Next Article in Journal
Current Applications and Immunological Considerations of Salmonella enterica Serovar Typhimurium as a Vaccine Vector
Previous Article in Journal
Characteristics, Whole Genome Analysis of a Virulent Phage from Avian-Derived Enterococcus faecalis and Its Application in Poultry Product Processing Safety
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Microorganisms
Volume 14
Issue 2
10.3390/microorganisms14020491
microorganisms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Use of Thermal and Emerging Non-Thermal Treatments Reveal Biomolecular and Morphological Changes in Pathogenic E. coli

by
Maxsueli Machado
1,2,3,4,
Jelmir Craveiro Andrade
2,3,
Eduardo Eustáquio de Souza Figueiredo
4,5 and
Carlos Adam Conte-Junior
1,2,3,*
1
Food Science Program (PPGCAL), Chemistry Institute (IQ), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 21941-909, RJ, Brazil
2
Center for Food Analysis (NAL), Technological Development Support Laboratory (LADETEC), Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 21941-598, RJ, Brazil
3
Laboratory of Advanced Analysis in Biochemistry and Molecular Biology (LAABBM), Department of Biochemistry, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 21941-909, RJ, Brazil
4
Nutrition, Food and Metabolism Program (PPGNAM), Federal University of Mato Grosso (UFMT), Cuiabá 78060-900, MT, Brazil
5
Animal Science Program (PPGCA), Federal University of Mato Grosso (UFMT), Cuiabá 78060-900, MT, Brazil
*
Author to whom correspondence should be addressed.
Microorganisms 2026, 14(2), 491; https://doi.org/10.3390/microorganisms14020491
Submission received: 29 November 2025 / Revised: 13 February 2026 / Accepted: 15 February 2026 / Published: 18 February 2026
(This article belongs to the Special Issue The Evolution of Foodborne Pathogens in the 4.0 Era: A Food Safety Perspective)
Browse Figures
Review Reports Versions Notes

Abstract

(1) Background: We sought to explore the changes in the biomolecular profile and morphology of Pathogenic heat-resistant E. coli isolated from animal-based food. (2) Methods: Six strains underwent heat (60 °C for 6 min), ultrasound (US; 299 W), UVC (4950 mJ/cm2), and combined treatments (UVC+US and heat+UVC). Afterwards, biomolecular characterization across four spectral regions was evaluated by Fourier transform infrared (FT-IR) spectroscopy and analyzed by principal component analysis (PCA) for treated and non-treated strains (control group). These regions are fatty acids (3010–2800 cm−1), proteins and peptides (1700–1200 cm−1), carbohydrates (1200–900 cm−1), and amide A (3280–3120 cm−1). Additionally, treated and untreated strains were assessed for surface damage using scanning electron microscopy (SEM). (3) Results: Among all the regions studied, the amide A and fatty acids regions exhibited the most significant variations in absorbance for treated strains compared to the control. Treatments such as US, heat, and UVC+US tended to increase Principal Components (PCs) and, consequently, absorbance. On the other hand, UVC and heat+UVC showed the opposite trend in these regions. SEM images showed filamentous cells for strains treated with UVC and UVC+US, indicating that cells continued to replicate under these conditions. These results highlight how thermal and non-thermal treatments influence specific biomolecular and morphological regions of E. coli. The methodologies used provide reliable data for understanding stress responses, which can guide the development of more effective technologies for eliminating multi-resistant pathogens.

Graphical Abstract

1. Introduction

Foodborne pathogens, especially Escherichia coli (E. coli), pose a primary global health concern, causing numerous outbreaks and foodborne illnesses each year [1,2,3,4,5,6,7]. In this context, uncooked meat and milk can be the primary sources of E. coli contamination [8,9,10,11]. In some cases, even cooked food can be a target since commensal E. coli and pathogenic strains exhibit heat resistance, allowing them to survive cooking temperatures between 60 and 80 °C [12,13,14]. In most of these cases, the heat resistance is primarily attributed to the transmissible locus of stress tolerance (tLST), a genetic factor that enhances bacterial survival under multiple stress conditions [15,16,17,18,19,20].
In addition to heat, E. coli can also resist emerging technologies such as ultrasound (US) and ultraviolet C (UVC) light, as evidenced in our previous studies [21,22]. Therefore, studies focused on understanding genotypic behavior in association with phenotype and macromolecular changes in bacteria under environmental stress can help develop future protocols to eliminate populations with multiple resistance profiles.
Traditional techniques such as quantitative PCR (qPCR), RNA sequencing (RNA-seq), microarrays, and other approaches that analyze metabolites and gene transcripts are commonly used to evaluate biomarkers of metabolic alterations in bacteria under stress conditions [23,24]. However, some of these can only verify or quantify bacterial gene expression and have limitations in terms of analysis time, complexity, and cost [25]. A promising alternative is Fourier transform infrared (FT-IR), which offers significant advantages in terms of speed and simplicity, enabling direct analysis without sample pretreatment [26].
FT-IR can be used to identify and classify microorganisms by analyzing the infrared spectra of polysaccharides, proteins, and lipids [26,27]. FT-IR has been investigated in research into bacterial behavior in relation to its macromolecular content for more than two decades. However, studies in this field that explore the tool are still limited [28,29,30]. Chemometric analyses, such as PCA, can be combined with FT-IR, as they allow the recognition of complex spectral patterns, helping to identify more informative spectral regions and increasing the accuracy of the test [21,22,31,32]. Additional instruments, such as scanning electron microscopy (SEM), can also be used to study bacteria under stress [33,34,35] and can be combined with FT-IR.
In the context of our previous data with E. coli strains isolated from animal-based food that exhibited the ability to resist high temperatures [19,20], UVC, and US [21,22], and knowing their genotypic history of resistance [19,36], the present study sought to study the characteristics of these strains using FT-IR and SEM combined with PCA to understand the morphological and macromolecular changes after using the aforementioned technologies.

2. Materials and Methods

2.1. Strains Preparation

In this study, we used six E. coli strains isolated from animal-based foods. Four of them harbor Extraintestinal Pathogenic Escherichia coli (ExPEC) genes and were previously described as C9, C31, C1145, and C97 [19]. The other two E. coli strains were isolated from pasteurized milk previously described as A21C1 and B20C3 [21]. These six strains were previously evaluated for their resistance to heat, UVC, US, and the combinations of these treatments [19,20,21,22]. Regarding the heat resistance phenotype, four strains with high heat resistance harbor tLST genes (C9, C31, C1145, and A21C1), and the other two strains (C97 and B20C3) showed moderate resistance and lack tLST.
In the present study, we focused exclusively on assessing their macromolecular and physiological behavior (via FT-IR and SEM of these strains when subjected to these treatments. These strains, previously stored in nutrient agar, were reactivated in Brain Heart infusion (BHI) broth (BHI; Kasvi®, Madrid, Spain) and incubated at 37 °C/18–24 h. Next, strains were streaked in MacConkey agar (Kasvi®, Madrid, Spain) and incubated at 37 °C/18–24 h. A colony was transferred to BHI broth and incubated until reaching the stationary growth phase (18–24 h), at which point the cell density was approximately 109 log CFU/mL. Bacteria in this condition were then submitted to thermal and non-thermal treatments. These treatments were conducted using previously validated conditions to maintain consistency with the established resistance characteristics of the strains [19,20,21,22].

2.2. Heat Treatment

The heat resistance experiments were replicated under the same conditions as in our previous studies to obtain results consistent with those previously reported for the strains [19,20]. For this, a 1.5 mL suspension of each strain (in BHI as described before) was transferred to 2 mL microtubes (11 × 40 mm, PP material; IonLab, Araucária–PR, Brazil). Then, closed microtubes were incubated in a water bath (Thermo Fisher, Waltham, MA, USA, TSCIR19 model) at 60 °C for 6 min. The come-up time (the time required for the sample to reach 60 °C) was measured in advance using BHI without bacteria. Each heat run consisted of ~3 min of heating to reach 60 °C, followed by 6 min at 60 °C. After the treatment ended, the samples were placed in an ice bath for approximately 30 min to stop heating.

2.3. Non-Thermal Treatments Alone and in Combination: US and UVC

For the US treatment, strains resuspended in BHI were transferred to 2 mL microtubes (11 × 40 mm, PP material; IonLab, Brazil) and exposed to an ultrasonic liquid processor (VCX 750, Sonics Vibra cell™, Newtown, CT, USA) at 20 kHz. The ultrasound power was set to 299 W and calibrated with a power meter (Sonics & Materials, Inc., Newtown, CT, USA). The sample was exposed directly to the ultrasound probe, which was submerged in a water bath at 22 °C to maintain thermal control. Each sample was sonicated for 12.5 min, and the temperature was monitored to ensure it remained below 25 °C during the exposure.
Regarding UVC treatment, E. coli strains were exposed to a UVC-LED device (Black Box Smart®, BioLambda, São Paulo, Brazil) equipped with a single-peak module at 275 nm. To simplify the nomenclature, we standardized the manuscript to use only UVC. The bacterial strains were aliquoted in 2 mL microtubes (11 × 40 mm, PP material; IonLab, Araucária–PR, Brazil) and were exposed to an intensity of 1650 mW/cm2 for 50 min, corresponding to a dose of 4950 mJ/cm2. The microtubes containing the sample were exposed to ensure uniform irradiation at a distance of 15 cm from the LEDs. The container was not shaken or mixed during treatment.
Next, treatments in combination (UVC+US and heat+UVC) were carried out. For UVC+US, a third group of the strains suspension in BHI was subjected to UVC (4950 mJ/cm2), and subsequently exposed to US conditions (299 W). For the heat+UVC combination, a fourth group of strains in suspension, submitted to heat treatment (60 °C for 6 min), was subsequently placed in the UVC cabin (4950 mJ/cm2). All of the treatments (UVC, US, heat, and their combination) were performed in triplicate.

2.4. Acquisition of FT-IR Spectra

The strains treated with heat, US, UVC, UVC+US, and heat+UVC were analyzed using a Shimadzu IRPrestige-21 spectrometer, with attenuation total reflectance (ATR) using a diamond crystal plate (IRIS module—PIKE Technologies; Madison, WI, USA). FT-IR was used to obtain molecular fingerprints of the cells, enabling analysis of variations in biomolecular profiles, including lipids, proteins, and carbohydrates, associated with the different treatments. The strains not exposed to any treatment (control) were also evaluated regarding the spectral regions. The strains were centrifuged at 13,000× g for 15 min after each treatment (heat, UVC, US, UVC+US, and heat+UVC). The bacterial pellets were washed in saline solution and resuspended in 40 µL of distilled water. Subsequently, approximately 35 µL of each suspension was applied directly to the ATR accessory. Spectra were recorded in triplicate within the range of 4000–500 cm−1, with 64 scans per spectrum, at a resolution of 8 cm−1. The environment in which the spectra were placed was maintained at 25 °C and with relative humidity below 15%. Before each new acquisition, the crystal surface was cleaned with ultrapure water, followed by an additional scan to subtract the background spectrum. For spectral processing, the IRsolutions software (version 1.04) was employed to analyze and remove the CO2 band from the acquired signal.

2.5. Scanning Electron Microscopy (SEM)

In this study, SEM was used to identify morphological changes in E. coli cells after the applied treatments. Thus, treated and untreated strains were evaluated for surface damage using SEM. After each therapy (heat, US, UVC, UVC+US, and heat+UVC), strains were resuspended by centrifugation at 8000× g for 5 min. The procedures were as described by Biswas et al. [37]. The bacterial suspensions were washed in PBS, transferred to coverslips, and incubated in 0.1 M glutaraldehyde for 2 h. The samples were post-fixed with 1% osmium tetroxide for 30 min, washed with sodium phosphate buffer 3×, and then dehydrated with serial ethanol treatments at 50, 70, 90, and 100% for 15 min each. The samples were then subjected to the critical point system for about 1.5 h. Finally, the samples were gold-coated and examined with SEM (Tescan Vega 3).

2.6. Multivariate Analysis

All pre-processing and multivariate analysis steps were performed using a user-friendly graphical interface, GAMMA-GUI (“Group of Multivariate Analysis in Food Matrices”), running on MATLAB® 2023a platform (The MathWorks Inc., Natick, MA, USA). The raw spectra were previously smoothed using Savitzky–Golay (SG) smoothing to remove unwanted noise, after which it was cut into four regions previously classified for bacteria [31,37,38,39]: 3010–2800 cm−1 for lipids/fatty acids; 1700–1200 cm−1 for a mixture of proteins (amide bands) and nucleic acids (phospholipids, DNA/RNA); 1200–900 cm−1 for carbohydrates (also nucleic acids); and 3280–3120 cm−1 for amide A region. These spectral regions studied provided greater precision in assessing the effects of the treatments on bacterial resistance, enabling a more targeted analysis of changes in macromolecules. PCA was used to examine the spectral behavior of bacteria exposed to the conditions of the applied treatments, aiming to reduce the dimensionality of the multivariate spectral data. All experiments were performed in triplicate to assess data variability and ensure the reliability of the results.
In addition, we include the statistical significance of the differences between the experimental groups which was assessed using Multivariate Analysis of Variance (MANOVA). MANOVA was applied to the scores of the first two principal components (PC1 and PC2), which captured most of the spectral variance. To validate the multivariate model, four test statistics were calculated: Wilks’ lambda, Pillai’s trace, Hotelling-Lawley trace, and Roy’s greatest root. Differences were considered significant when p < 0.05. To identify specific differences between pairs of groups within each principal component, a one-way ANOVA was performed, followed by Tukey’s multiple comparisons test (Tukey’s HSD). The results are graphically represented by boxplots, where distinct letters indicate a statistically significant difference (p < 0.05) (Table S1).

3. Results

Our results provide the positive and negative scores for each PC, indicating the direction of variation in the data: positive scores indicate higher intensity or greater presence of specific characteristics, and negative scores reflect the opposite. We emphasize the main trends observed for each spectral region before discussing strain-specific variations. This approach provides a clearer understanding of the overall patterns across treatments, highlighting the general trends observed in the spectral regions before exploring differences between individual strains.

3.1. Changes in E. coli Macromolecular Patterns Through FT-IR Assays

In this study, the most prominent variations observed in the spectra are associated with changes in band intensity and, in some cases, shifts in characteristic peaks linked to specific functional groups (Figure 1, Figure 2, Figure 3 and Figure 4). The spectral regions analyzed were selected based on the literature, where they are well-known and extensively reported for their association with these functional groups.
The amide I (~1650 cm−1) and amide II (~1540 cm−1) bands exhibited either increased or decreased intensity depending on the type of stress applied, suggesting structural modifications in proteins and variations in intracellular peptide content. These bands are well-established markers of protein secondary structure, and changes in their intensity and position have been correlated with alterations in α-helix, β-sheet, and random coil content in proteins under stress or undergoing conformational change, as reported in an FT-IR study of biological samples [40]. In the carbohydrates/nucleic acids region (1200–900 cm−1), alterations were detected in peaks attributed to cell wall polysaccharides, indicating changes in carbohydrate composition that may reflect cellular adaptation to the applied treatment conditions. Additionally, the peak at ~1740 cm−1, corresponding to lipid carbonyl groups, showed increased intensity under treatments that compromise membrane integrity, while systematic shifts toward lower wavenumbers suggest bond relaxation and possible conformational rearrangements of biochemical components. Moreover, PCA revealed that the bands at 1650, 1540, and 1075 cm−1 contributed most significantly to the discrimination among stress conditions, as shown by the loading plots (see Supplementary Material; Figures S1–S5). Furthermore, despite the known genotypic characteristics of the strains used in this study, we did not observe substantial differences in the regions studied between strains with distinct genotypes.

3.1.1. Region 1—Fatty Acids (3010–2800 cm−1)

In this region, specific treatments stood out for exhibiting positive scores in the areas with the highest weights in PC1 and PC2 (Figure 1), where the weight/score is associated with increased absorbance. The 3010–2800 cm−1 spectral range corresponds mainly to C–H stretching vibrations of CH3 and CH2 groups present in fatty acid chains and membrane lipids, which reflect changes in cellular lipid content and composition.
The UVC and heat+UVC treatments showed negative scores for most strains relative to PCs (Figure S2). This situation was the opposite for some strains regarding the US, heat, and UVC+US. Negative scores observed in the fatty acids region for these treatments could indicate a relative decrease in the spectral signature of lipid-associated vibrations, which may reflect membrane perturbation, lipid reorganization, or changes in fatty acid chain order induced by stress. This interpretation aligns with the use of FT-IR for monitoring lipid content and profile variation under different physiological or stress conditions, where changes in the intensity of C–H stretching bands can be linked to altered lipid composition and metabolic state [41].

3.1.2. Region 2—Proteins and Peptides Region (1700–1200 cm−1)

The second region showed a heterogeneous distribution in relation to treatments and strains (Figure 2). The spectral window between ~1700 and 1200 cm−1 primarily reflects absorbance from protein amide bands (e.g., amide I at ~1650 cm−1 and amide II at ~1550 cm−1), which arise from peptide backbone vibrations (C=O stretching and N–H bending/C–N stretching), making this region sensitive to protein conformation, composition, and structural changes in response to environmental or stress conditions. FT-IR studies have shown that shifts and intensity changes in these bands can indicate alterations in protein secondary structure or peptide content in response to stressors or treatments [34]. However, in the general context, we observed that the region of nucleic acids (DNA and RNA) can comprise ~1080 900–700 cm−1 and 1715 to 1415 cm−1 [39,42,43]. Spectral ranges previously assigned to vibrations involving phosphate groups and nucleic acid backbones, as well as overlapping contributions from proteins and nucleic acids in microbial FT-IR spectra, showed significant variation across treatments.
Some treatments, such as US (Figure S3) and UVC+US, showed a positive score in both PCs, as observed in the control group, for strains C1145, A21C1, and C31. Positive scores in these regions may indicate relative preservation or increased contributions of protein and nucleic acid vibrational signatures, suggesting either maintenance of structural integrity or specific molecular-level stress adaptations. On the other hand, we found the opposite result for UVC with strains B20C3 and A21C1.

3.1.3. Region 3—Carbohydrates (1200–900 cm−1)

Likewise, in the other regions, the US showed a positive score and was close to the control grouping in the PCs for most strains (Figure 3), with the exception of strains A21C1 and C97, which showed no or low significance for this treatment. Bands between ~1000 and 1100 cm−1 are typically attributed to C–O stretching of glycosidic bonds and ring vibrations in carbohydrates, and variations in these bands may reflect changes in carbohydrate composition or in the polysaccharide structures of the cell wall.
In contrast, for the heat-treated group, the results showed a positive score in the PCs for the strains A21C1, C97, C9, and B20C3. These behaviors suggest a relatively greater contribution from carbohydrate-associated vibrations, which can be interpreted as either maintenance of carbohydrate content or stress-induced modification of surface/extracellular polysaccharides. Other main changes in the strains for the treatments, compared to the control group, were observed mainly in the regions of approximately 920–950, 1080, and 1000–1100 cm−1 (see Figure S3).

3.1.4. Region 4—Amide A (3280–3120 cm−1)

For both PCs, the UVC was the one that showed a negative score for all strains, showing the most important changes between the 3220–3120 cm−1 regions (Figure S5). This same negative score was observed for almost all strains in relation to heat+UVC treatment, which consequently reflects a lower absorbance in the region of the spectrum and which may indicate reduced contributions from water bound by hydrogen bonds and alterations in protein surface hydration or secondary structure, especially when treatments disrupt cell envelopes or protein conformation [44]. On the other hand, in the same regions (3220–3120 cm−1), US (except strain C1145), heat, and UVC+US (except strain C9) treatments showed a positive score relative to PCs. These results suggest relatively larger absorbance contributions, which could be interpreted as an increase in hydrogen bonds or in the maintenance of bound water content and N-H interactions in the proteins. This may reflect less perturbation of hydration layers and intra- and intermolecular hydrogen-bond networks than treatments that decrease absorbance in this region [44,45].

3.2. MANOVA Analyses Regarding the Regions Studied

The results indicated statistically significant differences in all strains and spectral regions (Figures S6–S11) analyzed, as evidenced by the Pr > F values lower than 0.05. These findings corroborate the PCA conclusions, showing that the variation among treatments is well described by the PCs, with robust statistical evidence of significant differences between the groups. Thus, MANOVA, as an inferential analysis, complements PCA by providing statistical validation of the exploratory observations, reinforcing the interpretation of the variations observed in the spectral regions (Table S1).

3.3. Morphological Characteristics Revealed by SEM Analysis

Based on qualitative assessment of SEM images, no damage to the wall or membranes was observed in the US-treated group compared to the untreated/control group (Figure 5A,B). Similarly, for the US treatment, the treated strains showed no visible damage, demonstrating the integrity of the contents (Figure 5B). In contrast, the other treatments showed visible damage (Figure 5C–F). Most treatments showed some damage to the cell wall surface, such as wrinkled cells, as observed in heat (Figure 5C), UVC (Figure 5D), and the combined treatments (Figure 5E,F). Filamentous growth was also observed for heat (Figure 5C) and UVC+US (Figure 5E), characterized by cell elongation.

4. Discussion

Based on our results, we observed changes in the absorbance profile in the four regions of E. coli studied (Figure 1, Figure 2, Figure 3 and Figure 4). Under stress conditions, bacterial metabolism undergoes several changes [46]. Different stresses (heat, oxidative stress, UVC, antibiotics) drive distinct biochemical responses in E. coli, including carbohydrate remodeling, DNA injury responses, and lipid/membrane adjustments that affect fluidity [31,46,47]. These effects involve multiple macromolecular pools at once; therefore, FT-IR provides a rapid, global readout of stress-induced shifts in cellular composition [27,46].
In the fatty acids region (3010–2800 cm−1), our multivariate analysis revealed that the regions around 2860 and 2920 cm−1 were the most prominent for the strains across all treatments studied (Figure S1). In these spectral bands, characteristic group vibrations such as CH3 and CH2 are associated with fatty acids found in various amphiphilic membranes [39]. Although lipid compounds account for less than 10–15% of bacterial cell mass [39], increased absorption in the fatty acid region indicates greater membrane permeability, making it more fluid and flexible [48]. The treatment of US at 299 W was noted to increase absorption in those bands in this region (Figures S1 and S2), with a positive score in the PCs (Figure 1), for almost all the strains. Changes in lipid-related FT-IR bands have been associated with alterations in membrane fluidity and lipid organization following stress exposure, supporting the interpretation that membrane components are affected by the applied treatments [41,49].
The same situation occurred in the US for the carbohydrate band, especially between 1150 and 1000 cm−1 (Figure 3 and Figure S4). These regions are characterized by vibrations in PO-2 groups of nucleic acids, in addition to C-O, C-C, C-O-H, and C-O-C, which belong to polysaccharides (1200–900 cm−1) [39]. The increase in absorption in the carbohydrate band is associated with maintaining metabolite energy, leading cells to produce more polysaccharides and oligosaccharides in aggressive environments [50]. This increase typically occurs due to reduced vibrations in the C-O bonds of nucleic acids (deoxyribose), and in C-C bonds, leading to their subsequent loss [43,51]. According to a study by Wang et al. [52] on E. coli exposed to antibiotic treatment, some primary components and metabolites, such as polysaccharides, lipids, and proteins, may increase cell absorbance under harsh conditions. In contrast, others, such as nucleic acids, and amid I and II can decrease.
Similarly, our results showed prominent changes in the protein-associated regions, particularly in the amide A band (3220–3120 cm−1), and in the amide I and II bands (1700–1500 cm−1). Regarding amide A, such changes can be linked to modifications in peptidoglycan layers and surface polysaccharide structures during stress responses, which have been previously observed using FT-IR in studies of microbial and cellular systems, e.g., changes in C–O and C–C stretching vibrations associated with carbohydrate rings [28,49]. In addition, in these regions, the US did not show many differences compared to the control treatment in absorbance levels (Figures S2 and S4).
Furthermore, our qualitative SEM analyses demonstrated no deformity in the bacterial cells for this treatment compared to the control group (Figure 5A,B). Contributing to these results, when compared with our previous phenotypic data, we noticed that among all the treatments studied (heat, UVC, and UVC+US), US was the only one to show the lowest efficiency in reducing E. coli (<1 log CFU/mL) [19,20,21,22]. This demonstrates that, also at the macromolecular and morphological levels, no damage, or only minimal damage, occurred in E. coli cells under US exposure.
The aforementioned regions for amides A, I, and II showed lower absorption in the other treatments, especially in heat+UVC (Figures S3 and S5). This combined treatment was the only one for which we had not previously assessed the viable cell counts of these strains. However, the decrease in absorption in the amide A region for most strains (Figure 4 and Figure S5) clearly indicates significant alterations. While we did not perform logarithmic analysis, the spectral changes, which correlate with lethality in other treatments, suggest that SEM and FT-IR could be effective in screening for heat+UVC lethality without needing viable cell counts. The FT-IR results provide insight into the macromolecular level, where lower or higher absorption bands indicate imbalances in the net charge of proteins, leading to conformational changes that affect protein function [38]. Since amide A is an important molecule present in proteins [53,54], conformational changes in cellular content may have reduced the likelihood of interaction with other molecules, such as lipids, and consequently led to lower cell survival under heat+UVC conditions.
In general, the amide A region contains proteins that can also interact with lipid components of the lipid bilayer to stabilize the membrane under stress conditions [53,54]. Some regions around 3000, 2940, 2920, 2860 cm−1, and 2820 cm−1 showed significant changes in the absorption of our strains. These changes in absorption across different regions within the same band indicate greater or lesser interaction with other molecules, such as proteins. For example, certain proteins, including heat shock proteins (HSPs), have been reported to interact with phospholipids through their C-terminal domains [53].
In relation to phospholipids, DNA/RNA, as well as Wang et al. [52], we noticed that these were bands that tended to decrease in absorption (Figure S4), leading to negative scores for some strains (Figure 3). This situation could be expected in treatments such as US, heat, UVC, and UVC+US, where persistent cells lean towards reducing the replication rate and recover after a few hours in the absence of treatment and in the presence of culture medium without inhibitors [52,55]. Furthermore, culture media containing compatible solutes, such as sugars (as in the BHI used in our trials), can act as osmoprotectants, increasing cytoplasmic volume and free water content [56].
In the heat-treated group, we observed an increased absorbance in the amide A and lipid-associated regions compared with the control, indicating alterations in protein-related bands and membrane-associated components. In this context, E. coli harboring HSPs contribute to heat-stress tolerance by interacting with membrane phospholipids and stabilizing the lipid bilayer [24,53]. In addition, hsp genes are part of the tLST island and are among the key determinants of heat resistance in E. coli [19,57]. Nevertheless, it is essential to note that we did not observe any differences in the regions studied between strains that carried tLST and those that did not. Consequently, the spectral changes observed after heat exposure likely reflect only minor modifications involving protein-membrane interactions, which may be too subtle to be detected by the techniques used.
Contrary to the results for these bands under heat treatment, the UVC sample showed lower absorption or molecular vibration than the control. All strains exhibited the same tendency under this treatment, which is curious, as among the previously analyzed treatments, UVC at 4950 mJ/cm2 was reported to yield the greatest reduction compared to US and heat [21,22]. Meanwhile, an increase in absorbance in the amide A and lipid regions was observed when UVC was combined with US (UVC+US). This indicates changes in the cellular content of these strains, which may be associated with cellular recovery after UVC exposure.
We also observed damage to the E. coli membrane under UVC (Figure 5D), including a sunken surface, and filamentous growth after the combination of UVC+US (Figure 5E). In E. coli, the filamentous growth is characterized by growth without cell division. It is associated with a stress response that activates the emergency system (SOS genes) [58,59]. Some studies have reported cellular characteristics similar to those observed in our strains, using qualitative SEM analysis of E. coli and other bacteria after exposure to UVC, US, and other treatments [33,34,35]. These cellular damages are typically associated with the stress response of E. coli cells, which may reflect a dormant cellular state. Using WGS analysis, we recently reported the presence of genetic markers in some of these strains that are responsible for the SOS response [22,36]. These results can contribute to the hypotheses raised here and support the understanding that the spectral regions of UVC+US exhibit partial recovery of E. coli from UVC-induced damage. In addition, although our strains have been well characterized phenotypically and genetically in previous studies, genetic diversity can lead to slight differences in response to treatments, introducing some variability in the data. Therefore, future investigations in this area will further complement this study to consolidate the robustness and applicability of the proposed approach.

5. Conclusions

In conclusion, our study demonstrates that FT-IR, combined with chemometrics, can identify differences in the biomolecular responses of E. coli to thermal and non-thermal treatments. At the same time, SEM supports the morphological patterns associated with the treatments. Out of the examined spectral windows, the most consistent discrimination is reported in the lipid/fatty acids and amide A regions (3220–3120 cm−1) that show the most prominent shifts due to treatments with respect to the control. Heat, US, and UVC+US show increased absorbance and PCA scores, while UVC and heat+UVC show decreased absorbance and negative PCA scores in those regions. SEM indicates that US leaves cells intact, whereas heat, UVC, and the combined UVC+US treatments result in surface alterations and filamentous morphology. Overall, integrating FT-IR and PCA with SEM provides a relatively unexplored alternative for analyze the biochemical and morphological characteristics of heat-resistant E. coli under different stresses.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/microorganisms14020491/s1, Table S1: Detailed results of the MANOVA analysis for the four strains and the four spectral regions analyzed in the PCA. The table presents the Pr > F values for each E. coli strain and spectral region, highlighting statistically significant differences (values below 0.05) between the treatments.; Figure S1: FT-IR spectra medium of three replicates and the main highlighted regions for E. coli isolated from beef (C9, C31, C1145, and C97) and pasteurized milk (A21C1 and B20C3) treated with heat, ultrasound (US), UVC, UVC+US, heat+UVC, and untreated/control; Figure S2: Loadings plots of E. coli isolated from beef (C9, C31, C1145, and C97) and pasteurized milk (A21C1 and B20C3) treated with heat, ultrasound (US), UVC, UVC+US, heat+UVC, and untreated/control in region 1 of lipids/fatty acids (3010–2800 cm−1); Figure S3: Loadings plots of E. coli isolated from beef (C9, C31, C1145, and C97) and pasteurized milk (A21C1 and B20C3) treated with heat, ultrasound (US), UVC, UVC+US, heat+UVC, and untreated/control, in region 2 of proteins and peptides region (1700–1200 cm−1); Figure S4: Loadings plots of E. coli isolated from beef (C9, C31, C1145, and C97) and pasteurized milk (A21C1 and B20C3) treated with heat, ultrasound (US), UVC, UVC+US, heat+UVC, and untreated/control in region 3 of carbohydrates (1200–900 cm−1); Figure S5: Loadings plots of E. coli isolated from beef (C9, C31, C1145, and C97) and pasteurized milk (A21C1 and B20C3) treated with heat, ultrasound (US), UVC, UVC+US, heat+UVC, and untreated/control, in region 4 of Amide A (3280–3120 cm−1); Figure S6: Boxplots of PCA scores (PC1 and PC2) across treatment groups in four spectral regions of C31 strain, with MANOVA p-values; Figure S7: Boxplots of PCA scores (PC1 and PC2) across treatment groups in four spectral regions of C1145 strain, with MANOVA p-values; Figure S8: Boxplots of PCA scores (PC1 and PC2) across treatment groups in four spectral regions of C9 strain, with MANOVA p-values; Figure S9: Boxplots of PCA scores (PC1 and PC2) across treatment groups in four spectral regions of C97 strain, with MANOVA p-value; Figure S10: Boxplots of PCA scores (PC1 and PC2) across treatment groups in four spectral regions of A21C1 strain, with MANOVA p-value; Figure S11: Boxplots of PCA scores (PC1 and PC2) across treatment groups in four spectral regions of B20C3 strain, with MANOVA p-value.

Author Contributions

Conceptualization: M.M., J.C.A., E.E.d.S.F., C.A.C.-J. Data curation: M.M., J.C.A. Formal analysis: M.M., J.C.A. Funding acquisition: M.M., E.E.d.S.F., C.A.C.-J. Investigation: M.M. Methodology: M.M., J.C.A., E.E.d.S.F., C.A.C.-J. Project administration: M.M., E.E.d.S.F., C.A.C.-J. Resources: M.M., E.E.d.S.F., C.A.C.-J. Software: M.M., J.C.A. Supervision: E.E.d.S.F., C.A.C.-J. Validation/Visualization: M.M., J.C.A., E.E.d.S.F., C.A.C.-J. Writing—original draft: M.M. Writing—review and editing: M.M., J.C.A., E.E.d.S.F., C.A.C.-J. All authors have read and agreed to the published version of the manuscript.

Funding

The Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)/Brazil, with process codes: 140016/2021-0, 313119/2020-1, 310181/2021-6, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro—Brazil (FAPERJ), grant numbers E-26/200.891/2021 and E-26/204.145/2022, and Coordenação De Aperfeiçoamento de Pessoal de Nivel Superior (CAPES)/Brazil, process code: 88887.999872/2024-00 and PRJNA1166229.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Acknowledgments

We would also like to extend our appreciation to Edward G. Burgess for his assistance in re-viewing the spelling and grammar of this manuscript.

Conflicts of Interest

All the authors of this manuscript declare that there are no conflicts of interest.

References

  1. GBD 2019 Antimicrobial Resistance Collaborators. Global mortality associated with 33 bacterial pathogens in 2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet 2022, 400, 2221–2248. [Google Scholar] [CrossRef]
  2. Higuera-Ciapara, I.; Benitez-Vindiola, M.; Figueroa-Yañez, L.J.; Martínez-Benavidez, E. Polyphenols and CRISPR as Quorum Quenching Agents in Antibiotic-Resistant Foodborne Human Pathogens (Salmonella Typhimurium, Campylobacter jejuni and Escherichia coli 0157:H7). Foods 2024, 13, 584. [Google Scholar] [CrossRef] [PubMed]
  3. Vakili, M.; Goli, H.; Javidnia, J.; Alipour, T.; Eslami, M. Genetic diversity and antibiotic resistance patterns of Escherichia coli isolates causing septicemia: A phylogenetic typing and PFGE analysis. Diagn. Microbiol. Infect. Dis. 2025, 111, 116586. [Google Scholar] [CrossRef] [PubMed]
  4. Naghavi, M.; Mestrovic, T.; Gray, A.; Hayoon, A.G.; Swetschinski, L.R.; Aguilar, G.R.; Weaver, N.D.; Ikuta, K.S.; Chung, E.; Wool, E.E.; et al. Global burden associated with 85 pathogens in 2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet Infect. Dis. 2024, 24, 868–895. [Google Scholar] [CrossRef] [PubMed]
  5. Ayoub, H.; Kumar, M.S.; Dubal, Z.B.; Bhilegaonkar, K.N.; Naga, E.; Sneha, P.; Premkumar, D.; Kumar, V.; Rajendran, O. Systematic Review and Meta-Analysis on Prevalence and Antimicrobial Resistance Patterns of Important Foodborne Pathogens Isolated from Retail Chicken Meat and Associated Environments in India. Foods 2025, 14, 555. [Google Scholar] [CrossRef]
  6. World Health Organization (WHO). Five Keys to Safer Food Manual. Five Keys to Safer Food Man; WHO: Geneva, Switzerland, 2006; p. 30. [Google Scholar]
  7. European Food Safety Authority (EFSA); European Centre fo Disease Prevention and Control (ECDC). The European Union One Health 2019 Zoonoses Report. EFSA J 2021, 19, e06406. [Google Scholar] [CrossRef]
  8. Gill, A.; Tamber, S.; Yang, X. Relative response of populations of Escherichia coli and Salmonella enterica to exposure to thermal, alkaline and acidic treatments. Int. J. Food Microbiol. 2019, 293, 94–101. [Google Scholar] [CrossRef]
  9. Gill, C.O. Effects on the microbiological condition of product of decontaminating treatments routinely applied to carcasses at beef packing plants. J. Food Prot. 2009, 72, 1790–1801. [Google Scholar] [CrossRef]
  10. Johnson, M.E. A 100-Year Review: Cheese production and quality. J. Dairy Sci. 2017, 100, 9952–9965. [Google Scholar] [CrossRef]
  11. Schrijver, D.K.; Buvens, G.; Possé, B.; Branden, D.V.d.; Oosterlynck, O.; Zutter, L.D.; Eilers, K.; Piérard, D.; Dierick, K.; Damme-Lombaerts, R.V.; et al. Outbreak of verocytotoxin-producing E. coli O145 and O26 infections associated with the consumption of ice cream produced at a farm, Belgium, 2007. Eurosurveillance 2008, 13, 8041. [Google Scholar] [CrossRef]
  12. Mercer, R.G.; Walker, B.D.; Yang, X.Y.; McMullen, L.M.; Ganzle, M.G. The locus of heat resistance (LHR) mediates heat resistance in Salmonella enterica, Escherichia coli and Enterobacter cloacae. Food Microbiol 2017, 64, 96–103. [Google Scholar] [CrossRef] [PubMed]
  13. Figueiredo, E.E.S.; Yang, X.; Zhang, P.; Reuter, T.; Stanford, K. Comparison of heating block and water bath methods to determine heat resistance in Shiga-toxin producing Escherichia coli with and without the locus of heat resistance. J. Microbiol. Methods 2019, 164, 105679. [Google Scholar] [CrossRef]
  14. Guragain, M.; Smith, G.E.; King, D.A.; Bosilevac, J.M. Prevalence of Extreme Heat Resistant Gram Negative Bacteria carried by US Cattle at Harvest. J. Food Prot. 2020, 83, 1438–1443. [Google Scholar] [CrossRef] [PubMed]
  15. Mercer, R.G.; Zheng, J.; Garcia-Hernandez, R.; Ruan, L.; Gänzle, M.G.; McMullen, L.M. Genetic determinants of heat resistance in Escherichia coli. Front. Microbiol. 2015, 6, 932. [Google Scholar] [CrossRef] [PubMed]
  16. Guragain, M.; Bono, J.L.; Bosilevac, J.M. Complete Closed Genome Sequence of the Extremely Heat-Resistant Strain Escherichia coli AW1.7. Microbiol. Resour. Announc. 2021, 10, e0050221. [Google Scholar] [CrossRef]
  17. Boll, E.J.; Marti, R.; Hasman, H.; Overballe-Petersen, S.; Stegger, M.; Ng, K.; Knøchel, S.; Krogfelt, K.A.; Hummerjohann, J.; Struve, C. Turn up the heat-food and clinical Escherichia coli isolates feature two transferrable loci of heat resistance. Front. Microbiol. 2017, 8, 579. [Google Scholar] [CrossRef]
  18. Yang, X.; Tran, F.; Klassen, M.D. Heat resistance in Escherichia coli and its implications on ground beef cooking recommendations in Canada. J. Food Saf. 2020, 40, e12769. [Google Scholar] [CrossRef]
  19. Machado, M.A.M.; Castro, V.S.; Da Cunha-Neto, A.; Carvalho, R.C.T.; Stanford, K.; Conte-Junior, C.A.; Figueiredo, E.E.d.S. Genomic features and heat resistance profiles of Escherichia coli isolated from Brazilian beef. J. Appl. Microbiol. 2022, 134, lxac027. [Google Scholar] [CrossRef]
  20. Machado, M.A.M.; Castro, V.S.; da Cunha-Neto, A.; Vallim, D.C.; Pereira, R.d.C.L.; dos Reis, J.O.; de Almeida, P.V.; Galvan, D.; Conte-Junior, C.A.; Figueiredo, E.E.d.S. Heat-resistant and biofilm-forming Escherichia coli in pasteurized milk from Brazil. Brazilian J. Microbiol. 2023, 54, 1035–1046. [Google Scholar] [CrossRef]
  21. Machado, M.A.M.; Castro, V.S.; Monteiro, M.L.G.; Bernardo, Y.A.d.A.; Figueiredo, E.E.d.S.; Conte-Junior, C.A. Effect of UVC-LED and ultrasound alone and combined on heat-resistant Escherichia coli isolated from pasteurised milk. Int. J. Dairy Technol. 2023, 76, 74–80. [Google Scholar] [CrossRef]
  22. Machado, M.A.M.; Castro, V.S.; Monteiro, M.L.G.; Bernardo, Y.A.d.A.; Figueiredo, E.E.d.S.; Conte-Junior, C.A. Can Extraintestinal Pathogenic Escherichia coli with Heat Resistance Profile Overcome Nonthermal Technologies? Foodborne Pathog. Dis. 2023, 21, 168–173. [Google Scholar] [CrossRef]
  23. Joffre, E.; Nicklasson, M.; Álvarez-Carretero, S.; Xiao, X.; Sun, L.; Nookaew, I.; Zhu, B.; Sjöling, Å. The bile salt glycocholate induces global changes in gene and protein expression and activates virulence in enterotoxigenic Escherichia coli. Sci. Rep. 2019, 9, 108. [Google Scholar] [CrossRef] [PubMed]
  24. Ruan, L.; Pleitner, A.; Gänzle, M.G.; McMullen, L.M. Solute transport proteins and the outer membrane protein NmpC contribute to heat resistance of Escherichia coli AW1.7. Appl. Environ. Microbiol. 2011, 77, 2961–2967. [Google Scholar] [CrossRef] [PubMed][Green Version]
  25. Zhao, S.; Fung-Leung, W.P.; Bittner, A.; Ngo, K.; Liu, X. Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells. PLoS ONE 2014, 9, e78644. [Google Scholar] [CrossRef] [PubMed]
  26. Carlos, C.; Maretto, D.A.; Poppi, R.J.; Sato, M.I.Z.; Ottoboni, L.M.M. Fourier transform infrared microspectroscopy as a bacterial source tracking tool to discriminate fecal E. coli strains. Microchem. J. 2011, 99, 15–19. [Google Scholar] [CrossRef]
  27. Wang-Wang, J.H.; Bordoy, A.E.; Martró, E.; Quesada, M.D.; Pérez-Vázquez, M.; Guerrero-Murillo, M.; Tiburcio, A.; Navarro, M.; Castellà, L.; Sopena, N.; et al. Evaluation of Fourier Transform Infrared Spectroscopy as a First-Line Typing Tool for the Identification of Extended-Spectrum β-Lactamase-Producing Klebsiella pneumoniae Outbreaks in the Hospital Setting. Front. Microbiol. 2022, 13, 897161. [Google Scholar] [CrossRef]
  28. Kassem, A.; Abbas, L.; Coutinho, O.; Opara, S.; Tiquia-arashiro, S. Applications of Fourier Transform-Infrared spectroscopy in microbial cell biology and environmental microbiology: Advances, challenges, and future perspectives. Front. Microbiol. 2023, 14, 1304081. [Google Scholar] [CrossRef]
  29. Kamnev, A.A.; Dyatlova, Y.A.; Kenzhegulov, O.A.; Fedonenko, Y.P.; Evstigneeva, S.S.; Tugarova, A.V. Fourier Transform Infrared (FTIR) Spectroscopic Study of Biofilms Formed by the Rhizobacterium Azospirillum baldaniorum Sp245: Aspects of Methodology and Matrix Composition. Molecules 2023, 28, 1949. [Google Scholar] [CrossRef]
  30. Orsini, F.; Ami, D.; Villa, A.M.; Sala, G.; Bellotti, M.G.; Doglia, S.M. FT-IR microspectroscopy for microbiological studies. J. Microbiol. Methods 2000, 42, 17–27. [Google Scholar] [CrossRef]
  31. Moen, B.; Janbu, A.O.; Langsrud, S.; Langsrud, Ø.; Hobman, J.L.; Constantinidou, C.; Kohler, A.; Rudi, K. Global responses of Escherichia coli to adverse conditions determined by microarrays and FT-IR spectroscopy. Can. J. Microbiol. 2009, 55, 714–728. [Google Scholar] [CrossRef]
  32. Machado, M.A.M.; Andrade, J.C.; Torres Neto, L.; Cunha-Neto, A.; Figueiredo, E.E.d.S.; Conte-Junior, C.A. A novel analytical approach in Amazon fish chain: Using infrared-spectroscopy with chemometric tools to identify non-typhoid Salmonella. Food Control 2025, 168, 110842. [Google Scholar] [CrossRef]
  33. Xu, L.; Zhang, C.; Xu, P.; Wang, X.C. Mechanisms of ultraviolet disinfection and chlorination of Escherichia coli: Culturability, membrane permeability, metabolism, and genetic damage. J. Environ. Sci. 2018, 65, 356–366. [Google Scholar] [CrossRef] [PubMed]
  34. Guo, L.; Zhang, X.; Xu, L.; Li, Y.; Pang, B.; Sun, J.; Wang, B.; Huang, M.; Xu, X.; Ho, H. Efficacy and Mechanism of Ultrasound Combined with Slightly Acidic Electrolyzed Water for Inactivating Escherichia coli. J. Food Qual. 2021, 2021, 6689751. [Google Scholar] [CrossRef]
  35. Gera, N.; Doores, S. Kinetics and Mechanism of Bacterial Inactivation by Ultrasound Waves and Sonoprotective Effect of Milk Components. J. Food Sci. 2011, 76, 111–119. [Google Scholar] [CrossRef]
  36. Machado, M.; Panzenhagen, P.; Aburjaile, F.F.; Brenig, B.; Matiuzzi, M.; Ariston, V.; Azevedo, d.C.; Figueiredo, E.E.S.; Conte-Junior, C.A. Evolution of pathogenic Escherichia coli harboring the transmissible locus of stress tolerance: From food sources to clinical environments. Sci. Rep. 2025, 15, 5014. [Google Scholar] [CrossRef]
  37. Biswas, D.; Tiwari, M.; Tiwari, V. Molecular mechanism of antimicrobial activity of chlorhexidine against carbapenemresistant Acinetobacter baumannii. PLoS ONE 2019, 14, e0224107. [Google Scholar] [CrossRef]
  38. Barth, A. Infrared spectroscopy of proteins. Biochim. Biophys. Acta-Bioenerg. 2007, 1767, 1073–1101. [Google Scholar] [CrossRef]
  39. Naumann, D. FT-infrared and FT-Raman spectroscopy in biomedical research. Appl. Spectrosc. Rev. 2001, 36, 239. [Google Scholar] [CrossRef]
  40. Al-Kelani, M.; Buthelezi, N. Advancements in medical research: Exploring Fourier Transform Infrared (FTIR) spectroscopy for tissue, cell, and hair sample analysis. Ski. Res. Technol. 2024, 30, e13733. [Google Scholar] [CrossRef]
  41. Shapaval, V.; Brandenburg, J.; Blomqvist, J.; Tafintseva, V.; Passoth, V.; Sandgren, M.; Kohler, A. Biochemical profiling, prediction of total lipid content and fatty acid profile in oleaginous yeasts by FTIR spectroscopy. Biotechnol. Biofuels 2019, 12, 140. [Google Scholar] [CrossRef]
  42. Faghihzadeh, F.; Anaya, N.M.; Schifman, L.A.; Oyanedel-Craver, V. Fourier transform infrared spectroscopy to assess molecular-level changes in microorganisms exposed to nanoparticles. Nanotechnol. Environ. Eng. 2016, 1, 1–16. [Google Scholar] [CrossRef]
  43. Lu, X.; Liu, Q.; Wu, D.; Al-Qadiri, H.M.; Al-Alami, N.I.; Kang, D.H.; Shin, J.H.; Tang, J.; Jabal, J.M.F.; Aston, E.D.; et al. Using of infrared spectroscopy to study the survival and injury of Escherichia coli O157:H7, Campylobacter jejuni and Pseudomonas aeruginosa under cold stress in low nutrient media. Food Microbiol. 2011, 28, 537–546. [Google Scholar] [CrossRef] [PubMed]
  44. Vasylieva, A.; Doroshenko, I.; Vaskivskyi, Y.; Chernolevska, Y.; Pogorelov, V. FTIR study of condensed water structure. J. Mol. Struct. 2018, 1167, 232–238. [Google Scholar] [CrossRef]
  45. Mallamace, F.; Broccio, M.; Corsaro, C.; Faraone, A.; Majolino, D.; Venuti, V.; Liu, L.; Mou, C.Y.; Chen, S.H. Evidence of the existence of the low-density liquid phase in supercooled, confined water. Proc. Natl. Acad. Sci. USA 2007, 104, 424–428. [Google Scholar] [CrossRef]
  46. Grube, M.; Dimanta, I.; Gavare, M.; Strazdina, I.; Liepins, J.; Juhna, T.; Kalnenieks, U. Hydrogen-producing Escherichia coli strains overexpressing lactose permease: FT-IR analysis of the lactose-induced stress. Biotechnol. Appl. Biochem. 2014, 61, 111–117. [Google Scholar] [CrossRef]
  47. El-Gendy, A.O.; Ezzat, S.; Samad, F.A.; Dabbous, O.A.; Dahm, J.; Hamblin, M.R.; Mohamed, T. Studying the viability and growth kinetics of vancomycin-resistant Enterococcus faecalis V583 following femtosecond laser irradiation (420–465 nm). Lasers Med. Sci. 2024, 39, 144. [Google Scholar] [CrossRef]
  48. Beney, L.; Mille, Y.; Gervais, P. Death of Escherichia coli during rapid and severe dehydration is related to lipid phase transition. Appl. Microbiol. Biotechnol. 2004, 65, 457–464. [Google Scholar] [CrossRef]
  49. Ricciardi, V.; Portaccio, M.; Perna, G.; Lasalvia, M.; Capozzi, V.; Cammarata, F.P.; Pisciotta, P.; Petringa, G.; Delfino, I.; Manti, L.; et al. Ft-ir transflection micro-spectroscopy study on normal human breast cells after exposure to a proton beam. Appl. Sci. 2021, 11, 540. [Google Scholar] [CrossRef]
  50. Moen, B.; Oust, A.; Langsrud, Ø.; Dorrell, N.; Marsden, G.L.; Hinds, J.; Kohler, A.; Wren, B.W.; Rudi, K. Explorative multifactor approach for investigating global survival mechanisms of campylobacter jejuni under environmental conditions. Appl. Environ. Microbiol. 2005, 71, 2086–2094. [Google Scholar] [CrossRef]
  51. Muntean, C.M.; Lapusan, A.; Mihaiu, L.; Stefan, R. Strain dependent UV degradation of Escherichia coli DNA monitored by Fourier transform infrared spectroscopy. J. Photochem. Photobiol. B Biol. 2014, 130, 140–145. [Google Scholar] [CrossRef]
  52. Wang, C.; Chen, R.; Xu, J.; Jin, L. Single-cell Raman spectroscopy identifies Escherichia coli persisters and reveals their enhanced metabolic activities. Front Microbiol 2022, 13, 936726. [Google Scholar] [CrossRef] [PubMed]
  53. Li, P.; Wang, J.; Zou, Y.; Sun, Z.; Zhang, M.; Geng, Z.; Xu, W.; Wang, D. Interaction of Hsp90AA1 with phospholipids stabilizes membranes under stress conditions. Biochim. Biophys. Acta-Biomembr. 2019, 1861, 457–465. [Google Scholar] [CrossRef] [PubMed]
  54. Maity, J.P.; Kar, S.; Lin, C.M.; Chen, C.Y.; Chang, Y.F.; Jean, J.S.; Kulp, T.R. Identification and discrimination of bacteria using Fourier transform infrared spectroscopy. Spectrochim. Acta-Part A Mol. Biomol. Spectrosc. 2013, 116, 478–484. [Google Scholar] [CrossRef] [PubMed]
  55. Gill, A.O.; Delaquis, P.; Russo, P.; Holley, R.A. Evaluation of antilisterial action of cilantro oil on vacuum packed ham. Int. J. Food Microbiol. 2002, 73, 83–92. [Google Scholar] [CrossRef]
  56. Kempf, B.; Bremer, E. Uptake and synthesis of compatible solutes as microbial stress responses to high-osmolality environments. Arch. Microbiol. 1998, 170, 319–330. [Google Scholar] [CrossRef]
  57. Kamal, S.M.; Simpson, D.J.; Wang, Z.; Gänzle, M.; Römling, U. Horizontal Transmission of Stress Resistance Genes Shape the Ecology of Beta- and Gamma-Proteobacteria. Front. Microbiol. 2021, 12, 696522. [Google Scholar] [CrossRef]
  58. Dash, S.S.N.; Gummadi, S. Inhibitory Effect of Caffeine on Growth of Various Bacterial Strains. Res. J. Microbiol. 2008, 3, 457–465. [Google Scholar] [CrossRef]
  59. Whitney, A.K.; Weir, T.L. Interaction of caffeine with the SOS response pathway in Escherichia coli. Gut Pathog. 2015, 7, 21. [Google Scholar] [CrossRef]
Microorganisms 14 00491 g001
Figure 1. Score plots of E. coli isolated from beef (C9, C31, C1145, and C97) and pasteurized milk (A21C1 and B20C3) treated with heat, ultrasound (US), UVC, UVC+US, heat+UVC, and untreated/control, in region 1 of lipids/fatty acids (3010–2800 cm−1).
Figure 1. Score plots of E. coli isolated from beef (C9, C31, C1145, and C97) and pasteurized milk (A21C1 and B20C3) treated with heat, ultrasound (US), UVC, UVC+US, heat+UVC, and untreated/control, in region 1 of lipids/fatty acids (3010–2800 cm−1).
Microorganisms 14 00491 g001
Microorganisms 14 00491 g002
Figure 2. Score plots of E. coli isolated from beef (C9, C31, C1145, and C97) and pasteurized milk (A21C1 and B20C3) treated with heat, ultrasound (US), UVC, UVC+US, heat+UVC, and untreated/control, in the region 2 of proteins and peptides region (1700–1200 cm−1).
Figure 2. Score plots of E. coli isolated from beef (C9, C31, C1145, and C97) and pasteurized milk (A21C1 and B20C3) treated with heat, ultrasound (US), UVC, UVC+US, heat+UVC, and untreated/control, in the region 2 of proteins and peptides region (1700–1200 cm−1).
Microorganisms 14 00491 g002
Microorganisms 14 00491 g003
Figure 3. Score plots of E. coli isolated from beef (C9, C31, C1145, and C97) and pasteurized milk (A21C1 and B20C3) treated with heat, ultrasound (US), UVC, UVC+US, heat+UVC, and untreated/control, in region 3 of carbohydrates (1200–900 cm−1).
Figure 3. Score plots of E. coli isolated from beef (C9, C31, C1145, and C97) and pasteurized milk (A21C1 and B20C3) treated with heat, ultrasound (US), UVC, UVC+US, heat+UVC, and untreated/control, in region 3 of carbohydrates (1200–900 cm−1).
Microorganisms 14 00491 g003
Microorganisms 14 00491 g004
Figure 4. Score plots of E. coli isolated from beef (C9, C31, C1145, and C97) and pasteurized milk (A21C1 and B20C3) treated with heat, ultrasound (US), UVC, UVC+US, heat+UVC, and untreated/control in region 4 of Amide A (3280–3120 cm−1).
Figure 4. Score plots of E. coli isolated from beef (C9, C31, C1145, and C97) and pasteurized milk (A21C1 and B20C3) treated with heat, ultrasound (US), UVC, UVC+US, heat+UVC, and untreated/control in region 4 of Amide A (3280–3120 cm−1).
Microorganisms 14 00491 g004
Microorganisms 14 00491 g005
Figure 5. Scanning electron microscopy (SEM) images of E. coli isolated from animal-based food: untreated/control bacteria (A), bacteria treated with ultrasound (US) at power of 299 W (B); bacteria treated with heat at 60 °C/6 min (C); bacteria treated with UVC at dose of 4950 mJ/cm2 (D); bacteria treated with UVC+US (E); and bacteria treated with heat+UVC (F).
Figure 5. Scanning electron microscopy (SEM) images of E. coli isolated from animal-based food: untreated/control bacteria (A), bacteria treated with ultrasound (US) at power of 299 W (B); bacteria treated with heat at 60 °C/6 min (C); bacteria treated with UVC at dose of 4950 mJ/cm2 (D); bacteria treated with UVC+US (E); and bacteria treated with heat+UVC (F).
Microorganisms 14 00491 g005
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Machado, M.; Andrade, J.C.; Figueiredo, E.E.d.S.; Conte-Junior, C.A. Use of Thermal and Emerging Non-Thermal Treatments Reveal Biomolecular and Morphological Changes in Pathogenic E. coli. Microorganisms 2026, 14, 491. https://doi.org/10.3390/microorganisms14020491

AMA Style

Machado M, Andrade JC, Figueiredo EEdS, Conte-Junior CA. Use of Thermal and Emerging Non-Thermal Treatments Reveal Biomolecular and Morphological Changes in Pathogenic E. coli. Microorganisms. 2026; 14(2):491. https://doi.org/10.3390/microorganisms14020491

Chicago/Turabian Style

Machado, Maxsueli, Jelmir Craveiro Andrade, Eduardo Eustáquio de Souza Figueiredo, and Carlos Adam Conte-Junior. 2026. "Use of Thermal and Emerging Non-Thermal Treatments Reveal Biomolecular and Morphological Changes in Pathogenic E. coli" Microorganisms 14, no. 2: 491. https://doi.org/10.3390/microorganisms14020491

APA Style

Machado, M., Andrade, J. C., Figueiredo, E. E. d. S., & Conte-Junior, C. A. (2026). Use of Thermal and Emerging Non-Thermal Treatments Reveal Biomolecular and Morphological Changes in Pathogenic E. coli. Microorganisms, 14(2), 491. https://doi.org/10.3390/microorganisms14020491

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop