Biology subjects

Liu, Q.

Publications and source records attributed to Liu, Q..

At least 595 records · Page 33Linked to original sources

bioRxiv2019

A-Star, an Argonaute-directed System for Rare SNV Enrichment and Detection

The ability to detect rare mutations has revolutionized diagnosis and monitoring of tumors, but is limited by the shortage of sensitive, cost-effective and high coverage methods for identification of extremely low abundant mutations. Here, we establish a single-tube multiplex PCR system by employing thermophilic Argonaute-derived DNA-guided nuclease for highly efficient rare mutation detection, referred to as A-Star (Argonaute-directed specific target enrichment and detection), that combines the selective cleavage of the wild type DNA in the DNA denaturation step and the followed amplification of mutant DNA during PCR. A-Star enables easy detection and quantitation of rare mutations originally as low as 0.01% in allele frequency with a [>=] 5500-fold efficiency. We also demonstrate the feasibility of A-Star for detecting oncogenic mutations in complex biological systems such as solid tumors tissues and blood samples. Remarkably, A-Star could achieve the detection of multiple oncogenic genes simultaneously through a simple single-tube reaction. Taken together, our work illustrates a supersensitive and rapid nucleic acid detection system, thereby extending the utility for both research and therapeutic applications.

bioengineering↗

bioRxiv2019

3D Printed Microheater Sensor-Integrated, Drug-Encapsulated Microneedle Patch System for Pain Management

Microneedle patch device has been widely utilized for transdermal drug delivery in pain management, but is challenged by accurate control of drug release and subsequent diffusion to human body. The recent emerging wearable electronics that could be integrated with microneedle devices offers a facile approach to address such a challenge. Here a 3D printed microheater integrated drug-encapsulated microneedle patch system for drug delivery is presented. The ink solution comprised of polydimethylsiloxane (PDMS) and multiwalled carbon nanotubes (MWCNTs) with mass concentration of up to 45% is prepared and used to print crack-free stretchable microheaters on substrates with a broad range of materials and geometric curves. The adhesion strength of printed microheater on microneedle patch in elevated temperatures are measured to evaluate their integration performance. Assessments of encapsulated drug release into rats skin are confirmed by examining degradation of microneedles, skin morphologies, and released fluorescent signals. Results and demonstrations established here creates a new opportunity for developing sensor controlled smart microneedle patch systems by integrating with wearable electronics, potentially useful in clinic and biomedical research.

bioengineering↗

bioRxiv2019

Commensal-derived succinate enhances tuft cell specification and suppresses ileal inflammation

Longitudinal analysis of Crohn's disease (CD) incidence has identified an inverse correlation with helminth infestation and recent studies have revealed that intestinal tuft cell hyperplasia is critical for helminth response. Tuft cell frequency was decreased in the inflamed ilea of CD patients and a mouse model of TNF-induced Crohn's-like ileitis (TNF{Delta}ARE). Single-cell RNA sequencing paired with unbiased differential trajectory analysis of the tuft cell lineage in a genetic model of tuft cell hyperplasia (AtohKO) demonstrated that the tuft cell lineage had increased tricarboxylic acid (TCA) cycle gene signatures. Commensal microbiome-derived succinate was detected in the ileal lumen of these animals while microbiome depletion suppressed tuft cell hyperplasia. Therapeutic succinate treatment in TNF{Delta}ARE animals reduced pathology in correlation with induced tuft cell specification. We provide evidence implicating the modulatory role of intestinal tuft cells in chronic intestinal inflammation, which could facilitate leveraging this rare and elusive cell type for CD treatment.

cell biology↗

bioRxiv2019

Approaches for integrating heterogeneous RNA-seq data reveals cross-talk between microbes and genes in asthmatic patients

Sputum induction is a non-invasive method to evaluate the airway environment, particularly for asthma. RNA sequencing (RNAseq) can be used on sputum, but it can be challenging to interpret because sputum contains a complex and heterogeneous mixture of human cells and exogenous (microbial) material. In this study, we developed a methodology that integrates dimensionality reduction and statistical modeling to grapple with the heterogeneity. We use this to relate bulk RNAseq data from 115 asthmatic patients with clinical information, microscope images, and single-cell profiles. First, we mapped sputum RNAseq to human and exogenous sources. Next, we decomposed the human reads into cell-expression signatures and fractions of these in each sample; we validated the decomposition using targeted single-cell RNAseq and microscopy. We observed enrichment of immune-system cells (neutrophils, eosinophils, and mast cells) in severe asthmatics. Second, we inferred microbial abundances from the exogenous reads and then associated these with clinical variables -- e.g., Haemophilus was associated with increased white blood cell count and Candida, with worse lung function. Third, we applied a generative model, Latent Dirichlet allocation (LDA), to identify patterns of gene expression and microbial abundances and relate them to clinical data. Based on this, we developed a method called LDA-link that connects microbes to genes using reduced-dimensionality LDA topics. We found a number of known connections, e.g. between Haemophilus and the gene IL1B, which is highly expressed by mast cells. In addition, we identified novel connections, including Candida and the calcium-signaling gene CACNA1E, which is highly expressed by eosinophils. These results speak to the mechanism by which gene-microbe interactions contribute to asthma and define a strategy for making inferences in heterogeneous and noisy RNAseq datasets.

genomics↗

bioRxiv2019

Developing a dengue forecast model using Long Short Term Memory neural networks method

BackgroundDengue Fever (DF) is a tropical mosquito-borne disease that threatens public health and causes enormous economic burdens worldwide. In China, DF expanded from coastal region to inner land, and the incidence sharply increased in the last few years. In this study, we conduct the analysis of dengue using the Long Short Term Memory (LSTM) recurrent neural networks. This is an artificial intelligence technology, to develop a precise dengue forecast model.\n\nMethodology/Principal FindingsThe model is developed from monthly dengue cases and local meteorological data of 2005-2018 among top 20 Chinese cities with a record of the highest dengue incidence. The first 13 year data were used to construct the LSTM and to predict the dengue outbreaks in 2018. The results are compared with the estimated dengue cases of other previously published models. Model performance and prediction accuracy were assessed using Root Mean Square Error (RMSE). With the LSTM method, the prediction measurements of average RMSE drop by 54.79% and 34.76% as compared with the Susceptible Infected Recovered (SIR) model and Zero Inflated Generalized Additive Model (ZIGAM). Our results showed that if only local data were used to develop forecast models, the LSTM neural networks would fail to capture the transmission characteristics of dengue virus in areas with fewer dengue cases. Contrarily, transfer learning (TL) can improve the accuracy of prediction of the LSTM neural network model in areas with fewer dengue incidences.\n\nConclusion and significanceThe LSTM model is beneficial in predicting dengue incidence as compared with other previously published forecasting models. The findings provide a more precise forecast dengue model, which can help the local government and health-related departments respond early to dengue epidemics.\n\nAuthor summaryIn China, DF is a public health concern that poses a great economic burden on local governments. However, the incidence has sharply increased in recent years with growth in the sub-regions. With this issue, it will be challenging to develop an accurate and timely dengue forecast model. LSTM recurrent neural networks, deep learning methods and virus propagation rules by learning from observational data offer more advantages in predicting the prevalence of infectious disease dynamics than the traditional statistical model. The 2005-2017 data of the top 20 Chinese cities with the highest dengue incidence were used to construct the LSTM model, advantageous in predicting dengue in most cities. Moreover, the model helped to predict the dengue outbreaks in 2018 and used to compare the estimated dengue cases with the RMSE results of other previously published models. A thorough search of the literature shows that this is the first established dengue forecast model using the LSTM method, which is effective in predicting the trend of dengue dynamics.

bioinformatics↗

bioRxiv2019

Genomic variant identification methods alter Mycobacterium tuberculosis transmission inference

Pathogen genomic data are increasingly used to characterize global and local transmission patterns of important human pathogens and to inform public health interventions. Yet there is no current consensus on how to measure genomic variation. We investigated the effects of variant identification approaches on transmission inferences for M. tuberculosis by comparing variants identified by five different groups in the same sequence data from a clonal outbreak. We then measured the performance of commonly used variant calling approaches in recovering variation in a simulated tuberculosis outbreak and tested the effect of applying increasingly stringent filters on transmission inferences and phylogenies. We found that variant calling approaches used by different groups do not recover consistent sets of variants, often leading to conflicting transmission inferences. Further, performance in recovering true outbreak variation varied widely across approaches. Finally, stringent filters rapidly eroded the accuracy of transmission inferences and quality of phylogenies reconstructed from outbreak variation. We conclude that measurements of genetic distance and phylogenetic structure are dependent on variant calling approach. Variant calling algorithms trained upon true sequence data outperform other approaches and enable inclusion of repetitive regions typically excluded from genomic epidemiology studies, maximizing the information gleaned from outbreak genomes.

genomics↗

bioRxiv2019

Network-Based Matching of Patients and Targeted Therapies for Precision Oncology

The extensive acquisition of high-throughput molecular profiling data across model systems (human tumors and cancer cell lines) and drug sensitivity data, makes precision oncology possible - allowing clinicians to match the right drug to the right patient. Current supervised models for drug sensitivity prediction, often use cell lines as exemplars of patient tumors and for model training. However, these models are limited in their ability to accurately predict drug sensitivity of individual cancer patients to a large set of drugs, given the paucity of patient drug sensitivity data used for testing and high variability across different drugs. To address these challenges, we developed a multilayer network-based approach to impute individual patients responses to a large set of drugs. This approach considers the triplet of patients, cell lines and drugs as one inter-connected holistic system. We first use the omics profiles to construct a patient-cell line network and determine best matching cell lines for patient tumors based on robust measures of network similarity. Subsequently, these results are used to impute the \"missing link\" between each individual patient and each drug, called Personalized Imputed Drug Sensitivity Score (PIDS-Score), which can be construed as a measure of the therapeutic potential of a drug or therapy. We applied our method to two subtypes of lung cancer patients, matched these patients with cancer cell lines derived from 19 tissue types based on their functional proteomics profiles, and computed their PIDS-Scores to 251 drugs and experimental compounds. We identified the best representative cell lines that conserve lung cancer biology and molecular targets. The PIDS-Score based top sensitive drugs for the entire patient cohort as well as individual patients are highly related to lung cancer in terms of their targets, and their PIDS-Scores are significantly associated with patient clinical outcomes. These findings provide evidence that our method is useful to narrow the scope of possible effective patient-drug matchings for implementing evidence-based personalized medicine strategies.\n\nData and code availabilityhttps://github.com/bayesrx/bayesrx.github.io/tree/master/authors/liu-q./ Shiny app (data and results visualization tool): https://qingzliu.shinyapps.io/psb-app/

bioinformatics↗

bioRxiv2019

Biological Pathways and Gene Networks Link Inflammation and Vascular Remodeling to Both Heart Failure with Preserved and Reduced Ejection Fraction in Women across Ethnicities

IntroductionHeart failure (HF) is understudied among women; especially, genomic evidence implicating shared or unique mechanisms of HF with respect to reduced or preserved ejection fraction (HFrEF, HFpEF) is lacking across ethnic populations of women. Prior genome-wide association studies (GWAS) have identified approximately 30 suggestive genetic variants for HF, although none have been specifically linked to HFrEF or HFpEF.\n\nObjectivesWe aimed to define, replicate, and annotate genetic variants to HFrEF, HFpEF, or both, as well as to investigate potential biological mechanisms underlying HFrEF and HFpEF among African American (AA) and European American (EA) women in three well-characterized, high-quality prospective cohorts, the Womens Health Initiative (WHI) study, the Jackson Heart Study (JHS), and the Framingham Heart Study (FHS).\n\nMethodsGWAS analysis on HFrEF and HFpEF were first performed among 7,982 AA and 4,133 EA in the WHI, followed by pathway analysis employing two independent methodological platforms (GSA-SNP and Mergeomics) curating KEGG, Reactome, and BioCarta pathway databases. GWAS signals and biological pathways identified using the WHI were replicated in the JHS and FHS. For all replicated pathways, we performed cross-phenotype and cross-ethnicity validation analyses to examine shared pathways between HFrEF and HFpEF, and phenotype-specific pathways, across ethnicities. We further prioritized key driver genes for HF according to specific pathways identified.\n\nResultsWe validated one previously reported genetic locus and identified six new ones, among which one locus was allocated to HFrEF and five to HFpEF. Additionally, we defined five biological pathways shared between HFrEF and HFpEF and discovered six HFpEF-specific pathways. These pathways overlapped in two main domains for molecular signaling: 1) inflammation and 2) vascular remodeling (including angiogenesis and vascular patterning), involving key driver genes from collagen and HLA gene families.\n\nConclusionsOur network analysis of three large prospective cohorts of women in the United States defined several novel loci for HF and its subtypes. In particular, several key driver genes reinforce the mechanistic role of inflammation and vascular remodeling in the development of HF, especially HFpEF. Given that therapeutic strategies developed for left ventricular dysfunction have had limited success for HFpEF, several new targets and pathways identified and validated in this study should be further assessed in risk stratification as well as the design of potential new HF interventions.

genetics↗

bioRxiv2019

ProGeo-neo: a Customized Proteogenomic Workflow for Neoantigen Prediction and Selection

Neoantigens can be differentially recognized by T cell receptor (TCR) as these sequences are derived from mutant proteins and are unique to the tumor. The discovery of neoantigens is the first key step for tumor-specific antigen (TSA) based immunotherapy. Based on high-throughput tumor genomic analysis, each missense mutation can potentially give rise to multiple neopeptides, resulting in a vast total number, but only a small percentage of these peptides may achieve immune-dominant status with a given major histocompatibility complex (MHC) class I allele. Specific identification of immunogenic candidate neoantigens is consequently a major challenge. Currently almost all neoantigen prediction tools are based on genomics data. Here we report the construction of proteogenomics prediction of neoantigen (ProGeo-neo) pipeline, which incorporates the following modules: mining tumor specific antigens from next-generation sequencing genomic and mRNA expression data, predicting the binding mutant peptides to class I MHC molecules by latest netMHCpan (v.4.0), verifying MHC-peptides by MaxQuant with mass spectrometry proteomics data searched against customized protein database, and checking potential immunogenicity of T-cell-recognization by additional screening methods. ProGeo-neo pipeline achieves proteogenomics strategy and the neopeptides identified were of much higher quality as compared to those identified using genomic data only. The pipeline was constructed based on the genomics and proteomics data of Jurkat leukemia cell line but is generally applicable to other solid cancer research. With massively parallel sequencing and proteomics profiling increasing, this proteogenomics workflow should be useful for neoantigen oriented research and immunotherapy.

bioinformatics↗

bioRxiv2019

A Na2CO3-responsive chitinase gene from Chinese wildrye improve pathogen resistance and saline-alkali stress tolerance in transgenic tobacco and maize

Salinity and microbial pathogens are the major limiting factors for crop production. Although the manipulation of many genes could improve plant performance under either of these stresses, few genes have reported that could improve both pathogen resistance and saline-alkali stress tolerance. In this study, we identified a new chitinase gene CHITINASE 2 (LcCHI2) that encodes a class II chitinase from a Chinese wildrye (Leymus Chinensis), which grows naturally on alkaline-sodic soil. Overexpression of LcCHI2 increased chitinase activity in transgenic plants. The transgenic tobacco and maize exhibited improved pathogen resistance and enhanced both neutral salt and alkaline salt stress tolerance. Overexpression of LcCHI2 reduced sodium (Na+) accumulation, malondialdehyde content and relative electrical conductivity in transgenic tobacco under salt stress. In addition, the transgenic tobacco showed diminished lesion against bacterial and fungal pathogen challenge, suggesting an improved disease resistance. Similar improved performance was also observed in LcCHI2-overexpressed maize under both pathogen and salt stresses. It is worth noting that this genetic manipulation does not impair the growth and yield of transgenic tobacco and maize under normal cultivation condition. Apparently, application of LcCHI2 provides a new train of thought for genetically engineering saline-alkali and pathogen resistant crops of both dicots and monocots.

plant biology↗

bioRxiv2019

Combined inhibition of mTOR and PIKKs exploits replicative and checkpoint vulnerabilities to induce death of PI3K-activated triple-negative breast cancer cells

SUMMARY SUMMARY HIGHLIGHTS INTRODUCTION RESULTS DISCUSSION FUNDING AUTHOR CONTRIBUTIONS STAR METHODS Cell lines METHOD DETAILS Western blotting Evaluation of drug sensitivity Immunofluorescence microscopy Primary antibodies and dilutions Single-stranded DNA ssDNA and... Live cell microscopy Extraction of polar metabolites Metabolomics profiling by... Isobolograms and analysis of... Cell cycle model and... QUANTIFICATION AND STATISTICAL... Viable cell counts ...

cancer biology↗

bioRxiv2019

Domain-centric dissection and classification of prokaryotic poly(3-hydroxyalkanoate) synthases

Although many enzymes and multiple pathways involve in Polyhydroxyalkanoates (PHAs) synthesis, PHA synthases play a determinant role in the process, which include three subunits of PhaC, PhaE, and PhaR. Currently, PHA synthases are categorized into four classes according to its primary sequences, substrate specificity, and subunit composition. However, theoretical analysis of PHA synthases from the domain perspective has not been performed. In this study, we dissected PHA synthases thoroughly through analysis of domain organization. Both referenced bacterial and archaeal proteomes were then screened for the presence and absence of different PHA synthases along NCBI taxonomy ID-based phylogenetic tree. In addition, sequences annotated as bacterial and archaeal PhaCs in UniProt database were also analyzed for domain organizations and interactions. In sum, the in-silico study provided a better understanding of the domain features of PHA synthases in prokaryotes, which also assisted in the production of PHA polymers with optimized chemical properties.

genomics↗

bioRxiv2019

Synergistic interplay between PHF8 and HER2 signaling contributes to breast cancer development and drug resistance

HER2 plays a critical role in tumorigenesis and is associated with poor prognosis of HER2-positive breast cancers. Although, anti-HER2 drugs show benefits in breast cancer therapy, de novo or acquired resistance often develop. Epigenetic factors have been increasingly targeted for therapeutic purposes, however, such mechanisms interacting with HER2 signaling are poorly understood. This study reports the synergistic interplay between histone demethylase PHF8 and HER2 signaling, i.e. PHF8 is elevated in HER2-positive breast cancers and is upregulated by HER2; PHF8 plays coactivator roles in regulating HER2 expression and HER2-driven epithelial-to-mesenchymal transition (EMT) markers and cytokines. The HER2-PHF8-IL-6 regulatory axis was proved both in cell lines and in the newly established MMTV-Her2/MMTV-Cre/Phf8flox/flox models, with which the oncogenic function of Phf8 in breast cancer in vivo was revealed for the first time. Furthermore, PHF8-IL-6 axis contributes to the resistance of Trastuzumab in vitro and may play a critical role in the infiltration of T-cells in HER2-driven breast cancers. This study reveals novel epigenetic mechanisms underlying HER2-driven cancer development and anti-HER2 drug resistance.

cancer biology↗

bioRxiv2019

TRESK K+ Channel Activity Regulates Trigeminal Nociception and Headache

Although dominant-negative mutations of TRESK background K+ channel have been reported in migraine patients, whether TRESK activity controls the generation of trigeminal pain, especially headache, is not established. We found that loss of TRESK in all trigeminal ganglia (TG) neurons preferentially increased the intrinsic excitability of small-diameter TG nociceptors that express neuropeptide CGRP or TRPM8 channels. Surprisingly, loss of TRESK increased the number of TG neurons expressing TRPV1 channels. In dorsal root ganglia neurons, neither the persistent outward current nor the intrinsic excitability was affected by the loss of TRESK. Compared with wild-type controls, TRESK knockout mice exhibited more robust trigeminal pain, especially headache-like behaviors; but displayed normal body and visceral pain responses. Our findings indicate that endogenous TRESK activity is required for trigeminal pain regulation. A substantial reduction of TRESK activity may selectively affect the functions of TG nociceptors, thereby increasing the susceptibility to migraine headache in humans.

neuroscience↗

bioRxiv2019

A luciferase-based assay identifies niclosamide derivatives antagonizing Mcl-1 through post-translational down-regulation

The up-regulation of Mcl-1 expression is a major mechanism of cancer cell survival and therapy resistance. However, the underlying molecular mechanism remains incompletely understood, limiting the number of druggable approaches to selectively inhibit Mcl-1 function. In this study, we designed and employed a novel mechanistic high-throughput screening system to selectively uncover post-translational modulators of Mcl-1. We generated a cell-based high-throughput screening assay in which myeloid leukemia K562 cells constitutively express Mcl-1 or Bcl-xL fused with luciferase (Luc-Mcl-1 or Luc-Bcl-xL, respectively) under a viral promoter. 1,650 bioactive compounds were screened for their ability to selectively induce Mcl-1 down-regulation in a 2-hour assay. A family of niclosamide derivatives were eventually identified for their remarkable ability to decrease Mcl-1 protein stability, exemplified by N007. These salicylate derivatives did not alter Mcl-1 mRNA levels, but selectively induced proteasome-dependent Mcl-1 down-regulation independent of Noxa, Mule, or GSK3{beta}. We also demonstrate that N007 potently induced cell death in leukemia cell lines, including those resistant to Bcl-2 inhibitors. Our work highlights the versatility of the mechanistic high-throughput screening approach as a valuable tool in identifying novel agents with the ability to down-regulate proteins crucial to human diseases.

biochemistry↗

bioRxiv2019

Microstructural plasticity in the bilingual brain

The human brain has been uniquely equipped with the remarkable ability to acquire more than one language, as in bilingual individuals. Previous neuroimaging studies have indicated that learning a second language (L2) induced neuroplasticity at the macrostructural level. In this study, using the quantitative MRI (qMRI) combined with functional MRI (fMRI) techniques, we quantified the microstructural properties and tested whether second language learning modulates the microstructure in the bilingual brain. We found significant microstructural variations related to age of acquisition of second language in the left inferior frontal region and the left fusiform gyrus that are crucial for resolving lexical competition of bilinguals two languages. Early second language acquisition contributes to enhance cortical development at the microstructural level.\n\nSignificant statementThe ability to communicate in two languages is becoming more and more important in the increasingly global community. Does learning a second language (L2) affect the human brain development? At the macrostructural level, there has been neuroimaging evidence revealing neuroplasticity induced by the acquisition of L2. Here, we employed the quantitative MRI technique to investigate the microstructural variations related to L2 learning, and found that age of acquisition of L2, but not its proficiency, is associated with cortical proliferation. Early second language acquisition seems to enhance cortical development at the microstructural level.

neuroscience↗

bioRxiv2019

Verify Hub Genes of expression profile in aortic dissection

BackgroundTo assess the mRNAs expression profile and explore the hub mRNAs and potential molecular mechanisms in the pathogenesis of human thoracic aortic dissection (TAD). Methodology: mRNA microarray expression signatures of TAD tissues (n=6) and no TAD tissues (NT;n=6) were analyzed by Arraystar human mRNAs microarray. Real-time PCR (qRT-PCR) were used to validate the result of mRNAs microarray. Bioinformatic tools including gene ontology, and Kyoto Encyclopedia of Genes and Genomes pathway analysis were utilized. The protein-protein interaction networks were constructed based on data from the STRING database. Molecular Complex Detection (MCODE) and cytohubba analysis were used to infer the most hug gene and pathways. Results: The top 10 hub genes CDK1, CDC20, CCNB2, CCNB1, MAD2L1, AURKA, C3AR1, NCAPG,CXCL12 and ASPM were identified from the PPI network. Module analysis revealed that TAD was associated with cell cycle, oocyte meiosis, p53 signaling pathway, progesterone-mediated oocyte maturation. The qRT-PCR result showed that the expression of all hug genes was significantly increased in TAD samples (p < 0.05). Conclusions: These candidate genes could be used as potential diagnostic biomarkers and therapeutic targets of TAD.\n\nAuthor summaryMany basic characteristics underlying the establishment of aortic dissection have not been studied in detail. The presented work sought to understand the pathogenesis of human thoracic aortic dissection by employing bioinformatic tools to explore the hub mRNAs and potential molecular mechanisms of thoracic aortic dissection. Many pathway were thought to have relevant with this disease, but the most important pathway was not define. We used bio-mathematical analysis to explore the potential functions in thoracic aortic dissection and identified the hub genes and explored the intrinsic molecular mechanisms involved in thoracic aortic dissection between two microarray analysis. Finally, we indentified the cell cycle maybe the key pathway in thoracic aortic dissection.

bioinformatics↗

bioRxiv2019

Comparative genomic analysis reveals a monophyletic cold adapted Arthrobacter cluster from polar and alpine regions

Decrease in the frequency of arginine and increase in lysine are the trends that have been identified in the genomes of cold adapted bacteria. However, some cold adapted taxa show only limited or no detectable changes in the frequencies of amino acid composition. Here, we examined Arthrobacter spp. genomes from a wide range of environments on whether the genomic adaptations can be conclusively identified across genomes of taxa from polar and alpine regions. Phylogenetic analysis with a concatenated alignment of 119 orthologous proteins revealed a monophyletic clustering of seven polar and alpine isolated strains. Significant changes in amino acid composition related to cold adaptation were exclusive to seven of the twenty-nine strains from polar and alpine regions. Analysis of significant indicator genes and cold shock genes also revealed that clear differences could only be detected in the same seven strains. These unique characteristics may result from a vast exchange of genome content at the node leading to the monophyletic cold adapted Arthrobacter cluster predicted by the birth-and-death model. We then experimentally validated that strains with significant changes in amino acid composition have a better capacity to grow at low temperature than the mesophilic strains.\n\nImportanceAcquisition of novel traits through horizontal gene transfer at the early divergence of the monophyletic cluster may accelerate their adaptation to low temperature. Our study reached a clear relationship between adaptation to cold and genomic features and would advanced in understanding the ambiguous results produced by the previous studies on genomic adaption to cold temperature.

microbiology↗