j ourna l homepage: www.e lsev ie r .com/ locate /meeg id
Variable transcriptional adaptation between the laboratory (H37Rv) andclinical strains (S7 and S10) ofMycobacterium tuberculosis under hypoxia
Santhi Devasundaram, Alamelu Raja ⁎Department of Immunology, National Institute for Research in Tuberculosis (ICMR) (Formerly Tuberculosis Research Centre), No.1, Mayor Sathiyamoorthy Road, Chetpet, Chennai 600 031, India
⁎ Corresponding author at: National Institute for Re(Formerly Tuberculosis Research Centre), No.1, Sathiyam600 031, India.
Article history:Received 29 August 2015Received in revised form 12 November 2015Accepted 7 January 2016Available online 11 January 2016
Tuberculosis continues to be amajor public health problem inmany parts of theworld, despite intensified effortstaken to control the disease. The remarkable success ofM. tuberculosis as a pathogen is largely due to its ability topersist within the host for long periods. To develop the effective intervention strategies, understanding the biol-ogy of persistence is highly required. Accumulating evidences showed oxygen deprivation (hypoxia) as a poten-tial stimulus for triggering the transition of M. tuberculosis to a non-replicating persistent state analogous tolatency in vivo. To date, in vitro hypoxia experimental models used the laboratory adapted isolate H37Rv andvery little is known about the behavior of clinical isolates that are involved during disease outbreaks. Hence,we compared the transcription profiles of H37Rv and two south Indian clinical isolates (S7 and S10) under hyp-oxia to finddifferences in gene expression pattern. Themain objective of this current work is tofind “differential-ly regulated genes” (genes that are down regulated in H37Rv but upregulated in both the clinical isolates) underhypoxia. Microarray results showed, a total of 502 genes were down regulated in H37Rv under hypoxia and 10out of 502 genes were upregulated in both the clinical isolates. Thus, giving less importance to down regulatedgenes based on H37Rv model strain might exclude the true representative gene candidates in clinical isolates.Our study suggests the use of most prevalent clinical isolates for in vitro experimental model to minimize thevariation in understanding the adaptation mechanisms of the strains.
Aftermore than one century, the disease has not yet been eradicatedand in 2013 an estimated 9 million people developed tuberculosis (TB)and 1.5 million died from the disease (WHO, 2014). Efforts are to beaccelerated to combat TB deaths which are unacceptably high beyondit could be prevented. The success of Mycobacterium tuberculosis(M. tuberculosis) is due to its ability to reside in the host in a non-replicating state called “dormancy”, a condition characterized bylow metabolic activity that renders the bacteria resistant to killingby the host immune response and antibiotics (Fattorini et al., 2013).In an attempt to understand this dormant state at the genomic level,studies have been performed to explore the altered metabolism ofM. tuberculosis grown under defined conditions to mimic their intracel-lular niche during granuloma formation. Mimicking conditions arethought to reflect the environment inside the granuloma in vitro. Thisincludes hypoxia, starvation, macrophages, and murine infection andeach system mimic some of, but not the entire, clinical niche within
search in Tuberculosis (ICMR)oorthy Road, Chetpet, Chennai
the host. Of these, hypoxic condition (depletion of oxygen which pre-vents aerobic respiration by the obligate aerobe,) is best studied andconsidered to be a prime factor for inhibition of M. tuberculosis growth(Rustad et al., 2009; Fang et al., 2012). The most frequently usedexperimental model for hypoxia-induced M. tuberculosis dormancy isthe defined headspace model of non-replicating persistence (NRP)(Wayne and Hayes, 1996) and is adapted for the present study.
M. tuberculosis presents a very limited genetic variation. However,high degree of phenotypic variability, differences in clinical outcomeand epidemiological behavior amongM. tuberculosis isolates are report-ed. Number of studies reported variation in the virulence of differentstrains of M. tuberculosis (Manabe et al., 2003, Palanisamy et al., 2009,Mehaffy et al., 2010, Sohn et al., 2009). Host and bacterial factors playan important role in this variability (Tsolaki et al., 2005, Reed et al.,2004). H37Rv, the first strain isolated in 1905 (Betts et al., 2000), isthefirst strain ofM. tuberculosis species forwhich thewhole genome se-quence is available (Cole et al., 1998). H37Rv is the widely used labora-tory model strain, but might not represent the actual virulence ofnaturally occurring strains of TB outbreaks. Thus, it is highly importantto study themost prevalent clinical strains ofM. tuberculosis to elucidatethe realmechanismof virulence aswell to test the vaccine efficacy.Withthis goal, we have selected two south India prevalent clinical isolates, S7and S10 along with H37Rv to study transcriptional changes in the genelevel under in vitro hypoxia experimental model.
22 S. Devasundaram, A. Raja / Infection, Genetics and Evolution 40 (2016) 21–28
These two south Indian prevalent strains, S7 and S10, were firstidentified based on restriction fragment length polymorphism (RFLP)of various M. tuberculosis strains isolated from the BCG trial area,Tiruvallur district, south India (Das et al., 1995). Their results showedthat most (38–40%) of the isolated clinical isolates of M. tuberculosisfrom this area harbored IS6110 single copy in their genome. Distinct im-mune responseswere observed, in human donors, with the sonicate an-tigens of these two strains. S7 induced Th-2 response and the strain S10induced potent T-cell proliferation and IFN-γ secretion (Th-1 response)despite having similar IS6110 copy number at the same locus Rajaveluand Das (2005)). These factors attracted us to select S7 and S10 clinicalisolates to study gene regulation mechanism under depleted oxygencondition.
Evaluating the transcriptional response of M. tuberculosis underdifferent stress conditions has been the subject of intensive researchand this will likely lead to improve drug and vaccine design (Talaatet al., 2004). Vaccine based studies are majorly targeted on thegenes or proteins that are upregulated (over-expressed) duringin vitro stress conditions (Kim et al., 2008, Aagaard et al., 2011) orwithin the host or during in vitro infection of human cell lines likemacrophages (Triccas and Gicquel, 2000). In particular, these reportswere based on laboratory adapted strains such as H37Rv or Erdmanstrains as amodel organism. Genes that are down regulated (repressed)in these strains during in vitro stress experiments are often given lessimportance for the TB vaccine or drug development. But, expressionlevels of those down regulated genes in clinical strains are highly un-known under given stress conditions. Thus, we analyzed the expressionlevels of down regulated genes from H37Rv under hypoxia in clinicalisolates to highlight the need of studying more prevalent clinicalisolates for in vitro experiments to gain insight into the best vaccine can-didates. This approach would minimize the exclusion of probable truerepresentative genes and would help to reduce variations in vaccineformulations.
Our earlier microarray data included only 134 upregulated genesthat were common between all 3 strains used under hypoxia. TheDosR–DosS, the two components regulatory system plays a pivotalrole in mediating the adaptive response to hypoxia. Overexpressionof some of the DosR regulon genes like 3128c, Rv1997, Rv2004c,Rv2005c, Rv2007c, Rv3127c and dosS genes like Rv3132c andRv2025c indicated that the oxygen depletion was faithfully achievedin our culture methods (Devasundaram et al., 2015). In addition tothis, themicroarray datawere also analyzed for “differentially regulatedgenes” that is genes that were down regulated in H37Rv laboratorystrain, but upregulated in both clinical isolates under hypoxia ispresented here and the term “differentially regulated genes” is usedelsewhere in the text to represent genes down regulated in H37Rv butupregulated in both clinical isolates.
2. Methods
2.1. Aerobic and anaerobic culture methods
Stocks of the M. tuberculosis laboratory strain H37Rv (ATCC 27294),obtained from Colorado State university, USA and clinical strains S7 andS10 were obtained during the Model Dots study conducted at the BGCtrial area of Tiruvallur District (Das et al., 1995). H37Rv, clinical isolatesS7 and S10 were grown in Middlebrook 7H9 media supplementedwith 2% (v/v) glycerol, 10% albumin–dextrose–catalase (ADC) and0.05% (v/v) Tween 80 at 37 °C, 200 rpm for 25 to 30 days to obtainaerobic cultures.
To obtain anaerobic cultures (NRP-2) Wayne's method wasfollowed as described (Wayne and Hayes, 1996). Briefly, a laboratorystrain H37Rv and clinical isolates S7 and S10 were inoculated inscrew capped test tubes (20 × 125 mm, with a total fluid capacityof 25.5 ml) containing supplemented Middlebrook 7H9 (MB7H9).For uniform dispersion of cultures and to control the rate of O2
depletion stirring was done with 8-mm Teflon-coated magneticstirring bars in the tubes (120 rpm) and incubated at 37 °C. Oxygendepletion in the cultures was assessed by adding a sterile solutionof methylene blue in the medium at final dye concentrations of1.5 μg ml−1. Reduction and decolorization of this dye served as avisual indication of oxygen depletion. Methylene blue decolorizationstarts, in M. tuberculosis in vitro cultures, when the dissolved oxygenconcentration is below 3% (Leistikow et al., 2010). The cells werepelleted from triplicate cultures, by centrifugation at 2000 g for 5 minand frozen on dry ice.
2.2. Isolation of RNA from aerobic and anaerobic cultures of H37Rv, S7and S10
Cell pellets (107 bacterial cells) were suspended in 1 ml TRIzol re-agent (Sigma Aldrich, USA) and transferred to 2-ml screw cap tubescontaining 0.5 ml of 0.1 mm diameter zirconia/silica beads (BioSpecProducts, USA). Three 30-s pulses in a bead beater disrupted the cells.Cell debris was separated by centrifugation for 1 min at 16,000 g. Thesupernatant was transferred to 2-ml micro centrifuge tube containing300 μl chloroform: isoamyl alcohol (24:1), inverted rapidly for 15 s,and incubated 2 min at room temperature. Samples were centrifugedfor 5 min and the aqueous phase was precipitated using 2.5 volumeof isopropanol and 1/10th volume of 3 M sodium acetate. Sampleswere incubated 10 min at room temperature and centrifuged for15 min at 4 °C. The RNA pellets were washed with 1 ml 75% ethanol,centrifuged 5 min, air dried and resuspended with RNase free water.Final purification of RNA and DNase treatment to remove residualDNA was carried out by RNeasy columns (Qiagen,USA). RNA qualitywas assessed by measuring the ratio of absorbance of total RNA at260/280 and 260/230 nm.
2.3. cDNA synthesis, cRNA labeling and microarray hybridization
For cDNA synthesis, 2 μg of RNA from each sample was incubatedwith WT (Whole Transcript) primers according to manufacturer in-struction (Low Input Quick Amp LabelingWT kit, Agilent Technologies,USA) for 10min at 65 °C, cooled on ice, combinedwith5X standard buff-er, 0.1 M dithiothreitol (DTT), 10 mM deoxynucleotide triphosphate(dNTP) and RNase block mix to the final volume of 4.7 μl. This mixturewas incubated for 2 h at 40 °C.
Fluorescently labeled cRNA, transcribed from cDNA, by T7 polymer-ase transcription master mix (Low Input Quick Amp Labeling WT kit,Agilent Technologies, USA) containing 5× transcription buffer, 0.1 MDTT, nucleotide triphosphate (NTP), T7 polymerase and labeled withCy3-CTP (aerobic cultures of H37Rv, S7, S10) or Cy5-CTP (anaerobic cul-tures of H37Rv, S7, S10) and incubated for 2 h at 40 °C. Purification ofcRNAs was carried by Qiagen's RNeasy mini kit. An aliquot of 1 μl ofpurified cRNA was used to determine the yield and specific activitywith a Nanodrop ND-1000 UV–Vis Spectrophotomer. The specific activ-ity (pmol Cy3 or Cy5 per μg cRNA) was obtained by following calcula-tion: specific activity = (concentration of Cy3/Cy5)/((concentration ofcRNA) ∗ 1000) = pmol Cy3/Cy5 per μg cRNA. Samples with yield ofb0.825 μg and specific activity i.e. b15 pmol Cy3/Cy5 per μg cRNAwere only selected for competitive hybridization. Labeled cRNA wasalso checked on 1% agarose gel and scanned using the Typhoon 9210scanner (GE Life Sciences).
A 60mer oligonucleotide based custom array chip was used fromAgilent Technologies in 8x15K format. 300 ng of Cy5 labeled cRNAfrom anaerobic cultures of H37Rv, S7 and S10 was hybridized against300 ng Cy3 labeled cRNA from aerobic cultures of H37Rv, S7 and S10.Hybridization was done for 17 h, 10 rpm at 65 °C. Following imageanalysis, feature extraction was performed using Feature extractiontool version 9.5.3.1 (Agilent Technologies, USA).
23S. Devasundaram, A. Raja / Infection, Genetics and Evolution 40 (2016) 21–28
2.4. Microarray data analysis
Microarray data analysis was performed by R-Bioconductor LIMMApackage. The background-corrected raw intensity values were used foranalysis. LOWESS algorithm was used to normalize the data and foldchange (Fc) was calculated based on the ratio of Cy5/Cy3 (anaerobic/aerobic) intensities. For statistical analysis, Student's t-test againstzero was performed using Benjamini Hochberg multiple testing correc-tion. Hierarchical cluster was done byMev4.1 using Pearson correlationmethod. The data were clustered by averaged linkage. Adjusted p valuecut-off of 0.05 and fold change of ≥1.5was used for identifying differen-tially regulated genes. Gene expression data are deposited into the GEOdatabase (GEO accession no: GSE55863).
A 2 μg of the isolated total RNA from H37Rv, S7 and S10 grownunder aerobic and anaerobic culture conditions were reverse-tran-scribed by a genetically engineered, highly thermostable version ofMoloney Murine Leukemia Virus Reverse Transcriptase (MMLV-RT),supplied with High Capacity cDNA synthesis kit (Applied Biosystems,USA). For qRT-PCR 20 ng of synthesized cDNAwas taken per 20 μl reac-tions. qRT-PCRs were performed in triplicate using a Dynamo™SYBRgreen 2× mix kit (Finnzymes, Finland). PCR conditions: initial de-naturation 95 °C for 15 min, 40 cycles of 95 °C (30 s), 60 °C (30 s), and72 °C (1 min). Real-time PCR quantitations were performed in ABIPrism 7000 sequence detection system and analyzed with SDS 2.1 soft-ware (Applied Biosystems, USA).
For each RNA sample, the control transcript (16 s rRNA) and targetRNAs were reverse-transcribed together in one reaction and theresulting cDNAs were quantified by real-time PCR. The target cDNAwas normalized internally to the cDNA levels of 16 s rRNA in the samesample and expressed as (target mRNA)/(16 s rRNA mRNA).
3. Results
3.1. Growth pattern and culture conditions
Oxygen sufficient (aerobic) cultures are depicted as “Rv” for H37Rv,“S7” and “S10” for clinical isolates in the text. Oxygen depleted (anaer-obic) cultures are denoted as “RvD”, “S7D” and “S10D” for H37Rv, S7and S10 strains respectively and “D” indicates dormancy. The growthrate was higher in aerobic cultures where bacilli entered into log
Fig. 1.Growth curves and Venn diagram of altered transcriptional pattern forM. tuberculosisH3all three strains were obtained by growing at 37 °C at 200 rpm and 120 rpm respectively. Meansimilar between H37Rv and S10 whereas S7 strain had less OD value, but the pattern of growrepresentation by Venn diagram of the overlap of “differentially regulated genes” identifieddown regulated genes predicted by microarray in H37Rv was compared with upregulated geS10 and for anaerobic cultures it was denoted as RvD (H37Rv), S7D (for S7) and S10D (for S10
phase on day 9 and growth was stabilized on day 18 (considered as astationary phase entry). But in anaerobic cultures stationary phasewere reached on or after day 14 but log phasewas attained at day 9 sim-ilar to aerobic cultures. There were no significant differences found ingrowth pattern between these 3 strains (Fig.1a). Many researchersfollowed methylene blue decolorization as a sole indicator to confirmoxygen depletion during in vitro dormancy experiment; hence thesame was followed in our study (Fang et al., 2013; Rex et al., 2013;Sohaskey, 2008). Gradual decolorization of methylene blue was ob-served, with all 3 strains, during the incubation at 120 rpm, 37 °C andcomplete decolorization was observed by day 25. No decolorization ofmethylene blue dye in the “blank” tube was observed, as no inoculumwas introduced and it remained in the same color till 25 to 30 days(Supplementary Fig. 1) (Devasundaram et al., 2015). Growth kineticsand methylene blue decolorization pattern were in agreement withthe in vitroWayne's model (Wayne and Hayes, 1996).
After methylene blue decolorization, pellets were collected fromtriplicate cultures of H37Rv, S7 and S10 for RNA isolation. The isolatedRNA, which showed the ratio of ≥2 at A260/280 showed no DNA contam-ination andwasonly included formicroarray and qRT-PCRexperiments.Further, the integrity of RNA was also determined on a MOPS-formaldehyde denaturing agarose gel (data not given).
3.2. Genes that are repressed in H37Rv but expressed in clinical strains S7and S10
To identify the “differentially regulated genes”, a threshold value of1.5 fold change was assigned for each gene rather than the individualexpression values. Based on the mean expression value of triplicate mi-croarray experiments, about 502 genes were repressed in H37Rv, 453and 1463geneswere upregulated in S7 and S10 respectively under hyp-oxia when compared to aerobic growth (GEO accession no: GSE55863).This was represented by the Venn diagram in Fig 1b.
To identify “differentially regulated genes”, the expression levels ofthese 502 repressed genes of H37Rv were compared with S7 and S10over-expressed genes. A total of 10 genes was found in common asover-expressed in both the clinical isolates but repressed in H37Rvunder hypoxia. These sets of genes were termed as “differentiallyregulated genes” and followed throughout the text. These are Rv0812,Rv1463, Rv1544, Rv1582c, Rv1601, Rv2035, Rv2045c, Rv2430c,Rv2483c and Rv3538, their fold expression cluster is given Fig 2Table.1. These genes are predicted to be involved in intermediarymetabolism (Rv0812, Rv1544 and Rv2483c), energy metabolism(Rv1544), cell envelope synthesis (Rv2483c), fatty acid synthesis
7Rv, S7 and S10 under aerobic and anaerobic cultures. a) Aerobic and anaerobic cultures ofoptical density (OD) valueswith standard error at 600 nm are shown. The OD values wereth was same for all 3 strains. Growth is expressed as log of turbidity (A600). b) Graphicalbetween H37Rv and in both the clinical isolates S7 and S10 under hypoxia. Total of 502nes of S7 and S10. The aerobic cultures of 3 strains were denoted as Rv (H37Rv), S7 and).
Fig. 2. Heat map describing the differences in the transcriptional signature between H37Rv and clinical isolates S7 and S10 under hypoxia. Hierarchical clustering of “differentiallyregulated genes” that is, genes repressed in H37Rv and expressed in both the clinical isolates S7 and S10 under hypoxia is given based on triplicate microarray experiments. Anaerobiccultures of H37Rv, S7 and S10 are depicted as RvD, S7D and S10D respectively in the given image and triplicates are denoted as 1, 2 and 3. Expression ratios were log 2-transformed,and displayed according to the color code at the top of the figure. Each row is labeled with their corresponding gene names. Green bars represents down regulated genes and redrepresents upregulated genes.
24 S. Devasundaram, A. Raja / Infection, Genetics and Evolution 40 (2016) 21–28
(Rv2045c), amino acid biosynthesis (Rv0812, Rv1601), co-factor syn-thesis (Rv1463) and hypothetical protein (Rv2035).
The comparison of 502 repressed genes fromH37Rvwith any one ofthe clinical isolates led to the prediction of 8 genes (Rv1421, Rv1830,Rv2501c, Rv2764, Rv3275c, Rv3283, Rv3556c and Rv3843c) that areexpressed in S7 clinical strain and repressed in H37Rv under hypoxia(RvD vs S7D) (Fig 3, Supplementary Table 1). Interestingly, 128 geneswere predicted as repressed in H37Rv but expressed in S10 underhypoxia (RvD vs S10D) (Fig.4. and Supplementary Table 2).
3.3. Real-time PCR
The expression levels of 10 “differentially regulated genes” predictedbymicroarray as down regulated in H37Rv and upregulated in both theclinical isolates were also verified by qRT-PCR. Triplicate qRT-PCR reac-tionwas carried outwith 10 genes specific primers in triplicates for all 3strains in both aerobic and aerobic growth conditions. qRT-PCR resultsshowed that these 10 genes were repressed in H37Rv but their expres-sion was observed in both the clinical isolates (Fig. 5) in agreementto microarray results. qRT-PCR data is presented as fold difference ofexpression in all three strains relative to their control cultures i.e. aero-bic cultures. Gene specific primer sequences are given in the Table.2.The expression of a mycobacterial ribosomal protein coding gene
Table 1Genes that are repressed in H37Rv but expressed in both clinical isolates under hypoxia.
Microarray experiments, from 3mycobacterial strains (H37Rv, S7 and S10) which were grownstrain and 2 clinical isolates S7 and S10. These set of genes were predicted to be down regulatedexpression levels from triplicate cultures from both microarray (numbers in bold) and qRT-PC
(16srRNA), MTB000019, was shown to be consistent throughout differ-ent phases ofM. tuberculosis growth in vitro (Banaiee et al., 2006). Thus,16srRNA was taken as the normalizing internal control gene for allsamples.
Fold changes were calculated by taking average CT (threshold cycle)values from triplicate qRT-PCR reaction. The predicted fold change forthe gene Rv0812 in H37Rv was higher (−4.46) than the values obtain-ed by themicroarray (−0.28). The expression values predicted by qRT-PCR for other genes did not vary significantly from the microarray re-sults. Collectively, expression analysis of these 10 genes by qRT-PCRalso confirms that they are differentially regulated between H37Rvand in clinical isolates S7 and S10.
4. Discussion
Despite the different environment (acidic environment, low oxygen,and nutritional deficiency) within the host, M. tuberculosis has theability to infect and persist for longer periods without causing activeTB disease (Fontan et al., 2008). This non-symptomatic infection stateis known as latent TB and represents the future reservoir for active TBdisease (Kasprowicz et al., 2011). Adaptation to hypoxia environmentis a critical part of the ability of M. tuberculosis to persist within thehost (Muttucumaru et al., 2004).
e Predicted function
.28 Probable conserved ATP-binding protein ABC transporter
.78 Possible transmembrane phospholipid biosynthesis bifunctional enzyme PlsC
.14 Conserved hypothetical protein
.04 PPE family protein PPE41
.61 Carboxylesterase LipT
.14 Possible 2-enoyl acyl-CoA hydratase
under hypoxia, predicted 10 genes with differential expression between H37Rv laboratoryin H37Rv strain but upregulated in both the clinical isolates under hypoxia. Average gene
R, are given in this table. qRT-PCR results were also in agreement with microarray results.
Fig. 3. Heat map describing the differences in the transcriptional signature only betweenH37Rv and S7 under hypoxia. Hypoxia related genes that are repressed in H37Rv andexpressed only in S7 clinical isolate (H37Rv anaerobic vs S7 anaerobic) under hypoxia isgiven. Anaerobic cultures of H37Rv, S7 are depicted as RvD and S7D respectively in thegiven image. These results are based on triplicate microarray experiments with 1.5 foldchange. Green bars represent down regulated genes and red represents upregulatedgenes and their corresponding values are given above the cluster image. Rows arelabeled with gene names.
25S. Devasundaram, A. Raja / Infection, Genetics and Evolution 40 (2016) 21–28
Adaptation of M. tuberculosis to varying environmental conditionsduring the course of infection is likely mediated by differential geneexpression (Timm et al., 2003). Many researchers followed micro-array and RT-PCR to analyze the transcriptional response ofM. tuberculosis during in vitro dormancy experimental models thatmimic the conditions encountered by the bacteria within the host(Geiman et al., 2006; Rehren et al., 2007; Voskuil et al., 2011; Weiet al., 2013). It is also predicted that genes/proteins that are over-expressed inM. tuberculosis during in vitro stress are likely to be impor-tant for intracellular survival and can be targeted for drug and vaccinedevelopment (Betts, 2002; Kendall et al., 2004).
Themajority of the in vitro hypoxia experiments used the laboratorystrain H37Rv (Fisher et al., 2002, Bacon et al., 2004, Boshoff and Barry2005, Gengenbacher et al., 2010) and over-expressed genes during hyp-oxia are considered as potential drug and vaccine targets (Talaat et al.,2004). Often, less importance is given for the down regulated genes(predicted fromH37Rv) despite the lack of information on their expres-sion levels in clinical strains under the similar stimuli. The uniquefeature of our work is, we used 2 prevalent clinical isolates (S7 andS10) from TB endemic regions like India and showed 10 genes thatwere predicted as down regulated in H37Rv during hypoxia are actuallyover-expressed in both clinical strains under similar stimuli (hypoxia).This highlights the need of using the most prevalent clinical strains ofM. tuberculosis during in vitro stress experiments.
The expression of hypoxia associated genes like DosS-DosR regulongenes demonstrate the faithful achievement of our in vitro hypoxia ex-periments. We have already reported the microarray analysis of 134over-expressed genes that are common in all 3 strains (H37Rv, S7 andS10) during hypoxia (Devasundaram et al., 2015). The current analysisfocusesmainly on the set of genes that are predicted as down regulatedin the laboratory strain H37Rv but over-expressed in clinical isolates(S7 and S10).
Fig. 4. Heat map describing the differences in the transcriptional signature only betweenH37Rv and S10 under hypoxia. Hypoxia repressed genes in H37Rv and expressed onlyin S10 clinical isolate (H37Rv anaerobic vs S10 anaerobic) under hypoxia are given.Anaerobic cultures of H37Rv and S10 are depicted as RvD and S10D respectively in thegiven image. These results are based on triplicate microarray experiments with 1.5 foldchange. Green bars represent down regulated genes and red represents upregulatedgenes and their corresponding values are given above the cluster image. Rows arelabeled with gene names.
Fig 5. QRT-PCR validation of microarray results for genes repressed in H37Rv and expressed in S7 and S10. Quantitative RT-PCR was used to confirm the “differentially regulated genes”that are predicted bymicroarray result. Expression of these genes under anaerobic cultures (RvD, S7D and S10D)was compared to their respective aerobic cultures (Rv, S7 and S10). Genesthat were repressed in H37Rv were upregulated in clinical isolates under hypoxia. Data are mean with SEM calculated from three independent biological samples analyzed.
26 S. Devasundaram, A. Raja / Infection, Genetics and Evolution 40 (2016) 21–28
Dormancy associated genes ofM. tuberculosismight serve as poten-tial immunogens that can be targeted for vaccine development(Andersen, 2007). Evidently, 2 genes (Rv2660, Rv2659) that were pre-dicted to be expressed by microarray during in vitro non-replicatingpersistence model induced higher CD4+ T-cell response in humandonors (Govender et al., 2010). Expression of all resuscitation-promoting factors (rpf) genes from M. tuberculosis during variousin vitro stress experiments were shown by RT-PCR (Gupta et al.,2010). Later, the strong cellular immune response in human donorswas identified against all rpf genes and proposed as potential vaccinetargets (Commandeur et al., 2011). Collectively, these reports supportthe transcriptional analysis bymicroarray and RT-PCR is also a potentialtool to identify novel drug and vaccine targets. We followed the sametechnique for transcriptional analysis specifically in clinical isolates;hence genes that are predicted from our results would be beneficialfor anti-TB vaccine development.
Phage particles help M. tuberculosis to adapt to environmental fluc-tuations and predicted to be involved in bacterial virulence. Out of 10genes that are reported as down regulated in H37Rv but over-expressed in both the clinical isolates, Rv1582c is a probable PhiRv1phage protein. Rv1582 is present in RD3 region ofM. tuberculosis but ab-sent in BCG (Brosch et al., 2002). A recent report explicitly analyzedmost of the available gene expression data on mycobacterial phage
Table 2Primers used in real-time RT-PCR with SYBR green probes.
S. No Rv number Primer name Sequence 5′ - 3′ Template size
Primers sequences, specific for 10 differentially expressed genes (down regulated inH37Rv strain but upregulated in both the clinical isolates S7 and S10 under hypoxia),used for qPCR experiment to validate microarray results is given here. Primers namewith F indicate forward primer and with R denote reverse primer. 16srRNA was used asnormalizing control.
proteins under various stresses like, nutrient starvation, macrophageinfection and oxygen depletion. Almost the majority of the phagegenes were reported during various stress experiments exceptRv1582c (Fan et al., 2015). Notably, these studies used the laboratorystrain H37Rv for their in vitro experiments. This indicates that in vitroresults based on the laboratory strains might exclude the probable rep-resentative genes involved in clinical isolates during adaptationmecha-nism. We have also carried an independent microarray analysis (datanot shown) on genes that are expressed only in S7 and S10 (uniquegenes) during hypoxia. This showed the phage protein Rv1576c andRv2653cwas expressed in S7 and S10 respectively during hypoxia. Sur-prisingly, these phage genes were also not reported by any of the stressexperiments analyzed by Fan et al., (2015). This further supports ourhypothesis that transcription variations exhibited between the strainshence the use of clinical isolates would minimize these variations.
With agreement to our results, Rv1463 (ABC transporter ATP-binding protein) was down regulated under hypoxia in H37Rv(Sherman et al., 2001). But, our microarray analysis showed the over-expression of Rv1463 in both the clinical isolates (S7 and S10) whichwere also confirmed by RT-PCR. Collectively, these observations alarmthe variations existing between the laboratory and clinical strains ofM. tuberculosis. Hence, for a specialized study like anti-TB drug/vaccinedevelopment, inclusion of clinical isolates would bring more specificgene targets when compared to the laboratory strain.
Non-homologous proteins (unrelated to human proteins) aremajorly targeted for the drug development against infectious disease.The probable amino acid aminotransferase, Rv0812 was predicted tobe a unique enzyme in M. tuberculosis by protein interaction networkanalysis (Kushwaha and Shakya, 2010). Rv0812 expressionwas also ob-served during lung infection by M. tuberculosis in mice (Dubnau et al.,2005). Our results bring the first insight on Rv0812 expression underhypoxia especially in clinical isolates of M. tuberculosis and markingthis gene as a potential drug/vaccine target.
Proline–glutamic acid/proline–proline–glutamic acid (PE/PPE) pro-teins are exclusively present in pathogenic mycobacteria and constitute10% coding regions of M. tuberculosis genome. PE/PPE proteins role inmycobacterial virulence is yet to be determined (Kunnath-Velayudhanand Porcelli, 2013). PE/PPE proteins are closely associated with ESAT-6(esx) and modulate host immune response (Sampson, 2011). A strongB cell response by PPE41 (Rv2430c) was observed in TB patients(Choudhary et al., 2003) but its expression under hypoxia in clinical iso-lates ofM. tuberculosiswere first reported by our results. To our knowl-edge, we also bring the first insight on expression of another PE/PPEfamily of gene Rv3538, a double hotdog enzyme, under hypoxia in clin-ical isolates of M. tuberculosis.
The data presented here show the existence of variations betweenlaboratory and clinical strains ofM. tuberculosis in terms of gene expres-sion under hypoxia. An understanding of this variation opens the pathto identify a set of genes which have altered expressions (that is,down regulated in H37Rv but over-expressed in clinical isolates).Gene expression analysis in other clinically relevant M. tuberculosis
27S. Devasundaram, A. Raja / Infection, Genetics and Evolution 40 (2016) 21–28
strains during in vitro stress experiments could bring highly relevantrepresentative genes that can be targeted for the design of anti-tuberculosis drugs and vaccine.
Ethical Statement/Conflict of Interest Statement
The author declares no conflict of interest
Acknowledgments
We thank the Indian Council of Medical Research for the SeniorResearch Fellowship awarded to Santhi Devasundaram. We alsoacknowledge Prof. Paturu Kondaiah, MRDGDepartment, IISc, Bangalorefor providing microarray and qPCR facility to carry this work and forhelpful discussions. Wewould also like to thank Imran Khan and NeerajKumar for their help during microarray and RT-PCR experiments.
Appendix A. Supplementary data
Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.meegid.2016.01.007.
References
Aagaard, C., Hoang, T., Dietrich, J., Cardona, P.J., Izzo, A., Dolganov, G., Schoolnik, G.K.,Cassidy, J.P., Billeskov, R., Andersen, P., 2011. A multistage tuberculosis vaccine thatconfers efficient protection pre- and post-exposure. Nat. Med. 17, 189–194.
Bacon, J., James, B.W., Wernisch, L., Williams, A., Morley, K.A., et al., 2004. The influence ofreduced oxygen availability on pathogenicity and gene expression in Mycobacteriumtuberculosis. Tuberculosis (Edinb) 84, 205–217.
Banaiee, N., Jacobs Jr., W.R., Ernst, J.D., 2006. Regulation of Mycobacterium tuberculosiswhiB3 in the mouse lung and macrophages. Infect. Immun. 74 (11), 6449–6457.
Betts, J.C., 2002. Transcriptomics and proteomics: tools for the identification of novel drugtargets and vaccine candidates for tuberculosis. IUBMB life 53 (4–5), 239–242.
Betts, J.C., Dodson, P., Quan, S., Lewis, A.P., Thomas, P.J., et al., 2000. Comparison of theproteome of Mycobacterium tuberculosis strain H37Rv with clinical isolate CDC1551. Microbiology 146 (Pt 12), 3205–3216.
Boshoff, H.I., Barry 3rd, C.E., 2005. Tuberculosis — metabolism and respiration in theabsence of growth. Nat. Rev. Microbiol. 3, 70–80.
Brosch, R., Gordon, S.V., Marmiesse, M., Brodin, P., Buchrieser, C., Eiglmeier, K., Garnier, T.,Gutierrez, C., Hewinson, G., Kremer, K., Parsons, L.M., Pym, A.S., Samper, S., vanSoolingen, D., Cole, S.T., 2002. A new evolutionary scenario for the Mycobacteriumtuberculosis complex. Proc. Natl. Acad. Sci. U. S. A. 99 (6), 3684–3689.
Cole, S.T., Brosch, R., Parkhill, J., Garnier, T., Churcher, C., Harris, D., Gordon, S.V., Eiglmeier,K., Gas, S., Barry 3rd, C.E., Tekaia, F., Badcock, K., Basham, D., Brown, D., Chillingworth,T., Connor, R., Davies, R., Devlin, K., Feltwell, T., Gentles, S., Hamlin, N., Holroyd, S.,Hornsby, T., Jagels, K., Krogh, A., McLean, J., Moule, S., Murphy, L., Oliver, K.,Osborne, J., Quail, M.A., Rajandream, M.A., Rogers, J., Rutter, S., Seeger, K., Skelton, J.,Squares, R., Squares, S., Sulston, J.E., Taylor, K., Whitehead, S., Barrell, B.G., 1998.Deciphering the biology of Mycobacterium tuberculosis from the complete genomesequence. Nature 393 (6685), 537–544.
Commandeur, S., van Meijgaarden, K.E., Lin, M.Y., Franken, K.L., Friggen, A.H., Drijfhout,J.W., et al., 2011. Identification of human T-cell responses toMycobacterium tuberculosisresuscitation-promoting factors in long-term latently infected individuals. Clin. VaccineImmunol. 18 (4), 676–683.
Das, S., Paramasivan, C.N., Lowrie, D.B., Prabhakar, R., Narayanan, P.R., 1995. IS6110 re-striction fragment length polymorphism typing of clin-ical isolates of Mycobacteriumtuberculosis from patients with pulmonary tuberculosis in Madras, south India. Tuber.Lung Dis. 76, 550–554.
Devasundaram, S., Khan, I., Kumar, N., Das, S., Raja, A., 2015. The influence of reducedoxygen availability on gene expression in laboratory (H37Rv) and clinical strains(S7 and S10) of Mycobacterium tuberculosis. J. Biotechnol. 210, 70–80.
Dubnau, E., Chan, J., Mohan, V.P., Smith, I., 2005. Responses of Mycobacterium tuberculosisto growth in the mouse lung. Infect. Immun. 73 (6), 3754–3757. http://dx.doi.org/10.1128/IAI.73.6.3754-3757.2005.
Fan, X., Abd Alla, A.A., Xie, J., 2015. Distribution and function of prophage phiRv1 andphiRv2 among Mycobacterium tuberculosis complex. J. Biomol. Struct. Dyn. 1-6.
Fisher, M.A., Plikaytis, B.B., Shinnick, T.M., 2002. Microarray analysis of theMycobacteriumtuberculosis transcriptional response to the acidic conditions found in phagosomes.J. Bacteriol. 184 (14), 4025–4032.
Fontan, P., Aris, V., Ghanny, S., Soteropoulos, P., Smith, I., 2008. Global transcriptional pro-file ofMycobacterium tuberculosis during THP-1 human macrophage infection. Infect.Immun. 76 (2), 717–725.
Geiman, D.E., Raghunand, T.R., Agarwal, N., Bishai, W.R., 2006. Differential gene expres-sion in response to exposure to antimycobacterial agents and other stress conditionsamong seven Mycobacterium tuberculosis whiB-like genes. Antimicrob. AgentsChemother. 50 (8), 2836–2841.
Gengenbacher, M., Rao, S.P., Pethe, K., Dick, T., 2010. Nutrient-starved, non-replicatingMycobacterium tuberculosis requires respiration, ATP synthase and isocitrate lyasefor maintenance of ATP homeostasis and viability. Microbiology 156 (Pt 1), 81–87.
Govender, L., Abel, B., Hughes, E.J., Scriba, T.J., Kagina, B.M., de Kock, M., Walzl, G., Black, G.,Rosenkrands, I., Hussey, G.D., Mahomed, H., Andersen, P., Hanekom, W.A., 2010.Higher human CD4 T cell response to novel Mycobacterium tuberculosis latency asso-ciated antigens Rv2660 and Rv2659 in latent infection compared with tuberculosisdisease. Vaccine 29 (1), 51–57.
Gupta, R.K., Srivastava, B.S., Srivastava, R., 2010. Comparative expression analysis ofrpf-like genes of Mycobacterium tuberculosis H37Rv under different physiologicalstress and growth conditions. Microbiology 156 (Pt 9), 2714–2722.
Kasprowicz, V.O., Churchyard, G., Lawn, S.D., Squire, S.B., Lalvani, A., 2011. Diagnosinglatent tuberculosis in high-risk individuals: rising to the challenge in high-burdenareas. J. Infect Dis. 204 (Suppl. 4), S1168–S1178.
Kendall, S.L., Rison, S.C., Movahedzadeh, F., Frita, R., Stoker, N.G., 2004. What do microarraysreally tell us aboutM. tuberculosis? Trends Microbiol. 12 (12), 537–544.
Kim, S.Y., Lee, B.S., Shin, S.J., Kim, H.J., Park, J.K., 2008. Differentially expressed genes inMycobacterium tuberculosis H37Rv under mild acidic and hypoxic conditions.J. Med. Microbiol. 57, 1473–1480.
Kunnath-Velayudhan, S., Porcelli, S.A., 2013. Recent advances in defining the immuno-proteome of Mycobacterium tuberculosis. Front. Immunol. 4, 335.
Kushwaha, S.K., Shakya, M., 2010. Protein interaction network analysis—approach forpotential drug target identification in Mycobacterium tuberculosis. J. Theor. Biol. 262(2), 284–294.
Leistikow, R.L., Morton, R.A., Bartek, I.L., Frimpong, I., Wagner, K., Voskuil, M.I., 2010.The Mycobacterium tuberculosis DosR regulon assists in metabolic homeostasisand enables rapid recovery from nonrespiring dormancy. J. Bacteriol. 192 (6),1662–1670.
Manabe, Y.C., Dannenberg Jr., A.M., Tyagi, S.K., Hatem, C.L., Yoder, M., Woolwine, S.C.,Zook, B.C., Pitt, M.L., Bishai, W.R., 2003. Different strains ofMycobacterium tuberculosiscause various spectrums of disease in the rabbit model of tuberculosis. Infect. Immun.71 (10), 6004–6011.
Mehaffy, C., Hess, A., Prenni, J.E., Mathema, B., Kreiswirth, B., Dobos, K.M., 2010. Descrip-tive proteomic analysis shows protein variability between closely related clinicalisolates of Mycobacterium tuberculosis. Proteomics 10 (10), 1966–1984.
Muttucumaru, D.G., Roberts, G., Hinds, J., Stabler, R.A., Parish, T., 2004. Gene expressionprofile of Mycobacterium tuberculosis in a non-replicating state. Tuberculosis 84(3–4), 239–246.
Palanisamy, G.S., DuTeau, N., Eisenach, K.D., Cave, D.M., Theus, S.A., Kreiswirth, B.N.,Basaraba, R.J., Orme, I.M., 2009. Clinical strains of Mycobacterium tuberculosis displaya wide range of virulence in guinea pigs. Tuberculosis 89 (3), 203–209.
Rajavelu, P., Das, S.D., 2005. Th2-type immune response observed in healthy individualsto sonicate antigen prepared from the most prevalent Mycobacterium tuberculosisstrain with single copy of IS6110. FEMS Immunol. Med. Microbiol. 45, 95–102.
Reed, M.B., Domenech, P., Manca, C., Su, H., Barczak, A.K., et al., 2004. A glycolipid ofhypervirulent tuberculosis strains that inhibits the innate immune response. Nature431, 84–87.
Rehren, G., Walters, S., Fontan, P., Smith, I., Zarraga, A.M., 2007. Differential gene expres-sion between Mycobacterium bovis and Mycobacterium tuberculosis. Tuberculosis 87(4), 347–359.
Rex, K., Kurthkoti, K., Varshney, U., 2013. Hypersensitivity of hypoxia grownMycobacteriumsmegmatis to DNA damaging agents: implications of the DNA repair deficiencies inattenuation of mycobacteria. Mech. Ageing Dev. 134 (10), 516–522.
Sampson, S.L., 2011. Mycobacterial PE/PPE proteins at the host–pathogen interface. Clin.Dev. Immunol. 497203.
Sherman, D.R., Voskuil, M., Schnappinger, D., Liao, R., Harrell, M.I., et al., 2001. Regulationof the Mycobacterium tuberculosis hypoxic response gene encoding alpha-crystallin.Proc. Natl. Acad. Sci. U. S. A. 98, 7534–7539.
Sohaskey, C.D., 2008. Nitrate enhances the survival of Mycobacterium tuberculosis duringinhibition of respiration. J. Bacteriol. 190 (8), 2981–2986.
Sohn, H., Lee, K.S., Kim, S.Y., Shin, D.M., Shin, S.J., et al., 2009. Induction of cell death inhuman macrophages by a highly virulent Korean isolate ofMycobacterium tuberculosisand the virulent strain H37Rv. Scand. J. Immunol. 69, 43–50.
Talaat, A.M., Lyons, R., Howard, S.T., Johnston, S.A., 2004. The temporal expression profileofMycobacterium tuberculosis infection in mice. Proc. Natl. Acad. Sci. U. S. A. 101 (13),4602–4607.
Timm, J., Post, F.A., Bekker, L.G., Walther, G.B., Wainwright, H.C., Manganelli, R., Chan,W.T., Tsenova, L., Gold, B., Smith, I., Kaplan, G., McKinney, J.D., 2003. Differential ex-pression of iron-, carbon-, and oxygen-responsive mycobacterial genes in the lungsof chronically infected mice and tuberculosis patients. Proc. Natl. Acad. Sci. U. S. A.100 (24), 14321–14326.
28 S. Devasundaram, A. Raja / Infection, Genetics and Evolution 40 (2016) 21–28
Triccas, J.A., Gicquel, B., 2000. Life on the inside: probingMycobacterium tuberculosis geneexpression during infection. Immunol. Cell Biol. 78, 311–317.
Tsolaki, A.G., Gagneux, S., Pym, A.S., Goguet de la Salmoniere, Y.O., Kreiswirth, B.N., et al.,2005. Genomic deletions classify the Beijing/W strains as a distinct genetic lineage ofMycobacterium tuberculosis. J. Clin. Microbiol. 43, 3185–3191.
Voskuil, M.I., Bartek, I.L., Visconti, K., Schoolnik, G.K., 2011. The response ofMycobacterium tuberculosis to reactive oxygen and nitrogen species. Front.Microbiol. 2, 105.
Wayne, L.G., Hayes, L.G., 1996. An in vitro model for sequential study of shiftdown ofMycobacterium tuberculosis through two stages of nonreplicating persistence. Infect.Immun. 64, 2062–2069.
Wei, J., Guo, N., Liang, J., Yuan, P., Shi, Q., Tang, X., et al., 2013. DNA microarray geneexpression profile of Mycobacterium tuberculosis when exposed to osthole. Polish.J. Microbiol. 62 (1), 23–30.
