key: cord-0301564-sv0t5vl3 authors: Chac, Denise; Kordahi, Melissa; Brettner, Leandra; Verma, Arushi; McCleary, Paul; Crebs, Kelly; Yee, Cara; DePaolo, R. William title: Proteomic changes in bacteria caused by exposure to environmental conditions can be detected by Matrix-Assisted Laser Desorption/Ionization – Time of Flight (MALDI-ToF) Mass Spectrometry date: 2020-01-25 journal: bioRxiv DOI: 10.1101/2020.01.24.918938 sha: 643059679cd268bcfd1e5e8290f6659b2ac07a22 doc_id: 301564 cord_uid: sv0t5vl3 In the past decade, matrix-assisted laser desorption/ionization time-of-flight (MALDI-ToF) mass spectrometry (MS) has become a timely and cost-effective alternative to bacterial identification. The MALDI-ToF MS technique analyzes the total protein of culturable microorganisms at the species level and produces a mass spectra based on peptides which is compared to a database of identified profiles. Consequently, unique signatures of each microorganism are produced allowing identification at the species and, more importantly, strain level. Our present study proposes that the MALDI-ToF MS can be further used to screen functional and metabolic differences. While other studies applied the MALDI-ToF technique to identify subgroups within species, we investigated how various environmental factors could alter the unique bacterial signatures. We found that genetic and phenotypic differences between microorganisms belonging to the same species can be reflected in peptide-mass fingerprints generated by MALDI-ToF MS. These results suggest that MALDI-ToF MS can screen intra-species phenotypic differences of several microorganisms. The MALDI-ToF (matrix-assisted desorption/ionization time-of-flight) mass spectrometry (MS) technology offers a time-and cost-effective method of identifying microorganisms. Compared to previous time-consuming and expensive methods to identify microorganisms based on 16s rRNA or whole genome sequencing, the MALDI-ToF MS provides rapid, accurate and inexpensive identification within minutes via proteotyping1. While the MALDI-ToF is limited to culturable microorganisms and public databases, it has been quickly incorporated in the clinical setting and used for diagnosis. Studies have shown that MALDI-ToF MS can equally or even better identify sources of systemic infections2,3, urinary tract infections4 6, respiratory tract infections7 and intestinal infections8,9. The MALDI-ToF MS technology allows for identification down to the strain level and has been shown to discriminate between strains of methicillin-resistant (rep-PCR), housekeeping gene (e.g., PheS) sequence analysis20, and whole genome sequencing21, 22 . Each of these approaches has been shown to have adequately high resolution to distinguish microbial strains from one another; however, these approaches are labor-and time-intensive as well as costly techniques that might lack the required rapid high-throughput nature of MALDI-ToF. A big advantage of the MALDI-ToF MS strain typing application would be epidemiologic investigations that require rapid identification of a single strain within a single species to determine the origin and spread of an outbreak in order to mitigate risks to public safety posed by microbial food and water contamination or potential acts of bioterrorism 16,23. Moreover, in an era where microbiome-based diagnostics and therapeutic targets are envisioned24, this technology could provide a framework to link disease phenotypes to peptides translated by microorganisms isolated from various biological samples. The MALDI-ToF MS is an ionization process that became commercially available in the Raw spectra text files were analyzed using the R package, MALDIquant [https://www.ncbi.nlm.nih.gov/pubmed/22796955]. The raw data were trimmed to a spectra range of 3,000 to 15,000 m/z. The spectra intensities were then square-root transformed and smoothed using the Savitzky-Golay algorithm. Baseline noise was removed using the statistics-sensitive non-linear iterative peak clipping (or SNIP) algorithm with 100 iterations. The data were then normalized using total ion current (or TIC) calibration, which sets the total intensity to 1. Multiple spectra within the same analysis were aligned to the same x-axis using the Lowess warping method, a signal-tonoise ratio of 3, and a tolerance of 0.001. Peaks were detected from the average of at least 4 technical replicates using median absolute deviation. Principal components analyses and hierarchical clustering were also performed in R using the base stats package. Hierarchical clustering was performed on a calculated Euclidean distance a g Wa d e d. Peptide mass fingerprints distinguish genetic differences within microbial species MALDI-ToF based bacterial profiling at the genus and species levels has provided results that are superior to and less expensive and time consuming than those obtained from more conventional approaches such as 16S rRNA sequencing. Such successes can be found across diverse areas of research and across many disciplines such as clinical microbiology, biodefense, food safety and environmental health. Although many studies have reported strain-specific peaks generated by MALDI-ToF MS, identification of reliable peaks as strain-specific biomarkers has been hindered by poor profile reproducibility. Further, it appears that the limits of the taxonomic resolution of MALDI-ToF MS profiling at the strain level has been determined so far in large part by the nature of the particular bacterium profiled. The more genetically indistinguishable bacteria are, the more challenging their profiling has been28. In this study, we show that MALDI-ToF MS offers the possibility to discriminate between highly genetically similar genetic mutants or between different strains of the same bacterial species. To investigate the capabilities of MALDI-ToF to distinguish strain-level differences, we analyzed three different gramnegative bacteria. fragilis and Y. enterocolitica, the MALDI-ToF can also differentiate lab strains of E. coli from the highly pathogenic EPEC (Figure 1C ). This experiment clearly shows that MALDI-ToF MS is sensitive enough to detect strain-level differences within the same bacterial species as it was able to differentiate lab strains of E. coli from the highly pathogenic EPEC. As the MALDI-ToF MS uses whole protein of bacterial samples to generate unique profiles, lack of phenotypic or metabolic differences between subgroups of species can complicate identification. Meanwhile, our results confirm the findings of other studies. The technique has proved to be highly performant and reproducible in distinguishing peaks within highly genetically similar species. Several known strains can then be used to create a reference library that affords identification of unidentified strains with potentially useful applications such as diagnosing pathogenic strains in certain disease states or rapidly identifying the origin of an outbreak. The reproducibility of the mass spectra generated in MALDI-ToF MS analysis of proteins from bacterial extracts can be impacted by several experimental factors. Other researchers have discovered that differences in incubation and culturing conditions can alter peak intensities39 and identification rates40 ( (Figure 2A) . The two conditions clearly separate along the x-axis with over 50% of the various explained by PC1 (Figure 2A) . Interestingly, the clustering of L reuteri grown aerobically exhibit more variability in PMFs compared to L. reuteri grown anaerobically. These differences may be indicative of the impact of anaerobic stress exerting a more distinct response on the bacteria. (Figure 2B) . The overlapping of MC and EMB-grown E. coli is likely due to the lactose fermentation process undergone by E. coli when it is grown on these two types of agars. While the differences between MC and EMB agars did not discriminate between the E. coli colonies grown on these two types of media, the presence of 5% defibrinated sheep blood in the TSA was enough to trigger the expression of a different proteomic profile in E. coli grown on TSBA relative to TSA ( Figure 2B ). Thus the MALDI-ToF MS has the capacity to significantly discriminate between E. coli grown on various media depending on the components of the media. We next tested whether temperature affects the PMF profiles of microorganisms. Gut pathogen Y. enterocolitica is well adapted for survival and proliferation at room temperature (RT) and induces its virulence factors when consumed or introduced to host temperatures of 37°C42. These metabolic adaptations are clearly captured when we analyze Y. enterocolitica grown at these two temperatures on the MALDI-ToF MS with Y. enterocolitica grown at RT clustering away from the 37°C treated group (Figure 2C) Next we tested whether stressing a bacteria with high heat induces heat shock proteins43. For this experiment, liquid cultures of DH5 were incubated at 37°C or 50°C for 15 minutes or 30 minutes. Sure enough, we clearly saw that temperatures also affected the PMF profile and clustering of E. coli introduced to temperatures greater than 42°C. These differences, most likely due to the translation of heat-shock proteins, became even more dramatic with an increase in incubation time ( Figure 2D) . Y. enterocolitica applied to SW620s for 1 hour, or applied to SW620s for 1 hour and treated with gentamicin to remove extracellular bacteria were compared (Figure 3A) . After samples were collected, Y. enterocolitica was plated on TSA and incubated at room temperature for 48 hours. As we previously demonstrated with liquid cultures of DH5 plated on TSA, we found that Y. enterocolitica clustered depending on treatment group ( Figure 3B) . Cultures of intracellular Y. enterocolitica recovered following gentamicin had greater similarity whereas the Y. enterocolitica recovered after 1 hour had a more variable PMF profile. More divergent PMFs within a population could be due to natural heterogeneity within a clonal population45 or differences in transcriptional or virulent states. Our data using DH5 E. coli and Y. enterocolitica demonstrate that the MALDI-ToF can detect differences in metabolic and virulence states, respectively, even after those environmental influences are removed. This is especially important for trying to assess phenotypic differences of clinical isolates or samples that cannot be readily grown in its natural environments. These methods could be applied to situations in which metabolic or phenotypic difference of experimental groups can be compared against known culturing conditions. It can be used as a method of screening of known bacterial phenotypes. A very common use of the MALDI-ToF MS in clinical studies is to identify clinical isolates removed from biological samples for diagnostic purposes. Whereas the previous experiments described above used the protein extraction method in a well-controlled environment with well characterized strains, the use of MALDI-ToF in clinical settings is less likely to resort to this time-consuming method. In a clinical setting, using the protein extraction method is time consuming and not all samples are easily processed immediately. Additionally, freezing samples prior to culturing can reduce the number of bacteria recovered, skew the diversity of bacteria, and alter community composition46,47. Therefore these next experiments evaluated how frozen versus fresh conditions altered e PMF f c ca a e g e E e ded D ec T a fe e d a de c bed above. Using fresh human fecal samples, we tested whether culturing fresh or frozen microbiota samples altered the phenotype of clinical isolates ( Figure 4A ). In these experiments, whole fecal samples were plated aerobically on TSBA for 48 hours. Single colonies were then processed using the extended direct colony method. Bacteria identified as the same species were then compiled and examined retrospectively. Bacteroides ovatus from both fresh and frozen cultures. Despite distinct clustering between fresh and frozen isolates of C. sedlakii and E. coli, the clustering was not statistically significant as there was overlap seen in the PCA and dendrogram ( Figure 4B-C) . Freezing seemed to have a more profound impact on C. sedlakii compared to E. coli as these isolates appeared to have little overlap in PMFs ( Figure 4B ). In contrast, E. coli isolates from fresh and frozen stool had more similarities suggesting that the response to freezing may be bacteria dependent ( Figure 4C) . These experiments provide a rationale for why sample collection during microbiome studies must be standardized. We next compared the MALDI-ToF analysis of fresh and frozen B. ovatus taken from two different patients clustered. As expected, B. ovatus clustering was patient dependent, likely due to each patient containing a different strain of B. ovatus or exposure to patient dependent microenvironments that contribute to genetic diversity via phage and horizontal gene transfer ( Figure 4D ). Interestingly, fresh or frozen status had less of an impact on the clustering of B. ovatus from patient 2, while patient 1 had high variability of PMFs within fresh and frozen groups. The different sensitivity between patient 1 and patient 2 B. ovatus isolates from fresh and frozen stool may be due to the presence of more temperature sensitive genes in the isolates from patient 1 and indicate that freezing stool may alter the PMF of MALDI-ToF in a bacteria-dependent manner. Our results using clinical isolates derived from a mixed microbiota sample reveal that fresh versus frozen culturing techniques may alter the proteotyping of microorganisms. Further study is needed to analyze the freezing effects. 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