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Archive for the ‘Bacterial Resistance’ Category


A Nonlinear Methodology to Explain Complexity of the Genome and Bioinformatic Information

Reporter: Stephen J. Williams, Ph.D.

Multifractal bioinformatics: A proposal to the nonlinear interpretation of genome

The following is an open access article by Pedro Moreno on a methodology to analyze genetic information across species and in particular, the evolutionary trends of complex genomes, by a nonlinear analytic approach utilizing fractal geometry, coined “Nonlinear Bioinformatics”.  This fractal approach stems from the complex nature of higher eukaryotic genomes including mosaicism, multiple interdispersed  genomic elements such as intronic regions, noncoding regions, and also mobile elements such as transposable elements.  Although seemingly random, there exists a repetitive nature of these elements. Such complexity of DNA regulation, structure and genomic variation is felt best understood by developing algorithms based on fractal analysis, which can best model the regionalized and repetitive variability and structure within complex genomes by elucidating the individual components which contributes to an overall complex structure rather than using a “linear” or “reductionist” approach looking at individual coding regions, which does not take into consideration the aforementioned factors leading to genetic complexity and diversity.

Indeed, many other attempts to describe the complexities of DNA as a fractal geometric pattern have been described.  In a paper by Carlo Cattani “Fractals and Hidden Symmetries in DNA“, Carlo uses fractal analysis to construct a simple geometric pattern of the influenza A virus by modeling the primary sequence of this viral DNA, namely the bases A,G,C, and T. The main conclusions that

fractal shapes and symmetries in DNA sequences and DNA walks have been shown and compared with random and deterministic complex series. DNA sequences are structured in such a way that there exists some fractal behavior which can be observed both on the correlation matrix and on the DNA walks. Wavelet analysis confirms by a symmetrical clustering of wavelet coefficients the existence of scale symmetries.

suggested that, at least, the viral influenza genome structure could be analyzed into its basic components by fractal geometry.
This approach has been used to model the complex nature of cancer as discussed in a 2011 Seminars in Oncology paper
Abstract: Cancer is a highly complex disease due to the disruption of tissue architecture. Thus, tissues, and not individual cells, are the proper level of observation for the study of carcinogenesis. This paradigm shift from a reductionist approach to a systems biology approach is long overdue. Indeed, cell phenotypes are emergent modes arising through collective non-linear interactions among different cellular and microenvironmental components, generally described by “phase space diagrams”, where stable states (attractors) are embedded into a landscape model. Within this framework, cell states and cell transitions are generally conceived as mainly specified by gene-regulatory networks. However, the system s dynamics is not reducible to the integrated functioning of the genome-proteome network alone; the epithelia-stroma interacting system must be taken into consideration in order to give a more comprehensive picture. Given that cell shape represents the spatial geometric configuration acquired as a result of the integrated set of cellular and environmental cues, we posit that fractal-shape parameters represent “omics descriptors of the epithelium-stroma system. Within this framework, function appears to follow form, and not the other way around.

As authors conclude

” Transitions from one phenotype to another are reminiscent of phase transitions observed in physical systems. The description of such transitions could be obtained by a set of morphological, quantitative parameters, like fractal measures. These parameters provide reliable information about system complexity. “

Gene expression also displays a fractal nature. In a Frontiers in Physiology paper by Mahboobeh Ghorbani, Edmond A. Jonckheere and Paul Bogdan* “Gene Expression Is Not Random: Scaling, Long-Range Cross-Dependence, and Fractal Characteristics of Gene Regulatory Networks“,

the authors describe that gene expression networks display time series display fractal and long-range dependence characteristics.

Abstract: Gene expression is a vital process through which cells react to the environment and express functional behavior. Understanding the dynamics of gene expression could prove crucial in unraveling the physical complexities involved in this process. Specifically, understanding the coherent complex structure of transcriptional dynamics is the goal of numerous computational studies aiming to study and finally control cellular processes. Here, we report the scaling properties of gene expression time series in Escherichia coliand Saccharomyces cerevisiae. Unlike previous studies, which report the fractal and long-range dependency of DNA structure, we investigate the individual gene expression dynamics as well as the cross-dependency between them in the context of gene regulatory network. Our results demonstrate that the gene expression time series display fractal and long-range dependence characteristics. In addition, the dynamics between genes and linked transcription factors in gene regulatory networks are also fractal and long-range cross-correlated. The cross-correlation exponents in gene regulatory networks are not unique. The distribution of the cross-correlation exponents of gene regulatory networks for several types of cells can be interpreted as a measure of the complexity of their functional behavior.

 

Given that multitude of complex biomolecular networks and biomolecules can be described by fractal patterns, the development of bioinformatic algorithms  would enhance our understanding of the interdependence and cross funcitonality of these mutiple biological networks, particularly in disease and drug resistance.  The article below by Pedro Moreno describes the development of such bioinformatic algorithms.

Pedro A. Moreno
Escuela de Ingeniería de Sistemas y Computación, Facultad de Ingeniería, Universidad del Valle, Cali, Colombia
E-mail: pedro.moreno@correounivalle.edu.co

Eje temático: Ingeniería de sistemas / System engineering
Recibido: 19 de septiembre de 2012
Aceptado: 16 de diciembre de 2013


 

 


Abstract

The first draft of the human genome (HG) sequence was published in 2001 by two competing consortia. Since then, several structural and functional characteristics for the HG organization have been revealed. Today, more than 2.000 HG have been sequenced and these findings are impacting strongly on the academy and public health. Despite all this, a major bottleneck, called the genome interpretation persists. That is, the lack of a theory that explains the complex puzzles of coding and non-coding features that compose the HG as a whole. Ten years after the HG sequenced, two recent studies, discussed in the multifractal formalism allow proposing a nonlinear theory that helps interpret the structural and functional variation of the genetic information of the genomes. The present review article discusses this new approach, called: “Multifractal bioinformatics”.

Keywords: Omics sciences, bioinformatics, human genome, multifractal analysis.


1. Introduction

Omic Sciences and Bioinformatics

In order to study the genomes, their life properties and the pathological consequences of impairment, the Human Genome Project (HGP) was created in 1990. Since then, about 500 Gpb (EMBL) represented in thousands of prokaryotic genomes and tens of different eukaryotic genomes have been sequenced (NCBI, 1000 Genomes, ENCODE). Today, Genomics is defined as the set of sciences and technologies dedicated to the comprehensive study of the structure, function and origin of genomes. Several types of genomic have arisen as a result of the expansion and implementation of genomics to the study of the Central Dogma of Molecular Biology (CDMB), Figure 1 (above). The catalog of different types of genomics uses the Latin suffix “-omic” meaning “set of” to mean the new massive approaches of the new omics sciences (Moreno et al, 2009). Given the large amount of genomic information available in the databases and the urgency of its actual interpretation, the balance has begun to lean heavily toward the requirements of bioinformatics infrastructure research laboratories Figure 1 (below).

The bioinformatics or Computational Biology is defined as the application of computer and information technology to the analysis of biological data (Mount, 2004). An interdisciplinary science that requires the use of computing, applied mathematics, statistics, computer science, artificial intelligence, biophysical information, biochemistry, genetics, and molecular biology. Bioinformatics was born from the need to understand the sequences of nucleotide or amino acid symbols that make up DNA and proteins, respectively. These analyzes are made possible by the development of powerful algorithms that predict and reveal an infinity of structural and functional features in genomic sequences, as gene location, discovery of homologies between macromolecules databases (Blast), algorithms for phylogenetic analysis, for the regulatory analysis or the prediction of protein folding, among others. This great development has created a multiplicity of approaches giving rise to new types of Bioinformatics, such as Multifractal Bioinformatics (MFB) that is proposed here.

1.1 Multifractal Bioinformatics and Theoretical Background

MFB is a proposal to analyze information content in genomes and their life properties in a non-linear way. This is part of a specialized sub-discipline called “nonlinear Bioinformatics”, which uses a number of related techniques for the study of nonlinearity (fractal geometry, Hurts exponents, power laws, wavelets, among others.) and applied to the study of biological problems (https://pharmaceuticalintelligence.com/tag/fractal-geometry/). For its application, we must take into account a detailed knowledge of the structure of the genome to be analyzed and an appropriate knowledge of the multifractal analysis.

1.2 From the Worm Genome toward Human Genome

To explore a complex genome such as the HG it is relevant to implement multifractal analysis (MFA) in a simpler genome in order to show its practical utility. For example, the genome of the small nematode Caenorhabditis elegans is an excellent model to learn many extrapolated lessons of complex organisms. Thus, if the MFA explains some of the structural properties in that genome it is expected that this same analysis reveals some similar properties in the HG.

The C. elegans nuclear genome is composed of about 100 Mbp, with six chromosomes distributed into five autosomes and one sex chromosome. The molecular structure of the genome is particularly homogeneous along with the chromosome sequences, due to the presence of several regular features, including large contents of genes and introns of similar sizes. The C. elegans genome has also a regional organization of the chromosomes, mainly because the majority of the repeated sequences are located in the chromosome arms, Figure 2 (left) (C. elegans Sequencing Consortium, 1998). Given these regular and irregular features, the MFA could be an appropriate approach to analyze such distributions.

Meanwhile, the HG sequencing revealed a surprising mosaicism in coding (genes) and noncoding (repetitive DNA) sequences, Figure 2 (right) (Venter et al., 2001). This structure of 6 Gbp is divided into 23 pairs of chromosomes (diploid cells) and these highly regionalized sequences introduce complex patterns of regularity and irregularity to understand the gene structure, the composition of sequences of repetitive DNA and its role in the study and application of life sciences. The coding regions of the genome are estimated at ~25,000 genes which constitute 1.4% of GH. These genes are involved in a giant sea of various types of non-coding sequences which compose 98.6% of HG (misnamed popularly as “junk DNA”). The non-coding regions are characterized by many types of repeated DNA sequences, where 10.6% consists of Alu sequences, a type of SINE (short and dispersed repeated elements) sequence and preferentially located towards the genes. LINES, MIR, MER, LTR, DNA transposons and introns are another type of non-coding sequences which form about 86% of the genome. Some of these sequences overlap with each other; as with CpG islands, which complicates the analysis of genomic landscape. This standard genomic landscape was recently clarified, the last studies show that 80.4% of HG is functional due to the discovery of more than five million “switches” that operate and regulate gene activity, re-evaluating the concept of “junk DNA”. (The ENCODE Project Consortium, 2012).

Given that all these genomic variations both in worm and human produce regionalized genomic landscapes it is proposed that Fractal Geometry (FG) would allow measuring how the genetic information content is fragmented. In this paper the methodology and the nonlinear descriptive models for each of these genomes will be reviewed.

1.3 The MFA and its Application to Genome Studies

Most problems in physics are implicitly non-linear in nature, generating phenomena such as chaos theory, a science that deals with certain types of (non-linear) but very sensitive dynamic systems to initial conditions, nonetheless of deterministic rigor, that is that their behavior can be completely determined by knowing initial conditions (Peitgen et al, 1992). In turn, the FG is an appropriate tool to study the chaotic dynamic systems (CDS). In other words, the FG and chaos are closely related because the space region toward which a chaotic orbit tends asymptotically has a fractal structure (strange attractors). Therefore, the FG allows studying the framework on which CDS are defined (Moon, 1992). And this is how it is expected for the genome structure and function to be organized.

The MFA is an extension of the FG and it is related to (Shannon) information theory, disciplines that have been very useful to study the information content over a sequence of symbols. Initially, Mandelbrot established the FG in the 80’s, as a geometry capable of measuring the irregularity of nature by calculating the fractal dimension (D), an exponent derived from a power law (Mandelbrot, 1982). The value of the D gives us a measure of the level of fragmentation or the information content for a complex phenomenon. That is because the D measures the scaling degree that the fragmented self-similarity of the system has. Thus, the FG looks for self-similar properties in structures and processes at different scales of resolution and these self-similarities are organized following scaling or power laws.

Sometimes, an exponent is not sufficient to characterize a complex phenomenon; so more exponents are required. The multifractal formalism allows this, and applies when many subgroups of fractals with different scalar properties with a large number of exponents or fractal dimensions coexist simultaneously. As a result, when a spectrum of multifractal singularity measurement is generated, the scaling behavior of the frequency of symbols of a sequence can be quantified (Vélez et al, 2010).

The MFA has been implemented to study the spatial heterogeneity of theoretical and experimental fractal patterns in different disciplines. In post-genomics times, the MFA was used to study multiple biological problems (Vélez et al, 2010). Nonetheless, very little attention has been given to the use of MFA to characterize the content of the structural genetic information of the genomes obtained from the images of the Chaos Representation Game (CRG). First studies at this level were made recently to the analysis of the C. elegans genome (Vélez et al, 2010) and human genomes (Moreno et al, 2011). The MFA methodology applied for the study of these genomes will be developed below.

2. Methodology

The Multifractal Formalism from the CGR

2.1 Data Acquisition and Molecular Parameters

Databases for the C. elegans and the 36.2 Hs_ refseq HG version were downloaded from the NCBI FTP server. Then, several strategies were designed to fragment the genomic DNA sequences of different length ranges. For example, the C. elegans genome was divided into 18 fragments, Figure 2 (left) and the human genome in 9,379 fragments. According to their annotation systems, the contents of molecular parameters of coding sequences (genes, exons and introns), noncoding sequences (repetitive DNA, Alu, LINES, MIR, MER, LTR, promoters, etc.) and coding/ non-coding DNA (TTAGGC, AAAAT, AAATT, TTTTC, TTTTT, CpG islands, etc.) are counted for each sequence.

2.2 Construction of the CGR 2.3 Fractal Measurement by the Box Counting Method

Subsequently, the CGR, a recursive algorithm (Jeffrey, 1990; Restrepo et al, 2009) is applied to each selected DNA sequence, Figure 3 (above, left) and from which an image is obtained, which is quantified by the box-counting algorithm. For example, in Figure 3 (above, left) a CGR image for a human DNA sequence of 80,000 bp in length is shown. Here, dark regions represent sub-quadrants with a high number of points (or nucleotides). Clear regions, sections with a low number of points. The calculation for the D for the Koch curve by the box-counting method is illustrated by a progression of changes in the grid size, and its Cartesian graph, Table 1

The CGR image for a given DNA sequence is quantified by a standard fractal analysis. A fractal is a fragmented geometric figure whose parts are an approximated copy at full scale, that is, the figure has self-similarity. The D is basically a scaling rule that the figure obeys. Generally, a power law is given by the following expression:

Where N(E) is the number of parts required for covering the figure when a scaling factor E is applied. The power law permits to calculate the fractal dimension as:

The D obtained by the box-counting algorithm covers the figure with disjoint boxes ɛ = 1/E and counts the number of boxes required. Figure 4 (above, left) shows the multifractal measure at momentum q=1.

2.4 Multifractal Measurement

When generalizing the box-counting algorithm for the multifractal case and according to the method of moments q, we obtain the equation (3) (Gutiérrez et al, 1998; Yu et al, 2001):

Where the Mi number of points falling in the i-th grid is determined and related to the total number Mand ɛ to box size. Thus, the MFA is used when multiple scaling rules are applied. Figure 4 (above, right) shows the calculation of the multifractal measures at different momentum q (partition function). Here, linear regressions must have a coefficient of determination equal or close to 1. From each linear regression D are obtained, which generate an spectrum of generalized fractal dimensions Dfor all q integers, Figure 4 (below, left). So, the multifractal spectrum is obtained as the limit:

The variation of the q integer allows emphasizing different regions and discriminating their fractal a high Dq is synonymous of the structure’s richness and the properties of these regions. Negative values emphasize the scarce regions; a high Dindicates a lot of structure and properties in these regions. In real world applications, the limit Dqreadily approximated from the data using a linear fitting: the transformation of the equation (3) yields:

Which shows that ln In(Mi )= for set q is a linear function in the ln(ɛ), Dq can therefore be evaluated as q the slope of a fixed relationship between In(Mi )= and (q-1) ln(ɛ). The methodologies and approaches for the method of box-counting and MFA are detailed in Moreno et al, 2000, Yu et al, 2001; Moreno, 2005. For a rigorous mathematical development of MFA from images consult Multifractal system, wikipedia.

2.5 Measurement of Information Content

Subsequently, from the spectrum of generalized dimensions Dq, the degree of multifractality ΔDq(MD) is calculated as the difference between the maximum and minimum values of : ΔD qq Dqmax – Dqmin (Ivanov et al, 1999). When qmaxqmin ΔDis high, the multifractal spectrum is rich in information and highly aperiodic, when ΔDq is small, the resulting dimension spectrum is poor in information and highly periodic. It is expected then, that the aperiodicity in the genome would be related to highly polymorphic genomic aperiodic structures and those periodic regions with highly repetitive and not very polymorphic genomic structures. The correlation exponent t(q) = (– 1)DqFigure 4 (below, right ) can also be obtained from the multifractal dimension Dq. The generalized dimension also provides significant specific information. D(q = 0) is equal to the Capacity dimension, which in this analysis is the size of the “box count”. D(q = 1) is equal to the Information dimension and D(q = 2) to the Correlation dimension. Based on these multifractal parameters, many of the structural genomic properties can be quantified, related, and interpreted.

2.6 Multifractal Parameters and Statistical and Discrimination Analyses

Once the multifractal parameters are calculated (D= (-20, 20), ΔDq, πq, etc.), correlations with the molecular parameters are sought. These relations are established by plotting the number of genome molecular parameters versus MD by discriminant analysis with Cartesian graphs in 2-D, Figure 5 (below, left) and 3-D and combining multifractal and molecular parameters. Finally, simple linear regression analysis, multivariate analysis, and analyses by ranges and clusterings are made to establish statistical significance.

3 Results and Discussion

3.1 Non-linear Descriptive Model for the C. elegans Genome

When analyzing the C. elegans genome with the multifractal formalism it revealed what symmetry and asymmetry on the genome nucleotide composition suggested. Thus, the multifractal scaling of the C. elegans genome is of interest because it indicates that the molecular structure of the chromosome may be organized as a system operating far from equilibrium following nonlinear laws (Ivanov et al, 1999; Burgos and Moreno-Tovar, 1996). This can be discussed from two points of view:

1) When comparing C. elegans chromosomes with each other, the X chromosome showed the lowest multifractality, Figure 5 (above). This means that the X chromosome is operating close to equilibrium, which results in an increased genetic instability. Thus, the instability of the X could selectively contribute to the molecular mechanism that determines sex (XX or X0) during meiosis. Thus, the X chromosome would be operating closer to equilibrium in order to maintain their particular sexual dimorphism.

2) When comparing different chromosome regions of the C. elegans genome, changes in multifractality were found in relation to the regional organization (at the center and arms) exhibited by the chromosomes, Figure 5 (below, left). These behaviors are associated with changes in the content of repetitive DNA, Figure 5 (below, right). The results indicated that the chromosome arms are even more complex than previously anticipated. Thus, TTAGGC telomere sequences would be operating far from equilibrium to protect the genetic information encoded by the entire chromosome.

All these biological arguments may explain why C. elegans genome is organized in a nonlinear way. These findings provide insight to quantify and understand the organization of the non-linear structure of the C. elegans genome, which may be extended to other genomes, including the HG (Vélez et al, 2010).

3.2 Nonlinear Descriptive Model for the Human Genome

Once the multifractal approach was validated in C. elegans genome, HG was analyzed exhaustively. This allowed us to propose a nonlinear model for the HG structure which will be discussed under three points of view.

1) It was found that the HG high multifractality depends strongly on the contents of Alu sequences and to a lesser extent on the content of CpG islands. These contents would be located primarily in highly aperiodic regions, thus taking the chromosome far from equilibrium and giving to it greater genetic stability, protection and attraction of mutations, Figure 6 (A-C). Thus, hundreds of regions in the HG may have high genetic stability and the most important genetic information of the HG, the genes, would be safeguarded from environmental fluctuations. Other repeated elements (LINES, MIR, MER, LTRs) showed no significant relationship,

Figure 6 (D). Consequently, the human multifractal map developed in Moreno et al, 2011 constitutes a good tool to identify those regions rich in genetic information and genomic stability. 2) The multifractal context seems to be a significant requirement for the structural and functional organization of thousands of genes and gene families. Thus, a high multifractal context (aperiodic) appears to be a “genomic attractor” for many genes (KOGs, KEEGs), Figure 6 (E) and some gene families, Figure 6 (F) are involved in genetic and deterministic processes, in order to maintain a deterministic regulation control in the genome, although most of HG sequences may be subject to a complex epigenetic control.

3) The classification of human chromosomes and chromosome regions analysis may have some medical implications (Moreno et al, 2002; Moreno et al, 2009). This means that the structure of low nonlinearity exhibited by some chromosomes (or chromosome regions) involve an environmental predisposition, as potential targets to undergo structural or numerical chromosomal alterations in Figure 6 (G). Additionally, sex chromosomes should have low multifractality to maintain sexual dimorphism and probably the X chromosome inactivation.

All these fractals and biological arguments could explain why Alu elements are shaping the HG in a nonlinearly manner (Moreno et al, 2011). Finally, the multifractal modeling of the HG serves as theoretical framework to examine new discoveries made by the ENCODE project and new approaches about human epigenomes. That is, the non-linear organization of HG might help to explain why it is expected that most of the GH is functional.

4. Conclusions

All these results show that the multifractal formalism is appropriate to quantify and evaluate genetic information contents in genomes and to relate it with the known molecular anatomy of the genome and some of the expected properties. Thus, the MFB allows interpreting in a logic manner the structural nature and variation of the genome.

The MFB allows understanding why a number of chromosomal diseases are likely to occur in the genome, thus opening a new perspective toward personalized medicine to study and interpret the GH and its diseases.

The entire genome contains nonlinear information organizing it and supposedly making it function, concluding that virtually 100% of HG is functional. Bioinformatics in general, is enriched with a novel approach (MFB) making it possible to quantify the genetic information content of any DNA sequence and their practical applications to different disciplines in biology, medicine and agriculture. This novel breakthrough in computational genomic analysis and diseases contributes to define Biology as a “hard” science.

MFB opens a door to develop a research program towards the establishment of an integrative discipline that contributes to “break” the code of human life. (http://pharmaceuticalintelligence. com/page/3/).

5. Acknowledgements

Thanks to the directives of the EISC, the Universidad del Valle and the School of Engineering for offering an academic, scientific and administrative space for conducting this research. Likewise, thanks to co authors (professors and students) who participated in the implementation of excerpts from some of the works cited here. Finally, thanks to Colciencias by the biotechnology project grant # 1103-12-16765.


6. References

Blanco, S., & Moreno, P.A. (2007). Representación del juego del caos para el análisis de secuencias de ADN y proteínas mediante el análisis multifractal (método “box-counting”). In The Second International Seminar on Genomics and Proteomics, Bioinformatics and Systems Biology (pp. 17-25). Popayán, Colombia.         [ Links ]

Burgos, J.D., & Moreno-Tovar, P. (1996). Zipf scaling behavior in the immune system. BioSystem , 39, 227-232.         [ Links ]

C. elegans Sequencing Consortium. (1998). Genome sequence of the nematode C. elegans: a platform for investigating biology. Science , 282, 2012-2018.         [ Links ]

Gutiérrez, J.M., Iglesias A., Rodríguez, M.A., Burgos, J.D., & Moreno, P.A. (1998). Analyzing the multifractals structure of DNA nucleotide sequences. In, M. Barbie & S. Chillemi (Eds.) Chaos and Noise in Biology and Medicine (cap. 4). Hackensack (NJ): World Scientific Publishing Co.         [ Links ]

Ivanov, P.Ch., Nunes, L.A., Golberger, A.L., Havlin, S., Rosenblum, M.G., Struzikk, Z.R., & Stanley, H.E. (1999). Multifractality in human heartbeat dynamics. Nature , 399, 461-465.         [ Links ]

Jeffrey, H.J. (1990). Chaos game representation of gene structure. Nucleic Acids Research , 18, 2163-2175.         [ Links ]

Mandelbrot, B. (1982). La geometría fractal de la naturaleza. Barcelona. España: Tusquets editores.         [ Links ]

Moon, F.C. (1992). Chaotic and fractal dynamics. New York: John Wiley.         [ Links ]

Moreno, P.A. (2005). Large scale and small scale bioinformatics studies on the Caenorhabditis elegans enome. Doctoral thesis. Department of Biology and Biochemistry, University of Houston, Houston, USA.         [ Links ]

Moreno, P.A., Burgos, J.D., Vélez, P.E., Gutiérrez, J.M., & et al., (2000). Multifractal analysis of complete genomes. In P roceedings of the 12th International Genome Sequencing and Analysis Conference (pp. 80-81). Miami Beach (FL).         [ Links ]

Moreno, P.A., Rodríguez, J.G., Vélez, P.E., Cubillos, J.R., & Del Portillo, P. (2002). La genómica aplicada en salud humana. Colombia Ciencia y Tecnología. Colciencias , 20, 14-21.         [ Links ]

Moreno, P.A., Vélez, P.E., & Burgos, J.D. (2009). Biología molecular, genómica y post-genómica. Pioneros, principios y tecnologías. Popayán, Colombia: Editorial Universidad del Cauca.         [ Links ]

Moreno, P.A., Vélez, P.E., Martínez, E., Garreta, L., Díaz, D., Amador, S., Gutiérrez, J.M., et. al. (2011). The human genome: a multifractal analysis. BMC Genomics , 12, 506.         [ Links ]

Mount, D.W. (2004). Bioinformatics. Sequence and ge nome analysis. New York: Cold Spring Harbor Laboratory Press.         [ Links ]

Peitgen, H.O., Jürgen, H., & Saupe D. (1992). Chaos and Fractals. New Frontiers of Science. New York: Springer-Verlag.         [ Links ]

Restrepo, S., Pinzón, A., Rodríguez, L.M., Sierra, R., Grajales, A., Bernal, A., Barreto, E. et. al. (2009). Computational biology in Colombia. PLoS Computational Biology, 5 (10), e1000535.         [ Links ]

The ENCODE Project Consortium. (2012). An integrated encyclopedia of DNA elements in the human genome. Nature , 489, 57-74.         [ Links ]

Vélez, P.E., Garreta, L.E., Martínez, E., Díaz, N., Amador, S., Gutiérrez, J.M., Tischer, I., & Moreno, P.A. (2010). The Caenorhabditis elegans genome: a multifractal analysis. Genet and Mol Res , 9, 949-965.         [ Links ]

Venter, J.C., Adams, M.D., Myers, E.W., Li, P.W., & et al. (2001). The sequence of the human genome. Science , 291, 1304-1351.         [ Links ]

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Other articles on Bioinformatics on this Open Access Journal include:

Bioinformatics Tool Review: Genome Variant Analysis Tools

2017 Agenda – BioInformatics: Track 6: BioIT World Conference & Expo ’17, May 23-35, 2017, Seaport World Trade Center, Boston, MA

Better bioinformatics

Broad Institute, Google Genomics combine bioinformatics and computing expertise

Autophagy-Modulating Proteins and Small Molecules Candidate Targets for Cancer Therapy: Commentary of Bioinformatics Approaches

CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics & Computational Genomics

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Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

The relationship between gut microbial metabolism and mental health is one of the most intriguing and controversial topics in microbiome research. Bidirectional microbiota–gut–brain communication has mostly been explored in animal models, with human research lagging behind. Large-scale metagenomics studies could facilitate the translational process, but their interpretation is hampered by a lack of dedicated reference databases and tools to study the microbial neuroactive potential.

 

Out of all the many ways, the teeming ecosystem of microbes in a person’s gut and other tissues might affect health. But, its potential influences on the brain may be the most provocative for research. Several studies in mice had indicated that gut microbes can affect behavior, and small scale studies on human beings suggested this microbial repertoire is altered in depression. Studies by two large European groups have found that several species of gut bacteria are missing in people with depression. The researchers can’t say whether the absence is a cause or an effect of the illness, but they showed that many gut bacteria could make substances that affect the nerve cell function—and maybe the mood.

 

Butyrate-producing Faecalibacterium and Coprococcus bacteria were consistently associated with higher quality of life indicators. Together with DialisterCoprococcus spp. was also depleted in depression, even after correcting for the confounding effects of antidepressants. Two kinds of microbes, Coprococcus and Dialister, were missing from the microbiomes of the depressed subjects, but not from those with a high quality of life. The researchers also found the depressed people had an increase in bacteria implicated in Crohn disease, suggesting inflammation may be at fault.

 

Looking for something that could link microbes to mood, researchers compiled a list of 56 substances important for proper functioning of nervous system that gut microbes either produce or break down. They found, for example, that Coprococcus seems to have a pathway related to dopamine, a key brain signal involved in depression, although they have no evidence how this might protect against depression. The same microbe also makes an anti-inflammatory substance called butyrate, and increased inflammation is implicated in depression.

 

Still, it is very much unclear that how microbial compounds made in the gut might influence the brain. One possible channel is the vagus nerve, which links the gut and brain. Resolving the microbiome-brain connection might lead to novel therapies. Some physicians and companies are already exploring typical probiotics, oral bacterial supplements, for depression, although they don’t normally include the missing gut microbes identified in the new study.

 

References:

 

https://www.sciencemag.org/news/2019/02/evidence-mounts-gut-bacteria-can-influence-mood-prevent-depression?utm_source=Nature+Briefing

 

https://www.nature.com/articles/s41564-018-0337-x

 

https://www.ncbi.nlm.nih.gov/pubmed/22968153

 

https://www.ncbi.nlm.nih.gov/pubmed/24888394

 

https://www.ncbi.nlm.nih.gov/pubmed/27067014

 

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Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Clostridium difficile-associated disease, a significant problem in healthcare facilities, causes an estimated 15,000 deaths in the United States each year. Clostridium difficile, commonly referred to as C. diff, is a bacterium that infects the colon and can cause diarrhea, fever, and abdominal pain. Clostridium difficile-associated disease (CDAD) most commonly occurs in hospitalized older adults who have recently taken antibiotics. However, cases of CDAD can occur outside of healthcare settings as well.

 

Although antibiotics often cure the infection, C. diff can cause potentially life-threatening colon inflammation. People with CDAD usually are treated with a course of antibiotics, such as oral vancomycin or fidaxomicin. However, CDAD returns in approximately 20 percent of people who receive such treatment, according to the Centers for Disease Control and Prevention (CDC).

 

Multiple research studies have indicated that fecal microbiota transplantation (FMT) is an effective method for curing patients with repeat C. diff infections. However, the long-term safety of FMT has not been established. Although more research is needed to determine precisely how FMT effectively cures recurrent CDAD, the treatment appears to rapidly restore a healthy and diverse gut microbiome in recipients. Physicians perform FMT using various routes of administration, including oral pills, upper gastrointestinal endoscopy, colonoscopy, and enema.

 

A research consortium recently began enrolling patients in a clinical trial examining whether FMT by enema (putting stool from a healthy donor in the colon of a recipient) is safe and can prevent recurrent CDAD, a potentially life-threatening diarrheal illness. Investigators aim to enroll 162 volunteer participants 18 years or older who have had two or more episodes of CDAD within the previous six months.

 

Trial sites include Emory University in Atlanta, Duke University Medical Center in Durham, North Carolina, and Vanderbilt University Medical Center in Nashville, Tennessee. Each location is a Vaccine and Treatment Evaluation Unit (VTEU), clinical research sites joined in a network funded by the National Institute of Allergy and Infectious Diseases (NIAID), part of the National Institutes of Health. This randomized, controlled trial aims to provide critical data on the efficacy and long-term safety of using FMT by enema to cure C. diff infections.

 

Volunteers will be enrolled in the trial after completing a standard course of antibiotics for a recurrent CDAD episode, presuming their diarrhea symptoms cease on treatment. They will be randomly assigned to one of two groups. The first group (108 people) will take an anti-diarrheal medication and receive a stool transplant (FMT) delivered by retention enema. The second group (54 people) will take an anti-diarrheal medication and receive a placebo solution delivered by retention enema.

 

Participants in either group who have diarrhea with stools that test positive for C. diff shortly after the enema will be given an active stool transplant for a maximum of two FMTs. If participants in either group have another C. diff infection after receiving two FMTs, then they will be referred to other locally available treatment options. Investigators will evaluate the stool specimens for changes in gut microbial diversity and infectious pathogens and will examine the blood samples for metabolic syndrome markers.

 

To learn more about the long-term outcomes of FMT, the researchers will monitor all participants for adverse side effects for three years after completing treatment for recurrent CDAD. Investigators will also collect information on any new onset of CDAD, related chronic medical conditions or any other serious health issues they may have.

 

References:

 

https://www.nih.gov/news-events/news-releases/clinical-trial-testing-fecal-microbiota-transplant-recurrent-diarrheal-disease-begins

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4749851/

 

https://bmjopengastro.bmj.com/content/3/1/e000087

 

https://jamanetwork.com/journals/jama/fullarticle/2635633

 

https://www.hopkinsmedicine.org/gastroenterology_hepatology/clinical_services/advanced_endoscopy/fecal_transplantation.html

 

https://en.wikipedia.org/wiki/Fecal_microbiota_transplant

 

https://www.openbiome.org/about-fmt/

 

https://taymount.com/faecal-microbiota-transplantation-fmt

 

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Signaling through the T Cell Receptor (TCR) Complex and the Co-stimulatory Receptor CD28

Curator: Larry H. Bernstein, MD, FCAP

 

 

New connections: T cell actin dynamics

Fluorescence microscopy is one of the most important tools in cell biology research because it provides spatial and temporal information to investigate regulatory systems inside cells. This technique can generate data in the form of signal intensities at thousands of positions resolved inside individual live cells. However, given extensive cell-to-cell variation, these data cannot be readily assembled into three- or four-dimensional maps of protein concentration that can be compared across different cells and conditions. We have developed a method to enable comparison of imaging data from many cells and applied it to investigate actin dynamics in T cell activation. Antigen recognition in T cells by the T cell receptor (TCR) is amplified by engagement of the costimulatory receptor CD28. We imaged actin and eight core actin regulators to generate over a thousand movies of T cells under conditions in which CD28 was either engaged or blocked in the context of a strong TCR signal. Our computational analysis showed that the primary effect of costimulation blockade was to decrease recruitment of the activator of actin nucleation WAVE2 (Wiskott-Aldrich syndrome protein family verprolin-homologous protein 2) and the actin-severing protein cofilin to F-actin. Reconstitution of WAVE2 and cofilin activity restored the defect in actin signaling dynamics caused by costimulation blockade. Thus, we have developed and validated an approach to quantify protein distributions in time and space for the analysis of complex regulatory systems.

RELATED CONTENT

 

Triple-Color FRET Analysis Reveals Conformational Changes in the WIP-WASp Actin-Regulating Complex

 

RELATED CONTENT

T cell activation by antigens involves the formation of a complex, highly dynamic, yet organized signaling complex at the site of the T cell receptors (TCRs). Srikanth et al. found that the lymphocyte-specific large guanosine triphosphatase of the Rab family CRACR2A-a associated with vesicles near the Golgi in unstimulated mouse and human CD4+ T cells. Upon TCR activation, these vesicles moved to the immunological synapse (the contact region between a T cell and an antigen-presenting cell). The guanine nucleotide exchange factor Vav1 at the TCR complex recruited CRACR2A-a to the complex. Without CRACR2A-a, T cell activation was compromised because of defective calcium and kinase signaling.

More than 60 members of the Rab family of guanosine triphosphatases (GTPases) exist in the human genome. Rab GTPases are small proteins that are primarily involved in the formation, trafficking, and fusion of vesicles. We showed that CRACR2A (Ca2+ release–activated Ca2+ channel regulator 2A) encodes a lymphocyte-specific large Rab GTPase that contains multiple functional domains, including EF-hand motifs, a proline-rich domain (PRD), and a Rab GTPase domain with an unconventional prenylation site. Through experiments involving gene silencing in cells and knockout mice, we demonstrated a role for CRACR2A in the activation of the Ca2+ and c-Jun N-terminal kinase signaling pathways in response to T cell receptor (TCR) stimulation. Vesicles containing this Rab GTPase translocated from near the Golgi to the immunological synapse formed between a T cell and a cognate antigen-presenting cell to activate these signaling pathways. The interaction between the PRD of CRACR2A and the guanidine nucleotide exchange factor Vav1 was required for the accumulation of these vesicles at the immunological synapse. Furthermore, we demonstrated that GTP binding and prenylation of CRACR2A were associated with its localization near the Golgi and its stability. Our findings reveal a previously uncharacterized function of a large Rab GTPase and vesicles near the Golgi in TCR signaling. Other GTPases with similar domain architectures may have similar functions in T cells.

 

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Microbe meets cancer

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Microbes Meet Cancer

Understanding cancer’s relationship with the human microbiome could transform immune-modulating therapies.

By Kate Yandell | April 1, 2016  http://www.the-scientist.com/?articles.view/articleNo/45616/title/Microbes-Meet-Cancer

 © ISTOCK.COM/KATEJA_FN; © ISTOCK.COM/FRANK RAMSPOTT  http://www.the-scientist.com/images/April2016/feature1.jpg

In 2013, two independent teams of scientists, one in Maryland and one in France, made a surprising observation: both germ-free mice and mice treated with a heavy dose of antibiotics responded poorly to a variety of cancer therapies typically effective in rodents. The Maryland team, led by Romina Goldszmidand Giorgio Trinchieri of the National Cancer Institute, showed that both an investigational immunotherapy and an approved platinum chemotherapy shrank a variety of implanted tumor types and improved survival to a far greater extent in mice with intact microbiomes.1 The French group, led by INSERM’s Laurence Zitvogel, got similar results when testing the long-standing chemotherapeutic agent cyclophosphamide in cancer-implanted mice, as well as in mice genetically engineered to develop tumors of the lung.2

The findings incited a flurry of research and speculation about how gut microbes contribute to cancer cell death, even in tumors far from the gastrointestinal tract. The most logical link between the microbiome and cancer is the immune system. Resident microbes can either dial up inflammation or tamp it down, and can modulate immune cells’ vigilance for invaders. Not only does the immune system appear to be at the root of how the microbiome interacts with cancer therapies, it also appears to mediate how our bacteria, fungi, and viruses influence cancer development in the first place.

“We clearly see shifts in the [microbial] community that precede development of tumors,” says microbiologist and immunologist Patrick Schloss, who studies the influence of the microbiome on colon cancer at the University of Michigan.

But the relationship between the microbiome and cancer is complex: while some microbes promote cell proliferation, others appear to protect us against cancerous growth. And in some cases, the conditions that spur one cancer may have the opposite effect in another. “It’s become pretty obvious that the commensal microbiota affect inflammation and, through that or through other mechanisms, affect carcinogenesis,” says Trinchieri. “What we really need is to have a much better understanding of which species, which type of bug, is doing what and try to change the balance.”

Gut feeling

In the late 1970s, pathologist J. Robin Warren of Royal Perth Hospital in Western Australia began to notice that curved bacteria often appeared in stomach tissue biopsies taken from patients with chronic gastritis, an inflammation of the stomach lining that often precedes the development of stomach cancer. He and Barry J. Marshall, a trainee in internal medicine at the hospital, speculated that the bacterium, now called Helicobacter pylori, was somehow causing the gastritis.3 So committed was Marshall to demonstrating the microbe’s causal relationship to the inflammatory condition that he had his own stomach biopsied to show that it contained no H. pylori, then infected himself with the bacterium and documented his subsequent experience of gastritis.4 Scientists now accept that H. pylori, a common gut microbe that is present in about 50 percent of the world’s population, is responsible for many cases of gastritis and most stomach ulcers, and is a strong risk factor for stomach cancer.5 Marshall and Warren earned the 2005 Nobel Prize in Physiology or Medicine for their work.

H. pylori may be the most clear-cut example of a gut bacterium that influences cancer development, but it is likely not the only one. Researchers who study cancer in mice have long had anecdotal evidence that shifts in the microbiome influence the development of diverse tumor types. “You have a mouse model of carcinogenesis. It works beautifully,” says Trinchieri. “You move to another institution. It works completely differently,” likely because the animals’ microbiomes vary with environment.

IMMUNE INFLUENCE: In recent years, research has demonstrated that microbes living in and on the mammalian body can affect cancer risk, as well as responses to cancer treatment. Although the details of this microbe-cancer link remain unclear, investigators suspect that the microbiome’s ability to modulate inflammation and train immune cells to react to tumors is to blame.
See full infographic: WEB | PDF
© AL GRANBERG

Around the turn of the 21st century, cancer researchers began to systematically experiment with the rodent microbiome, and soon had several lines of evidence linking certain gut microbes with a mouse’s risk of colon cancer. In 2001, for example, Shoichi Kado of the Yakult Central Institute for Microbiological Research in Japan and colleagues found that a strain of immunocompromised mice rapidly developed colon tumors, but that germ-free versions of these mice did not.6 That same year, an MIT-based group led by the late David Schauer demonstrated that infecting mice with the bacterium Citrobacter rodentium spurred colon tumor development.7 And in 2003, MIT’s Susan Erdman and her colleagues found that they could induce colon cancer in immunocompromised mice by infecting them with Helicobacter hepaticus, a relative of? H. pylori that commonly exists within the murine gut microbiome.8

More recent work has documented a similar link between colon cancer and the gut microbiome in humans. In 2014, a team led by Schloss sequenced 16S rRNA genes isolated from the stool of 90 people, some with colon cancer, some with precancerous adenomas, and still others with no disease.9 The researchers found that the feces of people with cancer tended to have an altered composition of bacteria, with an excess of the common mouth microbes Fusobacterium or Porphyromonas. A few months later, Peer Bork of the European Molecular Biology Laboratory performed metagenomic sequencing of stool samples from 156 people with or without colorectal cancer. Bork and his colleagues found they could predict the presence or absence of cancer using the relative abundance of 22 bacterial species, including Porphyromonas andFusobacterium.10 They could also use the method to predict colorectal cancer with about the same accuracy as a blood test, correctly identifying about 50 percent of cancers while yielding false positives less than 10 percent of the time. When the two tests were combined, they caught more than 70 percent of cancers.

Whether changes in the microbiota in colon cancer patients are harbingers of the disease or a consequence of tumor development remained unclear. “What comes first, the change in the microbiome or tumor development?” asks Schloss. To investigate this question, he and his colleagues treated mice with microbiome-altering antibiotics before administering a carcinogen and an inflammatory agent, then compared the outcomes in those animals and in mice that had received only the carcinogenic and inflammatory treatments, no antibiotics. The antibiotic-treated animals had significantly fewer and smaller colon tumors than the animals with an undisturbed microbiome, suggesting that resident bacteria were in some way promoting cancer development. And when the researchers transferred microbiota from healthy mice to antibiotic-treated or germ-free mice, the animals developed more tumors following carcinogen exposure. Sterile mice that received microbiota from mice already bearing malignancies developed the most tumors of all.11

Most recently, Schloss and his colleagues showed that treating mice with seven unique combinations of antibiotics prior to exposing them to carcinogens yielded variable but predictable levels of tumor formation. The researchers determined that the number of tumors corresponded to the unique ways that each antibiotic cocktail modulated the microbiome.12

“We’ve kind of proven to ourselves, at least, that the microbiome is involved in colon cancer,” says Schloss, who hypothesizes that gut bacteria–driven inflammation is to blame for creating an environment that is hospitable to tumor development and growth. Gain or loss of certain components of the resident bacterial community could lead to the release of reactive oxygen species, damaging cells and their genetic material. Inflammation also involves increased release of growth factors and blood vessel proliferation, potentially supporting the growth of tumors. (See illustration above.)

Recent research has also yielded evidence that the gut microbiota impact the development of cancer in sites far removed from the intestinal tract, likely through similar immune-modulating mechanisms.

Systemic effects

In the mid-2000s, MIT’s Erdman began infecting a strain of mice predisposed to intestinal tumors withH. hepaticus and observing the subsequent development of colon cancer in some of the animals. To her surprise, one of the mice developed a mammary tumor. Then, more of the mice went on to develop mammary tumors. “This told us that something really interesting was going on,” Erdman recalls. Sure enough, she and her colleagues found that mice infected with H. hepaticus were more likely to develop mammary tumors than mice not exposed to the bacterium.13The researchers showed that systemic immune activation and inflammation could contribute to mammary tumors in other, less cancer-prone mouse models, as well as to the development of prostate cancer.

MICROBIAL STOWAWAYS: Bacteria of the human gut microbiome are intimately involved in cancer development and progression, thanks to their interactions with the immune system. Some microbes, such as Helicobacter pylori, increase the risk of cancer in their immediate vicinity (stomach), while others, such as some Bacteroides species, help protect against tumors by boosting T-cell infiltration.© EYE OF SCIENCE/SCIENCE SOURCE
http://www.the-scientist.com/images/April2016/immune_2.jpg

 

 

© DR. GARY GAUGLER/SCIENCE SOURCE  http://www.the-scientist.com/images/April2016/immune3.jpg

At the University of Chicago, Thomas Gajewski and his colleagues have taken a slightly different approach to studying the role of the microbiome in cancer development. By comparing Black 6 mice coming from different vendors—Taconic Biosciences (formerly Taconic Farms) and the Jackson Laboratory—Gajewski takes advantage of the fact that the animals’ different origins result in different gut microbiomes. “We deliberately stayed away from antibiotics, because we had a desire to model how intersubject heterogeneity [in cancer development] might be impacted by the commensals they happen to be colonized with,” says Gajewski in an email to The Scientist.

Last year, the researchers published the results of a study comparing the progression of melanoma tumors implanted under the mice’s skin, finding that tumors in the Taconic mice grew more aggressively than those in the Jackson mice. When the researchers housed the different types of mice together before their tumors were implanted, however, these differences disappeared. And transferring fecal material from the Jackson mice into the Taconic mice altered the latter’s tumor progression.14

Instead of promoting cancer, in these experiments the gut microbiome appeared to slow tumor growth. Specifically, the reduced tumor growth in the Jackson mice correlated with the presence of Bifidobacterium, which led to the greater buildup of T?cells in the Jackson mice’s tumors. Bifidobacteriaactivate dendritic cells, which present antigens from bacteria or cancer cells to T?cells, training them to hunt down and kill these invaders. Feeding Taconic mice bifidobacteria improved their response to the implanted melanoma cells.

“One hypothesis going into the experiments was that we might identify immune-suppressive bacteria, or commensals that shift the immune response towards a character that was unfavorable for tumor control,” says Gajewski.  “But in fact, we found that even a single type of bacteria could boost the antitumor immune response.”

http://www.the-scientist.com/images/April2016/immune4.jpg

 

Drug interactions

Ideally, the immune system should recognize cancer as invasive and nip tumor growth in the bud. But cancer cells display “self” molecules that can inhibit immune attack. A new type of immunotherapy, dubbed checkpoint inhibition or blockade, spurs the immune system to attack cancer by blocking either the tumor cells’ surface molecules or the receptors on T?cells that bind to them.

CANCER THERAPY AND THE MICROBIOME

In addition to influencing the development and progression of cancer by regulating inflammation and other immune pathways, resident gut bacteria appear to influence the effectiveness of many cancer therapies that are intended to work in concert with host immunity to eliminate tumors.

  • Some cancer drugs, such as oxaliplatin chemotherapy and CpG-oligonucleotide immunotherapy, work by boosting inflammation. If the microbiome is altered in such a way that inflammation is reduced, these therapeutic agents are less effective.
  • Cancer-cell surface proteins bind to receptors on T cells to prevent them from killing cancer cells. Checkpoint inhibitors that block this binding of activated T cells to cancer cells are influenced by members of the microbiota that mediate these same cell interactions.
  • Cyclophosphamide chemotherapy disrupts the gut epithelial barrier, causing the gut to leak certain bacteria. Bacteria gather in lymphoid tissue just outside the gut and spur generation of T helper 1 and T helper 17 cells that migrate to the tumor and kill it.

As part of their comparison of Jackson and Taconic mice, Gajewski and his colleagues decided to test a type of investigational checkpoint inhibitor that targets PD-L1, a ligand found in high quantities on the surface of multiple types of cancer cells. Monoclonal antibodies that bind to PD-L1 block the PD-1 receptors on T?cells from doing so, allowing an immune response to proceed against the tumor cells. While treating Taconic mice with PD-L1–targeting antibodies did improve their tumor responses, they did even better when that treatment was combined with fecal transfers from Jackson mice, indicating that the microbiome and the immunotherapy can work together to take down cancer. And when the researchers combined the anti-PD-L1 therapy with a bifidobacteria-enriched diet, the mice’s tumors virtually disappeared.14

Gajewski’s group is now surveying the gut microbiota in humans undergoing therapy with checkpoint inhibitors to better understand which bacterial species are linked to positive outcomes. The researchers are also devising a clinical trial in which they will give Bifidobacterium supplements to cancer patients being treated with the approved anti-PD-1 therapy pembrolizumab (Keytruda), which targets the immune receptor PD-1 on T?cells, instead of the cancer-cell ligand PD-L1.

Meanwhile, Zitvogel’s group at INSERM is investigating interactions between the microbiome and another class of checkpoint inhibitors called CTLA-4 inhibitors, which includes the breakthrough melanoma treatment ipilimumab (Yervoy). The researchers found that tumors in antibiotic-treated and germ-free mice had poorer responses to a CTLA-4–targeting antibody compared with mice harboring unaltered microbiomes.15 Particular Bacteroides species were associated with T-cell infiltration of tumors, and feedingBacteroides fragilis to antibiotic-treated or germ-free mice improved the animals’ responses to the immunotherapy. As an added bonus, treatment with these “immunogenic” Bacteroides species decreased signs of colitis, an intestinal inflammatory condition that is a dangerous side effect in patients using checkpoint inhibitors. Moreover, Zitvogel and her colleagues showed that human metastatic melanoma patients treated with ipilimumab tended to have elevated levels of B. fragilis in their microbiomes. Mice transplanted with feces from patients who showed particularly strong B. fragilis gains did better on anti-CTLA-4 treatment than did mice transplanted with feces from patients with normal levels of B. fragilis.

“There are bugs that allow the therapy to work, and at the same time, they protect against colitis,” says Trinchieri. “That is very exciting, because not only [can] we do something to improve the therapy, but we can also, at the same time, try to reduce the side effect.”

And these checkpoint inhibitors aren’t the only cancer therapies whose effects are modulated by the microbiome. Trinchieri has also found that an immunotherapy that combines antibodies against interleukin-10 receptors with CpG oligonucleotides is more effective in mice with unaltered microbiomes.1He and his NCI colleague Goldszmid further found that the platinum chemotherapy oxaliplatin (Eloxatin) was more effective in mice with intact microbiomes, and Zitvogel’s group has shown that the chemotherapeutic agent cyclophosphamide is dependent on the microbiota for its proper function.

Although the mechanisms by which the microbiome influences the effectiveness of such therapies remains incompletely understood, researchers once again speculate that the immune system is the key link. Cyclophosphamide, for example, spurs the body to generate two types of T?helper cells, T?helper 1 cells and a subtype of T?helper 17 cells referred to as “pathogenic,” both of which destroy tumor cells. Zitvogel and her colleagues found that, in mice with unaltered microbiomes, treatment with cyclophosphamide works by disrupting the intestinal mucosa, allowing bacteria to escape into the lymphoid tissues just outside the gut. There, the bacteria spur the body to generate T?helper 1 and T?helper 17 cells, which translocate to the tumor. When the researchers transferred the “pathogenic” T?helper 17 cells into antibiotic-treated mice, the mice’s response to chemotherapy was partly restored.

Microbiome modification

As the link between the microbiome and cancer becomes clearer, researchers are thinking about how they can manipulate a patient’s resident microbial communities to improve their prognosis and treatment outcomes. “Once you figure out exactly what is happening at the molecular level, if there is something promising there, I would be shocked if people don’t then go in and try to modulate the microbiome, either by using pharmaceuticals or using probiotics,” says Michael Burns, a postdoc in the lab of University of Minnesota genomicist Ran Blekhman.

Even if researchers succeed in identifying specific, beneficial alterations to the microbiome, however, molding the microbiome is not simple. “It’s a messy, complicated system that we don’t understand,” says Schloss.

So far, studies of the gut microbiome and colon cancer have turned up few consistent differences between cancer patients and healthy controls. And the few bacterial groups that have repeatedly shown up are not present in every cancer patient. “We should move away from saying, ‘This is a causal species of bacteria,’” says Blekhman. “It’s more the function of a community instead of just a single bacterium.”

But the study of the microbiome in cancer is young. If simply adding one type of microbe into a person’s gut is not enough, researchers may learn how to dose people with patient-specific combinations of microbes or antibiotics. In February 2016, a team based in Finland and China showed that a probiotic mixture dubbed Prohep could reduce liver tumor size by 40 percent in mice, likely by promoting an anti-inflammatory environment in the gut.16

“If it is true that, in humans, we can alter the course of the disease by modulating the composition of the microbiota,” says José Conejo-Garcia of the Wistar Institute in Philadelphia, “that’s going to be very impactful.”

Kate Yandell has been a freelance writer living Philadelphia, Pennsylvania. In February she became an associate editor at Cancer Today.

GENETIC CONNECTION

The microbiome doesn’t act in isolation; a patient’s genetic background can also greatly influence response to therapy. Last year, for example, the Wistar Institute’s José Garcia-Conejo and Melanie Rutkowski, now an assistant professor at the University of Virginia, showed that a dominant polymorphism of the gene for the innate immune protein toll-like receptor 5 (TLR5) influences clinical outcomes in cancer patients by changing how the patients’ immune cells interact with their gut microbes (Cancer Cell, 27:27-40, 2015).

More than 7 percent of people carry a specific mutation in TLR5 that prevents them from mounting a full immune response when exposed to bacterial flagellin. Analyzing both genetic and survival data from the Cancer Genome Atlas, Conejo-Garcia, Rutkowski, and their colleagues found that estrogen receptor–positive breast cancer patients who carry the TLR5 mutation, called the R392X polymorphism, have worse outcomes than patients without the mutation. Among patients with ovarian cancer, on the other hand, those with the TLR5 mutation were more likely to live at least six years after diagnosis than patients who don’t carry the mutation.

Investigating the mutation’s contradictory effects, the researchers found that mice with normal TLR5produce higher levels of the cytokine interleukin 6 (IL-6) than those carrying the mutant version, which have higher levels of a different cytokine called interleukin 17 (IL-17). But when the researchers knocked out the animals’ microbiomes, these differences in cytokine production disappeared, as did the differences in cancer progression between mutant and wild-type animals.

“The effectiveness of depleting specific populations or modulating the composition of the microbiome is going to affect very differently people who are TLR5-positive or TLR5-negative,” says Conejo-Garcia. And Rutkowski speculates that many more polymorphisms linked to cancer prognosis may act via microbiome–immune system interactions. “I think that our paper is just the tip of the iceberg.”

References

  1. N. Iida et al., “Commensal bacteria control cancer response to therapy by modulating the tumor microenvironment,” Science, 342:967-70, 2013.
  2. S. Viaud et al., “The intestinal microbiota modulates the anticancer immune effects of cyclophosphamide,” Science, 342:971-76, 2013.
  3. J.R. Warren, B. Marshall, “Unidentified curved bacilli on gastric epithelium in active chronic gastritis,”Lancet, 321:1273-75, 1983.
  4. B.J. Marshall et al., “Attempt to fulfil Koch’s postulates for pyloric Campylobacter,” Med J Aust, 142:436-39, 1985.
  5. J. Parsonnet et al., “Helicobacter pylori infection and the risk of gastric carcinoma,” N Engl J Med, 325:1127-31, 1991.
  6. S. Kado et al., “Intestinal microflora are necessary for development of spontaneous adenocarcinoma of the large intestine in T-cell receptor β chain and p53 double-knockout mice,” Cancer Res, 61:2395-98, 2001.
  7. J.V. Newman et al., “Bacterial infection promotes colon tumorigenesis in ApcMin/+ mice,” J Infect Dis, 184:227-30, 2001.
  8. S.E. Erdman et al., “CD4+ CD25+ regulatory T lymphocytes inhibit microbially induced colon cancer in Rag2-deficient mice,” Am J Pathol, 162:691-702, 2003.
  9. J.P. Zackular et al., “The human gut microbiome as a screening tool for colorectal cancer,” Cancer Prev Res, 7:1112-21, 2014.
  10. G. Zeller et al., “Potential of fecal microbiota for early-stage detection of colorectal cancer,” Mol Syst Biol, 10:766, 2014.
  11. J.P. Zackular et al., “The gut microbiome modulates colon tumorigenesis,” mBio, 4:e00692-13, 2013.
  12. J.P. Zackular et al., “Manipulation of the gut microbiota reveals role in colon tumorigenesis,”mSphere, doi:10.1128/mSphere.00001-15, 2015.
  13. V.P. Rao et al., “Innate immune inflammatory response against enteric bacteria Helicobacter hepaticus induces mammary adenocarcinoma in mice,” Cancer Res, 66:7395, 2006.
  14. A. Sivan et al., “Commensal Bifidobacterium promotes antitumor immunity and facilitates anti-PD-L1 efficacy,” Science, 350:1084-89, 2015.
  15. M. Vétizou et al., “Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota,”Science, 350:1079-84, 2015.

……..

 

Microbially Driven TLR5-Dependent Signaling Governs Distal Malignant Progression through Tumor-Promoting Inflammation

Melanie R. Rutkowski, Tom L. Stephen, Nikolaos Svoronos, …., Julia Tchou,  Gabriel A. Rabinovich, Jose R. Conejo-Garcia
Cancer cell    12 Jan 2015; Volume 27, Issue 1, p27–40  http://dx.doi.org/10.1016/j.ccell.2014.11.009
Figure thumbnail fx1
  • TLR5-dependent IL-6 mobilizes MDSCs that drive galectin-1 production by γδ T cells
  • IL-17 drives malignant progression in IL-6-unresponsive tumors
  • TLR5-dependent differences in tumor growth are abrogated upon microbiota depletion
  • A common dominant TLR5 polymorphism influences the outcome of human cancers

The dominant TLR5R392X polymorphism abrogates flagellin responses in >7% of humans. We report that TLR5-dependent commensal bacteria drive malignant progression at extramucosal locations by increasing systemic IL-6, which drives mobilization of myeloid-derived suppressor cells (MDSCs). Mechanistically, expanded granulocytic MDSCs cause γδ lymphocytes in TLR5-responsive tumors to secrete galectin-1, dampening antitumor immunity and accelerating malignant progression. In contrast, IL-17 is consistently upregulated in TLR5-unresponsive tumor-bearing mice but only accelerates malignant progression in IL-6-unresponsive tumors. Importantly, depletion of commensal bacteria abrogates TLR5-dependent differences in tumor growth. Contrasting differences in inflammatory cytokines and malignant evolution are recapitulated in TLR5-responsive/unresponsive ovarian and breast cancer patients. Therefore, inflammation, antitumor immunity, and the clinical outcome of cancer patients are influenced by a common TLR5 polymorphism.

see also… Immune Influence

In recent years, research has demonstrated that microbes living in and on the mammalian body can affect cancer risk, as well as responses to cancer treatment.

By Kate Yandell | April 1, 2016

http://www.the-scientist.com/?articles.view/articleNo/45644/title/Immune-Influence

Although the details of this microbe-cancer link remain unclear, investigators suspect that the microbiome’s ability to modulate inflammation and train immune cells to react to tumors is to blame. Here are some of the hypotheses that have come out of recent research in rodents for how gut bacteria shape immunity and influence cancer.

HOW THE MICROBIOME PROMOTES CANCER

Gut bacteria can dial up inflammation locally in the colon, as well as in other parts of the body, leading to the release of reactive oxygen species, which damage cells and DNA, and of growth factors that spur tumor growth and blood vessel formation.

http://www.the-scientist.com/images/April2016/ImmuneInfluence1_640px.jpg

http://www.the-scientist.com/images/April2016/ImmuneInfluence2_310px1.jpg

Helicobacter pylori can cause inflammation and high cell turnover in the stomach wall, which may lead to cancerous growth.

HOW THE MICROBIOME STEMS CANCER

Gut bacteria can also produce factors that lower inflammation and slow tumor growth. Some gut bacteria (e.g., Bifidobacterium)
appear to activate dendritic cells,
which present cancer-cell antigens to T cells that in turn kill the cancer cells.

http://www.the-scientist.com/images/April2016/ImmuneInfluence3_310px1.jpg

http://www.the-scientist.com/images/April2016/ImmuneInfluence4_310px1.jpg

Read the full story.

 

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Salmonella adaptive “switch”

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Molecular switch lets salmonella fight or evade immune system   

February 4, 2016   http://phys.org/news/2016-02-molecular-salmonella-evade-immune.html

 

 

http://cdn.phys.org/newman/csz/news/800/2016/salmonella.jpg

Salmonella forms a biofilm. Credit: CDC

 

Researchers at the University of Illinois at Chicago have discovered a molecular regulator that allows salmonella bacteria to switch from actively causing disease to lurking in a chronic but asymptomatic state called a biofilm.

http://phys.org/news/2016-02-molecular-salmonella-evade-immune.html#jCp

Their findings are published in the online journal, eLife.

Biofilms cling to surfaces in the body, such as the bronchial tubes or artificial joints, often without causing illness. But they can be a reservoir of bacteria that detach and cause disease or infect new hosts. The biofilms are resistant to host defenses and antibiotics because their tightly-packed structure exposes little surface area for drugs to reach. Many pathogenic bacteria are able to switch from an infectious to a dormant state as a strategy for survival inside their hosts.

 

Linda Kenney, professor of microbiology and immunology at the UIC College of Medicine and lead author of the study, had been studying how survive inside immune system cells called macrophages. These patrol the body and engulf viruses and bacteria they encounter. They encase their prey in a bubble called a vacuole that protects them from the invader until it can be destroyed.

Macrophages digest their quarry when the acidity inside the vacuole drops in response to the captive. But the bacteria have evolved a unique defense, enabling them to survive inside the vacuole and use the macrophage as a Trojan horse to travel elsewhere in the body undetected by other immune cells.

Kenney knew that a type of salmonella that causes typhoid fever in humans, called Salmonella typhi, and its mouse counterpart, Salmonella typhimurium, were able to survive inside macrophage vacuoles. She noticed that these bacteria did two things: inside the vacuole, they formed a kind of syringe – a long, hollow filament to inject the vacuole with a host of proteins that altered it. They also quickly assumed the same acidity of the vacuole.

“These two defenses, together, allow salmonella to survive and replicate in the harsh conditions of the vacuole,” Kenney said.

Further experiments revealed that sensing and mirroring the acidity, or pH, of the vacuole is what triggers salmonella to form the syringe.

“The syringe-forming and pH-adjusting genes are signaled to turn on by the lower pH inside the vacuole,” Kenney said. But these same salmonella, equipped to survive the hostile environment inside a macrophage vacuole, were also able to exist free in the body of the host—as biofilms.

“I wanted to know how Salmonella ‘decide’ between these two very different lifestyles,” Kenney said.

Studying S. typhimurium, Kenney discovered that the molecular switch is a bacterial molecule called SsrB. As the macrophage vacuole starts to acidify, SsrB is activated and it turns on the genes needed to form the syringe and adjust the pH. When salmonella lives outside the vacuole, where pH levels are neutral, SsrB instead turns on genes for sticky proteins in the membrane that help bacteria bind to one another to form biofilms.

Kenney said that many disease-causing salmonella evolved from harmless strains partly by acquiring new genes from other germs in a process called horizontal gene transfer.

“Salmonella acquired their pH-adjusting and syringe-forming genes in this way, as well as the switch that turns them on and off – SsrB,” she said. “The default mode, or its ancestral program, dictates that it make biofilms, cause no illness, and survive long enough to infect new hosts when the opportunity arises. The new genes allow it to survive the host’s main defense—the acidifying macrophage vacuole.”

Understanding how switch from the disease-causing state to the biofilm state could help scientists develop anticancer drugs that encourage the formation of biofilms on tumors, Kenney said.

“When salmonella forms biofilms on tumors, it releases TNF-alpha, a powerful anti-tumor molecule,” she said. “If we can better control the formation of biofilms, we can target them to tumors for cancer therapy.”

Explore further: Revealing camouflaged bacteria

More information: The horizontally-acquired response regulator SsrB drives a Salmonella lifestyle switch by relieving biofilm silencing, dx.doi.org/10.7554/eLife.10747 , elifesciences.org/content/5/e10747

The horizontally-acquired response regulator SsrB drives a Salmonella lifestyle switch by relieving biofilm silencing

 Stuti K Desai, 

A common strategy by which bacterial pathogens reside in humans is by shifting from a virulent lifestyle, (systemic infection), to a dormant carrier state. Two major serovars of Salmonella enterica, Typhi and Typhimurium, have evolved a two-component regulatory system to exist insideSalmonella-containing vacuoles in the macrophage, as well as to persist as asymptomatic biofilms in the gallbladder. Here we present evidence that SsrB, a transcriptional regulator encoded on the SPI-2 pathogenicity-island, determines the switch between these two lifestyles by controlling ancestral and horizontally-acquired genes. In the acidic macrophage vacuole, the kinase SsrA phosphorylates SsrB, and SsrB~P relieves silencing of virulence genes and activates their transcription. In the absence of SsrA, unphosphorylated SsrB directs transcription of factors required for biofilm formation specifically by activating csgD (agfD), the master biofilm regulator by disrupting the silenced, H-NS-bound promoter. Anti-silencing mechanisms thus control the switch between opposing lifestyles.

 

Introduction

Salmonella enterica serovar Typhimurium is a rod-shaped enteric bacterium which easily infects diverse hosts such as humans, cattle, poultry and reptiles through contaminated food or water, causing gastroenteritis. A human-restricted serovar of Salmonella enterica, serovar Typhi, causes typhoid fever and continues to be a dangerous pathogen throughout the world. Salmonella lives as a facultative pathogen in various natural and artificial environments as independent planktonic cells, cooperative swarms (Harshey and Matsuyama, 1994) or as multi-cellular communities called biofilms (see Steenackers et al., 2012 for a review). Upon successful invasion of host cells, Salmonella is phagocytosed by macrophages, where it resides in a modified vacuole in a self-nourishing niche called a Salmonella-Containing Vacuole (SCV). This intracellular lifestyle eventually adversely affects the host. Salmonella also resides as multi-cellular communities on intestinal epithelial cells (Boddicker et al., 2002), gallstones (Prouty et al., 2002) and tumors (Crull et al., 2011). It is believed that biofilms in the gall bladder are important for maintaining the carrier state, allowing Salmonella to persist (Crawford et al., 2010).

Each of these lifestyles of Salmonella are regulated by two-component regulatory systems (TCRS). TCRSs are comprised of a membrane-bound sensor histidine kinase and a cytoplasmic response regulator. The virulence genes of Salmonella are encoded on horizontally acquired AT-rich segments of the genome called Salmonella Pathogenecity Islands (SPIs), and are also tightly regulated by TCRSs. For example, the SsrA/B TCRS is essential for the activation of the SPI-2 regulon genes encoding a type-three secretory needle and effectors that are involved in formation of the SCV (Cirillo et al., 1998). Interestingly, the SsrA/B system itself is regulated by upstream two-component systems such as EnvZ/OmpR and PhoP/Q, which regulate gene expression in response to changes in osmolality, pH and the presence of anti-microbial peptides (Fields et al., 1989; Miller et al., 1989;Lee et al., 2000; Feng et al., 2003). The ssrA and ssrB genes are present on the SPI-2 pathogenecity island adjacent to each other and are regulated by a set of divergent promoters (Feng et al., 2003; Ochman et al., 1996). Under acidic pH and low osmolality, the ssrA and ssrB genes are transcriptionally activated by the binding of OmpR~P and PhoP~P to their promoters (Feng et al., 2003; Bijlsma and Groisman, 2005; Walthers and Kenney unpublished) whose levels are in turn regulated by the respective sensor kinases, EnvZ and PhoQ. SsrA is a tripartite membrane-bound histidine sensor kinase that undergoes a series of intra-molecular phosphorylation reactions before it transfers the phosphoryl group to the N-terminal aspartate residue of the response regulator, SsrB.

SsrB belongs to the NarL/FixJ family of transcriptional regulators that require phosphorylation-dependent dimerization to bind DNA. The X-ray crystal structure of NarL revealed that the C-terminal DNA binding domain was occluded by the N-terminus (Baikalov et al., 1996), and phosphorylation was predicted to relieve this inhibition. Full-length SsrB is unstable in solution, but an isolated C-terminal domain of SsrB, SsrBc, is capable of binding to the regulatory regions of nine genes belonging to the SPI-2 regulon, including ssrA and ssrB (Feng et al., 2004; Walthers et al., 2007) and activating transcription. A role for SsrB~P was identified by its dual function as a direct transcriptional activator and as an anti-silencer of H-NS-mediated repression (Walthers et al., 2007). The Histone like Nucleoid Structuring protein H-NS is involved in silencing many of the SPI-2 regulon genes in accordance with its role in binding to xenogenic AT-rich sequences and repressing their expression (Walthers et al., 2007; Navarre et al., 2006). H-NS binding to DNA leads to the formation of a stiff nucleoprotein filament which is essential in gene silencing (Lim et al., 2012; Liu et al., 2010; Amit et al., 2003; Winardhi et al., 2015). Moreover, relief of repression occurs due to the binding of SsrBc to this rigid H-NS-DNA complex (Walthers et al., 2011).

Salmonella reservoirs in host and non-host environments produce a three-dimensional extracellular matrix which consists of curli fimbriae, cellulose, proteins and extracellular DNA, to encase clusters of bacteria and form a mature biofilm. CsgD (AgfD) is the master regulator of biofilm formation (Gerstel et al., 2003); it is a LuxR family transcriptional activator that activates the expression of curli fimbriae encoded by csgDEFG/csgBAC operons (Collinson et al., 1996; Romling et al., 1998). CsgD also activates expression of adrA, increasing intracellular c-di-GMP levels, and activating the cellulose biosynthetic operon bcsABZC (Zogaj et al., 2001). Two other biofilm matrix components are also positively regulated by CsgD: BapA and the O-antigen capsule (Latasa et al., 2005; Gibson et al., 2006).

Transcriptional profiling of biofilms formed by S. Typhimurium SL1344 showed that many SPI-2 genes were down-regulated, yet SsrA was required for biofilms (Hamilton et al., 2009). This apparent paradox drove us to explore the underlying mechanism of biofilm formation. The role of SsrA/B in this process was of particular interest, since our previous comparison of SsrA and SsrB levels at neutral and acidic pH had shown that the expression of ssrA and ssrB was uncoupled (Feng et al., 2004).

We examined the ability of the wild type S. Typhimurium strain 14028s to form biofilms in the absence of ssrA and ssrB and found it to be dependent only on the expression of ssrB. We further showed that H-NS was a negative regulator of csgD. Surprisingly, the SsrB response regulator positively regulated the formation of biofilms by activating csgD expression in the absence of any phospho-donors. Moreover, AFM imaging revealed that unphosphorylated SsrB was able to bind to the csgD regulatory region and binding was sufficient to relieve H-NS-mediated repression and favor formation of S. Typhimurium biofilms.

As a result of these studies, we propose that SsrB, a pathogenicity island-2-encoded response regulator, sits at a pivotal position in governing Salmonella lifestyle fate: to either exist inside the host (in the SCV) as a promoter of virulence; or as a surface-attached multicellular biofilm, maintaining the carrier state. This switch is achieved merely by the ability of unphosphorylated SsrB to function as an anti-repressor of H-NS and the additional role of SsrB~P in activating SPI-2 transcription (Walthers et al., 2011).

 

eLife digest

Salmonella bacteria can infect a range of hosts, including humans and poultry, and cause sickness and diseases such as typhoid fever. Disease-causing Salmonella evolved from harmless bacteria in part by acquiring new genes from other organisms through a process called horizontal gene transfer. However, some strains of disease-causing Salmonella can also survive inside hosts as communities called biofilms without causing any illness to their hosts, who act as carriers of the disease and are able to pass their infection on to others.

So how do Salmonella bacteria ‘decide’ between these two lifestyles? Previous studies have uncovered a regulatory system that controls the decision in Salmonella, which is made up of two proteins called SsrA and SsrB. To trigger the disease-causing lifestyle, SsrA is activated and adds a phosphate group onto SsrB. This in turn causes SsrB to bind to and switch on disease-associated genes in the bacterium. However, it was less clear how the biofilm lifestyle was triggered.

Desai et al. now reveal that the phosphate-free form of SsrB – which was considered to be the inactive form of this protein – plays an important role in the formation of biofilms. Experiments involving an approach called atomic force microscopy showed that the unmodified SsrB acts to stop a major gene that controls biofilm formation from being switched off by a so-called repressor protein.

Salmonella acquired SsrB through horizontal gene transfer, and these findings show how this protein now acts as a molecular switch between disease-causing and biofilm-based lifestyles. SsrB protein is also involved in the decision to switch between these states, but how it does so remains a question for future work.

DOI:http://dx.doi.org/10.7554/eLife.10747.002

 

Figure 6.

https://elife-publishing-cdn.s3.amazonaws.com/10747/elife-10747-fig6-v1-480w.jpg

Figure 6.SsrB condenses H-NS bound csgD DNA.

(A) (i) AFM imaging in the presence of 600 nM H-NS shows a straight and rigid filament on csgD755. (ii) Addition of 600 nM SsrB to the H-NS bound csgD DNA resulted in areas of condensation (pink arrows; an ‘SsrB signature’) along with a few areas where the straight H-NS bound conformation persisted (yellow line; an ‘H-NS signature’); Scale bar = 200 nm as in Figure 5A. (B) A model for the mechanism of anti-silencing by SsrB at csgD wherein SsrB likely displaces H-NS from the ends of a stiffened nucleoprotein filament and relieves the blockade on the promoter for RNA polymerase to activate transcription. For details refer to (Winardhi et al., 2015).

 

Discussion

Pathogenic microbes constantly evolve novel means to counter the multitude of challenges posed by complex eukaryotic hosts. Successful acquisition and integeration of laterally acquired genes into the native genome of pathogens leads to novel capabilities enabling their survival in a wide range of environmental stresses. The present work demonstrates how the presence or absence of the horizontally acquired SsrA kinase controls post-translational modification of the transcription factor SsrB (i.e. phosphorylation at aspartate-56). This event controls the fate of Salmonella Typhimurium, resulting in either acute or chronic, but asymptomatic infection. A variation on two-component signaling in a similar lifestyle fate in Pseudomonas aeruginosa involved the presence or absence of the hybrid kinase RetS (Goodman et al., 2004).

SsrB sits at a pivotal decision point that determines Salmonella lifestyles

When the SsrA kinase is present and activated by acid stress, SsrB is phosphorylated and SsrB~P de-represses H-NS and activates transcription at SPI-2 and SPI-2 co-regulated genes, including: sifA(Walthers et al., 2011), ssaB, ssaM, sseA and ssaG (Walthers et al., 2007). In the absence of the SsrA kinase, SsrB is not phosphorylated, but it can counter H-NS silencing at csgD (Figure 4A–D andFigure 6A). SsrB binding and bending at the csgD promoter causes a sufficient change in the DNA secondary structure (Figure 5B,C) that likely enables access for RNA polymerase, stimulating csgDtranscription. It is interesting to note that SsrB is located on the SPI-2 pathogenicity island, and thus was acquired as Salmonella enterica diverged from Salmonella bongori. However, the capability to form biofilms is an ancestral trait, as phylogeny studies have shown that most of the natural or clinical isolates of Salmonella belonging to all the five sub-groups form rdar colonies (White and Surette, 2006). The SsrB response regulator can control two distinct lifestyle choices: the ability to assemble a type three secretory system and survive in the macrophage vacuole or the ability to form biofilms on gallstones in the gall bladder to establish the carrier state.

What then controls the presence or activation of the kinase SsrA? Our early experiments indicated that SsrA and SsrB were uncoupled from one another (i.e., SsrB was present in the absence of SsrA) and ssrA transcription was completely dependent on OmpR (Feng et al., 2004). The EnvZ/OmpR system is stimulated by a decrease in cytoplasmic pH when Salmonella enters the macrophage vacuole (Chakraborty et al., 2015). This may also be the stimulus for activating SsrA, since theSalmonella cytoplasm acidifies to pH 5.6 during infection and the cytoplasmic domain of EnvZ (EnvZc) was sufficient for signal transduction (Wang et al., 2012; Chakraborty et al., 2015). Previous reports also identified a role for PhoP in ssrA translation (Bijlsma and Groisman, 2005), which would further add to fluctuating SsrA levels. The present work describes a novel role for the unphosphorylated response regulator SsrB in de-repressing H-NS (Figure 6B). We show that under biofilm-inducing conditions, unphosphorylated SsrB is sufficient to activate the expression of csgD. There are only a few such examples of unphosphorylated response regulators playing a role in transcription such as DegU (Dahl et al., 1992) in Bacillus subtilis and RcsB (Latasa et al., 2012) in S.Typhimurium.

The importance of anti-silencing in gene regulation

In recent years, it has become apparent that H-NS silences pathogenicity island genes in Salmonella(Lucchini et al., 2006; Navarre et al., 2006; Walthers et al., 2007; 2011). Understanding how H-NS silences genes and how this silencing is relieved is an active area of research (Will et al., 2015;Winardhi et al., 2015). Because the anti-silencing style of gene regulation is indirect and does not rely on specific DNA interactions, searching for SsrB binding sites has not been informative in uncovering this type of regulation (Tomljenovic-Berube et al., 2010; Worley et al., 2000; Shea et al., 1996). Even a recent report in which the proteomes of wild type, hilA null (a transcriptional regulator of SPI-1 genes) and ssrB null were analyzed by SILAC and compared with an existing CHIP dataset failed to identify csgD as an SsrB-regulated locus (Brown et al., 2014), as sequence gazing alone does not help in identifying mechanisms of transcriptional regulation.

SsrB is well suited to this style of regulation, because it does not recognize a well-defined binding site (Feng et al., 2004; Walthers et al., 2007; Tomljenovic-Berube et al., 2010), it has a high non-specific binding component (Carroll et al., 2009) and it bends DNA upon binding (Carroll et al., 2009; Figure 6B, this work). Furthermore, previous microarray studies disrupted both ssrA and ssrB, which would not uncover a distinct role for SsrB in gene regulation under non SPI-2-inducing conditions in the absence of the SsrA kinase. It is worth mentioning here that in our AFM images, it was apparent that H-NS was still bound to some regions of the csgD promoter when SsrB condensed the DNA (Figure 6A(ii)). Thus, H-NS does not have to be completely stripped off the DNA for de-repression to occur, a finding that was also evident in our previous studies (Liu et al., 2010) and others (Will et al., 2014).

SsrB binds and bends DNA, resulting in highly curved DNA conformations. This DNA binding property of SsrB is distinct from H-NS, which forms rigid nucleoprotein filaments and thus straight DNA conformations (Figure 6A(i)). Bent DNA is therefore an energetically unfavorable substrate for H-NS binding, and a likely mechanism of SsrB-mediated anti-silencing of H-NS repressed genes. SsrB-dependent displacement of H-NS is more energetically favored to occur predominantly at the ends of H-NS-bound filaments, which requires disruption of fewer H-NS protein-protein interactions (Winardhi et al., 2015 and Figure 6B). In an equal mixture of H-NS and SsrB (Figure 6A(ii)), we do not see evidence of sharply bent filaments. This is expected because H-NS dissociation is likely restricted to the filament ends. Such events occur due to the cooperative nature of H-NS binding that results in a chain of linked H-NS proteins. Hence, H-NS displacement by SsrB likely occurs progressively from the filament end. This behavior has been observed in our single-molecule stretching experiments with H-NS filaments in the presence of SsrB. This ability of H-NS to re-orient on the DNA without being released would also promote its re-binding and silencing when SsrB or other anti-silencers are released (Figure 6B).

Structural homology does not indicate functional homology

Response regulators are grouped into subfamilies on the basis of the structures of their DNA binding domains. SsrB is in the NarL/FixJ subfamily, which possess a helix-turn-helix (HTH) motif in the C-terminus (Baikalov et al., 1996). NarL was the first full-length structure of a response regulator and it showed that the N-terminal phosphorylation domain physically blocked the recognition helix in the HTH motif (Maris et al., 2002). Thus, phosphorylation is required to relieve the inhibition of the N-terminus. In the results presented herein, it is apparent that SsrB has adapted to relieving H-NS-silencing and that phosphorylation is not required for this behavior, nor is it required for DNA binding (Figure 5B).

In summary, we showed that the response regulator SsrB is required for biofilm formation because it can de-repress H-NS at the csgD promoter (Figure 6B). This leads to the production of CsgD, the master regulator of biofilms. It is noteworthy that a laterally acquired gene product, SsrB, has evolved the job of regulating the levels of csgD, a transcriptional regulator encoded by the core genome. For this activity, phosphorylation of SsrB was not required, which is rare amongst response regulators. Furthermore, we identify H-NS as a repressor of csgD in Salmonella, instead of an activator (Gerstel et al., 2003). This unifies the regulation of CsgD by H-NS in E. coli (Ogasawara et al., 2010) and Salmonella. This work places SsrB at a unique decision point in the choice between lifestyles bySalmonella and makes it crucial for the entire gamut of pathogenesis, i.e., biofilms and virulence.

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Inflammatory Disorders: Articles published @ pharmaceuticalintelligence.com

Curators: Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

This is a compilation of articles on Inflammatory Disorders that were published 

@ pharmaceuticalintelligence.com, since 4/2012 to date

There are published works that have not been included.  However, there is a substantial amount of material in the following categories:

  1. The systemic inflammatory response
    https://pharmaceuticalintelligence.com/2014/11/08/introduction-to-impairments-in-pathological-states-endocrine-disorders-stress-hypermetabolism-cancer/
    https://pharmaceuticalintelligence.com/2014/11/09/summary-and-perspectives-impairments-in-pathological-states-endocrine-disorders-stress-hypermetabolism-cancer/
    https://pharmaceuticalintelligence.com/2015/12/19/neutrophil-serine-proteases-in-disease-and-therapeutic-considerations/
    https://pharmaceuticalintelligence.com/2014/03/21/what-is-the-key-method-to-harness-inflammation-to-close-the-doors-for-many-complex-diseases/
    https://pharmaceuticalintelligence.com/2012/08/20/therapeutic-targets-for-diabetes-and-related-metabolic-disorders/
    https://pharmaceuticalintelligence.com/2012/12/03/a-second-look-at-the-transthyretin-nutrition-inflammatory-conundrum/
    https://pharmaceuticalintelligence.com/2012/07/08/zebrafish-provide-insights-into-causes-and-treatment-of-human-diseases/
    https://pharmaceuticalintelligence.com/2016/01/25/ibd-immunomodulatory-effect-of-retinoic-acid-il-23il-17a-axis-correlates-with-the-nitric-oxide-pathway/
    https://pharmaceuticalintelligence.com/2015/11/29/role-of-inflammation-in-disease/
    https://pharmaceuticalintelligence.com/2013/03/06/can-resolvins-suppress-acute-lung-injury/
    https://pharmaceuticalintelligence.com/2015/02/26/acute-lung-injury/
  2. sepsis
    https://pharmaceuticalintelligence.com/2012/10/20/nitric-oxide-and-sepsis-hemodynamic-collapse-and-the-search-for-therapeutic-options/
  3. vasculitis
    https://pharmaceuticalintelligence.com/2015/02/26/acute-lung-injury/
    https://pharmaceuticalintelligence.com/2012/11/26/the-molecular-biology-of-renal-disorders/
    https://pharmaceuticalintelligence.com/2012/11/20/the-potential-for-nitric-oxide-donors-in-renal-function-disorders/
  4. neurodegenerative disease
    https://pharmaceuticalintelligence.com/2013/02/27/ustekinumab-new-drug-therapy-for-cognitive-decline-resulting-from-neuroinflammatory-cytokine-signaling-and-alzheimers-disease/
    https://pharmaceuticalintelligence.com/2016/01/26/amyloid-and-alzheimers-disease/
    https://pharmaceuticalintelligence.com/2016/02/15/alzheimers-disease-tau-art-thou-or-amyloid/
    https://pharmaceuticalintelligence.com/2016/01/26/beyond-tau-and-amyloid/
    https://pharmaceuticalintelligence.com/2015/12/10/remyelination-of-axon-requires-gli1-inhibition/
    https://pharmaceuticalintelligence.com/2015/11/28/neurovascular-pathways-to-neurodegeneration/
    https://pharmaceuticalintelligence.com/2015/11/13/new-alzheimers-protein-aicd-2/
    https://pharmaceuticalintelligence.com/2015/10/31/impairment-of-cognitive-function-and-neurogenesis/
    https://pharmaceuticalintelligence.com/2014/05/06/bwh-researchers-genetic-variations-can-influence-immune-cell-function-risk-factors-for-alzheimers-diseasedm-and-ms-later-in-life/
  5. cancer immunology
    https://pharmaceuticalintelligence.com/2013/04/12/innovations-in-tumor-immunology/
    https://pharmaceuticalintelligence.com/2016/01/09/signaling-of-immune-response-in-colon-cancer/
    https://pharmaceuticalintelligence.com/2015/05/12/vaccines-small-peptides-aptamers-and-immunotherapy-9/
    https://pharmaceuticalintelligence.com/2015/01/30/viruses-vaccines-and-immunotherapy/
    https://pharmaceuticalintelligence.com/2015/10/20/gene-expression-and-adaptive-immune-resistance-mechanisms-in-lymphoma/
    https://pharmaceuticalintelligence.com/2013/08/04/the-delicate-connection-ido-indolamine-2-3-dehydrogenase-and-immunology/
  6. autoimmune diseases: rheumatoid arthritis, colitis, ileitis, …
    https://pharmaceuticalintelligence.com/2016/02/11/intestinal-inflammatory-pharmaceutics/
    https://pharmaceuticalintelligence.com/2016/01/07/two-new-drugs-for-inflammatory-bowel-syndrome-are-giving-patients-hope/
    https://pharmaceuticalintelligence.com/2015/12/16/contribution-to-inflammatory-bowel-disease-ibd-of-bacterial-overgrowth-in-gut-on-a-chip/
    https://pharmaceuticalintelligence.com/2016/02/13/cytokines-in-ibd/
    https://pharmaceuticalintelligence.com/2016/01/23/autoimmune-inflammtory-bowl-diseases-crohns-disease-ulcerative-colitis-potential-roles-for-modulation-of-interleukins-17-and-23-signaling-for-therapeutics/
    https://pharmaceuticalintelligence.com/2014/10/14/autoimmune-disease-single-gene-eliminates-the-immune-protein-isg15-resulting-in-inability-to-resolve-inflammation-and-fight-infections-discovery-rockefeller-university/
    https://pharmaceuticalintelligence.com/2015/03/01/diarrheas-bacterial-and-nonbacterial/
    https://pharmaceuticalintelligence.com/2016/02/11/intestinal-inflammatory-pharmaceutics/
    https://pharmaceuticalintelligence.com/2014/01/28/biologics-for-autoimmune-diseases-cambridge-healthtech-institutes-inaugural-may-5-6-2014-seaport-world-trade-center-boston-ma/
    https://pharmaceuticalintelligence.com/2015/11/19/rheumatoid-arthritis-update/
    https://pharmaceuticalintelligence.com/2013/08/04/the-delicate-connection-ido-indolamine-2-3-dehydrogenase-and-immunology/
    https://pharmaceuticalintelligence.com/2013/07/31/confined-indolamine-2-3-dehydrogenase-controls-the-hemostasis-of-immune-responses-for-good-and-bad/
    https://pharmaceuticalintelligence.com/2012/09/13/tofacitinib-an-oral-janus-kinase-inhibitor-in-active-ulcerative-colitis/
    https://pharmaceuticalintelligence.com/2013/03/05/approach-to-controlling-pathogenic-inflammation-in-arthritis/
    https://pharmaceuticalintelligence.com/2013/03/05/rheumatoid-arthritis-risk/
    https://pharmaceuticalintelligence.com/2012/07/08/the-mechanism-of-action-of-the-drug-acthar-for-systemic-lupus-erythematosus-sle/
  7. T cells in immunity
    https://pharmaceuticalintelligence.com/2015/09/07/t-cell-mediated-immune-responses-signaling-pathways-activated-by-tlrs/
    https://pharmaceuticalintelligence.com/2015/05/14/allogeneic-stem-cell-transplantation-9-2/
    https://pharmaceuticalintelligence.com/2015/02/19/graft-versus-host-disease/
    https://pharmaceuticalintelligence.com/2014/10/14/autoimmune-disease-single-gene-eliminates-the-immune-protein-isg15-resulting-in-inability-to-resolve-inflammation-and-fight-infections-discovery-rockefeller-university/
    https://pharmaceuticalintelligence.com/2014/05/27/immunity-and-host-defense-a-bibliography-of-research-technion/
    https://pharmaceuticalintelligence.com/2013/08/04/the-delicate-connection-ido-indolamine-2-3-dehydrogenase-and-immunology/
    https://pharmaceuticalintelligence.com/2013/07/31/confined-indolamine-2-3-dehydrogenase-controls-the-hemostasis-of-immune-responses-for-good-and-bad/
    https://pharmaceuticalintelligence.com/2013/04/14/immune-regulation-news/

Proteomics, metabolomics and diabetes

https://pharmaceuticalintelligence.com/2015/11/16/reducing-obesity-related-inflammation/

https://pharmaceuticalintelligence.com/2015/10/25/the-relationship-of-stress-hypermetabolism-to-essential-protein-needs/

https://pharmaceuticalintelligence.com/2015/10/24/the-relationship-of-s-amino-acids-to-marasmic-and-kwashiorkor-pem/

https://pharmaceuticalintelligence.com/2015/10/24/the-significant-burden-of-childhood-malnutrition-and-stunting/

https://pharmaceuticalintelligence.com/2015/04/14/protein-binding-protein-protein-interactions-therapeutic-implications-7-3/

https://pharmaceuticalintelligence.com/2015/03/07/transthyretin-and-the-stressful-condition/

https://pharmaceuticalintelligence.com/2015/02/13/neural-activity-regulating-endocrine-response/

https://pharmaceuticalintelligence.com/2015/01/31/proteomics/

https://pharmaceuticalintelligence.com/2015/01/17/proteins-an-evolutionary-record-of-diversity-and-adaptation/

https://pharmaceuticalintelligence.com/2014/11/01/summary-of-signaling-and-signaling-pathways/

https://pharmaceuticalintelligence.com/2014/10/31/complex-models-of-signaling-therapeutic-implications/

https://pharmaceuticalintelligence.com/2014/10/24/diabetes-mellitus/

https://pharmaceuticalintelligence.com/2014/10/16/metabolomics-summary-and-perspective/

https://pharmaceuticalintelligence.com/2014/10/14/metabolic-reactions-need-just-enough/

https://pharmaceuticalintelligence.com/2014/11/03/introduction-to-protein-synthesis-and-degradation/

https://pharmaceuticalintelligence.com/2015/09/25/proceedings-of-the-nyas/

https://pharmaceuticalintelligence.com/2014/10/31/complex-models-of-signaling-therapeutic-implications/

https://pharmaceuticalintelligence.com/2014/03/21/what-is-the-key-method-to-harness-inflammation-to-close-the-doors-for-many-complex-diseases/

https://pharmaceuticalintelligence.com/2013/03/05/irf-1-deficiency-skews-the-differentiation-of-dendritic-cells/

https://pharmaceuticalintelligence.com/2012/11/26/new-insights-on-no-donors/

https://pharmaceuticalintelligence.com/2012/11/20/the-potential-for-nitric-oxide-donors-in-renal-function-disorders/

 

 

 

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