Effect of Quorum Sensing Molecules on the Quality of Bacterial Nanocellulose Materials.

Bacterial nanocellulose (BNC) biofilms, produced by various bacterial species, such as Gluconacetobacter xylinus, represent a highly promising multifunctional material characterized by distinctive physiochemical properties. These biofilms have demonstrated remarkable versatility as nano biomaterials, finding extensive applications across medical, defense, electronics, optics, and food industries. In contrast to plant cellulose, BNC biofilms exhibit numerous advantages, including elevated purity and crystallinity, expansive surface area, robustness, and excellent biocompatibility, making them exceptional multifunctional materials. However, their production with consistent morphological properties and their transformation into practical forms present challenges. This difficulty often arises from the heterogeneity in cell density, which is influenced by the presence of N-acyl-homoserine lactones (AHLs) serving as quorum sensing signaling molecules during the biosynthesis of BNC biofilms. In this study, we employed surface characterization methodologies including scanning electron microscopy, energy-dispersive spectroscopy, diffuse reflectance infrared Fourier transform spectroscopy, and atomic force microscopy to characterize BNC biofilms derived from growth media supplemented with varying concentrations of distinct N-acyl-homoserine lactone signaling molecules. The data obtained through these analytical techniques elucidated that the morphological properties of the BNC biofilms were influenced by the specific AHLs, signaling molecules, introduced into the growth media. These findings lay the groundwork for future exploration of leveraging synthetic biology and biomimetic methods for tailoring BNC with predetermined morphological properties.

First Organoid Intelligence (OI) workshop to form an OI community.

The brain is arguably the most powerful computation system known. It is extremely efficient in processing large amounts of information and can discern signals from noise, adapt, and filter faulty information all while running on only 20 watts of power. The human brain's processing efficiency, progressive learning, and plasticity are unmatched by any computer system. Recent advances in stem cell technology have elevated the field of cell culture to higher levels of complexity, such as the development of three-dimensional (3D) brain organoids that recapitulate human brain functionality better than traditional monolayer cell systems. Organoid Intelligence (OI) aims to harness the innate biological capabilities of brain organoids for biocomputing and synthetic intelligence by interfacing them with computer technology. With the latest strides in stem cell technology, bioengineering, and machine learning, we can explore the ability of brain organoids to compute, and store given information (input), execute a task (output), and study how this affects the structural and functional connections in the organoids themselves. Furthermore, understanding how learning generates and changes patterns of connectivity in organoids can shed light on the early stages of cognition in the human brain. Investigating and understanding these concepts is an enormous, multidisciplinary endeavor that necessitates the engagement of both the scientific community and the public. Thus, on Feb 22-24 of 2022, the Johns Hopkins University held the first Organoid Intelligence Workshop to form an OI Community and to lay out the groundwork for the establishment of OI as a new scientific discipline. The potential of OI to revolutionize computing, neurological research, and drug development was discussed, along with a vision and roadmap for its development over the coming decade.

Secretome analysis of diarrhea-inducing strains of Escherichia coli.

Secreted proteins constitute a major part of virulence factors that are responsible for pathogenesis caused by Gram-negative bacteria. Enterohemorrhagic Escherichia coli, O157:H7, is the major pathogen often causing outbreaks. However, studies have reported that the significant outbreaks caused by non-O157:H7 E. coli strains, also known as "Big-Six" serogroup strains, are increasing. There is no systematic study describing differential secreted proteins from these non-O157:H7 E. coli strains. In this study, we carried out MS-based differential secretome analysis using tandem mass tags labeling strategy of non-O157:H7 E. coli strains, O103, O111, O121, O145, O26, and O45. We identified 1241 proteins, of which 565 proteins were predicted to be secreted. We also found that 68 proteins were enriched in type III secretion system and several of them were differentially expressed across the strains. Additionally, we identified several strain-specific secreted proteins that could be used for developing potential markers for the identification and strain-level differentiation. To our knowledge, this study is the first comparative proteomic study on secretome of E. coli Big-Six serogroup and the several of these strain-specific secreted proteins can be further studied to develop potential markers for identification and strain-level differentiation. Moreover, the results of this study can be utilized in several applications, including food safety, diagnostics of E. coli outbreaks, and detection and identification of bio threats in biodefense.

Occurrence of Bacillus amyloliquefaciens as a systemic endophyte of vanilla orchids.

We report the occurrence of Bacillus amyloliquefaciens in vanilla orchids (Vanilla phaeantha) and cultivated hybrid vanilla (V. planifolia × V. pompona) as a systemic bacterial endophyte. We determined with light microscopy and isolations that tissues of V. phaeantha and the cultivated hybrid were infected by a bacterial endophyte and that shoot meristems and stomatal areas of stems and leaves were densely colonized. We identified the endophyte as B. amyloliquefaciens using DNA sequence data. Since additional endophyte-free plants and seed of this orchid were not available, additional studies were performed on surrogate hosts Amaranthus caudatus, Ipomoea tricolor, and I. purpurea. Plants of A. caudatus inoculated with B. amyloliquefaciens demonstrated intracellular colonization of guard cells and other epidermal cells, confirming the pattern observed in the orchids. Isolations and histological studies suggest that the bacterium may penetrate deeply into developing plant tissues in shoot meristems, forming endospores in maturing tissues. B. amyloliquefaciens produced fungal inhibitors in culture. In controlled experiments using morning glory seedlings we showed that the bacterium promoted seedling growth and reduced seedling necrosis due to pathogens. We detected the gene for phosphopantetheinyl transferase (sfp), an enzyme in the pathway for production of antifungal lipopeptides, and purified the lipopeptide "surfactin" from cultures of the bacterium. We hypothesize that B. amyloliquefaciens is a robust endophyte and defensive mutualist of vanilla orchids. Whether the symbiosis between this bacterium and its hosts can be managed to protect vanilla crops from diseases is a question that should be evaluated in future research.

Extracellular protein biomarkers for the characterization of enterohemorrhagic and enteroaggregative Escherichia coli strains.

The extracellular proteins (ECPs) of enterohemorrhagic Escherichia coli (EHEC) can cause hemorrhagic colitis which may cause life threatening hemolytic-uremic syndrome, while that of enteroaggregative E. coli (EAEC) can clump to intestinal membranes. Liquid chromatography-electrospray ionization-tandem mass spectrometry based proteomics is used to evaluate a preliminary study on the extracellular and whole cell protein extracts associated with E. coli strain pathogenicity. Proteomics analysis, which is independent of genomic sequencing, of EAEC O104:H4 (unsequenced genome) identified a number of proteins. Proteomics of EHEC O104:H4, causative agent of the Germany outbreak, showed a closest match with E. coli E55989, in agreement with genomic studies. Dendrogram analysis separated EHEC O157:H7 and EHEC/EAEC O104:H4. ECP analysis compared to that of whole cell processing entails few steps and convenient experimental extraction procedures. Bacterial characterization results are promising in exploring the impact of environmental conditions on E. coli ECP biomarkers with a few relatively straightforward protein extraction steps.

A protein processing filter method for bacterial identification by mass spectrometry-based proteomics.

A "one-pot" alternative method for processing proteins and isolating peptide mixtures from bacterial samples is presented for liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis and data reduction. The conventional in-solution digestion of the protein contents of bacteria is compared to a small disposable filter unit placed inside a centrifuge vial for processing and digestion of bacterial proteins. Each processing stage allows filtration of excess reactants and unwanted byproduct while retaining the proteins. Upon addition of trypsin, the peptide mixture solution is passed through the filter while retaining the trypsin enzyme. The peptide mixture is then analyzed by LC-MS/MS with an in-house BACid algorithm for a comparison of the experimental unique peptides to a constructed proteome database of bacterial genus, specie, and strain entries. The concentration of bacteria was varied from 10 × 10(7) to 3.3 × 10(3) cfu/mL for analysis of the effect of concentration on the ability of the sample processing, LC-MS/MS, and data analysis methods to identify bacteria. The protein processing method and dilution procedure result in reliable identification of pure suspensions and mixtures at high and low bacterial concentrations.

Iridovirus and microsporidian linked to honey bee colony decline.

BACKGROUND: In 2010 Colony Collapse Disorder (CCD), again devastated honey bee colonies in the USA, indicating that the problem is neither diminishing nor has it been resolved. Many CCD investigations, using sensitive genome-based methods, have found small RNA bee viruses and the microsporidia, Nosema apis and N. ceranae in healthy and collapsing colonies alike with no single pathogen firmly linked to honey bee losses. METHODOLOGY/PRINCIPAL FINDINGS: We used Mass spectrometry-based proteomics (MSP) to identify and quantify thousands of proteins from healthy and collapsing bee colonies. MSP revealed two unreported RNA viruses in North American honey bees, Varroa destructor-1 virus and Kakugo virus, and identified an invertebrate iridescent virus (IIV) (Iridoviridae) associated with CCD colonies. Prevalence of IIV significantly discriminated among strong, failing, and collapsed colonies. In addition, bees in failing colonies contained not only IIV, but also Nosema. Co-occurrence of these microbes consistently marked CCD in (1) bees from commercial apiaries sampled across the U.S. in 2006-2007, (2) bees sequentially sampled as the disorder progressed in an observation hive colony in 2008, and (3) bees from a recurrence of CCD in Florida in 2009. The pathogen pairing was not observed in samples from colonies with no history of CCD, namely bees from Australia and a large, non-migratory beekeeping business in Montana. Laboratory cage trials with a strain of IIV type 6 and Nosema ceranae confirmed that co-infection with these two pathogens was more lethal to bees than either pathogen alone. CONCLUSIONS/SIGNIFICANCE: These findings implicate co-infection by IIV and Nosema with honey bee colony decline, giving credence to older research pointing to IIV, interacting with Nosema and mites, as probable cause of bee losses in the USA, Europe, and Asia. We next need to characterize the IIV and Nosema that we detected and develop management practices to reduce honey bee losses.

Identification of Yersinia pestis and Escherichia coli strains by whole cell and outer membrane protein extracts with mass spectrometry-based proteomics.

Whole cell protein and outer membrane protein (OMP) extracts were compared for their ability to differentiate and delineate the correct database organism to an experimental sample and for the degree of dissimilarity to the nearest neighbor database organism strains. These extracts were isolated from pathogenic and nonpathogenic strains of Yersinia pestis and Escherichia coli using ultracentrifugation and a sarkosyl extraction method followed by protein digestion and analysis using liquid chromatography tandem mass spectrometry (MS). Whole cell protein extracts contain many different types of proteins resident in an organism at a given phase in its growth cycle. OMPs, however, are often associated with virulence in Gram-negative pathogens and could prove to be model biomarkers for strain differentiation among bacteria. The mass spectra of bacterial peptides were searched, using the SEQUEST algorithm, against a constructed proteome database of microorganisms in order to determine the identity and number of unique peptides for each bacterial sample. Data analysis was performed with the in-house BACid software. It calculated the probabilities that a peptide sequence assignment to a product ion mass spectrum was correct and used accepted spectrum-to-sequence matches to generate a sequence-to-bacterium (STB) binary matrix of assignments. Validated peptide sequences, either present or absent in various strains (STB matrices), were visualized as assignment bitmaps and analyzed by the BACid module that used phylogenetic relationships among bacterial species as part of a decision tree process. The bacterial classification and identification algorithm used assignments of organisms to taxonomic groups (phylogenetic classification) based on an organized scheme that begins at the phylum level and follows through the class, order, family, genus, and species to the strain level. For both Gram-negative organisms, the number of unique distinguishing proteins arrived at by the whole cell method was less than that of the OMP method. However, the degree of differentiation measured in linkage distance units on a dendrogram with the OMP extract showed similar or significantly better separation than the whole cell protein extract method between the sample and correct database match compared to the next nearest neighbor. The nonpathogenic Y. pestis A1122 strain used does not have its genome available, and thus, data analysis resulted in an equal similarity index to the nonpathogenic 91001 and pathogenic Antiqua and Nepal 516 strains for both extraction methods. Pathogenic and nonpathogenic strains of E. coli were correctly identified with both protein extraction methods, and the pathogenic Y. pestis CO92 strain was correctly identified with the OMP procedure. Overall, proteomic MS proved useful in the analysis of unique protein assignments for strain differentiation of E. coli and Y. pestis. The power of bacterial protein capture by the whole cell protein and OMP extraction methods was highlighted by the data analysis techniques and revealed differentiation and similarities between the two protein extraction approaches for bacterial delineation capability.

Double-blind characterization of non-genome-sequenced bacteria by mass spectrometry-based proteomics.

Due to the possibility of a biothreat attack on civilian or military installations, a need exists for technologies that can detect and accurately identify pathogens in a near-real-time approach. One technology potentially capable of meeting these needs is a high-throughput mass spectrometry (MS)-based proteomic approach. This approach utilizes the knowledge of amino acid sequences of peptides derived from the proteolysis of proteins as a basis for reliable bacterial identification. To evaluate this approach, the tryptic digest peptides generated from double-blind biological samples containing either a single bacterium or a mixture of bacteria were analyzed using liquid chromatography-tandem mass spectrometry. Bioinformatic tools that provide bacterial classification were used to evaluate the proteomic approach. Results showed that bacteria in all of the double-blind samples were accurately identified with no false-positive assignment. The MS proteomic approach showed strain-level discrimination for the various bacteria employed. The approach also characterized double-blind bacterial samples to the respective genus, species, and strain levels when the experimental organism was not in the database due to its genome not having been sequenced. One experimental sample did not have its genome sequenced, and the peptide experimental record was added to the virtual bacterial proteome database. A replicate analysis identified the sample to the peptide experimental record stored in the database. The MS proteomic approach proved capable of identifying and classifying organisms within a microbial mixture.

Bioaerosol analysis with Raman chemical imaging microspectroscopy.

Raman chemical imaging microspectroscopy is evaluated as a technology for waterborne pathogen and bioaerosol detection. Raman imaging produces a three-dimensional data cube consisting of a Raman spectrum at every pixel in a microscope field of view. Binary and ternary mixtures including combinations of polystyrene beads, gram-positive Bacillus anthracis, B. thuringiensis, and B. atrophaeus spores, and B. cereus vegetative cells were investigated by Raman imaging for differentiation and characterization purposes. Bacillus spore aerosol sizes were varied to provide visual proof for corroboration of spectral assignments. Conventional applications of Raman imaging consist of differentiating relatively broad areas of a sample in a microscope field of view. The spectral angle mapping data analysis algorithm was used to compare a library spectrum with experimental spectra from pixels in the microscope field of view. This direct one-to-one matching is straightforward, does not require a training set, is independent of absolute spectral intensity, and only requires univariate statistics. Raman imaging is expanded in its capabilities to differentiate and distinguish between discrete 1-6 microm size bacterial species in single particles, clusters of mixed species, and bioaerosols with interference background particles.

Waterborne pathogen detection using Raman spectroscopy.

Raman spectroscopy is being evaluated as a candidate technology for waterborne pathogen detection. We have investigated the impact of key experimental and background interference parameters on the bacterial species level identification performance of Raman detection. These parameters include laser-induced photodamage threshold, composition of water matrix, and organism aging in water. The laser-induced photodamage may be minimized by operating a 532 nm continuous wave laser excitation at laser power densities below 2300 W/cm(2) for Grampositive Bacillus atrophaeus (formerly Bacillus globigii, BG) vegetative cells, 2800 W/cm(2) for BG spores, and 3500 W/cm(2) for Gram-negative E. coli (EC) organisms. In general, Bacillus spore microorganism preparations may be irradiated with higher laser power densities than the equivalent Bacillus vegetative preparations. In order to evaluate the impact of background interference and organism aging, we selected a biomaterials set comprising Gram-positive (anthrax simulants) organisms, Gram-negative (plague simulant) organisms, and proteins (toxin simulants) and constructed a Raman signature classifier that identifies at the species level. Subsequently, we evaluated the impact of tap water and storage time in water (aging) on the classifier performance when characterizing B. thuringiensis spores, BG spores, and EC cell preparations. In general, the measured Raman signatures of biological organisms exhibited minimal spectral variability with respect to the age of a resting suspension and water matrix composition. The observed signature variability did not substantially degrade discrimination performance at the genus and species levels. In addition, Raman chemical imaging spectroscopy was used to distinguish a mixture of BG spores and EC cells at the single cell level.

Mass Spectrometry and Integrated Virus Detection System Characterization of MS2 Bacteriophage.

ABSTRACT In this study, we demonstrate the effect of sample matrix composition of MS2 virus on its characterization by ESI-MS and IVDS. MS2 samples grown and purified using various techniques showed different responses on ESI-MS than that on IVDS. The LC-MS of the specific biomarker of MS2 bacteriophage from an infected Escherichia coli sample was characterized by the presence of E. coli proteins. The significant impact of sample matrix was observed upon identification of MS2 using a database search. Infected E. coli with MS2 showed a matching score indifferent from uninfected ones. Only purified MS2, using CsCl and analyzed by LS-MS, showed a positive match using the database search. However, the variation in MS2 sample matrix had no effect on the deification of MS2.

Mass spectrometry-based proteomics combined with bioinformatic tools for bacterial classification.

Timely classification and identification of bacteria is of vital importance in many areas of public health. We present a mass spectrometry (MS)-based proteomics approach for bacterial classification. In this method, a bacterial proteome database is derived from all potential protein coding open reading frames (ORFs) found in 170 fully sequenced bacterial genomes. Amino acid sequences of tryptic peptides obtained by LC-ESI MS/MS analysis of the digest of bacterial cell extracts are assigned to individual bacterial proteomes in the database. Phylogenetic profiles of these peptides are used to create a matrix of sequence-to-bacterium assignments. These matrixes, viewed as specific assignment bitmaps, are analyzed using statistical tools to reveal the relatedness between a test bacterial sample and the microorganism database. It is shown that, if a sufficient amount of sequence information is obtained from the MS/MS experiments, a bacterial sample can be classified to a strain level by using this proteomics method, leading to its positive identification.

Detecting Bacteria by Direct Counting of Structural Protein Units by IVDS and Mass Spectrometry.

This report explores the direct counting of "hair-like" struc-tures specific for Gram-positive bacteria. Indications show that these structures are intact after removal from the cell and are sufficiently different from species to species of bacteria to give an indication of bacteria type if not actual identification. Their detection would represent a new approach to bacteria detection and identification. This report documents the detection of the bacterial structures using the physical nanometer counting methodology in the Integrated Virus Detection System (IVDS) and electrospray ionization-mass spectrometry.