Prioritizing prognostic-associated subpopulations and individualized recurrence risk signatures from single-cell transcriptomes of colorectal cancer.

Colorectal cancer (CRC) is one of the most common gastrointestinal malignancies. There are few recurrence risk signatures for CRC patients. Single-cell RNA-sequencing (scRNA-seq) provides a high-resolution platform for prognostic signature detection. However, scRNA-seq is not practical in large cohorts due to its high cost and most single-cell experiments lack clinical phenotype information. Few studies have been reported to use external bulk transcriptome with survival time to guide the detection of key cell subtypes in scRNA-seq data. We proposed scRankXMBD, a computational framework to prioritize prognostic-associated cell subpopulations based on within-cell relative expression orderings of gene pairs from single-cell transcriptomes. scRankXMBD achieves higher precision and concordance compared with five existing methods. Moreover, we developed single-cell gene pair signatures to predict recurrence risk for patients individually. Our work facilitates the application of the rank-based method in scRNA-seq data for prognostic biomarker discovery and precision oncology. scRankXMBD is available at https://github.com/xmuyulab/scRank-XMBD. (XMBD:Xiamen Big Data, a biomedical open software initiative in the National Institute for Data Science in Health and Medicine, Xiamen University, China.).

The extracellular cyclophilin A-integrin ß2 complex as a therapeutic target of viral pneumonia.

The acute respiratory virus infection can induce uncontrolled inflammatory responses, such as cytokine storm and viral pneumonia, which are the major causes of death in clinical cases. Cyclophilin A (CypA) is mainly distributed in the cytoplasm of resting cells and released into the extracellular space in response to inflammatory stimuli. Extracellular CypA (eCypA) is upregulated and promotes inflammatory response in severe COVID-19 patients. However, how eCypA promotes virus-induced inflammatory response remains elusive. Here, we observe that eCypA is induced by influenza A and B viruses and SARS-CoV-2 in cells, mice, or patients. Anti-CypA mAb reduces pro-inflammatory cytokines production, leukocytes infiltration, and lung injury in virus-infected mice. Mechanistically, eCypA binding to integrin ß2 triggers integrin activation, thereby facilitating leukocyte trafficking and cytokines production via the focal adhesion kinase (FAK)/GTPase and FAK/ERK/P65 pathways, respectively. These functions are suppressed by the anti-CypA mAb that specifically blocks eCypA-integrin ß2 interaction. Overall, our findings reveal that eCypA-integrin ß2 signaling mediates virus-induced inflammatory response, indicating that eCypA is a potential target for antibody therapy against viral pneumonia.

Application of individualized differential expression analysis in human cancer proteome.

Liquid chromatography-mass spectrometry-based quantitative proteomics can measure the expression of thousands of proteins from biological samples and has been increasingly applied in cancer research. Identifying differentially expressed proteins (DEPs) between tumors and normal controls is commonly used to investigate carcinogenesis mechanisms. While differential expression analysis (DEA) at an individual level is desired to identify patient-specific molecular defects for better patient stratification, most statistical DEP analysis methods only identify deregulated proteins at the population level. To date, robust individualized DEA algorithms have been proposed for ribonucleic acid data, but their performance on proteomics data is underexplored. Herein, we performed a systematic evaluation on five individualized DEA algorithms for proteins on cancer proteomic datasets from seven cancer types. Results show that the within-sample relative expression orderings (REOs) of protein pairs in normal tissues were highly stable, providing the basis for individualized DEA for proteins using REOs. Moreover, individualized DEA algorithms achieve higher precision in detecting sample-specific deregulated proteins than population-level methods. To facilitate the utilization of individualized DEA algorithms in proteomics for prognostic biomarker discovery and personalized medicine, we provide Individualized DEP Analysis IDEPAXMBD (XMBD: Xiamen Big Data, a biomedical open software initiative in the National Institute for Data Science in Health and Medicine, Xiamen University, China.) (https://github.com/xmuyulab/IDEPA-XMBD), which is a user-friendly and open-source Python toolkit that integrates individualized DEA algorithms for DEP-associated deregulation pattern recognition.

A community challenge to predict clinical outcomes after immune checkpoint blockade in non-small cell lung cancer.

BACKGROUND: Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. METHODS: Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. RESULTS: A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1. CONCLUSIONS: This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy. TRIAL REGISTRATION: CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.

cgMSI: pathogen detection within species from nanopore metagenomic sequencing data.

BACKGROUND: Metagenomic sequencing is an unbiased approach that can potentially detect all the known and unidentified strains in pathogen detection. Recently, nanopore sequencing has been emerging as a highly potential tool for rapid pathogen detection due to its fast turnaround time. However, identifying pathogen within species is nontrivial for nanopore sequencing data due to the high sequencing error rate. RESULTS: We developed the core gene alleles metagenome strain identification (cgMSI) tool, which uses a two-stage maximum a posteriori probability estimation method to detect pathogens at strain level from nanopore metagenomic sequencing data at low computational cost. The cgMSI tool can accurately identify strains and estimate relative abundance at 1× coverage. CONCLUSIONS: We developed cgMSI for nanopore metagenomic pathogen detection within species. cgMSI is available at https://github.com/ZHU-XU-xmu/cgMSI .

Snipe: highly sensitive pathogen detection from metagenomic sequencing data.

Metagenomics data provide rich information for the detection of foodborne pathogens from food and environmental samples that are mixed with complex background bacteria strains. While pathogen detection from metagenomic sequencing data has become an activity of increasing interest, shotgun sequencing of uncultured food samples typically produces data that contain reads from many different organisms, making accurate strain typing a challenging task. Particularly, as many pathogens may contain a common set of genes that are highly similar to those from normal bacteria in food samples, traditional strain-level abundance profiling approaches do not perform well at detecting pathogens of very low abundance levels. To overcome this limitation, we propose an abundance correction method based on species-specific genomic regions to achieve high sensitivity and high specificity in target pathogen detection at low abundance.

Comparison of high-throughput single-cell RNA sequencing data processing pipelines.

With the development of single-cell RNA sequencing (scRNA-seq) technology, it has become possible to perform large-scale transcript profiling for tens of thousands of cells in a single experiment. Many analysis pipelines have been developed for data generated from different high-throughput scRNA-seq platforms, bringing a new challenge to users to choose a proper workflow that is efficient, robust and reliable for a specific sequencing platform. Moreover, as the amount of public scRNA-seq data has increased rapidly, integrated analysis of scRNA-seq data from different sources has become increasingly popular. However, it remains unclear whether such integrated analysis would be biassed if the data were processed by different upstream pipelines. In this study, we encapsulated seven existing high-throughput scRNA-seq data processing pipelines with Nextflow, a general integrative workflow management framework, and evaluated their performance in terms of running time, computational resource consumption and data analysis consistency using eight public datasets generated from five different high-throughput scRNA-seq platforms. Our work provides a useful guideline for the selection of scRNA-seq data processing pipelines based on their performance on different real datasets. In addition, these guidelines can serve as a performance evaluation framework for future developments in high-throughput scRNA-seq data processing.

NLRC4 promotes the cGAS-STING signaling pathway by facilitating CBL-mediated K63-linked polyubiquitination of TBK1.

TANK-binding kinase 1 (TBK1) is crucial in producing type Ⅰ interferons (IFN-Ⅰ) that play critical functions in antiviral innate immunity. The tight regulation of TBK1, especially its activation, is very important. Here we identify NLRC4 as a positive regulator of TBK1. Ectopic expression of NLRC4 facilitates the activation of the IFN-ß promoter, the mRNA levels of IFN-ß, ISG54, and ISG56, and the nuclear translocation of interferon regulatory factor 3 induced by cGAS and STING. Consistently, under herpes simplex virus-1 (HSV-1) infection, knockdown or knockout of NLRC4 in BJ cells and primary peritoneal macrophages from Nlrc4-deficient (Nlrc4-/- ) mice show attenuated Ifn-ß, Isg54, and Isg56 mRNA transcription, TBK1 phosphorylation, and augmented viral replications. Moreover, Nlrc4-/- mice show higher mortality upon HSV-1 infection. Mechanistically, NLRC4 facilitates the interaction between TBK1 and the E3 ubiquitin ligase CBL to enhance the K63-linked polyubiquitination of TBK1. Our study elucidates a previously uncharacterized function for NLRC4 in upregulating the cGAS-STING signaling pathway and antiviral innate immunity.

Self-powered (In,Ga)N-nanowire-based photodetector with fast response speed for under-seawater detection.

Due to the requirements of oceanography exploration and detection, self-powered photodetectors (PDs) with low-power consumption are essential for the next-generation optoelectronic applications. In this work, we successfully demonstrate a self-powered photoelectrochemical (PEC) PD in seawater based on the (In,Ga)N/GaN core-shell heterojunction nanowires. Compared to those of the PD in pure water, it is found that the upward and downward overshooting features of current can be the key reason contributing to the much faster response speed of the PD in seawater. Thanks to the enhanced response speed, the rise time of PD can be reduced more than 80%, and the fall time remains only 30% by applying in seawater instead of pure water. The key factors of generating these overshooting features should be the instantaneous temperature gradient, carrier accumulation and elimination on the semiconductor/electrolyte interfaces at the moments of light on and off. By the analysis of experimental results, the Na+ and Cl- ions are proposed to be the main factors affecting the PD behavior in seawater, which can enhance the conductivity and accelerate the oxidation-reduction reaction significantly. This work paves an effective way to develop the new self-powered PDs for the wide applications in under-seawater detection and communication.

Enhancing microstructure and device performance of InGaN quantum dot micro-LEDs through substrate off-cut angle modulation.

InGaN quantum dots (QDs) are regarded as a compelling candidate material for the fabrication of high-quality GaN-based micro-LEDs. In this work, to study the impact of a substrate structure on InGaN QDs and QD-based micro-LEDs, GaN-on-sapphire substrates with off-cut angles toward the a-axis of 0.2°, 0.4°, and 0.7° were utilized as templates for the fabrication of InGaN QDs and InGaN QDs-based micro-LEDs. Experimental results show that GaN template with 0.4° off-cut angle exhibits the narrowest terrace width and enables InGaN QDs to be higher and more uniform. The InGaN QD sample grown on 0.4° substrate has a very small wavelength shift of 2.5 nm with temperature increasing and owns the longest photoluminescence peak wavelength implying the highest In content. Furthermore, electroluminescence (EL) spectra demonstrate that QD-based micro-LED array has excellent wavelength stability under various injection currents, and the stability can be improved further on a GaN template with narrower terraces. The results indicate that altering the terrace width of GaN template is a feasible scheme for improving the properties of GaN-based micro-LEDs.

Diamond: a multi-modal DIA mass spectrometry data processing pipeline.

SUMMARY: Currently, various software tools are used to support two mainstream workflows for data-independent acquisition (DIA) mass spectrometry (MS) data processing, namely, spectrum-centric scoring (SCS) and peptide-centric scoring (PCS). However, a fully automatic, easily reproducible and freely accessible pipeline that simultaneously integrates SCS and PCS strategies and supports both library-free and library-based modes is absent. We developed Diamond, a Nextflow-based, containerized, multi-modal DIA-MS data processing pipeline for peptide identification and quantification. Diamond integrated two mainstream workflows for DIA data analysis, namely, SCS and PCS, for use cases both with and without assay libraries. This multi-modal pipeline serves as a versatile, easy-to-use and easily extendable toolbox for large-scale DIA data processing. AVAILABILITY: Diamond is hosted on GitHub (https://github.com/xmuyulab/Diamond) and is released under the highly permissive MIT license to encourage further customization and modification. The Docker image for Diamond is freely accessible at https://hub.docker.com/r/zeroli/diamond.

Effects and mechanisms of In surfactant on high Al-content AlGaN grown by plasma-assisted molecular beam epitaxy.

High Al-content AlGaN epilayers were grown on AlN template by using indium (In) surfactant with plasma-assisted molecular beam epitaxy (PA-MBE), and deep ultraviolet emission at 235 nm was obtained at room temperature. The effects and mechanisms of In-surfactant on the crystalline quality and optical properties of AlGaN were investigated. It was found that In-surfactant could facilitate two-dimensional AlGaN growth by reducing activation barrier for Al/Ga atoms to cross steps and effectively increasing the migration rate on the growth surface, and thus improve surface morphology and decrease defect density. The photoluminescence measurements revealed that the optical properties were remarkably improved by adopting In as surfactant, and phase separation was also effectively eliminated. Furthermore, the concentration of impurities including oxygen and silicon was decreased, which is attributed to higher defects formation energy for these impurities with In-surfactant assisted epitaxy growth.

Cyclophilin A is a key positive and negative feedback regulator within interleukin-6 trans-signaling pathway.

Cyclophilin A (CypA), a member of the cyclophilin family, plays a vital role in microorganismal infections, inflammatory diseases, and cancers. Interleukin-6 (IL-6) is a pleiotropic cytokine, exerting variety of effects on inflammation, immune response, hematopoiesis, and tumor proliferation. Binding of IL-6 to soluble IL-6 receptor (sIL-6R) induces pro-inflammatory trans-signaling, which has been described to be stronger than anti-inflammatory classic signaling triggered by the binding of IL-6 to membrane-bound IL-6 receptor. Here we found that upon the treatment of IL-6 and sIL-6R, CypA inhibited the ubiquitination-mediated degradation of IL-6 membrane receptor gp130 and enhanced its dimerization, thereby positively regulated the IL-6 trans-signaling and increased the expression of downstream iNOS, IL-6, and CypA. Furthermore, CypA expression could be negatively regulated by suppressor of cytokine signaling 1 (SOCS1). The SH2 and Box domains of SOCS1 interacted with CypA and promoted its K48-linked ubiquitination-mediated degradation, which inhibited the IL-6 trans-signaling pathway. Collectively, our findings reveal an important role of CypA in the positive and negative feedback regulation of the IL-6 trans-signaling pathway.

MiR-125b enhances autophagic flux to improve septic cardiomyopathy via targeting STAT3/HMGB1.

We explore the role of miR-125b in septic cardiomyopathy, focusing on miR-125b/STAT3/HMGB1 axis. CLP mouse model and LPS-stimulated primary rat cardiomyocytes (CMs) and H9C2 cell were used as in vivo and in vitro models of septic cardiomyopathy, respectively. qRT-PCR and western blot were performed to measure expression levels of miR-125b, STAT3, HMGB1, and autophagy-related proteins. MTT assay was employed to examine LPS toxicity. Dual luciferase activity assay and CHIP were performed to validate interactions of miR-125b/STAT3 and STAT3/HMGB1 promoter. Immunostaining was used to assess the level of autophagic flux. ROS level was measured by fluorescence assay. Heart functions were examined via intracoronary Doppler ultrasound. miR-125b was diminished while STAT3 and HMGB1 were elevated in the heart tissue following CLP surgery and in LPS-treated H9C2 cells. LPS treatment up-regulated ROS generation and suppressed autophagic flux. Overexpression of miR-125b mimics or knockdown of STAT3 or HMGB1 alleviated LPS-induced hindrance of autophagic flux and ROS production. miR-125b directly targeted STAT3 mRNA and STAT3 bound with HMGB1 promoter. Overexpression of miR-125b mitigated myocardial dysfunction induced by CLP in vivo. Hyperactivation of STAT3/HMGB1 caused by reduced miR-125b contributes to ROS generation and the hindrance of autophagic flux during septic cardiomyopathy, leading to myocardial dysfunction.

An artificial neural network model based on autophagy-related genes in childhood systemic lupus erythematosus.

BACKGROUND: Childhood systemic lupus erythematosus (cSLE) is a multisystemic, life-threatening autoimmune disease. Compared to adults, SLE in childhood is more active, can cause multisystem involvement including renal, neurological and hematological, and can cause cumulative damage across systems more rapidly. Autophagy, one of the core functions of cells, is involved in almost every process of the immune response and has been shown to be associated with many autoimmune diseases, being a key factor in the interplay between innate and adaptive immunity. Autophagy influences the onset, progression and severity of SLE. This paper identifies new biomarkers for the diagnosis and treatment of childhood SLE based on an artificial neural network of autophagy-related genes. METHODS: We downloaded dataset GSE100163 from the Gene Expression Omnibus database and used Protein-protein Interaction Network (PPI) and Least Absolute Shrinkage and Selection Operator (LASSO) to screen the signature genes of autophagy-related genes in cSLE. A new artificial neural network model for cSLE diagnosis was constructed using the signature genes. The predictive efficiency of the model was also validated using the dataset GSE65391. Finally, "CIBERSORT" was used to calculate the infiltration of immune cells in cSLE and to analyze the relationship between the signature genes and the infiltration of immune cells. RESULTS: We identified 37 autophagy-related genes that differed in cSLE and normal samples, and finally obtained the seven most relevant signature genes for cSLE (DDIT3, GNB2L1, CTSD, HSPA8, ULK1, DNAJB1, CANX) by PPI and LASOO regression screening, and constructed an artificial neural network diagnostic model for cSLE. Using this model, we plotted the ROC curves for the training and validation group diagnoses with the area under the curve of 0.976 and 0.783, respectively. Finally, we performed immunoassays on cSLE samples, and the results showed that Plasma cells, Macrophages M0, Dendritic cells activated and Neutrophils were significantly infiltrated in cSLE. CONCLUSION: We constructed an artificial neural network diagnostic model of seven autophagy-related genes that can be used for the diagnosis of cSLE. Meanwhile, the characteristic genes affect the immune infiltration of cSLE, which may provide new perspectives for the exploration of cSLE treatment and related mechanisms.

ScaleQC: a scalable lossy to lossless solution for NGS data compression.

MOTIVATION: Per-base quality values in Next Generation Sequencing data take a significant portion of storage even after compression. Lossy compression technologies could further reduce the space used by quality values. However, in many applications, lossless compression is still desired. Hence, sequencing data in multiple file formats have to be prepared for different applications. RESULTS: We developed a scalable lossy to lossless compression solution for quality values named ScaleQC (Scalable Quality value Compression). ScaleQC is able to provide the so-called bit-stream level scalability that the losslessly compressed bit-stream by ScaleQC can be further truncated to lower data rates without incurring an expensive transcoding operation. Despite its scalability, ScaleQC still achieves comparable compression performance at both lossless and lossy data rates compared to the existing lossless or lossy compressors. AVAILABILITY AND IMPLEMENTATION: ScaleQC has been integrated with SAMtools as a special quality value encoding mode for CRAM. Its source codes can be obtained from our integrated SAMtools (https://github.com/xmuyulab/samtools) with dependency on integrated HTSlib (https://github.com/xmuyulab/htslib). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Improved photocatalytic activity and stability of InGaN quantum dots/C3N4heterojunction photoelectrode for CO2reduction and hydrogen production.

Photocatalytic conversion of CO2to produce fuel is considered a promising approach to reduce CO2emissions and tackle energy crisis. GaN-based materials have been studied for CO2reduction because of their excellent optical properties and band structure. However, low photocatalytic activity and severe photocorrosion of GaN-based photoelectrode greatly limit their applications. In this work, photocatalytic activity was improved by adopting InGaN quantum dots (QDs) combined with C3N4nano-sheets as photoanode, and thus the efficiency of CO2reduction and the selectivity of hydrogen production were increased significantly. In addition, the photoelectron-chemical corrosion of photoelectrodes has been apparently controlled. InGaN QDs/C3N4has the highest CO and H2productions rates of 14.69µmol mol-1h-1and 140µmol mol-1h-1which were 2.2 times and 14.5 times than that of InGaN film photoelectrode, respectively. The enhancement of photocatalytic activity is attributed to C3N4modification and a large electric dipole forming on the surface of InGaN QDs, which facilitate the separation and transfer of photo-generated carriers and thus promote CO2reduction reaction. This work provides a promising strategy for the development of GaN-based photoanodes with superior stability and efficiency.

Performance evaluation of lossy quality compression algorithms for RNA-seq data.

BACKGROUND: Recent advancements in high-throughput sequencing technologies have generated an unprecedented amount of genomic data that must be stored, processed, and transmitted over the network for sharing. Lossy genomic data compression, especially of the base quality values of sequencing data, is emerging as an efficient way to handle this challenge due to its superior compression performance compared to lossless compression methods. Many lossy compression algorithms have been developed for and evaluated using DNA sequencing data. However, whether these algorithms can be used on RNA sequencing (RNA-seq) data remains unclear. RESULTS: In this study, we evaluated the impacts of lossy quality value compression on common RNA-seq data analysis pipelines including expression quantification, transcriptome assembly, and short variants detection using RNA-seq data from different species and sequencing platforms. Our study shows that lossy quality value compression could effectively improve RNA-seq data compression. In some cases, lossy algorithms achieved up to 1.2-3 times further reduction on the overall RNA-seq data size compared to existing lossless algorithms. However, lossy quality value compression could affect the results of some RNA-seq data processing pipelines, and hence its impacts to RNA-seq studies cannot be ignored in some cases. Pipelines using HISAT2 for alignment were most significantly affected by lossy quality value compression, while the effects of lossy compression on pipelines that do not depend on quality values, e.g., STAR-based expression quantification and transcriptome assembly pipelines, were not observed. Moreover, regardless of using either STAR or HISAT2 as the aligner, variant detection results were affected by lossy quality value compression, albeit to a lesser extent when STAR-based pipeline was used. Our results also show that the impacts of lossy quality value compression depend on the compression algorithms being used and the compression levels if the algorithm supports setting of multiple compression levels. CONCLUSIONS: Lossy quality value compression can be incorporated into existing RNA-seq analysis pipelines to alleviate the data storage and transmission burdens. However, care should be taken on the selection of compression tools and levels based on the requirements of the downstream analysis pipelines to avoid introducing undesirable adverse effects on the analysis results.

Fault Diagnosis of a Rolling Bearing Based on Adaptive Sparest Narrow-Band Decomposition and RefinedComposite Multiscale Dispersion Entropy.

Condition monitoring and fault diagnosis of a rolling bearing is crucial to ensure the reliability and safety of a mechanical system. When local faults happen in a rolling bearing, the complexity of intrinsic oscillations of the vibration signals will change. Refined composite multiscale dispersion entropy (RCMDE) can quantify the complexity of time series quickly and effectively. To measure the complexity of intrinsic oscillations at different time scales, adaptive sparest narrow-band decomposition (ASNBD), as an improved adaptive sparest time frequency analysis (ASTFA), is introduced in this paper. Integrated, the ASNBD and RCMDE, a novel-fault diagnosis-model is proposed for a rolling bearing. Firstly, a vibration signal collected is decomposed into a number of intrinsic narrow-band components (INBCs) by the ASNBD to present the intrinsic modes of a vibration signal, and several relevant INBCs are prepared for feature extraction. Secondly, the RCMDE values are calculated as nonlinear measures to reveal the hidden fault-sensitive information. Thirdly, a basic Multi-Class Support Vector Machine (multiSVM) serves as a classifier to automatically identify the fault type and fault location. Finally, experimental analysis and comparison are made to verify the effectiveness and superiority of the proposed model. The results show that the RCMDE value lead to a larger difference between various states and the proposed model can achieve reliable and accurate fault diagnosis for a rolling bearing.

Use of plasma mitochondrial DNA levels for determining disease severity and prognosis in pediatric sepsis: a case control study.

BACKGROUND: The mortality rate due to severe sepsis is approximately 30-60%. Sepsis readily progresses to septic shock and multiple organ dysfunction, representing a significant problem in the pediatric intensive care unit (PICU). The aim of this study was to explore the value of plasma mitochondrial DNA (mtDNA) for early diagnosis and prognosis in children with sepsis. METHODS: A total of 123 children with sepsis who were hospitalized in the Hunan Children's Hospital PICU from July 2013 to December 2014 were divided into the general sepsis group (n = 70) and severe sepsis group (n = 53) based on diagnostic standards. An additional 30 children with non-sepsis infection and 30 healthy children were randomly selected as a control group. Patients' plasma was collected during admission to the PICU. A pediatric critical illness score (PCIS) was also calculated. The plasma mtDNA level was examined using real-time polymerase chain reaction technology, and other parameters including routine laboratory values; blood lactate, procalcitonin (PCT), and C-reactive protein (CRP) levels; and data on survival were collected and compared among the groups. RESULTS: The plasma mtDNA level in the sepsis group than that in the non-sepsis infection and healthy groups. The plasma mtDNA level was significantly higher in the severe sepsis than in the general sepsis group (p < 0.001). A lower PCIS was associated with a higher plasma mtDNA level (p < 0.001). A higher number of organs with dysfunction was associated with higher plasma mtDNA levels (p < 0.001). Plasma mtDNA levels were higher among patients with elevated alanine aminotransferase, aspartate aminotransferase, blood urea nitrogen, creatinine, lactate dehydrogenase, creatine kinase, myoglobin, creatine kinase MB, and troponin than in those with values within the normal range. The mtDNA level was higher among non-survivors than among survivors, and this difference was significant. mtDNA showed a prognostic prediction value similar to that of lactate, PCT, and CRP. CONCLUSIONS: Plasma mtDNA levels may be a suitable biomarker for diagnosis and prognosis in children with sepsis.