAS2R14 SDR39U1 TBCA SECC TC2N SECA2 TCN1 SECTM1 TDG SEL1L TECPR2 SENP6 TEX30 SEPSECS TFB2M SETDB2 TFEB SF3B1 TGFB1 SF3B2 THAP1 SF3B5 THOC1 SF3B6 TIA1 SGK1 TIAM2 SH2B3 TIGIT SH2D1A TIMM17A SH3BP1 TIMM8B SHISA5 TLE3 SHKBP1 TLN1 SIGLEC11 TM2D1 SIGLEC6 TMCC1 SIRPA TMCC2 SKA2 TMEM106C SKA3 TMEM116 SLA TMEM141 SLAMF6 TMEM181 SLAMF7 TMEM258 SLC11A2 TMEM260 SLC25A26 TMEM30B SLC25A29 TMEM39A SLC25A33 TMEM43 SLC25A36 TMEM60 SLC30A4 TMEM62 SLC35A5 TMOD2 SLC35D1 TMSB15B SLC35E1 TMTC3 SLC5A3 TNFAIP2 SLC9B2 TNFAIP6 SLCO4C1 TNFAIP8 SLIRP TNFRSF1A SMAP2 TOP2B SMC4 TPCN1 SMC5 TPD52L2 SMC6 TPP2 SMCHD1 TPR SMDT1 TRAM1 SMIM20 TRIAP1 SMIM8 TRIM25 SNRPA1 TRIM61 SNRPB TRMO SNRPD1 TRMT13 SNRPD2 TRPS1 SNRPE TSC22D3 SNX13 TTC14 SNX14 TUBE1 SNX24 TXN SNX5 U2SURP SOAT1 UBAP1 SOX6 UBE2L6 SPATA7 UBE2M SPECC1 UBE2R2 SPIDR UBE2T SRFBP1 UBE3A SRP14 UCHL3 SRSF7 UFC1 SS18 UFL1 SSB UFSP2 ST3GAL2 UHRF2 ST6GALNAC3 UMPS STAMBP UQCRQ STAP1 URI1 STARD3NL USP16 STARD4 USP24 STAT3 USP28 STAT4 USP45 STK26 VAMP4 STRBP VPS13A STT3A VPS13C STXBP2 VPS25 SUB1 VPS29 SUCLG1 VPS50 SUMO1 VSTM1 SUPT3H WBP2 SVIP WDPCP SYNE2 WDR49 TAF2 WDR61 TANK XCL1 TAOK2 XPO6 TARP XRCC4 TAS2R14 YBX3 TBC1D19 ZBED5 TBCA ZBED6 TBL1X ZBED6CL TC2N ZBTB14 TCEAL4 ZBTB8OS TCEAL8 ZC3H8 TCEANC2 ZDHHC20 TCN1 ZEB2 TECPR2 ZFAND1 TEX30 ZFAND2A TFB2M ZFAND3 TFEB ZFC3H1 TGFB1 ZFP36L1 THAP1 ZFP37 THOC1 ZFP69 THRAP3 ZFP82 TIA1 ZFYVE16 TIGIT ZKSCAN8 TIMM17A ZMYM2 TLE3 ZNF138 TLN1 ZNF14 TM2D1 ZNF141 TMA16 ZNF17 TMC5 ZNF22 TMCC1 ZNF227 TMCC2 ZNF230 TMEM116 ZNF235 TMEM126B ZNF253 TMEM181 ZNF254 TMEM258 ZNF260 TMEM260 ZNF280C TMEM43 ZNF280D TMEM62 ZNF302 TMOD2 ZNF32 TMSB10 ZNF322 TMSB15B ZNF420 TMTC3 ZNF429 TNFAIP2 ZNF43 TNFAIP6 ZNF430 TNFAIP8 ZNF431 TNFRSF1A ZNF441 TOMM6 ZNF467 TOP2B ZNF493 TOP3B ZNF501 TPCN1 ZNF506 TPD52L2 ZNF532 TPP2 ZNF559 TPR ZNF566 TRAM1 ZNF568 TRAPPC6B ZNF569 TRIAP1 ZNF571 TRIM25 ZNF585A TRIM61 ZNF607 TRIM8 ZNF626 TRMO ZNF638 TRMT13 ZNF649 TRNT1 ZNF675 TRPS1 ZNF678 TSC22D3 ZNF680 TTC14 ZNF746 TTC30A ZNF782 TUBB6 ZNF791 TUBE1 ZNF800 TUBGCP4 ZNF85 TXN ZNF91 TYW3 ZRANB2 U2SURP ZSCAN9 UBAP1 ZYX UBE2M UBE2O UBE2R2 UBE3A UBR7 UCHL3 UFC1 UFL1 UFSP2 UHRF2 UMPS UNC50 UNC93B1 UPF1 UQCRQ URI1 USP16 USP24 USP28 USP31 USP45 VAMP2 VAMP4 VPS13A VPS13C VPS25 VPS29 VPS50 VSIG10 VSTM1 WBP2 WDPCP WDR49 WDR61 WDR7 WEE1 WRN XCL1 XPO1 XPO6 XRCC4 YEATS4 YOD1 ZBED5 ZBED6 ZBED6CL ZBTB14 ZBTB8OS ZC3H8 ZDHHC20 ZEB2 ZFAND1 ZFAND2A ZFAND3 ZFC3H1 ZFP36L1 ZFP37 ZFP69 ZFP82 ZFYVE16 ZKSCAN8 ZMYM2 ZNF136 ZNF138 ZNF14 ZNF141 ZNF17 ZNF177 ZNF22 ZNF227 ZNF230 ZNF235 ZNF25 ZNF253 ZNF254 ZNF260 ZNF280C ZNF302 ZNF32 ZNF322 ZNF420 ZNF43 ZNF430 ZNF431 ZNF441 ZNF467 ZNF493 ZNF501 ZNF506 ZNF532 ZNF559 ZNF566 ZNF568 ZNF569 ZNF571 ZNF585A ZNF607 ZNF626 ZNF638 ZNF649 ZNF658 ZNF675 ZNF678 ZNF680 ZNF746 ZNF782 ZNF784 ZNF791 ZNF800 ZNF85 ZNF91 ZNF93 ZNHIT3 ZRANB2 ZSCAN9 ZYX

DEG in hypothyroidism and sepsis survivor, n = 964	DEG in hypothyroidism and sepsis nonsurvivor, n = 848
ABCA5	ABCA5
ABCB1	ABCB1
ABCB7	ABCB7
ABCC2	ABI1
ABI1	ACOT11
ABRACL	ACP1
ACAT2	ACP6
ACOT11	ACTN1
ACP1	ACTN4
ACP6	ACTR10
ACTN1	ADAMTS5
ACTN4	ADAP1
ADAMTS5	ADD1
ADAP1	ADGRA2
ADH5	ADH5
ADHFE1	ADHFE1
ADM	ADM
ADRB2	ADRB2
AIM2	AFDN
AKAP7	AIM2
AKT3	AKAP7
ALG6	AKT3
ALOX15	ALOX15
ANAPC4	AMPD2
ANKMY2	ANK1
ANKRA2	ANKMY2
ANKRD12	ANKRA2
ANKRD42	ANKRD12
ANKS1A	ANKS1A
ANXA1	ANXA1
ANXA11	ANXA11
AP3S1	AP3S1
APLF	APLP2
APLP2	APOBEC3G
APOBEC3G	APOBR
APOBR	ARF3
ARAP2	ARF4
ARF3	ARGLU1
ARF4	ARHGAP24
ARGLU1	ARHGAP4
ARHGAP24	ARL1
ARID5B	ARMC1
ARL1	ARPC1B
ARMC1	ARSB
ARMC8	ARV1
ARPC1B	ASB3
ARSB	ASCC2
ARV1	ASTE1
ASB3	ATAD2
ASCC2	ATG12
ASTE1	ATG4C
ATAD2	ATP10D
ATG12	ATP11B
ATG4C	ATP11C
ATP10D	ATP6V0A1
ATP11B	ATP6V0C
ATP11C	ATP6V0D1
ATP23	ATP6V1C1
ATP6V0A1	ATP6V1D
ATP6V0C	ATP6V1E1
ATP6V0D1	ATP6V1G1
ATP6V1C1	ATP8A1
ATP6V1D	B3GNT2
ATP6V1E1	BACE2
ATP6V1G1	BANK1
ATP8A1	BAZ2A
ATPAF1	BBIP1
B3GNT2	BBS9
BACE2	BBX
BANK1	BCAP31
BAZ2A	BCCIP
BBIP1	BDP1
BBS9	BEND4
BBX	BET1
BCAP29	BICD2
BCCIP	BLK
BDH2	BLOC1S1
BDP1	BLOC1S2
BEND4	BOLA2
BET1	BOLA3
BICD2	BRI3BP
BLK	BTAF1
BLOC1S1	BTF3
BOLA2	BTLA
BOLA3	BTN3A2
BORA	BTN3A3
BRI3BP	C12orf57
BTAF1	C12orf75
BTF3	C15orf39
BTLA	C1GALT1C1
BTN3A2	C1orf21
BTN3A3	C21orf91
C12orf57	CAMKK2
C12orf75	CAMP
C15orf39	CANT1
C18orf21	CAPN2
C1GALT1	CAPN7
C1GALT1C1	CAPNS2
C1orf21	CARD16
C21orf91	CARNMT1
CA1	CASK
CAMKK2	CASP3
CAMP	CBFA2T3
CANT1	CBR4
CAPN2	CCDC112
CAPN7	CCDC134
CAPNS2	CCDC141
CARD16	CCDC146
CARNMT1	CCDC15
CASK	CCDC66
CBR4	CCDC7
CBX3	CCDC82
CCDC134	CCDC90B
CCDC141	CCDC91
CCDC146	CCL5
CCDC15	CCND3
CCDC66	CCNJL
CCDC7	CCSER2
CCDC82	CD14
CCDC90B	CD160
CCDC91	CD180
CCL5	CD19
CCNC	CD1C
CCND3	CD200
CCNJL	CD244
CCNL1	CD300LB
CCSER2	CD3D
CD14	CD3G
CD160	CD47
CD180	CD52
CD19	CD53
CD1C	CD79A
CD200	CD79B
CD226	CD84
CD244	CD96
CD33	CDC42
CD3D	CDC5L
CD3G	CDK17
CD47	CDK18
CD52	CDKN2C
CD53	CDKN3
CD69	CEBPE
CD79A	CEP120
CD79B	CEP78
CD84	CETN2
CD96	CFAP44
CDC42	CHD3
CDC5L	CHI3L2
CDK17	CHML
CDK18	CKS2
CDKAL1	CLDND1
CDKN2C	CLEC2B
CDKN3	CLEC2D
CEBPE	CLTC
CENPP	CMC2
CEP120	CMPK2
CEP78	CNGA1
CETN2	CNOT7
CFAP44	CNTLN
CHD3	COA1
CHI3L2	COBLL1
CHML	COCH
CHORDC1	COL4A3
CKS2	COPS2
CLCN3	COPS4
CLDND1	COPS8
CLEC1A	COQ8A
CLEC2B	COX16
CLEC2D	COX17
CLIC2	COX6A1
CLK1	COX6B1
CLTC	COX7B
CMC1	COX7C
CMSS1	CPSF7
CNGA1	CPVL
CNOT7	CR2
CNOT9	CREBRF
CNTLN	CREBZF
COA1	CRTAM
COBLL1	CSK
COL7A1	CSNK1G2
COMMD3	CTNNA1
COPS4	CTSA
COPS8	CTSW
COQ8A	CUZD1
CORO1A	CWF19L2
COX10	CXorf65
COX16	CYFIP2
COX17	DBI
COX6A1	DCAF12
COX6B1	DCAF13
COX7B	DCP2
CPVL	DDA1
CR2	DEPDC1
CRADD	DGAT2
CREBRF	DGKA
CREBZF	DHFR2
CRTAM	DHX34
CRYGS	DLGAP5
CSNK1G2	DMTF1
CTBP1	DMTN
CTNNA1	DNA2
CTNNBL1	DNAJC15
CTSA	DNM2
CTSW	DPH3
CWF19L2	DPY19L3
CXCR6	DYNC1I2
CXorf65	DZIP3
CYFIP2	E2F5
DCAF13	EAF2
DCP2	ECE1
DDA1	EEF1A1
DENND4A	EEF1B2
DEPDC1	EGLN2
DGAT2	EIF2AK3
DGCR2	EIF3E
DGKA	EIF4E
DHX34	ELAC1
DMTF1	ELP6
DNAAF2	EMC2
DNAJC15	EMG1
DNAJC19	ENSA
DNM2	EPB41L4A
DPH3	EPM2AIP1
DPM1	ERCC8
DPY19L3	ERF
DYNC1I2	ERGIC2
DYNC1LI2	ERI2
DYNLT3	ERMARD
DZIP3	EXOSC10
E2F5	FAM126A
ECE1	FAM149B1
EEF1A1	FAM169A
EEF1B2	FAM204A
EEF1E1	FAM3C
EFCAB7	FANCB
EGLN2	FARP2
EIF2AK3	FASTKD1
EIF3E	FASTKD3
EIF4E	FAU
ELAC1	FBXO8
ELP6	FBXW11
EMG1	FBXW4
ENPP4	FCER1A
ENSA	FCER2
EPB41L4A	FCGR2B
EPM2AIP1	FCGR2C
EPS15L1	FCGRT
ERCC8	FCRL2
ERF	FCRLA
ERGIC2	FFAR2
ERI2	FGD4
ERMARD	FGFBP2
ESRRA	FGGY
EVI2A	FGR
EXOSC10	FKBP3
EXOSC8	FLI1
EXOSC9	FLII
FAM126A	FLOT2
FAM149B1	FNTA
FAM169A	FOSL2
FAM204A	FRA10AC1
FAM3C	FRG1
FANCB	FRY
FANCL	FXR1
FARP2	GCC2
FASTKD1	GCHFR
FAU	GCNT2
FBXO8	GEN1
FBXW11	GGH
FBXW4	GIMAP7
FCER1A	GLOD4
FCER2	GLS
FCGR2B	GNL3
FCGR2C	GPATCH2
FCRL2	GPD2
FCRLA	GPM6A
FFAR2	GPR141
FGD4	GPR174
FGFBP2	GPR55
FGFR1OP2	GPR89A
FGGY	GPRC5B
FGR	GPSM3
FKBP3	GRHL1
FLI1	GTF2H3
FLII	GUK1
FLOT2	GYPA
FMNL1	GZMA
FNTA	GZMH
FOSL2	GZMK
FRA10AC1	HACD3
FRG1	HACD4
FRY	HAT1
FXR1	HBS1L
GATAD2A	HCK
GBP6	HDAC4
GCC2	HEATR5B
GCHFR	HENMT1
GEMIN2	HERPUD2
GEN1	HIGD1A
GFM2	HIKESHI
GGH	HINT1
GIMAP7	HLA-DOB
GLMN	HLA-DQA1
GLOD4	HMGB2
GLS	HMGN3
GNAI1	HSF5
GNL3	HSH2D
GPATCH2	HSP90AA1
GPCPD1	HSPA1A
GPD2	HSPB11
GPM6A	HSPBAP1
GPR141	IAH1
GPR174	IFI44L
GPR21	IFIT1
GPR55	IFIT3
GPR89A	IFNGR1
GPRC5B	IFT20
GPSM3	IL15
GRK2	IL18RAP
GSAP	IL1RN
GTF2H3	IL7
GTSF1	ING3
GYPA	INTS6L
GZMA	IQCB1
GZMH	IQCE
GZMK	ISG15
HABP2	ITGA4
HACD3	ITGB2
HACD4	ITM2A
HACE1	ITPR2
HAUS1	KDM3A
HBS1L	KDM6B
HCK	KDM7A
HDAC4	KIAA0232
HEATR5B	KIAA0825
HERPUD2	KIAA0930
HIGD1A	KIAA1109
HINT1	KIF15
HK1	KIF19
HLA-DOB	KIZ
HLA-DQA1	KLF13
HMGB2	KLHDC2
HMGN3	KLRB1
HSF5	KLRC3
HSH2D	KLRC4
HSPA14	KLRD1
HSPB11	KLRF1
IAH1	KLRK1
IFIT1	KMO
IFIT3	KRBOX4
IFNG	KRCC1
IFNGR1	KRR1
IFT20	LAMTOR5
IL18RAP	LANCL1
IL1RN	LAX1
IL5RA	LCN2
IL7	LDAH
IMMP2L	LDHB
ING3	LGALS8
INTS6L	LGALS9
IQCB1	LILRA6
IQCE	LLPH
IRF2BPL	LONRF3
ISG15	LPXN
ITGA4	LRFN1
ITGAX	LRP10
ITGB2	LSM1
ITM2A	LSM8
ITPR2	LSP1
KDM3A	LTBR
KDM6B	LTN1
KDM7A	LTV1
KIAA0232	LUC7L3
KIAA0825	LY6E
KIAA0930	LYN
KIAA1109	LYRM2
KIAA1586	LYST
KIF18A	LYVE1
KIF19	LZTFL1
KIF5C	MAGOHB
KIZ	MAP2K3
KLHDC2	MAP3K3
KLHL9	MAP4K3
KLRB1	MAP7D3
KLRC3	MAPKAPK2
KLRC4	MAPKAPK5
KLRD1	MAPRE2
KLRF1	MBIP
KMO	MBNL3
KRBOX4	MBOAT7
KRCC1	MCOLN2
KRR1	MCPH1
LAMTOR5	MCUB
LANCL1	MED13
LAX1	MED14
LCN2	MELK
LDAH	MERTK
LDHB	METTL14
LGALS8	METTL18
LGALS9	METTL4
LILRA6	MIB1
LONRF3	MICU3
LPXN	MIDN
LRFN1	MIER1
LRP10	MIPEP
LSM8	MLLT6
LTBR	MMAA
LTN1	MMP25
LTV1	MRPL11
LUC7L3	MRPL19
LYN	MRPL22
LYRM2	MRPL24
LYST	MRPL35
LYVE1	MRPL36
LZTFL1	MRPL40
MAP3K3	MRPL43
MAP4K3	MRPL46
MAP7D3	MRPL47
MAPKAPK2	MRPL51
MAPKAPK5	MRPS11
MAPRE2	MRPS17
MBIP	MRPS18C
MBNL3	MS4A3
MBOAT7	MSN
MCAM	MTERF1
MCOLN2	MTERF4
MCPH1	MTHFD2
MCUB	MTM1
MDH1	MVP
MED13	MX1
MED14	MYBL1
MELK	MYC
MEMO1	MYL12B
MERTK	MYNN
METTL14	MYO1F
METTL15	MYO9A
METTL18	N4BP2L2
METTL2A	NAA38
METTL4	NAA50
METTL5	NADK
MIB1	NAP1L4
MICU3	NBEAL1
MIDN	NCK1
MIER1	NCOA3
MIPEP	NDC80
MITD1	NDUFA1
MLLT6	NDUFA4
MMAA	NDUFA7
MMP25	NDUFAF4
MORF4L2	NDUFB1
MRPL11	NDUFB2
MRPL15	NDUFB8
MRPL19	NDUFS3
MRPL24	NDUFS4
MRPL32	NEDD4
MRPL35	NEDD8
MRPL39	NEIL3
MRPL43	NEK11
MRPL46	NFKBIA
MRPL50	NFU1
MRPL58	NFXL1
MRPL9	NFYB
MRPS11	NIFK
MRPS18C	NIPAL2
MS4A3	NIPSNAP3B
MS4A6A	NLRP1
MSN	NME8
MTCL1	NOC3L
MTERF1	NOL11
MTERF4	NOP10
MTHFD2	NOTCH1
MTIF3	NPM1
MVP	NPRL3
MYBL1	NSMCE4A
MYC	NSUN6
MYL12B	NUDT7
MYNN	NUP107
MYO1F	NUP54
MYO1G	NUSAP1
MYO9A	OAS2
N4BP2L2	OASL
NAA38	OCIAD2
NAA50	ODF2L
NADK	OLA1
NAP1L4	OLIG1
NBEAL1	OLIG2
NCF1	OLR1
NCK1	OMA1
NDUFA7	OR2W3
NDUFAF4	ORC2
NDUFB2	ORC3
NDUFB8	OSBPL9
NEDD4	P2RY10
NEK11	PANK3
NFKBIA	PARP15
NFXL1	PAXBP1
NFYB	PBX2
NIFK	PCM1
NINJ1	PCMTD1
NIPSNAP3B	PDCD10
NIT2	PDE4D
NLRP1	PDE6D
NME8	PGAM1
NOC3L	PGBD2
NOD1	PHF20L1
NOL11	PHTF2
NOP10	PIAS1
NOTCH1	PIGP
NPM1	PIK3CB
NPRL3	PKN1
NSA2	PLAA
NSMCE4A	PLB1
NSUN3	PLEKHO2
NSUN6	PMS1
NUCB2	PNISR
NUDT7	PNRC2
NUP107	POC5
NUP54	POLA1
NUSAP1	POLQ
OASL	POLR2F
OCIAD2	PPA2
ODF2L	PPAT
ODF3B	PPIA
OLA1	PPIB
OLIG1	PPIL3
OLIG2	PPP4C
OMA1	PPP6R1
ORC2	PPWD1
ORC3	PREX1
OXER1	PRIMPOL
OXR1	PRKDC
OXSM	PRPF18
P2RY10	PRPF40A
PANK3	PRPS1
PARP15	PRR3
PAXBP1	PRRC2C
PBDC1	PSMA6
PBX2	PSMC6
PCM1	PTCD2
PCMTD1	PTCD3
PDCD10	PTGDR2
PDCL	PTGES3
PDE4D	PTOV1
PDE6D	PTPN12
PDPR	PTPN4
PDS5A	PTRHD1
PDZD11	PUS3
PFKL	PXN
PGAM1	PXYLP1
PGBD2	RAB12
PHF20L1	RAB20
PHF6	RAB28
PHTF2	RAB35
PIAS1	RAB3D
PIGC	RAB3IP
PIGP	RAB7A
PIK3CB	RABEP1
PIK3CD	RABGAP1L
PINK1	RABIF
PKN1	RABL3
PLAA	RAC2
PLB1	RAD17
PLEKHO2	RAD51C
PLRG1	RALGAPA1
PMS1	RARA
PNISR	RARS2
PNRC2	RASA3
POC5	RASGEF1B
POLA1	RASSF6
POLQ	RBM25
POLR3F	RBM27
POU5F2	RBM4B
PPA2	RBM7
PPAT	RBX1
PPIA	RETN
PPIL3	RFESD
PPP4C	RGS19
PPP4R3A	RHOBTB3
PPP6R1	RMDN1
PPWD1	RNASE2
PRELID3B	RNASE3
PREX1	RNF19A
PRIMPOL	RNF213
PRKDC	ROMO1
PROSER2	RPA4
PRPF18	RPL10A
PRPF40A	RPL12
PRPS1	RPL15
PRRC2C	RPL22
PSAP	RPL26L1
PSMC6	RPL29
PSMD10	RPL30
PTCD2	RPL34
PTCD3	RPL35
PTGDR2	RPL5
PTGES3	RPRD1A
PTPN12	RPS15A
PTPN22	RPS21
PTPN4	RPS23
PTPRC	RPS27
PXN	RPS27A
RAB11B	RPS29
RAB12	RPS5
RAB20	RPS6
RAB28	RPS6KA1
RAB35	RRAS2
RAB3D	RUFY3
RAB3IP	RWDD2A
RAB7A	S100A12
RABEP1	S100Z
RABGAP1L	SAMD3
RABIF	SBDS
RABL3	SBF2
RAD17	SCCPDH
RAD51C	SCOC
RAD51D	SDR39U1
RALGAPA1	SECC
RAN	SEL1L
RANBP2	SENP6
RARA	SEPSECS
RARS2	SETDB2
RASA2	SETX
RASA3	SF3B1
RASSF6	SF3B5
RBL1	SF3B6
RBM25	SGK1
RBM27	SH2B3
RBM4B	SH2D1A
RBM7	SHISA5
RERE	SHKBP1
RETN	SIGLEC6
RGS19	SIRPA
RHOBTB3	SKA2
RNASE2	SKA3
RNASE3	SLA
RNF10	SLAMF6
RNF135	SLC11A2
RNF19A	SLC25A26
RNF213	SLC25A29
RNFT1	SLC25A36
ROMO1	SLC30A4
RPA4	SLC38A11
RPE	SLC5A3
RPF1	SLCO4C1
RPL10A	SLIRP
RPL12	SMAP2
RPL15	SMC5
RPL22	SMC6
RPL24	SMCHD1
RPL27A	SMDT1
RPL29	SNRPA1
RPL30	SNRPD2
RPL32	SNRPE
RPL34	SNX13
RPL35	SNX14
RPL36A	SNX5
RPL37	SOAT1
RPL5	SOX6
RPL6	SPAG7
RPL7	SPECC1
RPL9	SPIDR
RPRD1A	SPIRE2
RPS15A	SRP14
RPS21	SRSF4
RPS23	SRSF7
RPS25	SS18
RPS27	SSR2
RPS27A	ST3GAL2
RPS29	ST6GALNAC3
RPS3A	STAMBP
RPS5	STAP1
RPS6	STARD3NL
RPS6KA1	STAT3
RRAGB	STAT4
RRAS2	STK26
RSL24D1	STRBP
RSPH14	STXBP2
RSRC2	SUB1
RUFY3	SUCLG1
RWDD1	SUMO1
RWDD2A	SUPT3H
RWDD3	SVIP
S100A12	SYCP2
S100Z	SYNE2
SAMD12	TAF12
SAMD3	TAF2
SBDS	TANK
SBF2	TAOK2
SCCPDH	TARP
SCOC	TAS2R14
SDR39U1	TBCA
SECC	TC2N
SECA2	TCN1
SECTM1	TDG
SEL1L	TECPR2
SENP6	TEX30
SEPSECS	TFB2M
SETDB2	TFEB
SF3B1	TGFB1
SF3B2	THAP1
SF3B5	THOC1
SF3B6	TIA1
SGK1	TIAM2
SH2B3	TIGIT
SH2D1A	TIMM17A
SH3BP1	TIMM8B
SHISA5	TLE3
SHKBP1	TLN1
SIGLEC11	TM2D1
SIGLEC6	TMCC1
SIRPA	TMCC2
SKA2	TMEM106C
SKA3	TMEM116
SLA	TMEM141
SLAMF6	TMEM181
SLAMF7	TMEM258
SLC11A2	TMEM260
SLC25A26	TMEM30B
SLC25A29	TMEM39A
SLC25A33	TMEM43
SLC25A36	TMEM60
SLC30A4	TMEM62
SLC35A5	TMOD2
SLC35D1	TMSB15B
SLC35E1	TMTC3
SLC5A3	TNFAIP2
SLC9B2	TNFAIP6
SLCO4C1	TNFAIP8
SLIRP	TNFRSF1A
SMAP2	TOP2B
SMC4	TPCN1
SMC5	TPD52L2
SMC6	TPP2
SMCHD1	TPR
SMDT1	TRAM1
SMIM20	TRIAP1
SMIM8	TRIM25
SNRPA1	TRIM61
SNRPB	TRMO
SNRPD1	TRMT13
SNRPD2	TRPS1
SNRPE	TSC22D3
SNX13	TTC14
SNX14	TUBE1
SNX24	TXN
SNX5	U2SURP
SOAT1	UBAP1
SOX6	UBE2L6
SPATA7	UBE2M
SPECC1	UBE2R2
SPIDR	UBE2T
SRFBP1	UBE3A
SRP14	UCHL3
SRSF7	UFC1
SS18	UFL1
SSB	UFSP2
ST3GAL2	UHRF2
ST6GALNAC3	UMPS
STAMBP	UQCRQ
STAP1	URI1
STARD3NL	USP16
STARD4	USP24
STAT3	USP28
STAT4	USP45
STK26	VAMP4
STRBP	VPS13A
STT3A	VPS13C
STXBP2	VPS25
SUB1	VPS29
SUCLG1	VPS50
SUMO1	VSTM1
SUPT3H	WBP2
SVIP	WDPCP
SYNE2	WDR49
TAF2	WDR61
TANK	XCL1
TAOK2	XPO6
TARP	XRCC4
TAS2R14	YBX3
TBC1D19	ZBED5
TBCA	ZBED6
TBL1X	ZBED6CL
TC2N	ZBTB14
TCEAL4	ZBTB8OS
TCEAL8	ZC3H8
TCEANC2	ZDHHC20
TCN1	ZEB2
TECPR2	ZFAND1
TEX30	ZFAND2A
TFB2M	ZFAND3
TFEB	ZFC3H1
TGFB1	ZFP36L1
THAP1	ZFP37
THOC1	ZFP69
THRAP3	ZFP82
TIA1	ZFYVE16
TIGIT	ZKSCAN8
TIMM17A	ZMYM2
TLE3	ZNF138
TLN1	ZNF14
TM2D1	ZNF141
TMA16	ZNF17
TMC5	ZNF22
TMCC1	ZNF227
TMCC2	ZNF230
TMEM116	ZNF235
TMEM126B	ZNF253
TMEM181	ZNF254
TMEM258	ZNF260
TMEM260	ZNF280C
TMEM43	ZNF280D
TMEM62	ZNF302
TMOD2	ZNF32
TMSB10	ZNF322
TMSB15B	ZNF420
TMTC3	ZNF429
TNFAIP2	ZNF43
TNFAIP6	ZNF430
TNFAIP8	ZNF431
TNFRSF1A	ZNF441
TOMM6	ZNF467
TOP2B	ZNF493
TOP3B	ZNF501
TPCN1	ZNF506
TPD52L2	ZNF532
TPP2	ZNF559
TPR	ZNF566
TRAM1	ZNF568
TRAPPC6B	ZNF569
TRIAP1	ZNF571
TRIM25	ZNF585A
TRIM61	ZNF607
TRIM8	ZNF626
TRMO	ZNF638
TRMT13	ZNF649
TRNT1	ZNF675
TRPS1	ZNF678
TSC22D3	ZNF680
TTC14	ZNF746
TTC30A	ZNF782
TUBB6	ZNF791
TUBE1	ZNF800
TUBGCP4	ZNF85
TXN	ZNF91
TYW3	ZRANB2
U2SURP	ZSCAN9
UBAP1	ZYX
UBE2M
UBE2O
UBE2R2
UBE3A
UBR7
UCHL3
UFC1
UFL1
UFSP2
UHRF2
UMPS
UNC50
UNC93B1
UPF1
UQCRQ
URI1
USP16
USP24
USP28
USP31
USP45
VAMP2
VAMP4
VPS13A
VPS13C
VPS25
VPS29
VPS50
VSIG10
VSTM1
WBP2
WDPCP
WDR49
WDR61
WDR7
WEE1
WRN
XCL1
XPO1
XPO6
XRCC4
YEATS4
YOD1
ZBED5
ZBED6
ZBED6CL
ZBTB14
ZBTB8OS
ZC3H8
ZDHHC20
ZEB2
ZFAND1
ZFAND2A
ZFAND3
ZFC3H1
ZFP36L1
ZFP37
ZFP69
ZFP82
ZFYVE16
ZKSCAN8
ZMYM2
ZNF136
ZNF138
ZNF14
ZNF141
ZNF17
ZNF177
ZNF22
ZNF227
ZNF230
ZNF235
ZNF25
ZNF253
ZNF254
ZNF260
ZNF280C
ZNF302
ZNF32
ZNF322
ZNF420
ZNF43
ZNF430
ZNF431
ZNF441
ZNF467
ZNF493
ZNF501
ZNF506
ZNF532
ZNF559
ZNF566
ZNF568
ZNF569
ZNF571
ZNF585A
ZNF607
ZNF626
ZNF638
ZNF649
ZNF658
ZNF675
ZNF678
ZNF680
ZNF746
ZNF782
ZNF784
ZNF791
ZNF800
ZNF85
ZNF91
ZNF93
ZNHIT3
ZRANB2
ZSCAN9
ZYX

Supplementary list 2.

Mitochondrial genes (mitGenes) between comparisons of hypothyroidism, sepsis survivors and sepsis nonsurvivors groups

mitGenes in Hypothyroidism and sepsis survivor, n = 95 mitGenes in Hypothyroidism and sepsis nonsurvivor, n = 88 ABCB7 ABCB7 ACOT11 ACOT11 ACP6 ACP6 ADHFE1 ADHFE1 ATP23 BLOC1S1 ATPAF1 BOLA3 BLOC1S1 CASP3 BOLA3 CBR4 CBR4 CCDC90B CCDC90B CMC2 CMC1 CMPK2 COA1 COA1 COQ8A COQ8A COX10 COX16 COX16 COX17 COX17 COX6A1 COX6A1 COX6B1 COX6B1 COX7B COX7B COX7C DNAJC15 DBI DNAJC19 DNA2 FASTKD1 DNAJC15 GFM2 FASTKD1 GLOD4 FASTKD3 GLS GLOD4 GPD2 GLS HIGD1A GPD2 HINT1 GUK1 IMMP2L HIGD1A KMO HINT1 LDHB KMO LYRM2 LDHB MCUB LYRM2 METTL15 MCUB METTL4 METTL4 METTL5 MICU3 MICU3 MIPEP MIPEP MMAA MMAA MRPL11 MRPL11 MRPL19 MRPL15 MRPL22 MRPL19 MRPL24 MRPL24 MRPL35 MRPL32 MRPL36 MRPL35 MRPL40 MRPL39 MRPL43 MRPL43 MRPL46 MRPL46 MRPL47 MRPL50 MRPL51 MRPL58 MRPS11 MRPL9 MRPS17 MRPS11 MRPS18C MRPS18C MTERF1 MTERF1 MTERF4 MTERF4 MTHFD2 MTHFD2 NDUFA1 MTIF3 NDUFA4 NDUFA7 NDUFA7 NDUFAF4 NDUFAF4 NDUFB2 NDUFB1 NDUFB8 NDUFB2 NIPSNAP3B NDUFB8 NIT2 NDUFS3 NSUN3 NDUFS4 OCIAD2 NFU1 OMA1 NIPSNAP3B OXR1 OCIAD2 OXSM OMA1 PDPR POLQ PINK1 PPA2 POLQ PRIMPOL PPA2 PTCD2 PRELID3B PTCD3 PRIMPOL RARS2 PTCD2 RMDN1 PTCD3 ROMO1 RARS2 SDR39U1 ROMO1 SLC25A26 SDR39U1 SLC25A29 SLC25A26 SLC25A36 SLC25A29 SLIRP SLC25A33 SMDT1 SLC25A36 SUCLG1 SLIRP TFB2M SMDT1 TIMM17A SMIM20 TIMM8B SMIM8 TRIAP1 SUCLG1 UQCRQ TFB2M TIMM17A TMEM126B TOMM6 TRIAP1 TRNT1 UQCRQ

mitGenes in Hypothyroidism and sepsis survivor, n = 95	mitGenes in Hypothyroidism and sepsis nonsurvivor, n = 88
ABCB7	ABCB7
ACOT11	ACOT11
ACP6	ACP6
ADHFE1	ADHFE1
ATP23	BLOC1S1
ATPAF1	BOLA3
BLOC1S1	CASP3
BOLA3	CBR4
CBR4	CCDC90B
CCDC90B	CMC2
CMC1	CMPK2
COA1	COA1
COQ8A	COQ8A
COX10	COX16
COX16	COX17
COX17	COX6A1
COX6A1	COX6B1
COX6B1	COX7B
COX7B	COX7C
DNAJC15	DBI
DNAJC19	DNA2
FASTKD1	DNAJC15
GFM2	FASTKD1
GLOD4	FASTKD3
GLS	GLOD4
GPD2	GLS
HIGD1A	GPD2
HINT1	GUK1
IMMP2L	HIGD1A
KMO	HINT1
LDHB	KMO
LYRM2	LDHB
MCUB	LYRM2
METTL15	MCUB
METTL4	METTL4
METTL5	MICU3
MICU3	MIPEP
MIPEP	MMAA
MMAA	MRPL11
MRPL11	MRPL19
MRPL15	MRPL22
MRPL19	MRPL24
MRPL24	MRPL35
MRPL32	MRPL36
MRPL35	MRPL40
MRPL39	MRPL43
MRPL43	MRPL46
MRPL46	MRPL47
MRPL50	MRPL51
MRPL58	MRPS11
MRPL9	MRPS17
MRPS11	MRPS18C
MRPS18C	MTERF1
MTERF1	MTERF4
MTERF4	MTHFD2
MTHFD2	NDUFA1
MTIF3	NDUFA4
NDUFA7	NDUFA7
NDUFAF4	NDUFAF4
NDUFB2	NDUFB1
NDUFB8	NDUFB2
NIPSNAP3B	NDUFB8
NIT2	NDUFS3
NSUN3	NDUFS4
OCIAD2	NFU1
OMA1	NIPSNAP3B
OXR1	OCIAD2
OXSM	OMA1
PDPR	POLQ
PINK1	PPA2
POLQ	PRIMPOL
PPA2	PTCD2
PRELID3B	PTCD3
PRIMPOL	RARS2
PTCD2	RMDN1
PTCD3	ROMO1
RARS2	SDR39U1
ROMO1	SLC25A26
SDR39U1	SLC25A29
SLC25A26	SLC25A36
SLC25A29	SLIRP
SLC25A33	SMDT1
SLC25A36	SUCLG1
SLIRP	TFB2M
SMDT1	TIMM17A
SMIM20	TIMM8B
SMIM8	TRIAP1
SUCLG1	UQCRQ
TFB2M
TIMM17A
TMEM126B
TOMM6
TRIAP1
TRNT1
UQCRQ

Publication Dates

• Publication in this collection
  05 June 2023
• Date of issue
  2023

History

• Received
  19 Aug 2022
• Accepted
  20 Dec 2022