Proteomic Insights into Strong and Weak Biofilm Formation in Acinetobacter baumannii for Potential Therapeutic Targets

Proteomic Insights into Strong and Weak Biofilm Formation in Acinetobacter baumannii for Potential Therapeutic Targets | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Proteomic Insights into Strong and Weak Biofilm Formation in Acinetobacter baumannii for Potential Therapeutic Targets Umarani Brahma , Akash Suresh , Aafreen Kamila , Usha S , Arun Kumar S.V , Harshala Baddi , Siva Singothu , Paresh Sharma , Vasundhra Bhandari doi: https://doi.org/10.1101/2025.10.02.680171 Umarani Brahma 1 Department of Biological Sciences, National Institute of Pharmaceutical Education and Research (NIPER) , Hyderabad, India , 500037 2 Department of Biotechnology and bioinformatics, Sambalpur University , Jyoti Vihar, Sambalpur, Odisha, India , 768019 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Akash Suresh 3 National Institute of Animal Biotechnology , Hyderabad, India , 500032 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Aafreen Kamila 1 Department of Biological Sciences, National Institute of Pharmaceutical Education and Research (NIPER) , Hyderabad, India , 500037 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Usha S 1 Department of Biological Sciences, National Institute of Pharmaceutical Education and Research (NIPER) , Hyderabad, India , 500037 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Arun Kumar S.V 1 Department of Biological Sciences, National Institute of Pharmaceutical Education and Research (NIPER) , Hyderabad, India , 500037 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Harshala Baddi 1 Department of Biological Sciences, National Institute of Pharmaceutical Education and Research (NIPER) , Hyderabad, India , 500037 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Siva Singothu 1 Department of Biological Sciences, National Institute of Pharmaceutical Education and Research (NIPER) , Hyderabad, India , 500037 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Paresh Sharma 3 National Institute of Animal Biotechnology , Hyderabad, India , 500032 Find this author on Google Scholar Find this author on PubMed Search for this author on this site Vasundhra Bhandari 1 Department of Biological Sciences, National Institute of Pharmaceutical Education and Research (NIPER) , Hyderabad, India , 500037 Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: vasundhra23{at}gmail.com Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Acinetobacter baumannii is notable for its biofilm-forming abilities, which aid in its tolerance to antibiotics, adding to antimicrobial resistance. The clinical isolates present varied biofilm-forming capacity; hence, understanding the molecular determinants that result in strong biofilm development is crucial for drug target identification. This study is the first of its kind to compare proteome profiling of strong and weak biofilm-forming A. baumannii clinical isolates. Comparative proteomic profiling revealed 42 differentially regulated proteins. It was observed that in strong biofilm forming isolate NlpA, uL16, DNA gyrase B, acetyl-CoA carboxylase, and purl etc. were upregulated highlighting a dynamic reprogramming of cellular functions that promotes biofilm formation, stress adaptation, and immune evasion. In contrast, EF-Tu, ribosome hibernation factors, and T6SS components were downregulated, suggesting a lack of biosynthesis and stress adaptability. These findings suggest a metabolic downshift and a possible energy conservation mechanism under conditions less favorable for strong biofilm development. Additionally, several uncharacterized proteins were identified, highlighting potential novel factors in biofilm regulation and virulence that warrant further investigation. The proteomics data correlated with qPCR findings, providing support for the unknown regulators of biofilm formation that were identified in this study. Key proteins such as nlpA , 6,7-dimethyl-8-ribityllumazine synthase and DNA gyrase B emerged as potential therapeutic targets. Download figure Open in new tab Graphical Abstract 1. Introduction Antimicrobial resistance (AMR) is an ascending global health crisis, and Acinetobacter baumannii stands out as one of the most daunting threats due to its exceptional ability to resist antibiotics and persist in hospital environments 1 . This Gram-negative opportunistic pathogen is a notable member of the “ESKAPE” group of bacteria, known for evading the effects of antimicrobial agents 2 . A. baumannii poses a significant therapeutic challenge, despite the development of new antibiotics and adjuvants 3 . The World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC) have classified it as a critical priority pathogen, underscoring the urgent need for novel therapeutic strategies 1 . Also reported by India’s AMR surveillance network, a staggering 80% of A. baumannii isolates exhibited resistance to imipenem, bringing attention to a critical threat in the fight against drug-resistant infections 4 . Due to its dynamic genome, the isolates are different from one another and make it hard to define them as a single species 1 . Another feather is its robust capacity to form biofilms, a structured microbial community encased in a self-produced extracellular matrix 5 . Their biofilm-forming ability confers them remarkable survival advantages, which include resistance to desiccation, disinfectants, oxidative stress, and host immune responses. Biofilm formation on medical devices such as catheters, ventilators, and prosthetics facilitates persistent infections and complicates treatment, often leading to chronic and recurrent infections 6 . Within biofilms, bacteria exhibit up to 1,000-fold increased resistance to antibiotics compared to their planktonic counterparts, rendering conventional therapies largely ineffective 7 . The biofilm lifestyle of A. baumannii is intricately linked to its pathogenicity 8 . It contributes to the persistence of infections such as ventilator-associated pneumonia, catheter-associated urinary tract infections, and bloodstream infections, particularly in intensive care settings 9 . These infections are associated with prolonged hospital stays, increased healthcare costs, and elevated mortality rates 10 . The bacterium’s ability to survive on abiotic surfaces and evade host defenses through biofilm-mediated dormancy and immune modulation makes it a formidable nosocomial pathogen 11 . Biofilm formation in A. baumannii is regulated by a complex network of genetic and environmental factors, including the csuA/BABCDE operon, quorum-sensing systems, and surface-associated proteins such as BfmS, chaperone-usher pili, outer membrane protein A and several others. These elements orchestrate adhesion, maturation, and maintenance of the biofilm architecture 5 , 12 . Clinical isolates often exhibit enhanced biofilm-forming capabilities compared to environmental strains, further complicating infection control and treatment efforts 13 . Despite the critical role of biofilms in A. baumannii pathogenesis, most drug discovery efforts have historically focused on its planktonic phenotype 14 . Recent advances in proteomics have opened new avenues for understanding the molecular underpinnings of biofilm formation 15 . This study employs a label-free global proteomic approach to compare the proteomes of strong and weak biofilm-forming multidrug-resistant (MDR) clinical isolates of A. baumannii . By elucidating differential protein expression profiles, this work aims to uncover biofilm-associated regulators and pathways, offering insights into potential therapeutic targets to combat biofilm-mediated infections. 2. Experimental Section 2.1 Sample collection and Molecular characterization of A. baumannii clinical isolates The current study used 25 clinical isolates obtained from patients with blood-borne, respiratory tract, and skin infections reporting to ESIC Hospital, Hyderabad, India. The cultures were maintained in TSB (Tryptone Soy Broth, Himedia, India) medium at 37 °C for 16 to 20 hours. Species confirmation was carried out through universal 16S rRNA gene sequencing. Genomic DNA was isolated using a Wizard genomic kit (Promega, Madison, WI, USA) with slight modifications. In brief, 3 mL of the culture was centrifuged for 10 minutes at 4000× g to form a pellet. The pellet was washed twice with 1x PBS and was thereafter resuspended in 500 µL of 50 mM Tris-EDTA containing 100 µl of 20mg/mL lysozyme for two hours at 37 °C before processing as per the manufacturer’s guidelines. The DNA was diluted in sterile deionized water, and its purity was assessed using a Nanodrop (Thermo Scientific, Waltham, MA, USA). The 16S rRNA gene was amplified as previously reported 16 and then sequenced using Sanger sequencing at Ira Biotech, Hyderabad, India. A quality control strain, ATCC 19606, was used in studies of biofilm and antimicrobial susceptibility, in accordance with the Clinical and Laboratory Standards Institute (CLSI) guidelines 17 . 2.2 Antimicrobial susceptibility studies Antimicrobial susceptibility testing was performed using both disk diffusion and microbroth dilution methods in A. baumannii clinical isolates (n=25), and ATCC 19606 was used as a control strain as per CLSI guidelines. The antibiotics tested for susceptibility included carbapenems (imipenem, meropenem), fluoroquinolones (ciprofloxacin), tetracyclines (minocycline, doxycycline), cephalosporins (ceftazidime, ceftriaxone), lipopeptides (colistin), and the β-lactam combination drug piperacillin-tazobactam (Himedia, India). Microbroth dilution experiment with resazurin dye (Sigma, Bangalore, India) as an indicator was used to evaluate the susceptibility against imipenem, colistin, ciprofloxacin, minocycline, and ceftazidime 18 . Disk diffusion assay was performed against ceftriaxone (30 µg), doxycycline (30 µg), and piperacillin-tazobactam (2 µg). Isolates were categorized according to resistance profiles as extensively drug-resistant (XDR; susceptible to one or two classes), Pan-drug resistant 19 . 2.3 Biofilm formation The biofilm formation was determined using crystal violet (CV) (Sigma, Bangalore, India) assay with slight modifications to the standard protocol 20 . In brief, 200 µL of an overnight culture grown in TSB + 2% glucose (1:200 dilution) was added into each well of a 96-well microtiter plate and incubated at 37°C for 24 h under static conditions. Subsequent to incubation, wells were washed thrice with 1× PBS and fixed with 200 µL of methanol for 15 minutes. The plates were air-dried for 20 minutes, followed by the addition of 100 µL of 0.2% CV solution and incubated at room temperature for 15 minutes. Excess dye was removed by washing with distilled water, and the adhered stain was solubilized using 100 µL of 33% glacial acetic acid. The biomass of the biofilm was assessed by measuring absorbance at 590 nm using a multimode plate reader (Cytation 5). The biofilm-forming capacity of isolates was categorized according to optical density (OD) values as follows: non-adherent (ODs ≤ ODc), weak (ODc < ODs ≤ 2×ODc), moderate (2×ODc 4×ODc), where ODc denotes the mean OD of the negative control and ODs signifies the OD of the examined strains 14 . 2.4 Protein extraction from A. baumannii biofilms The biofilms were grown as described previously 14 , 21 . Briefly, overnight cultures of the clinical isolates were grown in TSB with 2% glucose at 37°C overnight, and 10 7 cells/ml were then seeded into 6-well flat-bottomed polystyrene microtiter plates and incubated at 37 °C for 48 h under static conditions to facilitate biofilm formation. The supernatant was removed, and the plates were washed twice with 1x PBS to remove any unadhered cells. Biofilm formation for each of the two isolates was performed in thrice in triplicate. Biofilm cells from each culture were scraped, pooled, and processed separately, followed by centrifugation at 4000 xg for 10 minutes at 4°C and then washed twice with 1x PBS before protein extraction. The resultant pellet was then resuspended in 1x TE buffer, and 20 mg/mL lysozyme was added and incubated for 1 h at 37°C before adding RIPA lysis buffer (HiMedia, India) for isolating protein. The cells were mixed thoroughly, followed by sonication on ice for 5 min at a 10 sec ON/OFF pulse. The homogenized cells were centrifuged at 16,000 g for 10 min at 4 °C to obtain the protein in the supernatant. Protein estimation was carried out using the BCA assay (Pierce - Thermo-Scientific). 2.5 Sample preparation for quantitative label-free proteomics 100µg of protein was processed for LC-MS/MS analysis using the in-solution trypsin digestion method. To the protein 1% sodium deoxycholate was added, followed by reduction with 20 mM DTT at 57°C for 1 h. Next, alkylation was carried out with 200 mM iodoacetamide (IAA) for 1 h at room temperature in the dark. The proteins were then digested with trypsin-protease (Thermo-scientific) (1:100 wt/wt) overnight at 37°C. The digestion reaction was stopped by adding 0.1% formic acid. The digested peptides were then purified using C 18 spin columns. The purified peptides were concentrated using vacuum evaporator and finally resuspended in 0.1% trifluoroacetic acid 22 . 2.6 Peptide mixtures were subjected to mass spectrometric analysis The peptides were analyzed with a Q Exactive HF-Orbitrap mass spectrometer (Thermo Fisher Scientific) coupled with an Ultimate 3000 RSLCnano LC system (Thermo Fisher Scientific). The peptides were injected into a reverse-phase C18 column (PepMap RSLC C18, 2 μm, 100 Å, 75 μm by 50 cm; Thermo Fisher Scientific) and separated by a gradient flow of solvent B (0.1% formic acid in 80/20 acetonitrile/water) from 5% to 90% for 60 min. A parent ion scan was performed with a scan range of 375 to 1,600 m/z with a resolution of 60,000. The top 25 intense peaks were fragmented by higher energy collision-induced dissociation (HCD) fragmentation in MS/MS with a resolution of 15,000 22 . 2.7 Data Processing The experimental design consisted of two biological groups: AB1 (strong biofilm former) and AB2 (weak biofilm former), each comprising two biological replicates. Label-Free Quantitative (LFQ) proteomic analysis was performed using Perseus-MaxQuant software. Initially, RAW files were obtained from a Q Exactive HF-Orbitrap mass spectrometer and analyzed using MaxQuant (version 2.7.3). A. baumannii protein sequences were obtained from UniProt (version 2025_01). Within MaxQuant, “matching between runs” and “LFQ” were chosen with the default value settings. Downstream analysis was carried out in the Perseus (version 2.1.4). The ‘proteinGroups.txt’ output file obtained from MaxQuant was imported into the Perseus suite, and the pertinent columns were chosen for both strong and weak biofilm-forming strains and subsequently log transformed. Quantitative profiles underwent a filtration process to address missing values, with independent filtering applied to each strain, ensuring that only proteins quantified in both replicates were retained. Imputation of missing values was conducted (width 0.3, down shift 1.8) before the integration of the tables and the execution of the multi-volcano analysis. The s0 and FDR parameters for the multi-volcano analysis were selected based on visual inspection for the strong biofilm strain (higher confidence, s0 = 1, FDR = 0.01%) and the weak biofilm strain (lower confidence, s0 = 1, FDR = 0.2%), to minimize the number of significantly depleted proteins across all experiments 23 . 2.8 Experimental Design and Statistical Rationale The protein expression profiles of strains AB1 and AB2 served as the foundation for identifying the patterns of up-regulation and down-regulation. Values that were missing were filled in using the normal distribution method. Abundance values underwent log₂ normalization, subsequently followed by Z-score standardization. The Z-score values underwent additional processing through Student’s t-test to determine the significance of the proteins. Significance was calculated using Benjamini-Hochberg, with a 0.05 FDR established as the significance cutoff. The correlation plot, utilizing Pearson’s correlation coefficient, was employed to visualize the relationship between each sample. Principal component analysis (PCA) was conducted to assess variations among biological replicates and within biological replicates. Only the proteins that exhibited up-regulation or down-regulation with both AB1 and AB2 were selected for subsequent gene ontology and pathway enrichment analysis. The proteins exhibiting analogous expression patterns, in conjunction with cellular components and biological functions, were depicted through a heat map visualization. Four biological replicates were utilized for the real-time PCR analysis. The results’ significance was examined through the analysis of variance. Differences were deemed significant when p-values fell below 0.05 21 14 . 2.9 Gene Ontology Analysis The differentially expressed proteins have been studied through the STRING database (version 12.0), ( https://string-db.org ) to anticipate the functional interactions between proteins. The analysis was performed using a confidence score threshold of > 0.9, focusing exclusively on experimentally validated and database-derived interactions. The interaction networks generated were visualized and subjected to further analysis to pinpoint essential hubs and enriched pathways. 2.10 RNA Extraction and Real-time PCR RNA was extracted from 48 h-old A. baumannii biofilms using the Macherey Nagel RNA isolation kit (NucleoSpin® RNA, 740955.10) following the manufacturer’s instructions. Subsequently, cDNA was synthesized from approximately 2.5 µg of RNA using the Clontech cDNA synthesis kit (PrimeScript™ 1st strand cDNA Synthesis Kit, 6110B). The 2 −ΔΔCₜ method was used to calculate the relative gene expression using SYBR green dye and was carried out on the QuantStudio 7 Pro real-time detection equipment (Applied Biosystems). Table 1 presents the list of primers utilized in this study. View this table: View inline View popup Table 1: List of primers used in the study. 3. Results and Discussion 3.1 Antimicrobial and biofilm profiling of A.baumannii clinical isolates In the study, 25 confirmed 16S rRNA gene-sequenced A. baumannii clinical isolates were included. These isolates were subjected to antimicrobial susceptibility testing against the commonly used antibiotics using microbroth dilution and disk diffusion assay. The majority of isolates (92%, n = 23), exhibited resistance to carbapenems, cephalosporins, fluoroquinolones, tetracyclines, and β-lactam/β-lactamase inhibitor combination, and were classified as extensively drug-resistant (XDR). Only 8% (n = 2) of isolates were found to be resistant to the lipopeptide (colistin) class of antibiotics. None of the clinical isolates was found to be pan-sensitive, and one clinical isolate was found to be resistant to all the classes of antibiotics categorized as pan-resistant. The biofilm-forming capacity was assessed in all the isolates using the CV assay, and they were characterized as strong (n = 13; 52%), moderate (n = 8; 25%) and weak (n = 3; 12%) biofilm forming isolates ( Figure 1a and 1b ). Figure 1A: Antimicrobial resistance profile of A.baumannii clinical isolates. Antimicrobial resistance profile of A.baumannii clinical isolates. The susceptibility profile of all clinical isolates was determined using microbroth dilution and disc diffusion methods. Download figure Open in new tab Figure 1B: Distribution of biofilm capacity among clinical isolates of A.baumannii. Distribution of biofilm capacity among clinical isolates of A.baumannii. Biofilm biomass was quantified using the crystal violet dye by measuring the absorbance at 570 nm. The capacity of A. baumannii to form biofilms is an important virulence characteristic that contributes to the pathogenicity of the bacteria and is thought to play a significant role in its ability to survive in adverse habitats 24 . The biofilms enhance the virulence of A. baumannii by promoting bacterial survival under adverse environmental conditions, including exposure to disinfectants, antibiotics, and host immune defenses 25 . Importantly, antibiotic resistance phenotypes have a strong influence on biofilm-forming capacity. Previous studies have demonstrated that MDR strains exhibit greater biofilm thickness and higher expression of biofilm-associated virulence genes compared to drug-sensitive isolates. A significant increase in biofilm-forming, drug-resistant A. baumannii has been documented in intensive care units (ICUs), and numerous studies have indicated a strong relationship between antimicrobial resistance and biofilm-forming ability. A study reported that 97.1% of the clinical isolates could form biofilms, in which 4.3% possessed weak biofilm formation ability, while 41.4% and 51.4% were moderate and strong biofilm producers 26 . This observation aligns with our findings, whereby most isolates exhibited moderate to high biofilm-forming capability, hence reinforcing the established association between drug resistance and increased biofilm development. Another study reported that 58% of the isolates showed strong ability to form biofilm, whereas 42% isolates showed moderate biofilm formation 27 . There is also a report on strong biofilm formation capacity in A. baumannii isolated from burn units. These isolates exhibited significant resistance to antibiotics, including carbapenems, and demonstrated co-production of AmpC and ESBLs 28 . In contrast, a study reported that the sensitive isolates exhibited strong biofilm formation in the initial phase (< 24 h) relative to the resistant isolates, whereas their biofilm-forming capacity dropped in subsequent phases 26 . We found two isolates to be colistin resistant, and both displayed strong biofilm formation. This is in-line with a study where colistin-resistant A. baumannii clinical isolates exhibited an enhanced biofilm-forming capacity, suggesting that biofilm production may represent an adaptive response associated with the development of colistin resistance 25 . Whereas the previous study suggests that the correlation between colistin resistance and biofilm formation is dependent upon the specific resistance mechanism involved. Mutations and modifications to LPS maintain biofilm development, therefore enhancing persistence and transmission in healthcare environments 29 . These results align with our observation that colistin-resistant A. baumannii clinical isolates are capable of maintaining strong biofilm-forming ability. This confirms our results that colistin-resistant A. baumannii may maintain significant biofilm-forming capability. Whereas in contrast, one study revealed that the acquisition of colistin resistance in two clinical A. baumannii isolates was accompanied by a marked reduction in biofilm-forming ability. The limited available information comes from another study that revealed laboratory-evolved Colᴿ mutants, which also demonstrated less biofilm production relative to their susceptible counterparts 30 . 3.2 Identification of differentially regulated proteins in the Strong and Weak Biofilm-forming A. baumannii isolates In taking note of the observed variability in biofilm formation and the distinct phenotypes linked to XDR and colistin resistance associated with the clinical strains, relevant isolates were carefully selected for the proteomics study. Isolates showing strong and weak formation of biofilm, in addition to colistin-resistant and colistin-susceptible, as well as multidrug-resistant phenotypes, were included. This methodology is meant to elucidate the proteome variations that contribute to biofilm heterogeneity and resistance mechanisms, therefore offering insights into the molecular factors that influence virulence and persistence in A. baumannii . A label-free quantitative mass spectrometric analysis was carried out to identify the regulators linked to the persistence of infection related to the biofilm-forming capability of A. baumannii . A total of 1066 proteins were identified in both the strong and weak isolates from the proteomic analysis. An analysis of the fold changes between AB1 and AB2 was conducted to get the differential expression profiles. The student’s t-test was then used on all 712 differentially expressed proteins for identifying specific protein markers linked with biofilm characteristics. We observed protein enrichment in pathways associated with energy metabolism, lipid biosynthesis, oxidative stress adaptation, and transport functions critical for biofilm matrix synthesis and durability in the AB1 isolate. Conversely, in strains exhibiting weak biofilm formation, proteins related to ribosome maintenance, RNA processing, and translation initiation were prevalent, indicating a diminished anabolic profile. Differential expression thresholds were set at log₂ fold change > 2 and p < 0.05. A total of 41 proteins met the significance criteria, comprising 19 upregulated and 22 downregulated proteins in strong biofilm conditions compared to weak ( Figure 2 ). The obtained fold changes were then filtered using population statistics to get the significant threshold cutoff of up-regulated and down-regulated proteins for each of the comparisons performed in the study. To visualize differential expression trends, a heatmap of the 41 significantly altered proteins was generated using hierarchical clustering based on Euclidean distance and average linkage ( Figure 3 ). Download figure Open in new tab Figure 2: Comparative proteomic heatmap showing upregulated and downregulated proteins in biofilm-forming A. baumannii . Protein intensities were scaled and color-coded: red for high expression, green/blue for low expression, and black for intermediate levels. Distinct clustering patterns were observed, separating strong biofilm samples (AB1) from weak biofilm samples (AB2), and grouping proteins based on shared expression dynamics. Download figure Open in new tab Figure 3: A volcano plot showing significantly differentially expressed proteins between strong and weak biofilm conditions. The x-axis represents fold change (log₂), and the y-axis represents statistical significance (−log₁₀ p value). Proteins significantly upregulated in strong biofilm formers are shown in red, while proteins significantly upregulated in weak biofilm formers are shown in blue. Gray points represent non-significant proteins. Labelled points indicate proteins with the most significant differential expression. 3.3 Proteomic remodelling in strong biofilm-forming A. baumannii A. Upregulated proteins The bioinformatic analysis revealed significantly upregulated proteins that could play a vital role in the biofilm formation and antibiotic resistance of A. baumannii ( Table 2 ). These proteins include several functional categories, including adhesion, stress adaptation, DNA replication, metabolic reprogramming, and a few uncharacterized proteins. View this table: View inline View popup Table 2: List of upregulated proteins. i. Surface associated and adhesion related proteins Of these, the NlpA lipoprotein (Uniprot ID: D0CE28) showed a 4.6-fold increase. This protein is known for facilitating the production of outer membrane vesicles (OMVs) and enhancing cellular adhesion, both of which are essential for biofilm formation, nutrient transfer, and evading the immune system. It is also involved in methionine import and may boost immune responses. Research has identified it as a potential vaccine candidate against biofilm-forming A. baumannii 31 . This nonessential protein is associated with the inner membrane and was first identified in E. coli 32 . In E. coli , NlpA is regulated by CsgD, a key transcriptional regulator involved in biofilm development, whose expression begins in the mid-logarithmic phase and persists till the stationary phase 33 . Other upregulated surface-associated proteins include, histidine triad domain protein (UniProt ID: D0CFB3), previously reported to be associated with virulence in Streptococcus sp . The rapid development and advantageous selection of this protein beyond its conserved regions suggest an adaptive role in immune evasion 34 . Similarly, the META domain protein (Uniprot ID: D0C907) identified in different bacterial species is linked to motility and virulence 35 . Studies in E. coli and Pseudomonas revealed that adherence to softer surfaces activates essential biofilm regulators, resulting in reduced motility and increased biofilm formation 36 . Likewise, the ABC family transporter (UniProt ID: D0C617) protein enables the translocation of multiple substrates, like metal ions and proteins, across cellular membranes by pairing this activity with ATP hydrolysis. These are involved in key processes that include multidrug resistance, nutrition absorption, adhesion, sporulation, conjugation, biofilm development, and toxin secretion. Mutagenesis studies on E. coli, S. aureus , and S. pneumoniae highlight their significance in the pathogenicity of disease 37 . ii. Stress-response and adaptive defense mechanism related Upregulated stress-associated proteins suggest enhanced defense capabilities in strong biofilm formers. The DJ-1/PfpI family protein (Uniprot ID: D0C6X0) makes up part of the DJ-1/ThiJ/PfpI superfamily, with homologs found in several species, including humans, Drosophila melanogaster , Caenorhabditis elegans , E. coli , and yeast. Proteins within this family are mostly linked to activities like oxidative stress response and RNA binding. The E. coli , homolog referred to as Hsp31 has activities like chaperone action, acid resistance, and extensive aminopeptidase activity. While its direct participation in biofilm formation has not been shown in any bacterial species, its function in stress responses implies a possible indirect contribution to biofilm growth 38 , 39 . Additionally, the large ribosomal subunit protein uL16 (Uniprot ID: D0CD04), components of the translation machinery, was found elevated, suggesting an enhanced protein synthesis mechanism to support bacterial survival. A study reported that uL16, a crucial element of the 50S large ribosomal subunit, is essential for appropriate ribosome maturation and functionality. The lack of it may hinder the bacterium’s capacity for protein synthesis, thereby affecting biofilm stability 40 . iii. Translation-associated protein Further, the protein A of the UvrABC system (Uniprot ID: D0CF73) is known to be involved in the activation of the adaptive defensive mechanisms of bacteria. This protein is involved in the nucleotide excision repair (NER) pathway, where UvrA and UvrB form a complex when DNA damage is observed. After UvrA has identified the DNA helix aberrations, UvrB confirms the existence of a lesion and firmly binds to the damaged site, leading to the release of UvrA 41 . In a study on E.coli, uvrABC system protein A was found to be upregulated in the presence of a bioreductive agent 42 . iv. Replication and transcription machinery The ATP-dependent enzyme DNA gyrase subunit B (Uniprot ID: D0CG10) was also found to be upregulated, which is essential for chromosomal segregation, DNA replication, and transcription. Its ubiquitous presence in all bacteria makes it a compelling and feasible target for the development of new antibacterial drugs 43 . Prior research has emphasized DNA gyrase inhibitors from many origins as potent frameworks for drug development. Their synthesis methodologies, structure–activity correlations, and extensive efficacy provide them exemplary prospects for the development of fresh therapies targeting resistant bacteria and biofilms 44 . An integrated in vitro and in silico study in Bacillus subtilis and Staphylococcus aureus found marine-derived natural compounds as prospective DNA gyrase inhibitors, leading to identification of new antibacterial and anti-biofilm compounds 45 . v. Metabolic and biosynthetic enzymes Metabolic reprogramming was evident in strong biofilm formers, with multiple biosynthetic enzymes enriched. Acetyl-CoA carboxylase beta subunit (Uniprot ID: D0CDC5) appeared prominently, indicating a rise in fatty acid biosynthesis. It is a biotin-dependent enzyme that catalyzes the carboxylation of acetyl-CoA to malonyl-CoA, a crucial step in fatty acid biosynthesis. In bacteria, it is a multi-subunit complex (accD) which forms malonyl-coA. This serves as the building block for the elongation of fatty acids and essential components of the bacterial cell membrane. Fatty acids are the precursors of LPS (Lipopolysaccharides), providing structural integrity to the biofilms 46 . In Mycobacterium tuberculosis, the dense mycolate layer in the cell wall confers resistance to chemicals and desiccation while facilitating biofilm formation 47 , 48 . Another biosynthetic pathway enzyme phosphoribosyl formyl glycinamidine synthase (Uniprot ID: D0CBM2) showed increased expression, indicating an upsurge in purine biosynthesis. This enzyme participates in the purine biosynthesis process. This facilitates the ATP-dependent conversion of formylglycinamide ribonucleotide (FGAR) and glutamine into formyl glycinamidine ribonucleotide (FGAM) or purL and glutamate 49 . A study in E. coli identifies disrupted purL as essential for curli production and biofilm formation 50 . Research suggests that purine metabolism affects microbe-host interactions, however, the underlying mechanisms are not clear. In bacterium Photorhabdus temperata , purL was identified as increased in nematode infective juveniles, and its deletion reduced both biofilm formation and bacterial persistence, underscoring its significance in symbiosis 51 . The observed upregulation of phosphogluconate dehydratase (Uniprot ID: D0CAG4), an enzyme of the Entner-Doudoroff pathway, suggests the potential activation of non-classical sugar metabolism in gram-negative bacteria. A study indicates that this enzyme is crucial for both the use of glycerol and the enhancement of biofilm strength in its presence. This indicates that gluconeogenesis and glucose degradation influence both glycerol uses and the proliferation of glycerol-induced biofilms 52 . Certain enzymes have become potent antibacterial targets by interfering with important metabolic or survival processes, even if they do not directly affect the production of biofilms. This underlines their capability in formulating effective antimicrobial treatments. Like 6,7-dimethyl-8-ribityllumazine synthase (UniProt ID: D0CFF3) catalyzes the last reaction in riboflavin biosynthesis by condensing 5-amino-6-(D-ribitylamino) uracil with 3,4-dihydroxy-2-butanone 4-phosphate to produce 6,7-dimethyl-8-ribityllumazine. A study on MtB established RibH as a viable drug target for the development of novel antibacterial agents 53 . Proteins belonging to the aldehyde dehydrogenase family (UniProt ID: D0CAG0) are vital enzymes that maintain bacteria’s tremendous metabolic capabilities 54 . A restricted set of uncharacterized proteins (e.g., D0CA47, D0C686, and D0C928) exhibiting substantial fold change and importance suggests possible fresh targets that may be crucial for pathogenesis. Collectively, these findings illustrate how strong biofilm-forming A. baumannii orchestrates a coordinated response. The consistent enrichment of druggable enzymes such as DNA gyrase, riboflavin biosynthesis enzymes, and acetyl-CoA carboxylase further highlights potential intervention points for anti-biofilm therapeutics. B. Down-regulated proteins The proteins found to be downregulated in strong biofilm formers are listed in Table 3 . The reduced levels indicate a reprioritization of bacterial processes during biofilm development, namely in translation, energy conservation, secretion systems, and metabolic pathways. ( Table 2 or 3 ) View this table: View inline View popup i. Translation factors and ribosome-associated proteins The elongation factor thermo unstable (Ef-Tu) C-terminal domain protein (Uniprot ID: D0C9P9), central to translation elongation, showed the most significant downregulation, indicating a possible shift in protein synthesis strategy. It is one of the most abundant proteins in bacteria. It functions as an essential and universally conserved GTPase that ensures translational accuracy 55 . Likewise, proteins linked to energy-conserving processes, like the ribosome hibernation inducing factor (Uniprot ID: D0C973), were significantly reduced, perhaps indicating a metabolic compromise under stress. P. aeruginosa biofilms exhibit spatial physiological heterogeneity resulting from microenvironmental adaptation. Surface-associated cells remain metabolically active, characterized by elevated ribosomal protein transcript levels, while ribosomal RNA is distributed throughout, indicating ribosome stability in dormant areas. However, deeper biofilm layers are slow-growing and are in a dormant condition expressing ribosome hibernation factors such as rmf. Ribosomal RNA is, however, spread throughout the biofilm, suggesting ribosome stability within the dormant regions. This stratification renders surface cells more vulnerable to antibiotics, while deeper cells exhibit increased tolerance, emphasizing spatially different roles that combined enhance biofilm resistance 56 . ii. Secretion System and Virulence Factor The type VI secretion system (T6SS) (Uniprot ID: D0C8P5), widely distributed in Gram-negative bacteria, facilitates stress responses to stimuli such as reactive oxygen species, temperature fluctuations, and pH alterations. It also plays crucial functions in bacterial growth, invasion, and virulence. The type VI secretion system (T6SS) has been discovered as a newly characterized virulence factor common in A. baumannii . T6SS-positive strains exhibited significantly reduced biofilm formation compared to T6SS-negative bacteria, consistent with our findings 57 . iii. Central Metabolism and related Stress-Adaptation proteins Among metabolic enzymes, MTA/SAH nucleosidase (Uniprot ID: D0C9H1) is essential in the active methyl cycle, facilitating the recycling of adenine and methionine while aiding quorum-sensing and SAM-dependent processes. It eliminates inhibitory metabolites such as MTA and SAH. Inhibiting this enzyme hinders biofilm development and pathogenicity, making it a potential broad-spectrum antibacterial target 58 . A study on this bacterium identified it as a homologous substrate of the type II secretion system (T2SS) 59 . Additionally, fatty acid metabolism-related enzyme 3-Hydroxyacyl-CoA dehydrogenase (Uniprot ID: D0C8J8) facilitates the third step in fatty acid β-oxidation by oxidizing the hydroxyl group of 3-hydroxyacyl-CoA to a keto group. Distinct subfamilies of this group of enzymes have been categorized according to their roles in certain metabolic processes, like fatty acid β-oxidation and amino acid degradation, as well as their substrate specificities 60 61 . Similarly, another enzyme related to vitamin biosynthesis pyridoxine 5’-phosphate synthase, (Uniprot ID: D0CBQ7), catalyzes the complex ring-closure reaction between the acyclic substrates 1-deoxy-D-xylulose-5-phosphate (DXP) and 3-amino-2-oxopropyl phosphate (1-amino-acetone-3-phosphate, AAP), yielding the formation of pyridoxine 5′-phosphate (PNP) and inorganic phosphate 62 . Another enzyme, Isocitrate lyase (Uniprot ID: D0C8Y6), catalyzes the cleavage of isocitrate into glyoxylate and succinate 63 . A study revealed that these enzymes play a role in antioxidant defense, thereby contributing to antibiotic tolerance 64 . 3.3 Pathway enrichment analysis of up and down-regulated proteins of biofilm phenotypes The protein–protein interaction network demonstrated clusters of connectivity among the differentially expressed proteins. Translation-related proteins, such as rplP (ribosomal protein L16/uL16), infA (translation initiation factor IF-1), and relA (GTP pyrophosphokinase), constituted a densely linked hub, with chaperone fkpA and AGQ13237.1 (hypothetical protein). A second module was constituted by gyrB (DNA gyrase subunit B) and uvrA (UvrABC repair protein A), showing a strong association between DNA topology and repair mechanisms. Smaller clusters were identified, including fxsA –AGQ13440.1 and AGQ13875.1–AGQ14720.1, indicating limited functional connections ( Figure 4 ). Download figure Open in new tab Figure 4: Principal Component Analysis (PCA) of A. baumannii clinical isolates. PCA plot showing distinct clustering of strong (red) and weak (blue) biofilm-forming isolates. Replicates from each group cluster closely together, while strong and weak biofilm formers are clearly separated along PC1 (95.9%), indicating major proteomic differences associated with biofilm phenotype. 3.4 Correlation of proteomics data with Quantitative Real-time PCR (qPCR) To ensure the precision of the proteomic analysis, qPCR was used to obtain gene expression profiles of specific genes. It was performed on two additional strong biofilm-forming isolates. The genes chosen for qPCR analysis were representative of the pathways that were most significantly impacted. The majority of genes exhibiting upregulation in the proteomics dataset similarly showed substantially increased expression across all strong biofilm formers, hence confirming the reproducible nature of the results. A subset of three genes showed downregulation in these isolates, suggesting potential strain-specific variations in the regulation of particular biofilm-associated genes. Likewise, in the downregulated genes we observed substantial decreased expression, except two genes. This variability highlights the intricacy of biofilm control in A. baumannii and the need to examine and analyze multiple isolates to include the whole range of biofilm-related responses ( Figure 5 ). The comparison of qPCR findings with proteomic data using a Pearson correlation matrix indicated little expression overlap between strong and weak biofilm profiles. This data substantiates the notion that proteins linked to strong biofilms operate within coordinated functional groups, while such co-regulation is absent under situations conducive to weaker biofilm formation ( Figure 6 ). Our proteomics results point to NlpA , DNA gyrase B, and 6,7-dimethyl-8-ribityllumazine synthase as potential therapeutic targets. Download figure Open in new tab Figure 5: Correlation analysis of proteomic profiles from strong and weak biofilm-forming A. baumannii isolates. Correlation matrix showing pairwise Pearson correlation coefficients among biological replicates of strong (AB1-SET1, AB1-SET2) and weak (AB2-SET1, AB2-SET2) biofilm formers. Strong intra-group correlations (r ≈ 0.95–0.96) confirm reproducibility within each phenotype, while weak inter-group correlations (r ≈ −0.05 to −0.07) highlight clear differences between strong and weak biofilm-forming isolates. Download figure Open in new tab Figure 6A: Real-time PCR validation - Upregulated proteins in strong biofilm-forming A. baumannii isolates compared to weak biofilm formers. Bar graph showing the log₂ fold change of significantly upregulated proteins in strong biofilm-forming isolates (AB_1, AB_2, AB_5, AB_6) relative to weak biofilm strain. Proteins involved in metabolic processes, stress response, ribosomal function, and lipoproteins were enriched, suggesting their potential roles as key biofilm determinants. Download figure Open in new tab Download figure Open in new tab Figure 6B: Real-time PCR validation - Downregulated proteins in strong biofilm-forming A. baumannii isolates compared to weak biofilm formers. Bar graph showing the log₂ fold change of significantly downregulated proteins in strong biofilm-forming isolates (AB_1, AB_2, AB_5, AB_6) relative to the weak biofilm strain. Proteins involved in translation, energy conservation, secretion systems, and metabolic pathways were enriched, suggesting their potential roles as key determinants of biofilms. Conclusion By integrating quantitative protein expression profiling with statistical and functional bioinformatics analyses, this work delineates the molecular basis of biofilm robustness in A. baumannii . The biofilm phenotypes identify specific patterns of upregulated and downregulated proteins, highlighting the intricacy of biofilm formation and adaptive survival mechanisms. The current study presents the first comprehensive proteomic profiling among strong and weak biofilm-forming A. baumannii clinical isolates. Validation of the unknown regulators of biofilm formation found from this work is backed by the correlation of the acquired proteomics data with qPCR outcomes. NlpA lipoprotein showed promise as a target against biofilm-related infections. To confirm its role and therapeutic value, future research must include gene knockdown studies with in-silico inhibitor screening. These studies might lead to new approaches to combat against biofilm-forming A. baumannii . Conflict of interest None declared Ethical approval Not required Data availability Not applicable Funding No extramural funding available. Acknowledgements We acknowledge the support of National Institute of Pharmaceutical Education and Research, Hyderabad, and Department of Pharmaceuticals (DoP), Ministry of Chemical and Fertilizer. UB thanks ICMR-SRF grant (AMR/Fellowship/23/2022-ECD-ll) and HB thanks ICMR IIRP (2023-4907) grant for providing fellowship. References 1. ↵ Whiteway , C. ; Breine , A. ; Philippe , C. ; Van der Henst , C. , Acinetobacter baumannii . Trends Microbiol 2022 , 30 ( 2 ), 199 – 200 . OpenUrl CrossRef PubMed 2. ↵ Maure , A. ; Robino , E. ; Van der Henst , C ., The intracellular life of Acinetobacter baumannii . Trends Microbiol 2023 , 31 ( 12 ), 1238 – 1250 . OpenUrl CrossRef PubMed 3. ↵ Miller , W. R. ; Arias , C. A ., ESKAPE pathogens: antimicrobial resistance, epidemiology, clinical impact and therapeutics . Nat Rev Microbiol 2024 , 22 ( 10 ), 598 – 616 . 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Share Proteomic Insights into Strong and Weak Biofilm Formation in Acinetobacter baumannii for Potential Therapeutic Targets Umarani Brahma , Akash Suresh , Aafreen Kamila , Usha S , Arun Kumar S.V , Harshala Baddi , Siva Singothu , Paresh Sharma , Vasundhra Bhandari bioRxiv 2025.10.02.680171; doi: https://doi.org/10.1101/2025.10.02.680171 Share This Article: Copy Citation Tools Proteomic Insights into Strong and Weak Biofilm Formation in Acinetobacter baumannii for Potential Therapeutic Targets Umarani Brahma , Akash Suresh , Aafreen Kamila , Usha S , Arun Kumar S.V , Harshala Baddi , Siva Singothu , Paresh Sharma , Vasundhra Bhandari bioRxiv 2025.10.02.680171; doi: https://doi.org/10.1101/2025.10.02.680171 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Microbiology Subject Areas All Articles Animal Behavior and Cognition (7635) Biochemistry (17697) Bioengineering (13895) Bioinformatics (41953) Biophysics (21456) Cancer Biology (18595) Cell Biology (25521) Clinical Trials (138) Developmental Biology (13381) Ecology (19903) Epidemiology (2067) Evolutionary Biology (24323) Genetics (15612) Genomics (22511) Immunology (17738) Microbiology (40401) Molecular Biology (17184) Neuroscience (88623) Paleontology (667) Pathology (2833) Pharmacology and Toxicology (4825) Physiology (7644) Plant Biology (15158) Scientific Communication and Education (2046) Synthetic Biology (4296) Systems Biology (9825) Zoology (2271)