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Tags: pipeline, DNA-seq
HF samples (Enterobacter hormaechei strain HKEH-1 vs. CP095177, CP095178, CP095179)
Definition: If genome comparison verify the bacterium is Enterobacter hormaechei, we can name it as “Enterobacter hormaechei strain HKEH-1”
Authors: 1) Ding, Mohan, 2) Huang, Jiabin, 3) Foong, Wuen-Ee, 4) Tam, Heng-Keat
Title: Draft genome sequence of Enterobacter hormaechei HKEH1, isolated from a blood sample from a type II diabetic patient
Source: mol_type="genomic DNA" strain="HKEH-1" isolation_source="blood" host="type II diabetic patient" geo_loc_name="China" collection_date="2024"
Preparing fasta-files for submitting the genome to NCBI using Ragtag
python ~/Scripts/filter_fasta_by_length.py HKEH-1.fasta HKEH-1_l500.fasta -l 500
mamba create -n ragtag_env -c bioconda ragtag
mamba activate ragtag_env
cd /mnt/md1/DATA/Data_Tam_DNAseq_2025_adeABadeIJ_adeIJK_CM1_CM2/gffs/
#(ragtag_env) ragtag.py scaffold -i 0.18 HKEH-1_l500.fasta CP097342.fasta
#(ragtag_env) ragtag.py scaffold -f 500 -q 5 -d 200000 -i 0.1 -a 0.1 -s 0.1 -g 200 -m 200000 -w -u HKEH-1_l500.fasta CP157756.fasta
(ragtag_env) ragtag.py scaffold -f 500 -q 5 -d 200000 -i 0.1 -a 0.1 -s 0.1 -g 200 -m 200000 -w -u HKEH-1_l500.fasta CP097342.fasta
mv ragtag_output ragtag_output5
#(ragtag_env) ragtag.py scaffold -f 500 -q 5 -d 200000 -i 0.1 -a 0.1 -s 0.1 -g 200 -m 200000 -w -u HKEH-1_l500.fasta Enterobacter_hormaechei_ATCC_49162.fasta
#mv ragtag_output ragtag_output7
cut -d$'\t' -f6 ragtag_output5/ragtag.scaffold.asm.paf | sort -u > contigs_selected5
#cut -d$'\t' -f6 ragtag_output7/ragtag.scaffold.asm.paf | sort -u > contigs_selected7
samtools faidx HKEH-1_l500.fasta
samtools faidx HKEH-1_l500.fasta contig00027 > putative_plasmids.fasta #NODE_27_length_6475
samtools faidx HKEH-1_l500.fasta contig00029 >> putative_plasmids.fasta #NODE_29_length_4093
samtools faidx HKEH-1_l500.fasta contig00030 >> putative_plasmids.fasta #NODE_30_length_2606
samtools faidx HKEH-1_l500.fasta contig00033 >> putative_plasmids.fasta
#ADD the xxxxxxxxxxxxxx NNNN contig00033
samtools faidx HKEH-1_l500.fasta contig00033 > contig00033.fasta
cat ragtag.scaffold.fasta N_100x.fasta contig00033.fasta > ragtag.scaffold_and_contig00033.fasta
#remove the second until last '>'
seqkit seq ragtag.scaffold_and_contig00033.fasta > ragtag.scaffold_and_contig00033_.fasta
cat ragtag.scaffold_and_contig00033_.fasta contig00027_circular.fasta contig00029_circular.fasta contig00030_circular.fasta > pHKEH-1_submit.fasta
#change the fasta headers to HKEH-1_chr, pHKEH-1_6k, pHKEH-1_4k, pHKEH-1_2k
#HKEH-1_chr 4786112 12 60 61
#pHKEH-1_6k 6364 4865905 60 61
#pHKEH-1_4k 3982 4872388 60 61
#pHKEH-1_2k 2495 4876449 60 61
Manual inspection of the mate-pair read mapping at the start and end confirmed that three of the contigs were circular plasmids.
# -- Circularity_method1: Confirm the contigs are circular using blastn --
makeblastdb -in HKEH-1_l500.fasta -dbtype nucl -out contigs_db
blastn -query HKEH-1_l500.fasta -db contigs_db -outfmt 6 -evalue 1e-10 > blast_results.txt
#[In ChatGPT] please check if the contigs are circular based on the following blast-results:
contig00027 contig00027 100.000 111 0 0 6365 6475 1 111 2.64e-52 206
contig00027 contig00027 100.000 111 0 0 1 111 6365 6475 2.64e-52 206
contig00029 contig00029 100.000 111 0 0 3983 4093 1 111 1.67e-52 206
contig00029 contig00029 100.000 111 0 0 1 111 3983 4093 1.67e-52 206
contig00030 contig00030 100.000 111 0 0 2496 2606 1 111 1.06e-52 206
contig00030 contig00030 100.000 111 0 0 1 111 2496 2606 1.06e-52 206
samtools faidx HKEH-1_l500.fasta contig00027 > contig00027.fasta
samtools faidx HKEH-1_l500.fasta contig00029 > contig00029.fasta
samtools faidx HKEH-1_l500.fasta contig00030 > contig00030.fasta
python3 ~/Scripts/process_circular.py contig00027.fasta contig00027_circular.fasta --overlap_len 111
python3 ~/Scripts/process_circular.py contig00029.fasta contig00029_circular.fasta --overlap_len 111
python3 ~/Scripts/process_circular.py contig00030.fasta contig00030_circular.fasta --overlap_len 111
# -- Circularity_method2: using PlasmidFinder --
#https://cge.food.dtu.dk/services/PlasmidFinder/
# -- Mapping the contigs to the reference genome to have an overview --
bwa mem CP157756.fasta HKEH-1.fasta > aligned2.sam
samtools view -bS aligned2.sam | samtools sort -o aligned2.sorted.bam
Summary of the genomic characteristics
# -- Fill the table: summary of sequence data and genome features --
# calculate the read number
zcat HF_trimmed_P_1.fastq.gz | echo $((`wc -l` / 4))
zcat HF_trimmed_P_2.fastq.gz | echo $((`wc -l` / 4))
# calculate the base number for paired-end reads
zcat HF_trimmed_P_1.fastq.gz | awk 'NR%4==2 {sum+=length($0)} END {print sum}' > bases_R1.txt
zcat HF_trimmed_P_2.fastq.gz | awk 'NR%4==2 {sum+=length($0)} END {print sum}' > bases_R2.txt
# Then add them:
#1142394820+1142327087=2284721907
samtools faidx HKEH-1_submit.fasta HKEH-1_chr > HKEH-1_chr.fasta
samtools faidx HKEH-1_submit.fasta pHKEH-1_6k > pHKEH-1_6k.fasta
samtools faidx HKEH-1_submit.fasta pHKEH-1_4k > pHKEH-1_4k.fasta
samtools faidx HKEH-1_submit.fasta pHKEH-1_2k > pHKEH-1_2k.fasta
for sample in HKEH-1_chr pHKEH-1_6k pHKEH-1_4k pHKEH-1_2k; do
bakta --db /mnt/nvme1n1p1/bakta_db --output bakta/${sample} --genus Enterobacter --species hormaechei --strain HKEH-1 --prefix ${sample} --locus-tag ${sample} ${sample}.fasta --threads 120
done
#--Calculate coverage--
bwa index HKEH-1_submit.fasta
bwa mem HKEH-1_submit.fasta ../HF_trimmed_P_1.fastq.gz ../HF_trimmed_P_2.fastq.gz > aligned_reads.sam
samtools view -bS aligned_reads.sam | samtools sort -o aligned_reads.sorted.bam
samtools index aligned_reads.sorted.bam
samtools depth aligned_reads.sorted.bam > coverage.txt
awk '{sum+=$3} END {print "Average coverage:", sum/NR}' coverage.txt
#Average coverage: 589.421
#--Using BV-BRC--
https://www.bv-brc.org/view/Bacteria/2#view_tab=overview
Edit manuscript
Genomic DNA extractions were performed using the Wizard Genomic DNA Purification Kit (Promega Corp.) from single colonies cultured in nutrient broth overnight at 28°C. For genome sequencing, libraries were constructed using the TruSeq DNA Sample Preparation Kit (Illumina, USA), and paired‐end (2 × 150 bp) raw reads were generated using the Illumina NovaSeq 6000 platform from Génome Québec, Montréal, Canada.
A total of 76,719,388, 127,982,014, and 108,524,345 paired-end reads were generated for strains MHB01, MHB02, and MHB03, respectively (Table 1). Trimmomatic v0.39 (2) was used for quality control and trimming of paired-end reads and de novo assemblies was performed using Unicycler v0.4.8 (3) as implemented in the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) pipeline [4]. Contigs < 500 bp were discarded. All contigs Not mapped
#The mate-pair reads refer here to the Illumina short paired-end reads. If the chromosomes/plasmids are circular, some paired reads should be located on both ends. Following the principle, we performed
the following mapping process to check the circularity. At first, one of a read pair is checked if it is
located on the left of the chromosome/plasmid. If yes, then we check the other one of a read pair
whether located on the right of the chromosome/plasmid or not. If true, the circularity of the
chromosome/plasmid is confirmed.
Further filtering removed reads with Q-score <10, leaving 55,537 reads with a mean length of 3.3 kb and a maximal length of 110.7 kb. The resulting high-quality long-reads together with Illumina short-reads were fed into the hybrid assembler Unicycler version 0.4.9 [3], resulting in a genome including a complete and circular chromosome of 4.3Mb and four plasmids (94.1, 37.8, 8.4 and 2.3kb, respectively) with mean coverage depths of 41.2x (long-reads) and 126.2x (short-reads). GC-content was 54.43%. Whole genome-based taxonomic analysis was conducted with Type Strain Genome Server (TYGS)(https://tygs.dsmz.de) [4]. Briefly, the TYGS analysis comprised of automatic determination of the ten most closely related type strains [[5], [6], [7]] plus a user added number of Mixta sp. non-type strains (see Fig. 1 and Supplementary Table 2) and phylogenomic inference based on pairwise comparisons among this set of genomes with calculation of 100 distance replicates each. Digital DNA-DNA-Hybridization (dDDH) values and confidence intervals were calculated using the recommended settings of the GGDC 2.1 [8]. A type-based species clustering using a 70% dDDH radius around each of the strains was done as previously described for species discrimination [4].
In addition, the average nucleotide identity (ANI) was calculated with the ChunLab's online Average Nucleotide Identity (ANI) calculator between X22927 and its closest relative Mixta theicola (GenBank accession number NZ_CP028273) [9,10].
The phylogenetic analysis revealed 83.23% average nucleotide identity (ANI) and a dDDH value by formula d4 of 39.1% compared to its closest relative Mixta theicola (GenBank accession number NZ_CP028273) [4,9]. Thus, the criteria for a new species with recommended cut-offs of 95–96% and 70 %, respectively, were met and the name Mixta hanseatica sp. nov. is proposed.
#Ragtag [5] was used to scaffold those contigs > 500 bp with its closest relative Enterobacter hormaechei strain 2024CK-00459 chromosome (CP157756), resulting in 4,786,112 bt chromosome and 3 unmapped contigs.
# ---- FINAL -----
A total of 18,254,594 reads were generated. Quality control and trimming of paired-end reads were performed using Trimmomatic v0.39 [1]. De novo assembly was performed using SPAdes v3.15.5 [2], resulting in 73 contigs. Contig annotation was carried out using the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) pipeline [3]. Contigs shorter than 500 bp were excluded from further analysis. Manual inspection of the mate-pair read mapping at the start and end confirmed that three of the contigs were circular plasmids. Sequencing and genome assembly statistics are summarized in Table 1. The Average Nucleotide Identity (ANI) was calculated using ChunLab’s online ANI calculator [5]. ANI analysis revealed that the isolate shared 94.85% identity with E. hormaechei ATCC 49162 (NCBI accession NZ_MKEQ01000001.1-NZ_MKEQ01000004.1), thereby confirming species-level classification (Fig. 1).
11. Bolger, A. M., Lohse, M., & Usadel, B. (2014). Trimmomatic: A flexible read trimming tool for Illumina NGS data. Bioinformatics, 30(15), 2114-2120.
12. Bankevich, A., Nurk, S., Antipov, D., Gurevich, A. A., Dvorkin, M., Kulikov, A. S., ... & Pevzner, P. A. (2012). SPAdes: A new genome assembly algorithm and its applications to single-cell sequencing. Journal of Computational Biology, 19(5), 455-477.
13. Joachimiak, M. P., Gorrell, R., Gamage, D. M., & Arzika, A. (2021). The Bacterial and Viral Bioinformatics Resource Center (BV-BRC): A comprehensive resource for bacterial, viral, and antimicrobial resistance research. Microorganisms, 9(1), 1-10.
14. Yoon, S. H., Ha, S. M., Lim, J. M., Kwon, S.J. & Chun, J. (2017). A large-scale evaluation of algorithms to calculate average nucleotide identity. Antonie van Leeuwenhoek. 110:1281–1286
#Candidate_SENTENCE_1: The remaing contigs were susccessfully scaffolded using RagTag (v2.1.0) [4] with the reference CP097342.
#ANI of contigs vs. Enterobacter hormaechei ATCC 49162
OrthoANIu value (%) 94.85
Genome A length (bp) 4,493,100
Genome B length (bp) 4,884,780
Average aligned length (bp) 2,888,871
Genome A coverage (%) 64.30
Genome B coverage (%) 59.14
#ANI of contigs vs. CP097342.fasta
OrthoANIu value (%) 97.20
Genome A length (bp) 4,493,100
Genome B length (bp) 4,784,820
Average aligned length (bp) 2,978,624
Genome A coverage (%) 66.29
Genome B coverage (%) 62.25
#ANI of submitted vs. CP157756
OrthoANIu value (%) 97.18
Genome A length (bp) 4,785,840
Genome B length (bp) 4,676,700
Average aligned length (bp) 2,938,856
Genome A coverage (%) 61.41
Genome B coverage (%) 62.84
#ANI of submitted vs. CP097342 (?)
OrthoANIu value (%) 100.00
Genome A length (bp) 4,785,840
Genome B length (bp) 4,784,820
Average aligned length (bp) 4,784,820
Genome A coverage (%) 99.98
Genome B coverage (%) 100.00
(NOT_USED) Construct a phylogenetic tree using roary and raxml. It is not used, since we take the tree directly from bv-brc.org
How to use Roary for pan-genome analysis and phylogenetic tree construction?
1. Annotate genomes with Prokka to generate GFF3 files.
2. Run Roary on the GFF3 files to perform pan-genome analysis and generate the core genome alignment.
3. Build a phylogenetic tree using the core genome alignment with FastTree or RAxML.
4. Visualize the tree in tools like iTOL or FigTree.
gi|2239477175|gb|CP095177.1|
gi|2741317321|gb|CP157756.1|
gi|1733463143|gb|CP043382.1|
gi|2458231726|gb|CP118279.1|
gi|2707115791|gb|CP149841.1|
gi|2523241526|gb|CP106894.1|
gi|2567072061|gb|CP114997.1|
gi|1395308702|gb|CP021162.1|
gi|2514450999|gb|CP109748.1|
gi|2587549373|gb|CP135270.1|
gi|2245290217|gb|CP088221.1|
gi|2738776003|gb|CP157339.1|
#gi|2587897827|gb|CP135475.1|
mamba activate /home/jhuang/miniconda3/envs/bengal3_ac3
#(bengal3_ac3) prokka --force --outdir prokka/HKEH-1 --cpus 2 --usegenus --genus Enterobacter --kingdom Bacteria --species hormaechei --addgenes --addmrna --prefix HKEH-1 --locustag HKEH-1 HKEH-1.fasta #-hmm /media/jhuang/Titisee/GAMOLA2/TIGRfam_db/TIGRFAMs_15.0_HMM.LIB
for sample in HKEH-1 CP095177 CP157756 CP043382 CP118279 CP149841 CP106894 CP114997 CP021162 CP109748 CP135270 CP088221 CP157339 CP135475; do
for sample in CP097342; do
bakta --db /mnt/nvme1n1p1/bakta_db --output bakta/${sample} --genus Enterobacter --species hormaechei --strain ${sample} --prefix ${sample} --locus-tag ${sample} ${sample}.fasta
done
#Used commands
#(bacto) export PERL5LIB=$CONDA_PREFIX/lib/perl5/site_perl/5.22.0/
(bacto) roary -p 100 -f ./roary -i 95 -cd 99 -s -e -n -v ./bakta/*.gff3
(bacto) cd roary
(bacto) ./roary$ samtools faidx core_gene_alignment.aln
(bacto) ./roary$ fasttree -gtr -nt core_gene_alignment.aln > core_gene_alignment.tree
(bacto) ./roary$ snp-sites core_gene_alignment.aln > core_gene_alignment_.aln #1945680 --> 213613 --> 120367
(bacto) ./roary$ raxml-ng --all --model GTR+G+ASC_LEWIS --prefix core_gene_raxml --threads 100 --msa core_gene_alignment_.aln --bs-trees 1000
Clinical samples (Acinetobacter baumannii strain HKAB-1 vs CP149838, NZ_CP149839.1 and NZ_CP149840.1)
Definition: If genome comparison verify the bacterium is Acinetobacter baumannii, we can name it as “ Acinetobacter baumannii strain HKAB-1”
Authors: 1) Wenjun, He, 2) Huang, Jiabin, 3) Foong, Wuen-Ee, 4) Tam, Heng-Keat
Title: Whole genome sequence data of Acinetobacter baumannii HKAB-1 strain, isolated from the sputum of a patient with suspected bacterial infection and heart failure.
Source: mol_type="genomic DNA" strain="HKAB-1" isolation_source="sputum" host="left-sided heart failure patient" geo_loc_name="China" collection_date="2024"
Summarize the genomic characteristics
quast contigs.fa -o quast_output
# count base-number: for paired-end reads
zcat clinical_trimmed_P_1.fastq.gz | awk 'NR%4==2 {sum+=length($0)} END {print sum}' > bases_R1.txt
zcat clinical_trimmed_P_2.fastq.gz | awk 'NR%4==2 {sum+=length($0)} END {print sum}' > bases_R2.txt
# Then add them:
#paste -sd+ bases_R1.txt bases_R2.txt | bc
1142394820+1142327087=2284721907
#46+62+5369=5477
#62(Number of pseudogenes)+5369(Number of CDSs (with protein), Number of CDSs (total))=5431 (Number of CDSs (total))
Number of CDSs (total) + Number of genes (RNA)= Number of genes (total)
Preparing the fasta files for submitting the genome to NCBI
python ~/Scripts/filter_fasta_by_length.py HKAB-1.fasta HKAB-1_l500.fasta -l 500
#ragtag.py scaffold -f 500 -q 5 -d 2000000 -i 0.02 -a 0.1 -s 0.1 -g 20000 -m 2000000 -w -u HKAB-1_l500.fasta CP140420.fasta #2023CK-00893
#(ragtag_env) ragtag.py scaffold HKAB-1_l500.fasta CP149838.fasta
(ragtag_env) ragtag.py scaffold -f 500 -q 5 -d 200000 -i 0.1 -a 0.1 -s 0.1 -g 200 -m 200000 -w -u HKAB-1_l500.fasta CP149838.fasta
#cut -d$'\t' -f6 ragtag_output4/ragtag.scaffold.asm.paf | sort -u > contigs_selected4 #CP140420
cut -d$'\t' -f6 ragtag_output/ragtag.scaffold.asm.paf | sort -u > contigs_selected #CP149838
samtools faidx HKAB-1_l500.fasta
samtools faidx HKAB-1_l500.fasta contig00015 > putative_plasmids.fasta #len=33067
samtools faidx HKAB-1_l500.fasta contig00018 >> putative_plasmids.fasta #len=2677
samtools faidx HKAB-1_l500.fasta contig00019 >> putative_plasmids.fasta #len=1998
samtools faidx HKAB-1_l500.fasta contig00023 >> putative_plasmids.fasta #len=592
#ADD the xxxxxxxxxxxxxx NNNN contig00015 NNNNN contig00018 NNNNN contig00019 NNNNN contig00023
samtools faidx HKAB-1_l500.fasta contig00015 > contig00015.fasta
samtools faidx HKAB-1_l500.fasta contig00018 > contig00018.fasta
samtools faidx HKAB-1_l500.fasta contig00019 > contig00019.fasta
samtools faidx HKAB-1_l500.fasta contig00023 > contig00023.fasta
cat ragtag.scaffold.fasta N_100x.fasta contig00015.fasta N_100x.fasta contig00018.fasta N_100x.fasta contig00019.fasta N_100x.fasta contig00023.fasta > ragtag.scaffold_and_other_contigs.fasta
#remove the second until last '>' and change the first header to "HKAB-1_chr"
seqkit seq ragtag.scaffold_and_other_contigs.fasta > ragtag.scaffold_and_other_contigs_submit.fasta
Mapping reads to NZ_CP149839.1 and NZ_CP149840.1, so that confirm the two plasmids don't exist (for the question 3) --
bwa index NZ_CP149839.fasta
bwa mem -t 80 NZ_CP149839.fasta clinical_trimmed_P_1.fastq.gz clinical_trimmed_P_1.fastq.gz > aligned_on_CP149839.sam
samtools view -@ 60 -bS aligned_on_CP149839.sam > aligned_on_CP149839.bam
samtools sort -@ 60 aligned_on_CP149839.bam -o aligned_on_CP149839_sorted.bam
samtools index aligned_on_CP149839_sorted.bam
samtools flagstat aligned_on_CP149839_sorted.bam
#0 + 0 mapped (0.00% : N/A)
bwa index NZ_CP149840.fasta
bwa mem -t 60 NZ_CP149840.fasta clinical_trimmed_P_1.fastq.gz clinical_trimmed_P_1.fastq.gz > aligned_on_CP149840.sam
samtools view -@ 60 -bS aligned_on_CP149840.sam > aligned_on_CP149840.bam
samtools sort -@ 60 aligned_on_CP149840.bam -o aligned_on_CP149840_sorted.bam
samtools index aligned_on_CP149840_sorted.bam
samtools flagstat aligned_on_CP149840_sorted.bam
samtools depth -m 0 -a aligned_on_CP149839_sorted.bam > coverage1.txt
samtools depth -m 0 -a aligned_on_CP149840_sorted.bam > coverage2.txt
grep "CP149839" coverage1.txt > CP149839_coverage.txt
grep "CP149840" coverage2.txt > CP149840_coverage.txt
import pandas as pd
import matplotlib.pyplot as plt
# Load coverage data
df = pd.read_csv("CP149840_coverage.txt", sep="\t", header=None, names=["chr", "pos", "coverage"])
# Plot
plt.figure(figsize=(10,4))
plt.plot(df["pos"], df["coverage"], color="blue", linewidth=0.5)
plt.xlabel("Genomic Position")
plt.ylabel("Coverage Depth")
plt.title("BAM Coverage Plot")
plt.show()
Confirm no contigs are circular using blastn
makeblastdb -in HKAB-1_l500.fasta -dbtype nucl -out contigs_db
blastn -query HKAB-1_l500.fasta -db contigs_db -outfmt 6 -evalue 1e-10 > blast_results.txt
#--> no contigs were confirmed as circular --> no plasmids.
鲍曼不动杆菌 HKAB-1 菌株的全基因组序列数据，该菌株分离自一名疑似细菌感染并伴有心力衰竭患者的痰液样本
#摘要
#引言
在不动杆菌属中，鲍曼不动杆菌（A. baumannii）是临床中最常分离的物种，已成为研究多重耐药性（MDR）的模型生物（Peleg 2008；Vila 2007）。近年来，该病原菌已对全球医疗系统构成日益严重的威胁，导致广泛的严重医院内感染，包括呼吸道、皮肤、尿路、伤口和血流感染（Dijkshoorn 2007）。其在临床环境中的持久性主要归因于其显著的耐受能力，如耐干燥、耐消毒剂和长时间暴露于抗微生物药物（Fournier 2006a；Harding 2018）。这种韧性还得益于其高度可塑的基因组，能够快速获取外源基因，从而迅速适应环境压力（Adams 2008；Touchon 2014）。例如，多重耐药菌株A. baumannii AYE拥有一个包含45个耐药基因的86 kb基因组耐药岛（Fournier 2006b）。在过去十年中，A. baumannii分离株的抗生素耐药性显著上升，包括多重耐药（MDR）、广泛耐药（XDR）和泛耐药（PDR）菌株的出现（Turton 2004；Doi 2009；Nowak 2017）。
在多种耐药机制中，外排泵的过表达在赋予A. baumannii抗生素耐药性方面起着关键作用（Rumbo 2013；Yoon 2013）。尤其是，A. baumannii固有编码大量的多重耐药外排泵，这些外排泵可分为单组分转运蛋白和三部分系统（Lee 2000；Verma 2021）。这些外排系统通过独立或协同作用，主动排除多种结构和化学性质各异的底物，包括抗生素、杀生物剂、染料和清洁剂，从而减少细胞内药物积累并提高最小抑菌浓度（MIC）（Foong 2019；Foong 2020）。
除了抗微生物耐药性外，A. baumannii在生物和非生物表面形成生物膜的能力以及表面附着性运动性也在其作为医院内致病菌的成功中起着重要作用（Skiebe 2012；Orsinger-Jacobsen 2013；Greene 2016）。这些特征在医疗设备的定植和侵入性操作中尤为重要，有助于持久的医院内感染（Rodríguez-Baño 2008）。此外，生物膜形成和运动性被认为促进耐药基因的传播和获取，进一步增强了A. baumannii在医院环境中的适应性和持久性（Wilharm 2013；Penesyan 2019）。总的来说，生物膜形成、运动性和多重耐药性之间的相互作用支撑了A. baumannii在临床环境中的持久生存和传播能力。
#方法与材料
生长曲线测量
A. baumannii菌落在37°C下于Mueller-Hinton II Broth (MH2B)液体培养基中培养16-18小时。接种新鲜的120毫升MH2B培养基，初始OD600为0.05，37°C下摇床150 rpm培养。使用OD600读数根据“baranyi_without_lag”模型绘制细菌生长曲线，使用R 4.3.3版本的“nlsMicrobio”包（Baty 2014；R Core Team 2021）。
最小抑菌浓度测量
最小抑菌浓度的测量方法如先前所述（Tam 2020）。简而言之，将A. baumannii菌株的过夜培养液稀释至OD600为0.02，并接种到含有不同抗生素浓度的MH2B液体培养基中的96孔板，培养16小时，37°C下180 rpm。使用Tecan Infinite M200 Pro微孔板读数仪（瑞士Tecan）测量OD600值。最小抑菌浓度定义为OD600值低于0.1的最低抗生素浓度。
生物膜形成实验
在微孔板中进行的生物膜形成实验如前所述略作修改（Haney 2018）。简要来说，将A. baumannii菌株的过夜培养液接种到含有1毫升LB培养基的聚苯乙烯试管中，初始OD600为0.05，30°C或37°C下静置培养24小时。液体培养基中的细菌生长通过OD600测定。随后，丢弃细菌培养物上清液，用蒸馏水冲洗生物膜细胞三次，再用0.1%结晶紫染色20分钟。丢弃结晶紫染色溶液后，再次用蒸馏水冲洗三次，最后用无水乙醇溶解染色的生物膜细胞。溶解的生物膜细胞通过OD595进行定量。生物膜形成以OD595/OD600的比值表示，以标准化总细菌生长。
游动实验
游动实验如前所述略作修改（Clemmer 2011）。过夜培养液稀释至OD600为0.1，将2微升液体点在含0.4%琼脂的LB培养基上。将琼脂平板在37°C下培养24小时。
滑行实验
滑行实验如前所述略作修改（Clemmer 2011）。过夜培养液稀释至OD600为0.1，将2微升液体接种到含有0.8%琼脂的LB培养基上，打孔后培养。将琼脂平板在37°C下培养24小时。培养结束后，丢弃琼脂并用0.2%结晶紫染色，之后观察结果。
全基因组测序与数据分析
使用Covaris LE220R-plus系统（Covaris，美国）将基因组DNA剪切至平均350 bp大小，进行末端修整、A尾处理并连接完整的Illumina接头。构建的文库使用Illumina平台进行测序，采用2 x 150 bp的配对末端读数，由Novogene生物信息技术有限公司（北京，中国）进行处理。
#结果
菌株收集与鉴定
从一位左心衰竭且怀疑为细菌感染的患者的痰样本中分离到一种细菌菌株HKAB-1。根据初步的表型特征，初步鉴定该菌株为鲍曼不动杆菌（A. baumannii）。随后，将该菌株送至南华大学衡阳医学院医学微生物学系进行分子鉴定和表型特征分析。之后，使用PCR扩增16S rRNA基因，并通过Sanger测序进行分析。通过BLAST分析16S rDNA序列，确认该菌株为A. baumannii。
A. baumannii临床分离株HKAB-1对多种抗生素的敏感性高于A. baumannii ATCC19606
近年来，A. baumannii相关感染因其多重耐药性表型在临床上日益受到关注。因此，我们评估了HKAB-1菌株和对照菌株ATCC19606的抗生素耐药性谱。我们的结果显示，与ATCC19606菌株相比，HKAB-1菌株对大多数测试的抗生素较为敏感，除了多粘菌素B和利福平（见表1）。
参考文献：Darby, EM2
A. baumannii临床分离株HKAB-1与ATCC 19606相比，具有更强的生物膜形成能力和运动性
A. baumannii在临床环境中的持久性主要归因于其形成生物膜的能力（Lee 2008；Espinal 2012）。为了确保生物膜实验的准确标准化，我们首先分析了临床分离株HKAB-1和实验室株ATCC19606在MH2B培养基中的生长动力学。HKAB-1菌株的生长速率高于ATCC19606，最大比生长速率（μmax）分别为0.87 ± 0.03 h⁻¹和0.63 ± 0.02 h⁻¹（图1A-B）。一致地，在5小时的OD₆₀₀测量值也高于HKAB-1（图1C）。这些发现表明，HKAB-1菌株在测试条件下的生长优于ATCC19606（图1A）。鉴于生长动力学的显著差异，生物膜定量通过结晶紫染色进行标准化，以确保与静态培养的浮游文化的OD₆₀₀相比，具有可比性和准确性。
由于A. baumannii能够在聚苯乙烯表面形成生物膜（33），我们使用聚苯乙烯试管作为生物膜生长的非生物表面（图1A）。我们发现，大多数来自血液和痰的分离株都能够在聚苯乙烯表面形成不同程度的生物膜。我们还发现血液分离株具有较强的生物膜形成能力。
此外，先前的研究报告表明，表面附着性运动性是A. baumannii临床分离株的常见特征（Skiebe 2012；参考文献）。因此，
全基因组测序和计算分析
为了说明临床分离株与ATCC19606菌株之间抗生素敏感性的差异，进行了HKAB-1的全基因组测序。
A. baumannii的生物膜形成：相关特征与临床意义
一些A. baumannii临床分离株能够在体外形成生物膜，这可能是导管介导的尿路感染、败血症感染和依赖呼吸机的院内肺炎等感染过程中重要的致病机制（Rodriguez-Bano等，2008）。有研究表明，生物膜的产生可能与抗生素耐药性相关（Li等，2007；Rodriguez-Bano等，2008），高产生者对常用抗生素的耐药性较低，而耐药性菌株则表现出较高的抗药性（King, LB）。
#讨论
在28°C和37°C下对一组A. baumannii菌株进行了生物膜形成的初步研究。结果显示，不同A. baumannii分离株之间的生物膜形成存在较大差异；28°C和37°C的结果没有显著差异（De Briej, A）。
先前的研究表明，生物膜是A. baumannii相关感染中的关键致病因子之一（参见Greene）。
在A. baumannii菌株中，来自医院环境的环境菌株通常承受有一定的适应性成本，其中耐药多药（MDR）表型会导致脱水耐受性的降低，而较强的生物膜表型则可以缓解这一成本。先前的研究表明，细菌的多药耐药性表型存在基因适应性成本，临床菌株在体内的抗生素耐药成本也得到了充分证明（Greene, C）。
在抗生素压力下，能够形成生物膜的A. baumannii菌株可能会被选择出来，或者相反，A. baumannii可能在生物膜群落中获得多重抗药性。当前研究的一个有趣发现是，携带blaPER-1的A. baumannii分离株在与不携带blaPER-1的A. baumannii分离株相比时，表现出显著更强的上皮细胞附着能力和生物膜形成能力（Lee, H-W）。
A. baumannii中发现的多重抗生素耐药机制也可能在其环境生存中发挥作用。Boll等（13）证明了对阳离子抗微生物肽药物（如粘菌素）的耐药性也增加了A. baumannii对干燥的耐受性。Gayoso等（12）证明，某些与抗生素耐药相关的蛋白质（这些蛋白质也与耐受清洁剂相关）在A. baumannii经历干燥应激时被过度表达。这些发现表明，一些抗药性机制可能利用细菌在开放环境中的生存能力（参见Greene, C）。
如上所述，生物膜不仅作为对抗细菌因子的保护，更重要的是它有助于细菌在不同表面的生存。已经证明，生物膜形成株在干燥表面上比非生物膜形成株存活时间更长（Espinal等，2012），一些菌株在玻璃和塑料表面在静态和动态条件下都能形成生物膜（Tomaras等，2003）。因此，生物膜形成有助于A. baumannii对干燥的耐受性以及其在医院环境中家具和医疗设备上的持久性。应答调节因子BfmR是双组分系统BmfRS的一部分，负责生物膜的形成（Draughn等，2018），同时它也在A. baumannii的致病性中起着重要作用（Farrow等，2018）。此外，它调节与应激相关的蛋白质的表达，如KatE，它负责降解H₂O₂。应激相关的蛋白质保护细胞免受反应性氧种的伤害，而干燥正是其中的一种应激因素，因此这些蛋白质在A. baumannii的脱水耐受性中起着重要作用（Farrow等，2018）。
#数据可用性
HKAB-1的全基因组原始数据已提交至NCBI GenBank（编号xxxx）。
#参考文献（略，格式保留原文）
#图表
表1 HKAB-1与ATCC19606的抗生素敏感性比较
图1 生长曲线与生物被膜定量
图2 运动性表型分析
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