Augmentation of immune response to vaccinations through osteopathic manipulative treatment: a study of procedure.

CONTEXT: Anecdotal evidence suggested that osteopathic manipulative treatment (OMT) may have imparted survivability to patients in osteopathic hospitals during the 1918 influenza pandemic. In addition, previous OMT research publications throughout the past century have shown evidence of increased lymphatic movement, resulting in improved immunologic function qualitatively and quantitatively. OBJECTIVES: The following is a description of a proposed protocol to evaluate OMT effects on antibody generation in the peripheral circulation in response to a vaccine and its possible use in the augmentation of various vaccines. This protocol will serve as a template for OMT vaccination studies, and by adhering to the gold standard of randomized controlled trials (RCTs), future studies utilizing this outline may contribute to the much-needed advancement of the scientific literature in this field. METHODS: This manuscript intends to describe a protocol that will demonstrate increased antibody titers to a vaccine through OMT utilized in previous historical studies. Confirmation data will follow this manuscript validating the protocol. Study participants will be divided into groups with and without OMT with lymphatic pumps. Each group will receive the corresponding vaccine and have antibody titers measured against the specific vaccine pathogen drawn at determined intervals. RESULTS: These results will be statistically evaluated. Our demonstration of a rational scientific OMT vaccine antibody augmentation will serve as the standard for such investigation that will be reported in the future. These vaccines could include COVID-19 mRNA, influenza, shingles, rabies, and various others. The antibody response to vaccines is the resulting conclusion of its administration. Osteopathic manipulative medicine (OMM) lymphatic pumps have, in the past through anecdotal reports and smaller pilot studies, shown effectiveness on peripheral immune augmentation to vaccines. CONCLUSIONS: This described protocol will be the template for more extensive scientific studies supporting osteopathic medicine's benefit on vaccine response. The initial vaccine studies will include the COVID-19 mRNA, influenza, shingles, and rabies vaccines.

A bacterial Goldilocks mechanism.

Bacillus subtilis can measure the activity of the enzymes that remodel the cell wall to ensure that the levels of activity are 'just right'.

Synthesis of histidine kinase inhibitors and their biological properties.

Infections caused by multidrug-resistant bacteria represent a significant and ever-increasing cause of morbidity and mortality. There is thus an urgent need to develop efficient and well-tolerated antibacterials targeting unique cellular processes. Numerous studies have led to the identification of new biological targets to fight bacterial resistance. Two-component signal transduction systems are widely employed by bacteria to translate external and cellular signals into a cellular response. They are ubiquitous in bacteria, absent in the animal kingdom and are integrated into various virulence pathways. Several chemical series, including isothiazolidones, imidazolium salts, benzoxazines, salicylanilides, thiophenes, thiazolidiones, benzimidazoles, and other derivatives deduced by different approaches have been reported in the literature to have histidine kinase (HK) inhibitory activity. In this review, we report on the design and the synthesis of these HKs inhibitors and their potential to serve as antibacterial agents.

A multi-scale coevolutionary approach to predict interactions between protein domains.

Interacting proteins and protein domains coevolve on multiple scales, from their correlated presence across species, to correlations in amino-acid usage. Genomic databases provide rapidly growing data for variability in genomic protein content and in protein sequences, calling for computational predictions of unknown interactions. We first introduce the concept of direct phyletic couplings, based on global statistical models of phylogenetic profiles. They strongly increase the accuracy of predicting pairs of related protein domains beyond simpler correlation-based approaches like phylogenetic profiling (80% vs. 30-50% positives out of the 1000 highest-scoring pairs). Combined with the direct coupling analysis of inter-protein residue-residue coevolution, we provide multi-scale evidence for direct but unknown interaction between protein families. An in-depth discussion shows these to be biologically sensible and directly experimentally testable. Negative phyletic couplings highlight alternative solutions for the same functionality, including documented cases of convergent evolution. Thereby our work proves the strong potential of global statistical modeling approaches to genome-wide coevolutionary analysis, far beyond the established use for individual protein complexes and domain-domain interactions.

Evolutionary couplings of amino acid residues reveal structure and function of bacterial signaling proteins.

The genomic era along with major advances in high-throughput sequencing technology has led to a rapid expansion of the genomic and consequently the protein sequence space. Bacterial extracytoplasmic function sigma factors have emerged as an important group of signaling proteins in bacteria involved in many regulatory decisions, most notably the adaptation to cell envelope stress. Their wide prevalence and amplification among bacterial genomes has led to sub-group classification and the realization of diverse signaling mechanisms. Mathematical frameworks have been developed to utilize extensive protein sequence alignments to extract co-evolutionary signals of interaction. This has proven useful in a number of different biological fields, including de novo structure prediction, protein-protein partner identification and the elucidation of alternative protein conformations for signal proteins, to name a few. The mathematical tools, commonly referred to under the name 'Direct Coupling Analysis' have now been applied to deduce molecular mechanisms of activation for sub-groups of extracytoplasmic sigma factors adding to previous successes on bacterial two-component signaling proteins. The amplification of signal transduction protein genes in bacterial genomes made them the first to be amenable to this approach but the sequences are available now to aid the molecular microbiologist, no matter their protein pathway of interest.

Inter-residue, inter-protein and inter-family coevolution: bridging the scales.

Interacting proteins coevolve at multiple but interconnected scales, from the residue-residue over the protein-protein up to the family-family level. The recent accumulation of enormous amounts of sequence data allows for the development of novel, data-driven computational approaches. Notably, these approaches can bridge scales within a single statistical framework. Although being currently applied mostly to isolated problems on single scales, their immense potential for an evolutionary informed, structural systems biology is steadily emerging.

Large-scale identification of coevolution signals across homo-oligomeric protein interfaces by direct coupling analysis.

Proteins have evolved to perform diverse cellular functions, from serving as reaction catalysts to coordinating cellular propagation and development. Frequently, proteins do not exert their full potential as monomers but rather undergo concerted interactions as either homo-oligomers or with other proteins as hetero-oligomers. The experimental study of such protein complexes and interactions has been arduous. Theoretical structure prediction methods are an attractive alternative. Here, we investigate homo-oligomeric interfaces by tracing residue coevolution via the global statistical direct coupling analysis (DCA). DCA can accurately infer spatial adjacencies between residues. These adjacencies can be included as constraints in structure prediction techniques to predict high-resolution models. By taking advantage of the ongoing exponential growth of sequence databases, we go significantly beyond anecdotal cases of a few protein families and apply DCA to a systematic large-scale study of nearly 2,000 Pfam protein families with sufficient sequence information and structurally resolved homo-oligomeric interfaces. We find that large interfaces are commonly identified by DCA. We further demonstrate that DCA can differentiate between subfamilies with different binding modes within one large Pfam family. Sequence-derived contact information for the subfamilies proves sufficient to assemble accurate structural models of the diverse protein-oligomers. Thus, we provide an approach to investigate oligomerization for arbitrary protein families leading to structural models complementary to often-difficult experimental methods. Combined with ever more abundant sequential data, we anticipate that this study will be instrumental to allow the structural description of many heteroprotein complexes in the future.

Molecular Mechanisms of Two-Component Signal Transduction.

Two-component systems (TCS) comprising sensor histidine kinases and response regulator proteins are among the most important players in bacterial and archaeal signal transduction and also occur in reduced numbers in some eukaryotic organisms. Given their importance to cellular survival, virulence, and cellular development, these systems are among the most scrutinized bacterial proteins. In the recent years, a flurry of bioinformatics, genetic, biochemical, and structural studies have provided detailed insights into many molecular mechanisms that underlie the detection of signals and the generation of the appropriate response by TCS. Importantly, it has become clear that there is significant diversity in the mechanisms employed by individual systems. This review discusses the current knowledge on common themes and divergences from the paradigm of TCS signaling. An emphasis is on the information gained by a flurry of recent structural and bioinformatics studies.

The Rational Design, Synthesis, and Antimicrobial Properties of Thiophene Derivatives That Inhibit Bacterial Histidine Kinases.

The emergence of multidrug-resistant bacteria emphasizes the urgent need for novel antibacterial compounds targeting unique cellular processes. Two-component signal transduction systems (TCSs) are commonly used by bacteria to couple environmental stimuli to adaptive responses, are absent in mammals, and are embedded in various pathogenic pathways. To attenuate these signaling pathways, we aimed to target the TCS signal transducer histidine kinase (HK) by focusing on their highly conserved adenosine triphosphate-binding domain. We used a structure-based drug design strategy that begins from an inhibitor-bound crystal structure and includes a significant number of structurally simplifiying "intuitive" modifications to arrive at the simple achiral, biaryl target structures. Thus, ligands were designed, leading to a series of thiophene derivatives. These compounds were synthesized and evaluated in vitro against bacterial HKs. We identified eight compounds with significant inhibitory activities against these proteins, two of which exhibited broad-spectrum antimicrobial activity. The compounds were also evaluated as adjuvants for the treatment of resistant bacteria. One compound was found to restore the sensivity of these bacteria to the respective antibiotics.

Inter-Protein Sequence Co-Evolution Predicts Known Physical Interactions in Bacterial Ribosomes and the Trp Operon.

Interaction between proteins is a fundamental mechanism that underlies virtually all biological processes. Many important interactions are conserved across a large variety of species. The need to maintain interaction leads to a high degree of co-evolution between residues in the interface between partner proteins. The inference of protein-protein interaction networks from the rapidly growing sequence databases is one of the most formidable tasks in systems biology today. We propose here a novel approach based on the Direct-Coupling Analysis of the co-evolution between inter-protein residue pairs. We use ribosomal and trp operon proteins as test cases: For the small resp. large ribosomal subunit our approach predicts protein-interaction partners at a true-positive rate of 70% resp. 90% within the first 10 predictions, with areas of 0.69 resp. 0.81 under the ROC curves for all predictions. In the trp operon, it assigns the two largest interaction scores to the only two interactions experimentally known. On the level of residue interactions we show that for both the small and the large ribosomal subunit our approach predicts interacting residues in the system with a true positive rate of 60% and 85% in the first 20 predictions. We use artificial data to show that the performance of our approach depends crucially on the size of the joint multiple sequence alignments and analyze how many sequences would be necessary for a perfect prediction if the sequences were sampled from the same model that we use for prediction. Given the performance of our approach on the test data we speculate that it can be used to detect new interactions, especially in the light of the rapid growth of available sequence data.

Analysis of periplasmic sensor domains from Anaeromyxobacter dehalogenans 2CP-C: structure of one sensor domain from a histidine kinase and another from a chemotaxis protein.

Anaeromyxobacter dehalogenans is a δ-proteobacterium found in diverse soils and sediments. It is of interest in bioremediation efforts due to its dechlorination and metal-reducing capabilities. To gain an understanding on A. dehalogenans' abilities to adapt to diverse environments we analyzed its signal transduction proteins. The A. dehalogenans genome codes for a large number of sensor histidine kinases (HK) and methyl-accepting chemotaxis proteins (MCP); among these 23 HK and 11 MCP proteins have a sensor domain in the periplasm. These proteins most likely contribute to adaptation to the organism's surroundings. We predicted their three-dimensional folds and determined the structures of two of the periplasmic sensor domains by X-ray diffraction. Most of the domains are predicted to have either PAS-like or helical bundle structures, with two predicted to have solute-binding protein fold, and another predicted to have a 6-phosphogluconolactonase like fold. Atomic structures of two sensor domains confirmed the respective fold predictions. The Adeh_2942 sensor (HK) was found to have a helical bundle structure, and the Adeh_3718 sensor (MCP) has a PAS-like structure. Interestingly, the Adeh_3718 sensor has an acetate moiety bound in a binding site typical for PAS-like domains. Future work is needed to determine whether Adeh_3718 is involved in acetate sensing by A. dehalogenans.

Statistical analyses of protein sequence alignments identify structures and mechanisms in signal activation of sensor histidine kinases.

Statistical analyses of genome sequence-derived protein sequence data can identify amino acid residues that interact between proteins or between domains of a protein. These statistical methods are based on evolution-directed amino acid variation responding to structural and functional constraints in proteins. The identified residues form a basis for determining structure and folding of proteins as well as inferring mechanisms of protein function. When applied to two-component systems, several research groups have shown they can be used to identify the amino acid interactions between response regulators and histidine kinases and the specificity therein. Recently, statistical studies between the HisKA and HATPase-ATP-binding domains of histidine kinases identified amino acid interactions for both the inactive and the active catalytic states of such kinases. The identified interactions generated a model structure for the domain conformation of the active state. This conformation requires an unwinding of a portion of the C-terminal helix of the HisKA domain that destroys the inactive state residue contacts and suggests how signal-binding determines the equilibrium between the inactive and active states of histidine kinases. The rapidly accumulating protein sequence databases from genome, metagenome and microbiome studies are an important resource for functional and structural understanding of proteins and protein complexes in microbes.

Structural basis of histidine kinase autophosphorylation deduced by integrating genomics, molecular dynamics, and mutagenesis.

Signal transduction proteins such as bacterial sensor histidine kinases, designed to transition between multiple conformations, are often ruled by unstable transient interactions making structural characterization of all functional states difficult. This study explored the inactive and signal-activated conformational states of the two catalytic domains of sensor histidine kinases, HisKA and HATPase. Direct coupling analyses, a global statistical inference approach, was applied to >13,000 such domains from protein databases to identify residue contacts between the two domains. These contacts guided structural assembly of the domains using MAGMA, an advanced molecular dynamics docking method. The active conformation structure generated by MAGMA simultaneously accommodated the sequence derived residue contacts and the ATP-catalytic histidine contact. The validity of this structure was confirmed biologically by mutation of contact positions in the Bacillus subtilis sensor histidine kinase KinA and by restoration of activity in an inactive KinA(HisKA):KinD(HATPase) hybrid protein. These data indicate that signals binding to sensor domains activate sensor histidine kinases by causing localized strain and unwinding at the end of the C-terminal helix of the HisKA domain. This destabilizes the contact positions of the inactive conformation of the two domains, identified by previous crystal structure analyses and by the sequence analysis described here, inducing the formation of the active conformation. This study reveals that structures of unstable transient complexes of interacting proteins and of protein domains are accessible by applying this combination of cross-validating technologies.

Bacillus subtilis RapA phosphatase domain interaction with its substrate, phosphorylated Spo0F, and its inhibitor, the PhrA peptide.

Rap proteins in Bacillus subtilis regulate the phosphorylation level or the DNA-binding activity of response regulators such as Spo0F, involved in sporulation initiation, or ComA, regulating competence development. Rap proteins can be inhibited by specific peptides generated by the export-import processing pathway of the Phr proteins. Rap proteins have a modular organization comprising an amino-terminal alpha-helical domain connected to a domain formed by six tetratricopeptide repeats (TPR). In this study, the molecular basis for the specificity of the RapA phosphatase for its substrate, phosphorylated Spo0F (Spo0F∼P), and its inhibitor pentapeptide, PhrA, was analyzed in part by generating chimeric proteins with RapC, which targets the DNA-binding domain of ComA, rather than Spo0F∼P, and is inhibited by the PhrC pentapeptide. In vivo analysis of sporulation efficiency or competence-induced gene expression, as well as in vitro biochemical assays, allowed the identification of the amino-terminal 60 amino acids as sufficient to determine Rap specificity for its substrate and the central TPR3 to TPR5 (TPR3-5) repeats as providing binding specificity toward the Phr peptide inhibitor. The results allowed the prediction and testing of key residues in RapA that are essential for PhrA binding and specificity, thus demonstrating how the widespread structural fold of the TPR is highly versatile, using a common interaction mechanism for a variety of functions in eukaryotic and prokaryotic organisms.

Transposon-mediated random mutagenesis of Bacillus subtilis.

The depth of knowledge concerning its physiology and genetics make Bacillus subtilis an attractive system for strain engineering and analysis. Transposon-based mutagenesis strategies generate large libraries of mutant strains that can be used to investigate molecular mechanisms relevant in fundamental research or to generate desirable phenotypes in applied research. This section presents a mini-Tn10-based transposon mutagenesis system that is capable of genome-wide insertional mutagenesis in B. subtilis and related organisms. Using appropriately designed selections or screens, the desired strain phenotypes can be isolated from transposon mutant libraries. This transposon system then allows rapid identification of the genetic locus responsible for the desired phenotype, and, due to the natural competence of B. subtilis, the identified genotypic change can easily be confirmed as responsible for the phenotypic change.

Dissecting the specificity of protein-protein interaction in bacterial two-component signaling: orphans and crosstalks.

Predictive understanding of the myriads of signal transduction pathways in a cell is an outstanding challenge of systems biology. Such pathways are primarily mediated by specific but transient protein-protein interactions, which are difficult to study experimentally. In this study, we dissect the specificity of protein-protein interactions governing two-component signaling (TCS) systems ubiquitously used in bacteria. Exploiting the large number of sequenced bacterial genomes and an operon structure which packages many pairs of interacting TCS proteins together, we developed a computational approach to extract a molecular interaction code capturing the preferences of a small but critical number of directly interacting residue pairs. This code is found to reflect physical interaction mechanisms, with the strongest signal coming from charged amino acids. It is used to predict the specificity of TCS interaction: Our results compare favorably to most available experimental results, including the prediction of 7 (out of 8 known) interaction partners of orphan signaling proteins in Caulobacter crescentus. Surveying among the available bacterial genomes, our results suggest 15∼25% of the TCS proteins could participate in out-of-operon "crosstalks". Additionally, we predict clusters of crosstalking candidates, expanding from the anecdotally known examples in model organisms. The tools and results presented here can be used to guide experimental studies towards a system-level understanding of two-component signaling.

A role for the essential YycG sensor histidine kinase in sensing cell division.

The YycG sensor histidine kinase co-ordinates cell wall remodelling with cell division in Gram-positive bacteria by controlling the transcription of genes for autolysins and their inhibitors. Bacillus subtilis YycG senses cell division and is enzymatically activated by associating with the divisome at the division septum. Here it is shown that the cytoplasmic PAS domain of this multi-domain transmembrane kinase is a determining factor translocating the kinase to the division septum. Furthermore, translocation to the division septum, per se, is insufficient to activate YycG, indicating that specific interactions and/or ligands produced there are required to stimulate kinase activity. N-terminal truncations of YycG lose negative regulation of their activity inferring that this regulation is accomplished through its transmembrane and extramembrane domains interacting with the membrane associated YycH and YycI proteins that do not localize to the divisome. The data indicate that YycG activity in non-dividing cells is suppressed by its interaction with YycH and YycI and its activation is co-ordinated to cell division in dividing cells by specific interactions that occur within the divisome.

Computational modeling of phosphotransfer complexes in two-component signaling.

Two-component signal transduction systems enable cells in bacteria, fungi, and plants to react to extracellular stimuli. A sensor histidine kinase (SK) detects such stimuli with its sensor domains and transduces the input signals to a response regulator (RR) by trans-phosphorylation. This trans-phosphorylation reaction requires the formation of a complex formed by the two interacting proteins. The complex is stabilized by transient interactions. The nature of the transient interactions makes it challenging for experimental techniques to gain structural information. X-ray crystallography requires stable crystals, which are difficult to grow and stabilize. Similarly, the mere size of these systems proves problematic for NMR. Theoretical methods can, however, complement existing data. The statistical direct coupling analysis presented in the previous chapter reveals the interacting residues at the contact interface of the SK/RR pair. This information can be combined with the structures of the individual proteins in molecular dynamical simulation to generate structural models of the complex. The general approach, referred to as MAGMA, was tested on the sporulation phosphorelay phosphotransfer complex, the Spo0B/Spo0F pair, delivering crystal resolution accuracy. The MAGMA method is described here in a step-by-step explanation. The developed parameters are transferrable to other SK/RR systems.

Inference of direct residue contacts in two-component signaling.

Since the onset of the genomic era more than 1000 bacterial genomes have been sequenced and several fold more are expected to be completed in the near future. These genome sequences supply a wealth of information that can be exploited by statistical methods to gain significant insights into cellular processes. In Volume 422 of Methods in Enzymology we described a covariance-based method, which was able to identify coevolving residue pairs between the ubiquitous bacterial two-component signal transduction proteins, the sensor kinase and the response regulator. Such residue position pairs supply interaction specificity in the light of highly amplified but structurally conserved two-component systems in a typical bacterium and are enriched with interaction surface residue pairings. In this chapter we describe an extended version of this method, termed "direct coupling analysis" (DCA), which greatly enhances the predictive power of traditional covariance analysis. DCA introduces a statistical inference step to covariance analysis, which allows to distinguish coevolution patterns introduced by direct correlations between two-residue positions, from those patterns that arise via indirect correlations, that is, correlations that are introduced by covariance with other residues in the respective proteins. This method was shown to reliably identify residue positions in spatial proximity within a protein or at the interface between two interaction partners. It is the goal of this chapter to allow an experienced programmer to reproduce our techniques and results so that DCA can soon be applied to new targets.

Interaction fidelity in two-component signaling.

Two component signal transduction systems and phosphorelays have been adapted and amplified by bacteria to respond to a multitude of environmental, metabolic and cell cycle signals while maintaining essentially identical structures for the domains responsible for recognition and phosphotransfer between the sensor histidine kinase and the response regulator. Co-crystal structures of these domains have revealed the variable residues at the interaction surface of the two components responsible for interaction specificity in signal transfer. This information has formed the basis for the development and validation of statistical methods to identify interaction residues and surfaces from compiled databases of interacting proteins and holds forth the promise of determining structures of multi-protein complexes and signaling networks.