CHOP regulated by METTL14-m6A affects cell cycle arrest and regorafenib sensitivity in HCC cells.

BACKGROUND: Regorafenib, a multi-targeted kinase inhibitor, has been used in the treatment of Hepatocellular carcinoma (HCC). The purpose of this study is to investigate the mechanism of Regorafenib in HCC. METHODS: Regorafenib's impact on the sensitivity of HCC cells was assessed using CCK8. Differential gene expression analysis was performed by conducting mRNA sequencing after treatment with Regorafenib. The m6A methylation status of CHOP and differential expression of m6A methylation-related proteins were assessed by RIP and Western Blot. To explore the molecular mechanisms involved in the therapeutic effects of Regorafenib in HCC and the impact of METTL14 and CHOP on Regorafenib treatment, we employed shRNA/overexpression approaches to transfect METTL14 and CHOP genes, as well as conducted in vivo experiments. RESULTS: Treatment with Regorafenib led to a notable decrease in viability and proliferation of SK-Hep-1 and HCC-LM3 cells. The expression level of CHOP was upregulated after Regorafenib intervention, and CHOP underwent m6A methylation. Among the m6A methylation-related proteins, METTL14 exhibited the most significant downregulation. Mechanistic studies revealed that Regorafenib regulated the cell cycle arrest in HCC through METTL14-mediated modulation of CHOP, and the METTL14/CHOP axis affected the sensitivity of HCC to Regorafenib. In vivo, CHOP enhanced the anticancer effect of Regorafenib. CONCLUSION: The inhibition of HCC development by Regorafenib is attributed to its modulation of m6A expression of CHOP, mediated by METTL14, and the METTL14/CHOP axis enhances the sensitivity of HCC to Regorafenib. These findings provide insights into the treatment of HCC and the issue of drug resistance to Regorafenib.

Image-Guided Photothermal and Immune Therapy of Tumors via Melanin-Producing Genetically Engineered Bacteria.

Photothermal therapy (PTT) is a new treatment modality for tumors. However, the efficient delivery of photothermal agents into tumors remains difficult, especially in hypoxic tumor regions. In this study, an approach to deliver melanin, a natural photothermal agent, into tumors using genetically engineered bacteria for image-guided photothermal and immune therapy is developed. An Escherichia coli MG1655 is transformed with a recombinant plasmid harboring a tyrosinase gene to produce melanin nanoparticles. Melanin-producing genetically engineered bacteria (MG1655-M) are systemically administered to 4T1 tumor-bearing mice. The tumor-targeting properties of MG1655-M in the hypoxic environment integrate the properties of hypoxia targeting, photoacoustic imaging, and photothermal therapeutic agents in an "all-in-one" manner. This eliminates the need for post-modification to achieve image-guided hypoxia-targeted cancer photothermal therapy. Tumor growth is significantly suppressed by irradiating the tumor with an 808 nm laser. Furthermore, strong antitumor immunity is triggered by PTT, thereby producing long-term immune memory effects that effectively inhibit tumor metastasis and recurrence. This work proposes a new photothermal and immune therapy guided by an "all-in-one" melanin-producing genetically engineered bacteria, which can offer broad potential applications in cancer treatment.

Ultrasound Molecular Imaging of Bladder Cancer via Extradomain B Fibronectin-Targeted Biosynthetic GVs.

Purpose: Ultrasound molecular imaging (UMI) has proven promising to diagnose the onset and progression of diseases such as angiogenesis, inflammation, and thrombosis. However, microbubble-based acoustic probes are confined to intravascular targets due to their relatively large particle size, greatly reducing the application value of UMI, especially for extravascular targets. Extradomain B fibronectin (ED-B FN) is an important glycoprotein associated with tumor genesis and development and highly expressed in many types of tumors. Here, we developed a gas vesicles (GVs)-based nanoscale acoustic probe (ZD2-GVs) through conjugating ZD2 peptides which can specially target to ED-B FN to the biosynthetic GVs. Materials and Methods: ED-B FN expression was evaluated in normal liver and tumor tissues with immunofluorescence and Western blot. ZD2-GVs were prepared by conjugating ZD2 to the surface of GVs by amide reaction. The inverted microscope was used to analyze the targeted binding capacity of ZD2-GVs to MB49 cells (bladder cancer cell line). The contrast-enhanced imaging features of GVs, non-targeted control GVs (CTR-GVs), and targeted GVs (ZD2-GVs) were compared in three MB49 tumor mice. The penetration ability of ZD2-GVs in tumor tissues was assessed by fluorescence immunohistochemistry. The biosafety of GVs was evaluated by CCK8, blood biochemistry, and HE staining. Results: Strong ED-B FN expression was observed in tumor tissues while little expression in normal liver tissues. The resulting ZD2-GVs had only 267.73 ± 2.86 nm particle size and exhibited excellent binding capability to the MB49 tumor cells. The in vivo UMI experiments showed that ZD2-GVs produced stronger and longer retention in the BC tumors than that of the non-targeted CTR-GVs and GVs. Fluorescence immunohistochemistry confirmed that ZD2-GVs could penetrate the tumor vascular into the interstitial space of the tumors. Biosafety analysis revealed there was no significant cytotoxicity to these tested mice. Conclusion: Thus, ZD2-GVs can function as a potential UMI probe for the early diagnosis of bladder cancer.

Negative regulation of TREM2-mediated C9orf72 poly-GA clearance by the NLRP3 inflammasome.

Expansion of the hexanucleotide repeat GGGGCC in the C9orf72 gene is the most common genetic factor in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Poly-Gly-Ala (poly-GA), one form of dipeptide repeat proteins (DPRs) produced from GGGGCC repeats, tends to form neurotoxic protein aggregates. The C9orf72 GGGGCC repeats and microglial receptor TREM2 are both associated with risk for ALS/FTD. The role and regulation of TREM2 in C9orf72-ALS/FTD remain unclear. Here, we found that poly-GA proteins activate the microglial NLRP3 inflammasome to produce interleukin-1ß (IL-1ß), which promotes ADAM10-mediated TREM2 cleavage and inhibits phagocytosis of poly-GA. The inhibitor of the NLRP3 inflammasome, MCC950, reduces the TREM2 cleavage and poly-GA aggregates, resulting in the alleviation of motor deficits in poly-GA mice. Our study identifies a crosstalk between NLRP3 and TREM2 signaling, suggesting that targeting the NLRP3 inflammasome to sustain TREM2 is an approach to treat C9orf72-ALS/FTD.

Comparison of CT findings and histopathological characteristics of pulmonary cryptococcosis in immunocompetent and immunocompromised patients.

Pulmonary cryptococcosis (PC) is a common fungal infectious disease, and infection can occur in patients with any immune function. To better understand PC, we compared the CT findings and histopathological results in immunocompetent and immunocompromised patients. The clinical data of 68 patients with PC were collected retrospectively and divided into the immunocompetent group and immunocompromised group. The clinical characteristics, CT manifestations and histopathological characteristics of the two groups of patients were compared. Forty-two patients (61.8%) were immunocompetent, and 26 patients (38.2%) were immunocompromised. Compared with immunocompromised patients, 57.14% (24/42) of immunocompetent patients were asymptomatic (p = 0.002). Compared with immunocompetent patients, cough (14/26, 53.9%) and fever (13/26, 50.0%) were the main symptoms in immunocompromised patients (p = 0.044, p = 0.007). Nodular lesions (97.6%, 41/42) were the most common CT type in immunocompetent patients, and the CT characteristic was a single lesion (25/42, 59.5%); the main histopathological type was nodular fibrogranuloma (30/42, 71.4%), and the main histopathological characteristic was inflammatory granuloma (31/42, 73.81%) formed by macrophage phagocytosis of Cryptococcus. Consolidation (15/26, 57.7%) was more common in the CT type of immunocompromised patients. Multiple lesions (24/26, 92.31%), air bronchial signs (19/26, 73.081%) and cavities (9/26, 34.62%) were the main CT characteristics. The mucinous colloid type (19/26, 73.1%) was its main histopathological type, which was mainly characterized by a small amount of surrounding inflammatory cell infiltration (17/26, 65.4%). There were significant differences in the classification and characteristics of CT and pathology between the two groups (p < 0.05). Through the CT manifestations and histopathological characteristics of PC under different immune function states, it was found that immune function has a significant impact on the CT manifestations and histopathological characteristics of patients with PC.

GPR177 in A-fiber sensory neurons drives diabetic neuropathic pain via WNT-mediated TRPV1 activation.

Diabetic neuropathic pain (DNP) is a common and devastating complication in patients with diabetes. The mechanisms mediating DNP are not completely elucidated, and effective treatments are lacking. A-fiber sensory neurons have been shown to mediate the development of mechanical allodynia in neuropathic pain, yet the molecular basis underlying the contribution of A-fiber neurons is still unclear. Here, we report that the orphan G protein-coupled receptor 177 (GPR177) in A-fiber neurons drives DNP via WNT5a-mediated activation of transient receptor potential vanilloid receptor-1 (TRPV1) ion channel. GPR177 is mainly expressed in large-diameter A-fiber dorsal root ganglion (DRG) neurons and required for the development of DNP in mice. Mechanistically, we found that GPR177 mediated the secretion of WNT5a from A-fiber DRG neurons into cerebrospinal fluid (CSF), which was necessary for the maintenance of DNP. Extracellular perfusion of WNT5a induced rapid currents in both TRPV1-expressing heterologous cells and nociceptive DRG neurons. Computer simulations revealed that WNT5a has the potential to bind the residues at the extracellular S5-S6 loop of TRPV1. Using a peptide able to disrupt the predicted WNT5a/TRPV1 interaction suppressed DNP- and WNT5a-induced neuropathic pain symptoms in rodents. We confirmed GPR177/WNT5A coexpression in human DRG neurons and WNT5A secretion in CSF from patients with DNP. Thus, our results reveal a role for WNT5a as an endogenous and potent TRPV1 agonist, and the GPR177-WNT5a-TRPV1 axis as a driver of DNP pathogenesis in rodents. Our findings identified a potential analgesic target that might relieve neuropathic pain in patients with diabetes.

pDHS-DSET: Prediction of DNase I hypersensitive sites in plant genome using DS evidence theory.

Predicting DNase I hypersensitive sites (DHSs) is an essential topic in the field of transcriptional regulatory elements, which provides clues for deciphering the function of noncoding genomic regions. To the best of our knowledge, several computational approaches are currently available for prediction of DHSs in the plant genome, but there is still room for improvement. In the present work, a DS evidence theory-based method was proposed. At first, four sequence-derived feature representation methods, i.e., kmer, reverse complement kmers, mismatch profile, and pseudo dinucleotide composition, were utilized to encode the sequences. Then, four support vector machine based sub-classifiers was built with these sequence-derived features. Finally, the DS evidence theory was applied to obtain the final results by fusing the outputs of these four base learners. In this work, to solve the data imbalance problem, a bidirectional synthetic sampling algorithm was proposed to obtain balanced dataset during training the models. In the computational experiments, the proposed method achieved accuracy up to 88.85%, and 88.60% in Arabidopsis thaliana and rice genome, respectively. Compared with existing DHSs prediction methods, the proposed method can achieve comparable or better performances, suggesting the usefulness of the method for DHSs prediction.

Prediction of DNase I hypersensitive sites in plant genome using multiple modes of pseudo components.

DNase I hypersensitive sites (DHSs) are accessible chromatin zones hypersensitive to DNase I endonucleases in plant genome. DHSs have been used as markers for the presence of transcriptional regulatory elements. It is an important complement to develop computational methods to identify DHSs for discovering potential regulatory elements. To the best of our knowledge, several machine learning approaches have been proposed for the DHSs prediction, but there is still room for improvements. In this work, a new predictor called pDHS-WE was proposed for prediction of DHSs in plant genome by using weighted ensemble learning framework. Here, five classes of heterogeneous features were used to represent the sequences. Five random forest (RF) operators were constructed based on these five classes of features. The proposed pDHS-WE was formed by fusing the five individual RF classifiers into an ensemble predictor. Genetic algorithm was employed to obtain the weights of different classes of features. In the experiments, pDHS-WE obtained accuracy of 88.5%, sensitivity of 89.1%, specificity of 88.0%, and AUC of 0.958, which was more than 2.7%, 2%, 3.5% and 2.6% higher than state-of-the-art methods, respectively. The results suggested that pDHS-WE may become a useful tool for transcriptional regulatory elements analysis in plant genome.

pDHS-ELM: computational predictor for plant DNase I hypersensitive sites based on extreme learning machines.

DNase I hypersensitive sites (DHSs) are hallmarks of chromatin zones containing transcriptional regulatory elements, making them critical in understanding the regulatory mechanisms of gene expression. Although large amounts of DHSs in the plant genome have been identified by high-throughput techniques, current DHSs obtained from experimental methods cover only a fraction of plant species and cell processes. Furthermore, these experimental methods are both time-consuming and expensive. Hence, it is urgent to develop automated computational means to efficiently and accurately predict DHSs in the plant genome. Recently, several methods have been proposed to predict the DHSs. However, all these methods took a lot of time to build the model, making them inappropriate for data with massive volume. In the present work, a new ensemble extreme learning machine (ELM)-based model called pDHS-ELM was proposed to predict the DHSs in the plant genome by fusing two different modes of pseudo-nucleotide composition. Here, two kinds of features including reverse complement kmer and pseudo-nucleotide composition were used to represent the DHSs. The ELM model was used to build the base classifiers. Then, an ensemble framework was employed to combine the outputs of these base classifiers. When applied to DHSs in Arabidopsis thaliana and rice (Oryza sativa) genome, the proposed method could obtain accuracies up to 88.48 and 87.58%, respectively. Compared with the state-of-the-art techniques, pDHS-ELM achieved higher sensitivity, specificity, and Matthew's correlation coefficient with much less training and test time. By employing pDHS-ELM, we identified 42,370 and 103,979 DHSs in A. thaliana and rice genome, respectively. The predicted DHSs were depleted of bulk nucleosomes and were tightly associated with transcription factors. Approximately 90% of the predicted DHSs were overlapped with transcription factors. Meanwhile, we demonstrated that the predicted DHSs were also associated with DNA methylation, nucleosome positioning/occupancy, and histone modification. This result suggests that pDHS-ELM can be considered as a new promising and powerful tool for transcriptional regulatory elements analysis. Our pDHS-ELM tool is available from the following website https://github.com/shanxinzhang/pDHS-ELM/ .

pDHS-SVM: A prediction method for plant DNase I hypersensitive sites based on support vector machine.

DNase I hypersensitive sites (DHSs) are accessible chromatin regions hypersensitive to cleavages by DNase I endonucleases. DHSs are indicative of cis-regulatory DNA elements (CREs), all of which play important roles in global gene expression regulation. It is helpful for discovering CREs by recognition of DHSs in genome. To accelerate the investigation, it is an important complement to develop cost-effective computational methods to identify DHSs. However, there is a lack of tools used for identifying DHSs in plant genome. Here we presented pDHS-SVM, a computational predictor to identify plant DHSs. To integrate the global sequence-order information and local DNA properties, reverse complement kmer and dinucleotide-based auto covariance of DNA sequences were applied to construct the feature space. In this work, fifteen physical-chemical properties of dinucleotides were used and Support Vector Machine (SVM) was employed. To further improve the performance of the predictor and extract an optimized subset of nucleotide physical-chemical properties positive for the DHSs, a heuristic nucleotide physical-chemical property selection algorithm was introduced. With the optimized subset of properties, experimental results of Arabidopsis thaliana and rice (Oryza sativa) showed that pDHS-SVM could achieve accuracies up to 87.00%, and 85.79%, respectively. The results indicated the effectiveness of proposed method for predicting DHSs. Furthermore, pDHS-SVM could provide a helpful complement for predicting CREs in plant genome. Our implementation of the novel proposed method pDHS-SVM is freely available as source code, at https://github.com/shanxinzhang/pDHS-SVM.

[Expression of microRNA-100 and its relation with prognosis of colorectal cancer].

OBJECTIVE: The aim of this study was to investigate the expression of microRNA-100 (miR-100) and its relation with prognosis in colorectal cancer (CRC). METHODS: The expression of miR-100 was analyzed by quantitative real-time PCR (qRT-PCR) in 172 CRC tissue samples. The relation of miR-100 expression patterns with clinical pathological significance in CRC was analyzed. The effects of alterations of miR-100 expression and its consequences on CRC cell proliferation, apoptosis and migration were demonstrated in cells cultured in vitro. RESULTS: The relative expression of miR-100 in CRC tissues and peritumoral tissues were -6.185 ± 1.921 and -3.698 ± 1.786, respectively, with a significant difference between the two groups (P<0.01). There was a significant difference between the relative expression of miR-100 in CRC with lymph node metastasis (-5.706 ± 1.809) and without lymph node metastasis (-6.775 ± 1.902, P<0.01). The relative expression of miR-100 in tumors of different TNM stages were -7.267 ± 1.888 in stage I, -6.443 ± 1.859 in stage II, -5.923 ± 1.796 in stage III, and -4.639 ± 1.516 in stage IV, with a significant difference among them (P<0.01). Different differentiation grades showed different expression of miR-100, i.e. -7.389 ± 1.828 in well differentiated tumors, -6.095 ± 1.843 in moderately differentiated tumors, and -5.476 ± 2.088 in poorly differentiated tumors (P<0.01). There was no significant correlation between miR-100 expression and overall survival rates of the CRC patients (P=0.179). Overexpression of miR-100 in the CRC cell line HCT-8 inhibited cell proliferation, but promoted cell apoptosis and migration. CONCLUSIONS: The expression of miR-100 is correlated with lymph node metastasis, TNM stage and differentiation grade, and may be a potential biomarker indicating the development of CRC.

Inhibition of Diethylnitrosamine-Induced Hepatocarcinogenesis in Mice by a High Dietary Protein Intake.

Epidemiological and experimental evidence supports the key role of diet in the development of many types of cancer. Recent studies have suggested that dietary modifications may be beneficial for individuals at high risk for hepatocellular carcinoma (HCC). In this study, we investigated the effect of a high-protein (HP; 20% casein) dietondiethylnitrosamine (DEN)-induced hepatocarcinogenesis. Mice were given free access to water with 30 µg/ml DEN and fed a normal or HP diet for 22 wk. The results showed mice consuming HP diets had reduced mortality rates and body weights and lower hepatic enzyme activity compared to DEN-treated mice on a normal diet. HP consumption also promoted collagen accumulation in the liver, and reduced numbers of proliferating hepatocytes and infiltrating inflammatory cells, as well as decreased expression of inflammatory factor interleukin-1ß, and nuclear factor κB activation. These data indicate that HP diets can inhibit DEN-induced hepatocarcinogenesis via suppression of the inflammatory response and provide a new evidence for the dietary management of clinical patients with hepatocellular carcinoma.

An improved poly(A) motifs recognition method based on decision level fusion.

Polyadenylation is the process of addition of poly(A) tail to mRNA 3' ends. Identification of motifs controlling polyadenylation plays an essential role in improving genome annotation accuracy and better understanding of the mechanisms governing gene regulation. The bioinformatics methods used for poly(A) motifs recognition have demonstrated that information extracted from sequences surrounding the candidate motifs can differentiate true motifs from the false ones greatly. However, these methods depend on either domain features or string kernels. To date, methods combining information from different sources have not been found yet. Here, we proposed an improved poly(A) motifs recognition method by combing different sources based on decision level fusion. First of all, two novel prediction methods was proposed based on support vector machine (SVM): one method is achieved by using the domain-specific features and principle component analysis (PCA) method to eliminate the redundancy (PCA-SVM); the other method is based on Oligo string kernel (Oligo-SVM). Then we proposed a novel machine-learning method for poly(A) motif prediction by marrying four poly(A) motifs recognition methods, including two state-of-the-art methods (Random Forest (RF) and HMM-SVM), and two novel proposed methods (PCA-SVM and Oligo-SVM). A decision level information fusion method was employed to combine the decision values of different classifiers by applying the DS evidence theory. We evaluated our method on a comprehensive poly(A) dataset that consists of 14,740 samples on 12 variants of poly(A) motifs and 2750 samples containing none of these motifs. Our method has achieved accuracy up to 86.13%. Compared with the four classifiers, our evidence theory based method reduces the average error rate by about 30%, 27%, 26% and 16%, respectively. The experimental results suggest that the proposed method is more effective for poly(A) motif recognition.

Genomic signatures of near-extinction and rebirth of the crested ibis and other endangered bird species.

BACKGROUND: Nearly one-quarter of all avian species is either threatened or nearly threatened. Of these, 73 species are currently being rescued from going extinct in wildlife sanctuaries. One of the previously most critically-endangered is the crested ibis, Nipponia nippon. Once widespread across North-East Asia, by 1981 only seven individuals from two breeding pairs remained in the wild. The recovering crested ibis populations thus provide an excellent example for conservation genomics since every individual bird has been recruited for genomic and demographic studies. RESULTS: Using high-quality genome sequences of multiple crested ibis individuals, its thriving co-habitant, the little egret, Egretta garzetta, and the recently sequenced genomes of 41 other avian species that are under various degrees of survival threats, including the bald eagle, we carry out comparative analyses for genomic signatures of near extinction events in association with environmental and behavioral attributes of species. We confirm that both loss of genetic diversity and enrichment of deleterious mutations of protein-coding genes contribute to the major genetic defects of the endangered species. We further identify that genetic inbreeding and loss-of-function genes in the crested ibis may all constitute genetic susceptibility to other factors including long-term climate change, over-hunting, and agrochemical overuse. We also establish a genome-wide DNA identification platform for molecular breeding and conservation practices, to facilitate sustainable recovery of endangered species. CONCLUSIONS: These findings demonstrate common genomic signatures of population decline across avian species and pave a way for further effort in saving endangered species and enhancing conservation genomic efforts.

Genome-wide identification and predictive modeling of lincRNAs polyadenylation in cancer genome.

Long noncoding RNAs (lncRNAs) play essential regulatory roles in the human cancer genome. Many identified lncRNAs are transcribed by RNA polymerase II in which they are polyadenylated, whereby the long intervening noncoding RNAs (lincRNAs) have been widely used for the researches of lncRNAs. To date, the mechanism of lincRNAs polyadenylation related to cancer is rarely fully understood yet. In this paper, first we reported a comprehensive map of global lincRNAs polyadenylation sites (PASs) in five human cancer genomes; second we proposed a grouping method based on the pattern of genes expression and the manner of alternative polyadenylation (APA); third we investigated the distribution of motifs surrounding PASs. Our analysis reveals that about 70% of PASs are located in the sense strand of lincRNAs. Also more than 90% PASs in the antisense strand of lincRNAs are located in the intron regions. In addition, around 40% of lincRNA genes with PASs has APA sites. Four obvious motifs i.e., AATAAA, TTTTTTTT, CCAGSCTGG, and RGYRYRGTGG were detected in the sequences surrounding PASs in the normal and cancer tissues. Furthermore, a novel algorithm was proposed to recognize the lincRNAs PASs of tumor tissues based on support vector machine (SVM). The algorithm can achieve the accuracies up to 96.55% and 89.48% for identification the tumor lincRNAs PASs from the non-polyadenylation sites and the non-lincRNA PASs, respectively.

A novel genome-wide polyadenylation sites recognition system based on condition random field.

Polyadenylation including the cleavage of pre-mRNA and addition of a stretch of adenosines to the 3'-end is an essential step of pre-mRNA processing in eukayotes. The known regulatory role of polyadenylation in mRNA localization, stability, and translation and the emerging link between poly(A) and disease states underline the necessary to fully characterize polyadenylation sites. Several artificial intelligence methods have been proposed for poly(A) sites recognition. However, these methods are suitable to small subsets of genome sequences. It is necessary to propose a method for genome-wide recognition of poly(A) sites. Recent efforts have found a lot of poly(A) related factors on DNA level. Here, we proposed a novel genome-wide poly(A) recognition method based on the Condition Random Field (CRF) by integrating multiple features. Compared with the polya_svm (the most accurate program for prediction of poly(A) sites till date), our method had a higher performance with the area under ROC curve(0.8621 versus 0.6796). The result suggests that our method is an effective method in genome wide poly(A) sites recognition.

Analysis of selective constraints on mitochondrial DNA, flight ability and physiological index on avian.

For most of the birds in the word, they can be divided into two main groups, i.e. resident birds and migratory ones. Most of the energy required for long-distance migration is supplied by mitochondria via oxidative phosphorylation. Therefore, the evolutionary constraints acted on the mitochondria DNA (mtDNA) are considered to vary with the locomotive abilities and flight speed. The flight speed is assumed to increase with mass and wing loading according to the fundamental aerodynamic theories, which is common between aves and aircrafts. We compared 148 avian mitochondrial genomes and main physiological parameters. More nonsynonymous nucleotide substitutions than synonymous ones are accumulated in low-speed and flightless birds rather than high-speed flying birds. No matter how the speed is obtained, directly measured or estimated through physiological index. Our results demonstrated that, besides artificial and environmental factors, selective constraints relevant to flight ability play an essential role in the evolution of mtDNA, even it might cause the extinction of avian species.