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Archive for the ‘Bacterial Resistance’ Category

From High-Throughput Assay to Systems Biology: New Tools for Drug Discovery

Curator: Stephen J. Williams, PhD

Marc W. Kirschner*

Department of Systems Biology
Harvard Medical School

Boston, Massachusetts 02115

With the new excitement about systems biology, there is understandable interest in a definition. This has proven somewhat difficult. Scientific fields, like spe­cies, arise by descent with modification, so in their ear­liest forms even the founders of great dynasties are only marginally different than their sister fields and spe­cies. It is only in retrospect that we can recognize the significant founding events. Before embarking on a def­inition of systems biology, it may be worth remember­ing that confusion and controversy surrounded the in­troduction of the term “molecular biology,” with claims that it hardly differed from biochemistry. Yet in retro­spect molecular biology was new and different. It intro­duced both new subject matter and new technological approaches, in addition to a new style.

As a point of departure for systems biology, consider the quintessential experiment in the founding of molec­ular biology, the one gene one enzyme hypothesis of Beadle and Tatum. This experiment first connected the genotype directly to the phenotype on a molecular level, although efforts in that direction can certainly be found in the work of Archibald Garrod, Sewell Wright, and others. Here a protein (in this case an enzyme) is seen to be a product of a single gene, and a single function; the completion of a specific step in amino acid biosynthesis is the direct result. It took the next 30 years to fill in the gaps in this process. Yet the one gene one enzyme hypothesis looks very different to us today. What is the function of tubulin, of PI-3 kinase or of rac? Could we accurately predict the phenotype of a nonle­thal mutation in these genes in a multicellular organ­ism? Although we can connect structure to the gene, we can no longer infer its larger purpose in the cell or in the organism. There are too many purposes; what the protein does is defined by context. The context also includes a history, either developmental or physiologi­cal. Thus the behavior of the Wnt signaling pathway depends on the previous lineage, the “where and when” questions of embryonic development. Similarly the behavior of the immune system depends on previ­ous experience in a variable environment. All of these features stress how inadequate an explanation for function we can achieve solely by trying to identify genes (by annotating them!) and characterizing their transcriptional control circuits.

That we are at a crossroads in how to explore biology is not at all clear to many. Biology is hardly in its dotage; the process of discovery seems to have been per­fected, accelerated, and made universally applicable to all fields of biology. With the completion of the human genome and the genomes of other species, we have a glimpse of many more genes than we ever had before to study. We are like naturalists discovering a new con­tinent, enthralled with the diversity itself. But we have also at the same time glimpsed the finiteness of this list of genes, a disturbingly small list. We have seen that the diversity of genes cannot approximate the diversity of functions within an organism. In response, we have argued that combinatorial use of small numbers of components can generate all the diversity that is needed. This has had its recent incarnation in the sim­plistic view that the rules of cis-regulatory control on DNA can directly lead to an understanding of organ­isms and their evolution. Yet this assumes that the gene products can be linked together in arbitrary combina­tions, something that is not assured in chemistry. It also downplays the significant regulatory features that in­volve interactions between gene products, their local­ization, binding, posttranslational modification, degra­dation, etc. The big question to understand in biology is not regulatory linkage but the nature of biological systems that allows them to be linked together in many nonlethal and even useful combinations. More and more we come to realize that understanding the con­served genes and their conserved circuits will require an understanding of their special properties that allow them to function together to generate different pheno­types in different tissues of metazoan organisms. These circuits may have certain robustness, but more impor­tant they have adaptability and versatility. The ease of putting conserved processes under regulatory control is an inherent design feature of the processes them­selves. Among other things it loads the deck in evolu­tionary variation and makes it more feasible to generate useful phenotypes upon which selection can act.

Systems biology offers an opportunity to study how the phenotype is generated from the genotype and with it a glimpse of how evolution has crafted the pheno­type. One aspect of systems biology is the develop­ment of techniques to examine broadly the level of pro­tein, RNA, and DNA on a gene by gene basis and even the posttranslational modification and localization of proteins. In a very short time we have witnessed the development of high-throughput biology, forcing us to consider cellular processes in toto. Even though much of the data is noisy and today partially inconsistent and incomplete, this has been a radical shift in the way we tear apart problems one interaction at a time. When coupled with gene deletions by RNAi and classical methods, and with the use of chemical tools tailored to proteins and protein domains, these high-throughput techniques become still more powerful.

High-throughput biology has opened up another im­portant area of systems biology: it has brought us out into the field again or at least made us aware that there is a world outside our laboratories. Our model systems have been chosen intentionally to be of limited genetic diversity and examined in a highly controlled and repro­ducible environment. The real world of ecology, evolu­tion, and human disease is a very different place. When genetics separated from the rest of biology in the early part of the 20th century, most geneticists sought to understand heredity and chose to study traits in the organism that could be easily scored and could be used to reveal genetic mechanisms. This was later ex­tended to powerful effect to use genetics to study cell biological and developmental mechanisms. Some ge­neticists, including a large school in Russia in the early 20th century, continued to study the genetics of natural populations, focusing on traits important for survival. That branch of genetics is coming back strongly with the power of phenotypic assays on the RNA and pro­tein level. As human beings we are most concerned not with using our genetic misfortunes to unravel biology’s complexity (important as that is) but with the role of our genetics in our individual survival. The context for understanding this is still not available, even though the data are now coming in torrents, for many of the genes that will contribute to our survival will have small quan­titative effects, partially masked or accentuated by other genetic and environmental conditions. To under­stand the genetic basis of disease will require not just mapping these genes but an understanding of how the phenotype is created in the first place and the messy interactions between genetic variation and environ­mental variation.

Extracts and explants are relatively accessible to syn­thetic manipulation. Next there is the explicit recon­struction of circuits within cells or the deliberate modifi­cation of those circuits. This has occurred for a while in biology, but the difference is that now we wish to construct or intervene with the explicit purpose of de­scribing the dynamical features of these synthetic or partially synthetic systems. There are more and more tools to intervene and more and more tools to measure. Although these fall short of total descriptions of cells and organisms, the detailed information will give us a sense of the special life-like processes of circuits, pro­teins, cells in tissues, and whole organisms in their en­vironment. This meso-scale systems biology will help establish the correspondence between molecules and large-scale physiology.

You are probably running out of patience for some definition of systems biology. In any case, I do not think the explicit definition of systems biology should come from me but should await the words of the first great modern systems biologist. She or he is probably among us now. However, if forced to provide some kind of label for systems biology, I would simply say that systems biology is the study of the behavior of complex biologi­cal organization and processes in terms of the molecu­lar constituents. It is built on molecular biology in its special concern for information transfer, on physiology for its special concern with adaptive states of the cell and organism, on developmental biology for the impor­tance of defining a succession of physiological states in that process, and on evolutionary biology and ecol­ogy for the appreciation that all aspects of the organ­ism are products of selection, a selection we rarely understand on a molecular level. Systems biology attempts all of this through quantitative measurement, modeling, reconstruction, and theory. Systems biology is not a branch of physics but differs from physics in that the primary task is to understand how biology gen­erates variation. No such imperative to create variation exists in the physical world. It is a new principle that Darwin understood and upon which all of life hinges. That sounds different enough for me to justify a new field and a new name. Furthermore, the success of sys­tems biology is essential if we are to understand life; its success is far from assured—a good field for those seeking risk and adventure.

Source: “Meaning of Systems Biology” Cell, Vol. 121, 503–504, May 20, 2005, DOI 10.1016/j.cell.2005.05.005

Old High-throughput Screening, Once the Gold Standard in Drug Development, Gets a Systems Biology Facelift

From Phenotypic Hit to Chemical Probe: Chemical Biology Approaches to Elucidate Small Molecule Action in Complex Biological Systems

Quentin T. L. Pasquer, Ioannis A. Tsakoumagkos and Sascha Hoogendoorn 

Molecules 202025(23), 5702; https://doi.org/10.3390/molecules25235702

Abstract

Biologically active small molecules have a central role in drug development, and as chemical probes and tool compounds to perturb and elucidate biological processes. Small molecules can be rationally designed for a given target, or a library of molecules can be screened against a target or phenotype of interest. Especially in the case of phenotypic screening approaches, a major challenge is to translate the compound-induced phenotype into a well-defined cellular target and mode of action of the hit compound. There is no “one size fits all” approach, and recent years have seen an increase in available target deconvolution strategies, rooted in organic chemistry, proteomics, and genetics. This review provides an overview of advances in target identification and mechanism of action studies, describes the strengths and weaknesses of the different approaches, and illustrates the need for chemical biologists to integrate and expand the existing tools to increase the probability of evolving screen hits to robust chemical probes.

5.1.5. Large-Scale Proteomics

While FITExP is based on protein expression regulation during apoptosis, a study of Ruprecht et al. showed that proteomic changes are induced both by cytotoxic and non-cytotoxic compounds, which can be detected by mass spectrometry to give information on a compound’s mechanism of action. They developed a large-scale proteome-wide mass spectrometry analysis platform for MOA studies, profiling five lung cancer cell lines with over 50 drugs. Aggregation analysis over the different cell lines and the different compounds showed that one-quarter of the drugs changed the abundance of their protein target. This approach allowed target confirmation of molecular degraders such as PROTACs or molecular glues. Finally, this method yielded unexpected off-target mechanisms for the MAP2K1/2 inhibitor PD184352 and the ALK inhibitor ceritinib [97]. While such a mapping approach clearly provides a wealth of information, it might not be easily attainable for groups that are not equipped for high-throughput endeavors.

All-in-all, mass spectrometry methods have gained a lot of traction in recent years and have been successfully applied for target deconvolution and MOA studies of small molecules. As with all high-throughput methods, challenges lie in the accessibility of the instruments (both from a time and cost perspective) and data analysis of complex and extensive data sets.

5.2. Genetic Approaches

Both label-based and mass spectrometry proteomic approaches are based on the physical interaction between a small molecule and a protein target, and focus on the proteome for target deconvolution. It has been long realized that genetics provides an alternative avenue to understand a compound’s action, either through precise modification of protein levels, or by inducing protein mutations. First realized in yeast as a genetically tractable organism over 20 years ago, recent advances in genetic manipulation of mammalian cells have opened up important opportunities for target identification and MOA studies through genetic screening in relevant cell types [98]. Genetic approaches can be roughly divided into two main areas, with the first centering on the identification of mutations that confer compound resistance (Figure 3a), and the second on genome-wide perturbation of gene function and the concomitant changes in sensitivity to the compound (Figure 3b). While both methods can be used to identify or confirm drug targets, the latter category often provides many additional insights in the compound’s mode of action.

Figure 3. Genetic methods for target identification and mode of action studies. Schematic representations of (a) resistance cloning, and (b) chemogenetic interaction screens.

5.2.1. Resistance Cloning

The “gold standard” in drug target confirmation is to identify mutations in the presumed target protein that render it insensitive to drug treatment. Conversely, different groups have sought to use this principle as a target identification method based on the concept that cells grown in the presence of a cytotoxic drug will either die or develop mutations that will make them resistant to the compound. With recent advances in deep sequencing it is now possible to then scan the transcriptome [99] or genome [100] of the cells for resistance-inducing mutations. Genes that are mutated are then hypothesized to encode the protein target. For this approach to be successful, there are two initial requirements: (1) the compound needs to be cytotoxic for resistant clones to arise, and (2) the cell line needs to be genetically unstable for mutations to occur in a reasonable timeframe.

In 2012, the Kapoor group demonstrated in a proof-of-concept study that resistance cloning in mammalian cells, coupled to transcriptome sequencing (RNA-seq), yields the known polo-like kinase 1 (PLK1) target of the small molecule BI 2536. For this, they used the cancer cell line HCT-116, which is deficient in mismatch repair and consequently prone to mutations. They generated and sequenced multiple resistant clones, and clustered the clones based on similarity. PLK1 was the only gene that was mutated in multiple groups. Of note, one of the groups did not contain PLK1 mutations, but rather developed resistance through upregulation of ABCBA1, a drug efflux transporter, which is a general and non-specific resistance mechanism [101]. In a following study, they optimized their pipeline “DrugTargetSeqR”, by counter-screening for these types of multidrug resistance mechanisms so that these clones were excluded from further analysis (Figure 3a). Furthermore, they used CRISPR/Cas9-mediated gene editing to determine which mutations were sufficient to confer drug resistance, and as independent validation of the biochemical relevance of the obtained hits [102].

While HCT-116 cells are a useful model cell line for resistance cloning because of their genomic instability, they may not always be the cell line of choice, depending on the compound and process that is studied. Povedana et al. used CRISPR/Cas9 to engineer mismatch repair deficiencies in Ewing sarcoma cells and small cell lung cancer cells. They found that deletion of MSH2 results in hypermutations in these normally mutationally silent cells, resulting in the formation of resistant clones in the presence of bortezomib, MLN4924, and CD437, which are all cytotoxic compounds [103]. Recently, Neggers et al. reasoned that CRISPR/Cas9-induced non-homologous end-joining repair could be a viable strategy to create a wide variety of functional mutants of essential genes through in-frame mutations. Using a tiled sgRNA library targeting 75 target genes of investigational neoplastic drugs in HAP1 and K562 cells, they generated several KPT-9274 (an anticancer agent with unknown target)-resistant clones, and subsequent deep sequencing showed that the resistant clones were enriched in NAMPT sgRNAs. Direct target engagement was confirmed by co-crystallizing the compound with NAMPT [104]. In addition to these genetic mutation strategies, an alternative method is to grow the cells in the presence of a mutagenic chemical to induce higher mutagenesis rates [105,106].

When there is already a hypothesis on the pathway involved in compound action, the resistance cloning methodology can be extended to non-cytotoxic compounds. Sekine et al. developed a fluorescent reporter model for the integrated stress response, and used this cell line for target deconvolution of a small molecule inhibitor towards this pathway (ISRIB). Reporter cells were chemically mutagenized, and ISRIB-resistant clones were isolated by flow cytometry, yielding clones with various mutations in the delta subunit of guanine nucleotide exchange factor eIF2B [107].

While there are certainly successful examples of resistance cloning yielding a compound’s direct target as discussed above, resistance could also be caused by mutations or copy number alterations in downstream components of a signaling pathway. This is illustrated by clinical examples of acquired resistance to small molecules, nature’s way of “resistance cloning”. For example, resistance mechanisms in Hedgehog pathway-driven cancers towards the Smoothened inhibitor vismodegib include compound-resistant mutations in Smoothened, but also copy number changes in downstream activators SUFU and GLI2 [108]. It is, therefore, essential to conduct follow-up studies to confirm a direct interaction between a compound and the hit protein, as well as a lack of interaction with the mutated protein.

5.2.3. “Chemogenomics”: Examples of Gene-Drug Interaction Screens

When genetic perturbations are combined with small molecule drugs in a chemogenetic interaction screen, the effect of a gene’s perturbation on compound action is studied. Gene perturbation can render the cells resistant to the compound (suppressor interaction), or conversely, result in hypersensitivity and enhanced compound potency (synergistic interaction) [5,117,121]. Typically, cells are treated with the compound at a sublethal dose, to ascertain that both types of interactions can be found in the final dataset, and often it is necessary to use a variety of compound doses (i.e., LD20, LD30, LD50) and timepoints to obtain reliable insights (Figure 3b).

An early example of successful coupling of a phenotypic screen and downstream genetic screening for target identification is the study of Matheny et al. They identified STF-118804 as a compound with antileukemic properties. Treatment of MV411 cells, stably transduced with a high complexity, genome-wide shRNA library, with STF-118804 (4 rounds of increasing concentration) or DMSO control resulted in a marked depletion of cells containing shRNAs against nicotinamide phosphoribosyl transferase (NAMPT) [122].

The Bassik lab subsequently directly compared the performance of shRNA-mediated knockdown versus CRISPR/Cas9-knockout screens for the target elucidation of the antiviral drug GSK983. The data coming out of both screens were complementary, with the shRNA screen resulting in hits leading to the direct compound target and the CRISPR screen giving information on cellular mechanisms of action of the compound. A reason for this is likely the level of protein depletion that is reached by these methods: shRNAs lead to decreased protein levels, which is advantageous when studying essential genes. However, knockdown may not result in a phenotype for non-essential genes, in which case a full CRISPR-mediated knockout is necessary to observe effects [123].

Another NAMPT inhibitor was identified in a CRISPR/Cas9 “haplo-insufficiency (HIP)”-like approach [124]. Haploinsuffiency profiling is a well-established system in yeast which is performed in a ~50% protein background by heterozygous deletions [125]. As there is no control over CRISPR-mediated loss of alleles, compound treatment was performed at several timepoints after addition of the sgRNA library to HCT116 cells stably expressing Cas9, in the hope that editing would be incomplete at early timepoints, resulting in residual protein levels. Indeed, NAMPT was found to be the target of phenotypic hit LB-60-OF61, especially at earlier timepoints, confirming the hypothesis that some level of protein needs to be present to identify a compound’s direct target [124]. This approach was confirmed in another study, thereby showing that direct target identification through CRISPR-knockout screens is indeed possible [126].

An alternative strategy was employed by the Weissman lab, where they combined genome-wide CRISPR-interference and -activation screens to identify the target of the phase 3 drug rigosertib. They focused on hits that had opposite action in both screens, as in sensitizing in one but protective in the other, which were related to microtubule stability. In a next step, they created chemical-genetic profiles of a variety of microtubule destabilizing agents, rationalizing that compounds with the same target will have similar drug-gene interactions. For this, they made a focused library of sgRNAs, based on the most high-ranking hits in the rigosertib genome-wide CRISPRi screen, and compared the focused screen results of the different compounds. The profile for rigosertib clustered well with that of ABT-571, and rigorous target validation studies confirmed rigosertib binding to the colchicine binding site of tubulin—the same site as occupied by ABT-571 [127].

From the above examples, it is clear that genetic screens hold a lot of promise for target identification and MOA studies for small molecules. The CRISPR screening field is rapidly evolving, sgRNA libraries are continuously improving and increasingly commercially available, and new tools for data analysis are being developed [128]. The challenge lies in applying these screens to study compounds that are not cytotoxic, where finding the right dosage regimen will not be trivial.

SYSTEMS BIOLOGY AND CANCER RESEARCH & DRUG DISCOVERY

Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence

Youngjun Park, Dominik Heider and Anne-Christin Hauschild. Cancers 202113(13), 3148; https://doi.org/10.3390/cancers13133148

Abstract

The rapid improvement of next-generation sequencing (NGS) technologies and their application in large-scale cohorts in cancer research led to common challenges of big data. It opened a new research area incorporating systems biology and machine learning. As large-scale NGS data accumulated, sophisticated data analysis methods became indispensable. In addition, NGS data have been integrated with systems biology to build better predictive models to determine the characteristics of tumors and tumor subtypes. Therefore, various machine learning algorithms were introduced to identify underlying biological mechanisms. In this work, we review novel technologies developed for NGS data analysis, and we describe how these computational methodologies integrate systems biology and omics data. Subsequently, we discuss how deep neural networks outperform other approaches, the potential of graph neural networks (GNN) in systems biology, and the limitations in NGS biomedical research. To reflect on the various challenges and corresponding computational solutions, we will discuss the following three topics: (i) molecular characteristics, (ii) tumor heterogeneity, and (iii) drug discovery. We conclude that machine learning and network-based approaches can add valuable insights and build highly accurate models. However, a well-informed choice of learning algorithm and biological network information is crucial for the success of each specific research question

1. Introduction

The development and widespread use of high-throughput technologies founded the era of big data in biology and medicine. In particular, it led to an accumulation of large-scale data sets that opened a vast amount of possible applications for data-driven methodologies. In cancer, these applications range from fundamental research to clinical applications: molecular characteristics of tumors, tumor heterogeneity, drug discovery and potential treatments strategy. Therefore, data-driven bioinformatics research areas have tailored data mining technologies such as systems biology, machine learning, and deep learning, elaborated in this review paper (see Figure 1 and Figure 2). For example, in systems biology, data-driven approaches are applied to identify vital signaling pathways [1]. This pathway-centric analysis is particularly crucial in cancer research to understand the characteristics and heterogeneity of the tumor and tumor subtypes. Consequently, this high-throughput data-based analysis enables us to explore characteristics of cancers with a systems biology and a systems medicine point of view [2].Combining high-throughput techniques, especially next-generation sequencing (NGS), with appropriate analytical tools has allowed researchers to gain a deeper systematic understanding of cancer at various biological levels, most importantly genomics, transcriptomics, and epigenetics [3,4]. Furthermore, more sophisticated analysis tools based on computational modeling are introduced to decipher underlying molecular mechanisms in various cancer types. The increasing size and complexity of the data required the adaptation of bioinformatics processing pipelines for higher efficiency and sophisticated data mining methodologies, particularly for large-scale, NGS datasets [5]. Nowadays, more and more NGS studies integrate a systems biology approach and combine sequencing data with other types of information, for instance, protein family information, pathway, or protein–protein interaction (PPI) networks, in an integrative analysis. Experimentally validated knowledge in systems biology may enhance analysis models and guides them to uncover novel findings. Such integrated analyses have been useful to extract essential information from high-dimensional NGS data [6,7]. In order to deal with the increasing size and complexity, the application of machine learning, and specifically deep learning methodologies, have become state-of-the-art in NGS data analysis.

Figure 1. Next-generation sequencing data can originate from various experimental and technological conditions. Depending on the purpose of the experiment, one or more of the depicted omics types (Genomics, Transcriptomics, Epigenomics, or Single-Cell Omics) are analyzed. These approaches led to an accumulation of large-scale NGS datasets to solve various challenges of cancer research, molecular characterization, tumor heterogeneity, and drug target discovery. For instance, The Cancer Genome Atlas (TCGA) dataset contains multi-omics data from ten-thousands of patients. This dataset facilitates a variety of cancer researches for decades. Additionally, there are also independent tumor datasets, and, frequently, they are analyzed and compared with the TCGA dataset. As the large scale of omics data accumulated, various machine learning techniques are applied, e.g., graph algorithms and deep neural networks, for dimensionality reduction, clustering, or classification. (Created with BioRender.com.)

Figure 2. (a) A multitude of different types of data is produced by next-generation sequencing, for instance, in the fields of genomics, transcriptomics, and epigenomics. (b) Biological networks for biomarker validation: The in vivo or in vitro experiment results are considered ground truth. Statistical analysis on next-generation sequencing data produces candidate genes. Biological networks can validate these candidate genes and highlight the underlying biological mechanisms (Section 2.1). (c) De novo construction of Biological Networks: Machine learning models that aim to reconstruct biological networks can incorporate prior knowledge from different omics data. Subsequently, the model will predict new unknown interactions based on new omics information (Section 2.2). (d) Network-based machine learning: Machine learning models integrating biological networks as prior knowledge to improve predictive performance when applied to different NGS data (Section 2.3). (Created with BioRender.com).

Therefore, a large number of studies integrate NGS data with machine learning and propose a novel data-driven methodology in systems biology [8]. In particular, many network-based machine learning models have been developed to analyze cancer data and help to understand novel mechanisms in cancer development [9,10]. Moreover, deep neural networks (DNN) applied for large-scale data analysis improved the accuracy of computational models for mutation prediction [11,12], molecular subtyping [13,14], and drug repurposing [15,16]. 

2. Systems Biology in Cancer Research

Genes and their functions have been classified into gene sets based on experimental data. Our understandings of cancer concentrated into cancer hallmarks that define the characteristics of a tumor. This collective knowledge is used for the functional analysis of unseen data.. Furthermore, the regulatory relationships among genes were investigated, and, based on that, a pathway can be composed. In this manner, the accumulation of public high-throughput sequencing data raised many big-data challenges and opened new opportunities and areas of application for computer science. Two of the most vibrantly evolving areas are systems biology and machine learning which tackle different tasks such as understanding the cancer pathways [9], finding crucial genes in pathways [22,53], or predicting functions of unidentified or understudied genes [54]. Essentially, those models include prior knowledge to develop an analysis and enhance interpretability for high-dimensional data [2]. In addition to understanding cancer pathways with in silico analysis, pathway activity analysis incorporating two different types of data, pathways and omics data, is developed to understand heterogeneous characteristics of the tumor and cancer molecular subtyping. Due to its advantage in interpretability, various pathway-oriented methods are introduced and become a useful tool to understand a complex diseases such as cancer [55,56,57].

In this section, we will discuss how two related research fields, namely, systems biology and machine learning, can be integrated with three different approaches (see Figure 2), namely, biological network analysis for biomarker validation, the use of machine learning with systems biology, and network-based models.

2.1. Biological Network Analysis for Biomarker Validation

The detection of potential biomarkers indicative of specific cancer types or subtypes is a frequent goal of NGS data analysis in cancer research. For instance, a variety of bioinformatics tools and machine learning models aim at identify lists of genes that are significantly altered on a genomic, transcriptomic, or epigenomic level in cancer cells. Typically, statistical and machine learning methods are employed to find an optimal set of biomarkers, such as single nucleotide polymorphisms (SNPs), mutations, or differentially expressed genes crucial in cancer progression. Traditionally, resource-intensive in vitro analysis was required to discover or validate those markers. Therefore, systems biology offers in silico solutions to validate such findings using biological pathways or gene ontology information (Figure 2b) [58]. Subsequently, gene set enrichment analysis (GSEA) [50] or gene set analysis (GSA) [59] can be used to evaluate whether these lists of genes are significantly associated with cancer types and their specific characteristics. GSA, for instance, is available via web services like DAVID [60] and g:Profiler [61]. Moreover, other applications use gene ontology directly [62,63]. In addition to gene-set-based analysis, there are other methods that focuse on the topology of biological networks. These approaches evaluate various network structure parameters and analyze the connectivity of two genes or the size and interconnection of their neighbors [64,65]. According to the underlying idea, the mutated gene will show dysfunction and can affect its neighboring genes. Thus, the goal is to find abnormalities in a specific set of genes linked with an edge in a biological network. For instance, KeyPathwayMiner can extract informative network modules in various omics data [66]. In summary, these approaches aim at predicting the effect of dysfunctional genes among neighbors according to their connectivity or distances from specific genes such as hubs [67,68]. During the past few decades, the focus of cancer systems biology extended towards the analysis of cancer-related pathways since those pathways tend to carry more information than a gene set. Such analysis is called Pathway Enrichment Analysis (PEA) [69,70]. The use of PEA incorporates the topology of biological networks. However, simultaneously, the lack of coverage issue in pathway data needs to be considered. Because pathway data does not cover all known genes yet, an integration analysis on omics data can significantly drop in genes when incorporated with pathways. Genes that can not be mapped to any pathway are called ‘pathway orphan.’ In this manner, Rahmati et al. introduced a possible solution to overcome the ‘pathway orphan’ issue [71]. At the bottom line, regardless of whether researchers consider gene-set or pathway-based enrichment analysis, the performance and accuracy of both methods are highly dependent on the quality of the external gene-set and pathway data [72].

2.2. De Novo Construction of Biological Networks

While the known fraction of existing biological networks barely scratches the surface of the whole system of mechanisms occurring in each organism, machine learning models can improve on known network structures and can guide potential new findings [73,74]. This area of research is called de novo network construction (Figure 2c), and its predictive models can accelerate experimental validation by lowering time costs [75,76]. This interplay between in silico biological networks building and mining contributes to expanding our knowledge in a biological system. For instance, a gene co-expression network helps discover gene modules having similar functions [77]. Because gene co-expression networks are based on expressional changes under specific conditions, commonly, inferring a co-expression network requires many samples. The WGCNA package implements a representative model using weighted correlation for network construction that leads the development of the network biology field [78]. Due to NGS developments, the analysis of gene co-expression networks subsequently moved from microarray-based to RNA-seq based experimental data [79]. However, integration of these two types of data remains tricky. Ballouz et al. compared microarray and NGS-based co-expression networks and found the existence of a bias originating from batch effects between the two technologies [80]. Nevertheless, such approaches are suited to find disease-specific co-expressional gene modules. Thus, various studies based on the TCGA cancer co-expression network discovered characteristics of prognostic genes in the network [81]. Accordingly, a gene co-expression network is a condition-specific network rather than a general network for an organism. Gene regulatory networks can be inferred from the gene co-expression network when various data from different conditions in the same organism are available. Additionally, with various NGS applications, we can obtain multi-modal datasets about regulatory elements and their effects, such as epigenomic mechanisms on transcription and chromatin structure. Consequently, a gene regulatory network can consist of solely protein-coding genes or different regulatory node types such as transcription factors, inhibitors, promoter interactions, DNA methylations, and histone modifications affecting the gene expression system [82,83]. More recently, researchers were able to build networks based on a particular experimental setup. For instance, functional genomics or CRISPR technology enables the high-resolution regulatory networks in an organism [84]. Other than gene co-expression or regulatory networks, drug target, and drug repurposing studies are active research areas focusing on the de novo construction of drug-to-target networks to allow the potential repurposing of drugs [76,85].

2.3. Network Based Machine Learning

A network-based machine learning model directly integrates the insights of biological networks within the algorithm (Figure 2d) to ultimately improve predictive performance concerning cancer subtyping or susceptibility to therapy. Following the establishment of high-quality biological networks based on NGS technologies, these biological networks were suited to be integrated into advanced predictive models. In this manner, Zhang et al., categorized network-based machine learning approaches upon their usage into three groups: (i) model-based integration, (ii) pre-processing integration, and (iii) post-analysis integration [7]. Network-based models map the omics data onto a biological network, and proper algorithms travel the network while considering both values of nodes and edges and network topology. In the pre-processing integration, pathway or other network information is commonly processed based on its topological importance. Meanwhile, in the post-analysis integration, omics data is processed solely before integration with a network. Subsequently, omics data and networks are merged and interpreted. The network-based model has advantages in multi-omics integrative analysis. Due to the different sensitivity and coverage of various omics data types, a multi-omics integrative analysis is challenging. However, focusing on gene-level or protein-level information enables a straightforward integration [86,87]. Consequently, when different machine learning approaches tried to integrate two or more different data types to find novel biological insights, one of the solutions is reducing the search space to gene or protein level and integrated heterogeneous datatypes [25,88].

In summary, using network information opens new possibilities for interpretation. However, as mentioned earlier, several challenges remain, such as the coverage issue. Current databases for biological networks do not cover the entire set of genes, transcripts, and interactions. Therefore, the use of networks can lead to loss of information for gene or transcript orphans. The following section will focus on network-based machine learning models and their application in cancer genomics. We will put network-based machine learning into the perspective of the three main areas of application, namely, molecular characterization, tumor heterogeneity analysis, and cancer drug discovery.

3. Network-Based Learning in Cancer Research

As introduced previously, the integration of machine learning with the insights of biological networks (Figure 2d) ultimately aims at improving predictive performance and interpretability concerning cancer subtyping or treatment susceptibility.

3.1. Molecular Characterization with Network Information

Various network-based algorithms are used in genomics and focus on quantifying the impact of genomic alteration. By employing prior knowledge in biological network algorithms, performance compared to non-network models can be improved. A prominent example is HotNet. The algorithm uses a thermodynamics model on a biological network and identifies driver genes, or prognostic genes, in pan-cancer data [89]. Another study introduced a network-based stratification method to integrate somatic alterations and expression signatures with network information [90]. These approaches use network topology and network-propagation-like algorithms. Network propagation presumes that genomic alterations can affect the function of neighboring genes. Two genes will show an exclusive pattern if two genes complement each other, and the function carried by those two genes is essential to an organism [91]. This unique exclusive pattern among genomic alteration is further investigated in cancer-related pathways. Recently, Ku et al. developed network-centric approaches and tackled robustness issues while studying synthetic lethality [92]. Although synthetic lethality was initially discovered in model organisms of genetics, it helps us to understand cancer-specific mutations and their functions in tumor characteristics [91].

Furthermore, in transcriptome research, network information is used to measure pathway activity and its application in cancer subtyping. For instance, when comparing the data of two or more conditions such as cancer types, GSEA as introduced in Section 2 is a useful approach to get an overview of systematic changes [50]. It is typically used at the beginning of a data evaluation [93]. An experimentally validated gene set can provide information about how different conditions affect molecular systems in an organism. In addition to the gene sets, different approaches integrate complex interaction information into GSEA and build network-based models [70]. In contrast to GSEA, pathway activity analysis considers transcriptome data and other omics data and structural information of a biological network. For example, PARADIGM uses pathway topology and integrates various omics in the analysis to infer a patient-specific status of pathways [94]. A benchmark study with pan-cancer data recently reveals that using network structure can show better performance [57]. In conclusion, while the loss of data is due to the incompleteness of biological networks, their integration improved performance and increased interpretability in many cases.

3.2. Tumor Heterogeneity Study with Network Information

The tumor heterogeneity can originate from two directions, clonal heterogeneity and tumor impurity. Clonal heterogeneity covers genomic alterations within the tumor [95]. While de novo mutations accumulate, the tumor obtains genomic alterations with an exclusive pattern. When these genomic alterations are projected on the pathway, it is possible to observe exclusive relationships among disease-related genes. For instance, the CoMEt and MEMo algorithms examine mutual exclusivity on protein–protein interaction networks [96,97]. Moreover, the relationship between genes can be essential for an organism. Therefore, models analyzing such alterations integrate network-based analysis [98].

In contrast, tumor purity is dependent on the tumor microenvironment, including immune-cell infiltration and stromal cells [99]. In tumor microenvironment studies, network-based models are applied, for instance, to find immune-related gene modules. Although the importance of the interaction between tumors and immune cells is well known, detailed mechanisms are still unclear. Thus, many recent NGS studies employ network-based models to investigate the underlying mechanism in tumor and immune reactions. For example, McGrail et al. identified a relationship between the DNA damage response protein and immune cell infiltration in cancer. The analysis is based on curated interaction pairs in a protein–protein interaction network [100]. Most recently, Darzi et al. discovered a prognostic gene module related to immune cell infiltration by using network-centric approaches [101]. Tu et al. presented a network-centric model for mining subnetworks of genes other than immune cell infiltration by considering tumor purity [102].

3.3. Drug Target Identification with Network Information

In drug target studies, network biology is integrated into pharmacology [103]. For instance, Yamanishi et al. developed novel computational methods to investigate the pharmacological space by integrating a drug-target protein network with genomics and chemical information. The proposed approaches investigated such drug-target network information to identify potential novel drug targets [104]. Since then, the field has continued to develop methods to study drug target and drug response integrating networks with chemical and multi-omic datasets. In a recent survey study by Chen et al., the authors compared 13 computational methods for drug response prediction. It turned out that gene expression profiles are crucial information for drug response prediction [105].

Moreover, drug-target studies are often extended to drug-repurposing studies. In cancer research, drug-repurposing studies aim to find novel interactions between non-cancer drugs and molecular features in cancer. Drug-repurposing (or repositioning) studies apply computational approaches and pathway-based models and aim at discovering potential new cancer drugs with a higher probability than de novo drug design [16,106]. Specifically, drug-repurposing studies can consider various areas of cancer research, such as tumor heterogeneity and synthetic lethality. As an example, Lee et al. found clinically relevant synthetic lethality interactions by integrating multiple screening NGS datasets [107]. This synthetic lethality and related-drug datasets can be integrated for an effective combination of anticancer therapeutic strategy with non-cancer drug repurposing.

4. Deep Learning in Cancer Research

DNN models develop rapidly and become more sophisticated. They have been frequently used in all areas of biomedical research. Initially, its development was facilitated by large-scale imaging and video data. While most data sets in the biomedical field would not typically be considered big data, the rapid data accumulation enabled by NGS made it suitable for the application of DNN models requiring a large amount of training data [108]. For instance, in 2019, Samiei et al. used TCGA-based large-scale cancer data as benchmark datasets for bioinformatics machine learning research such as Image-Net in the computer vision field [109]. Subsequently, large-scale public cancer data sets such as TCGA encouraged the wide usage of DNNs in the cancer domain [110]. Over the last decade, these state-of-the-art machine learning methods have been incorporated in many different biological questions [111].

In addition to public cancer databases such as TCGA, the genetic information of normal tissues is stored in well-curated databases such as GTEx [112] and 1000Genomes [113]. These databases are frequently used as control or baseline training data for deep learning [114]. Moreover, other non-curated large-scale data sources such as GEO (https://www.ncbi.nlm.nih.gov/geo/, accessed on 20 May 2021) can be leveraged to tackle critical aspects in cancer research. They store a large-scale of biological data produced under various experimental setups (Figure 1). Therefore, an integration of GEO data and other data requires careful preprocessing. Overall, an increasing amount of datasets facilitate the development of current deep learning in bioinformatics research [115].

4.1. Challenges for Deep Learning in Cancer Research

Many studies in biology and medicine used NGS and produced large amounts of data during the past few decades, moving the field to the big data era. Nevertheless, researchers still face a lack of data in particular when investigating rare diseases or disease states. Researchers have developed a manifold of potential solutions to overcome this lack of data challenges, such as imputation, augmentation, and transfer learning (Figure 3b). Data imputation aims at handling data sets with missing values [116]. It has been studied on various NGS omics data types to recover missing information [117]. It is known that gene expression levels can be altered by different regulatory elements, such as DNA-binding proteins, epigenomic modifications, and post-transcriptional modifications. Therefore, various models integrating such regulatory schemes have been introduced to impute missing omics data [118,119]. Some DNN-based models aim to predict gene expression changes based on genomics or epigenomics alteration. For instance, TDimpute aims at generating missing RNA-seq data by training a DNN on methylation data. They used TCGA and TARGET (https://ocg.cancer.gov/programs/target/data-matrix, accessed on 20 May 2021) data as proof of concept of the applicability of DNN for data imputation in a multi-omics integration study [120]. Because this integrative model can exploit information in different levels of regulatory mechanisms, it can build a more detailed model and achieve better performance than a model build on a single-omics dataset [117,121]. The generative adversarial network (GAN) is a DNN structure for generating simulated data that is different from the original data but shows the same characteristics [122]. GANs can impute missing omics data from other multi-omics sources. Recently, the GAN algorithm is getting more attention in single-cell transcriptomics because it has been recognized as a complementary technique to overcome the limitation of scRNA-seq [123]. In contrast to data imputation and generation, other machine learning approaches aim to cope with a limited dataset in different ways. Transfer learning or few-shot learning, for instance, aims to reduce the search space with similar but unrelated datasets and guide the model to solve a specific set of problems [124]. These approaches train models with data of similar characteristics and types but different data to the problem set. After pre-training the model, it can be fine-tuned with the dataset of interest [125,126]. Thus, researchers are trying to introduce few-shot learning models and meta-learning approaches to omics and translational medicine. For example, Select-ProtoNet applied the ProtoTypical Network [127] model to TCGA transcriptome data and classified patients into two groups according to their clinical status [128]. AffinityNet predicts kidney and uterus cancer subtypes with gene expression profiles [129].

Figure 3. (a) In various studies, NGS data transformed into different forms. The 2-D transformed form is for the convolution layer. Omics data is transformed into pathway level, GO enrichment score, or Functional spectra. (b) DNN application on different ways to handle lack of data. Imputation for missing data in multi-omics datasets. GAN for data imputation and in silico data simulation. Transfer learning pre-trained the model with other datasets and fine-tune. (c) Various types of information in biology. (d) Graph neural network examples. GCN is applied to aggregate neighbor information. (Created with BioRender.com).

4.2. Molecular Charactization with Network and DNN Model

DNNs have been applied in multiple areas of cancer research. For instance, a DNN model trained on TCGA cancer data can aid molecular characterization by identifying cancer driver genes. At the very early stage, Yuan et al. build DeepGene, a cancer-type classifier. They implemented data sparsity reduction methods and trained the DNN model with somatic point mutations [130]. Lyu et al. [131] and DeepGx [132] embedded a 1-D gene expression profile to a 2-D array by chromosome order to implement the convolution layer (Figure 3a). Other algorithms, such as the deepDriver, use k-nearest neighbors for the convolution layer. A predefined number of neighboring gene mutation profiles was the input for the convolution layer. It employed this convolution layer in a DNN by aggregating mutation information of the k-nearest neighboring genes [11]. Instead of embedding to a 2-D image, DeepCC transformed gene expression data into functional spectra. The resulting model was able to capture molecular characteristics by training cancer subtypes [14].

Another DNN model was trained to infer the origin of tissue from single-nucleotide variant (SNV) information of metastatic tumor. The authors built a model by using the TCGA/ICGC data and analyzed SNV patterns and corresponding pathways to predict the origin of cancer. They discovered that metastatic tumors retained their original cancer’s signature mutation pattern. In this context, their DNN model obtained even better accuracy than a random forest model [133] and, even more important, better accuracy than human pathologists [12].

4.3. Tumor Heterogeneity with Network and DNN Model

As described in Section 4.1, there are several issues because of cancer heterogeneity, e.g., tumor microenvironment. Thus, there are only a few applications of DNN in intratumoral heterogeneity research. For instance, Menden et al. developed ’Scaden’ to deconvolve cell types in bulk-cell sequencing data. ’Scaden’ is a DNN model for the investigation of intratumor heterogeneity. To overcome the lack of training datasets, researchers need to generate in silico simulated bulk-cell sequencing data based on single-cell sequencing data [134]. It is presumed that deconvolving cell types can be achieved by knowing all possible expressional profiles of the cell [36]. However, this information is typically not available. Recently, to tackle this problem, single-cell sequencing-based studies were conducted. Because of technical limitations, we need to handle lots of missing data, noises, and batch effects in single-cell sequencing data [135]. Thus, various machine learning methods were developed to process single-cell sequencing data. They aim at mapping single-cell data onto the latent space. For example, scDeepCluster implemented an autoencoder and trained it on gene-expression levels from single-cell sequencing. During the training phase, the encoder and decoder work as denoiser. At the same time, they can embed high-dimensional gene-expression profiles to lower-dimensional vectors [136]. This autoencoder-based method can produce biologically meaningful feature vectors in various contexts, from tissue cell types [137] to different cancer types [138,139].

4.4. Drug Target Identification with Networks and DNN Models

In addition to NGS datasets, large-scale anticancer drug assays enabled the training train of DNNs. Moreover, non-cancer drug response assay datasets can also be incorporated with cancer genomic data. In cancer research, a multidisciplinary approach was widely applied for repurposing non-oncology drugs to cancer treatment. This drug repurposing is faster than de novo drug discovery. Furthermore, combination therapy with a non-oncology drug can be beneficial to overcome the heterogeneous properties of tumors [85]. The deepDR algorithm integrated ten drug-related networks and trained deep autoencoders. It used a random-walk-based algorithm to represent graph information into feature vectors. This approach integrated network analysis with a DNN model validated with an independent drug-disease dataset [15].

The authors of CDRscan did an integrative analysis of cell-line-based assay datasets and other drug and genomics datasets. It shows that DNN models can enhance the computational model for improved drug sensitivity predictions [140]. Additionally, similar to previous network-based models, the multi-omics application of drug-targeted DNN studies can show higher prediction accuracy than the single-omics method. MOLI integrated genomic data and transcriptomic data to predict the drug responses of TCGA patients [141].

4.5. Graph Neural Network Model

In general, the advantage of using a biological network is that it can produce more comprehensive and interpretable results from high-dimensional omics data. Furthermore, in an integrative multi-omics data analysis, network-based integration can improve interpretability over traditional approaches. Instead of pre-/post-integration of a network, recently developed graph neural networks use biological networks as the base structure for the learning network itself. For instance, various pathways or interactome information can be integrated as a learning structure of a DNN and can be aggregated as heterogeneous information. In a GNN study, a convolution process can be done on the provided network structure of data. Therefore, the convolution on a biological network made it possible for the GNN to focus on the relationship among neighbor genes. In the graph convolution layer, the convolution process integrates information of neighbor genes and learns topological information (Figure 3d). Consequently, this model can aggregate information from far-distant neighbors, and thus can outperform other machine learning models [142].

In the context of the inference problem of gene expression, the main question is whether the gene expression level can be explained by aggregating the neighboring genes. A single gene inference study by Dutil et al. showed that the GNN model outperformed other DNN models [143]. Moreover, in cancer research, such GNN models can identify cancer-related genes with better performance than other network-based models, such as HotNet2 and MutSigCV [144]. A recent GNN study with a multi-omics integrative analysis identified 165 new cancer genes as an interactive partner for known cancer genes [145]. Additionally, in the synthetic lethality area, dual-dropout GNN outperformed previous bioinformatics tools for predicting synthetic lethality in tumors [146]. GNNs were also able to classify cancer subtypes based on pathway activity measures with RNA-seq data. Lee et al. implemented a GNN for cancer subtyping and tested five cancer types. Thus, the informative pathway was selected and used for subtype classification [147]. Furthermore, GNNs are also getting more attention in drug repositioning studies. As described in Section 3.3, drug discovery requires integrating various networks in both chemical and genomic spaces (Figure 3d). Chemical structures, protein structures, pathways, and other multi-omics data were used in drug-target identification and repurposing studies (Figure 3c). Each of the proposed applications has a specialty in the different purposes of drug-related tasks. Sun et al. summarized GNN-based drug discovery studies and categorized them into four classes: molecular property and activity prediction, interaction prediction, synthesis prediction, and de novo drug design. The authors also point out four challenges in the GNN-mediated drug discovery. At first, as we described before, there is a lack of drug-related datasets. Secondly, the current GNN models can not fully represent 3-D structures of chemical molecules and protein structures. The third challenge is integrating heterogeneous network information. Drug discovery usually requires a multi-modal integrative analysis with various networks, and GNNs can improve this integrative analysis. Lastly, although GNNs use graphs, stacked layers still make it hard to interpret the model [148].

4.6. Shortcomings in AI and Revisiting Validity of Biological Networks as Prior Knowledge

The previous sections reviewed a variety of DNN-based approaches that present a good performance on numerous applications. However, it is hardly a panacea for all research questions. In the following, we will discuss potential limitations of the DNN models. In general, DNN models with NGS data have two significant issues: (i) data requirements and (ii) interpretability. Usually, deep learning needs a large proportion of training data for reasonable performance which is more difficult to achieve in biomedical omics data compared to, for instance, image data. Today, there are not many NGS datasets that are well-curated and -annotated for deep learning. This can be an answer to the question of why most DNN studies are in cancer research [110,149]. Moreover, the deep learning models are hard to interpret and are typically considered as black-boxes. Highly stacked layers in the deep learning model make it hard to interpret its decision-making rationale. Although the methodology to understand and interpret deep learning models has been improved, the ambiguity in the DNN models’ decision-making hindered the transition between the deep learning model and translational medicine [149,150].

As described before, biological networks are employed in various computational analyses for cancer research. The studies applying DNNs demonstrated many different approaches to use prior knowledge for systematic analyses. Before discussing GNN application, the validity of biological networks in a DNN model needs to be shown. The LINCS program analyzed data of ’The Connectivity Map (CMap) project’ to understand the regulatory mechanism in gene expression by inferring the whole gene expression profiles from a small set of genes (https://lincsproject.org/, accessed on 20 May 2021) [151,152]. This LINCS program found that the gene expression level is inferrable with only nearly 1000 genes. They called this gene list ’landmark genes’. Subsequently, Chen et al. started with these 978 landmark genes and tried to predict other gene expression levels with DNN models. Integrating public large-scale NGS data showed better performance than the linear regression model. The authors conclude that the performance advantage originates from the DNN’s ability to model non-linear relationships between genes [153].

Following this study, Beltin et al. extensively investigated various biological networks in the same context of the inference of gene expression level. They set up a simplified representation of gene expression status and tried to solve a binary classification task. To show the relevance of a biological network, they compared various gene expression levels inferred from a different set of genes, neighboring genes in PPI, random genes, and all genes. However, in the study incorporating TCGA and GTEx datasets, the random network model outperformed the model build on a known biological network, such as StringDB [154]. While network-based approaches can add valuable insights to analysis, this study shows that it cannot be seen as the panacea, and a careful evaluation is required for each data set and task. In particular, this result may not represent biological complexity because of the oversimplified problem setup, which did not consider the relative gene-expressional changes. Additionally, the incorporated biological networks may not be suitable for inferring gene expression profiles because they consist of expression-regulating interactions, non-expression-regulating interactions, and various in vivo and in vitro interactions.

“ However, although recently sophisticated applications of deep learning showed improved accuracy, it does not reflect a general advancement. Depending on the type of NGS data, the experimental design, and the question to be answered, a proper approach and specific deep learning algorithms need to be considered. Deep learning is not a panacea. In general, to employ machine learning and systems biology methodology for a specific type of NGS data, a certain experimental design, a particular research question, the technology, and network data have to be chosen carefully.”

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Use of Systems Biology in Anti-Microbial Drug Development

Genomics, Computational Biology and Drug Discovery for Mycobacterial Infections: Fighting the Emergence of Resistance. Asma Munir, Sundeep Chaitanya Vedithi, Amanda K. Chaplin and Tom L. Blundell. Front. Genet., 04 September 2020 | https://doi.org/10.3389/fgene.2020.00965

In an earlier review article (Waman et al., 2019), we discussed various computational approaches and experimental strategies for drug target identification and structure-guided drug discovery. In this review we discuss the impact of the era of precision medicine, where the genome sequences of pathogens can give clues about the choice of existing drugs, and repurposing of others. Our focus is directed toward combatting antimicrobial drug resistance with emphasis on tuberculosis and leprosy. We describe structure-guided approaches to understanding the impacts of mutations that give rise to antimycobacterial resistance and the use of this information in the design of new medicines.

Genome Sequences and Proteomic Structural Databases

In recent years, there have been many focused efforts to define the amino-acid sequences of the M. tuberculosis pan-genome and then to define the three-dimensional structures and functional interactions of these gene products. This work has led to essential genes of the bacteria being revealed and to a better understanding of the genetic diversity in different strains that might lead to a selective advantage (Coll et al., 2018). This will help with our understanding of the mode of antibiotic resistance within these strains and aid structure-guided drug discovery. However, only ∼10% of the ∼4128 proteins have structures determined experimentally.

Several databases have been developed to integrate the genomic and/or structural information linked to drug resistance in Mycobacteria (Table 1). These invaluable resources can contribute to better understanding of molecular mechanisms involved in drug resistance and improvement in the selection of potential drug targets.

There is a dearth of information related to structural aspects of proteins from M. leprae and their oligomeric and hetero-oligomeric organization, which has limited the understanding of physiological processes of the bacillus. The structures of only 12 proteins have been solved and deposited in the protein data bank (PDB). However, the high sequence similarity in protein coding genes between M. leprae and M. tuberculosis allows computational methods to be used for comparative modeling of the proteins of M. leprae. Mainly monomeric models using single template modeling have been defined and deposited in the Swiss Model repository (Bienert et al., 2017), in Modbase (Pieper et al., 2014), and in a collection with other infectious disease agents (Sosa et al., 2018). There is a need for multi-template modeling and building homo- and hetero-oligomeric complexes to better understand the interfaces, druggability and impacts of mutations.

We are now exploiting Vivace, a multi-template modeling pipeline developed in our lab for modeling the proteomes of M. tuberculosis (CHOPIN, see above) and M. abscessus [Mabellini Database (Skwark et al., 2019)], to model the proteome of M. leprae. We emphasize the need for understanding the protein interfaces that are critical to function. An example of this is that of the RNA-polymerase holoenzyme complex from M. leprae. We first modeled the structure of this hetero-hexamer complex and later deciphered the binding patterns of rifampin (Vedithi et al., 2018Figures 1A,B). Rifampin is a known drug to treat tuberculosis and leprosy. Owing to high rifampin resistance in tuberculosis and emerging resistance in leprosy, we used an approach known as “Computational Saturation Mutagenesis”, to identify sites on the protein that are less impacted by mutations. In this study, we were able to understand the association between predicted impacts of mutations on the structure and phenotypic rifampin-resistance outcomes in leprosy.

FIGURE 2

Figure 2. (A) Stability changes predicted by mCSM for systematic mutations in the ß-subunit of RNA polymerase in M. leprae. The maximum destabilizing effect from among all 19 possible mutations at each residue position is considered as a weighting factor for the color map that gradients from red (high destabilizing effects) to white (neutral to stabilizing effects) (Vedithi et al., 2020). (B) One of the known mutations in the ß-subunit of RNA polymerase, the S437H substitution which resulted in a maximum destabilizing effect [-1.701 kcal/mol (mCSM)] among all 19 possibilities this position. In the mutant, histidine (residue in green) forms hydrogen bonds with S434 and Q438, aromatic interactions with F431, and other ring-ring and π interactions with the surrounding residues which can impact the shape of the rifampin binding pocket and rifampin affinity to the ß-subunit [-0.826 log(affinity fold change) (mCSM-lig)]. Orange dotted lines represent weak hydrogen bond interactions. Ring-ring and intergroup interactions are depicted in cyan. Aromatic interactions are represented in sky-blue and carbonyl interactions in pink dotted lines. Green dotted lines represent hydrophobic interactions (Vedithi et al., 2020).

Examples of Understanding and Combatting Resistance

The availability of whole genome sequences in the present era has greatly enhanced the understanding of emergence of drug resistance in infectious diseases like tuberculosis. The data generated by the whole genome sequencing of clinical isolates can be screened for the presence of drug-resistant mutations. A preliminary in silico analysis of mutations can then be used to prioritize experimental work to identify the nature of these mutations.

FIGURE 3

Figure 3. (A) Mechanism of isoniazid activation and INH-NAD adduct formation. (B) Mutations mapped (Munir et al., 2019) on the structure of KatG (PDB ID:1SJ2; Bertrand et al., 2004).

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A Nonlinear Methodology to Explain Complexity of the Genome and Bioinformatic Information, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 1: Next Generation Sequencing (NGS)

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 (http://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

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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 ]

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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

 

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

Proteomics, metabolomics and diabetes

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 

 

 

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New anti-Malarial treatment

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Malaria Proteasome Inhibitors Could Reverse Parasite Drug Resistance

http://www.genengnews.com/gen-news-highlights/malaria-proteasome-inhibitors-could-reverse-parasite-drug-resistance/81252358/

 

http://www.genengnews.com/Media/images/GENHighlight/thumb_108676_web2680362491.jpg

This structure (bottom left) of the malaria parasite’s proteasome, obtained using the revolutionary Cryo-Electron Microscopy technique, enabled the design of a specific inhibitor (front) against the mosquito-borne malaria parasite (pictured at back). [University of Melbourne]

 

  • With media attention recently focused on the spread of the Zika virus, it’s easy to forget about the mosquito-borne disease that has been credited with killing one out of every two people who have ever lived—malaria. Currently, close to 50 percent of the world’s population live in malaria-endemic areas, leading to between 200–500 million new cases and close to 500,000 deaths annually (mostly children under the age of five).

    Adding to the complexities of trying to control this disease is that resistance to the most effective antimalarial drug, artemisinin, has developed in Southeast Asia, with fears it will soon reach Africa. Artemisinin-resistant species have spread to six countries in five years.

    A collaborative team of scientists from Stanford University, University of California, San Francisco, University of Melbourne, and the MRC in Cambridge have used cutting-edge technology to design a smarter drug to combat the resistant strain.

    “Artemisinin causes damage to the proteins in the malaria parasite that kill the human cell, but the parasite has developed a way to deal with that damage. So new drugs that work against resistant parasites are desperately needed,” explained coauthor Leann Tilley, Ph.D., professor and deputy head of biochemistry and molecular biology in the Bio21 Molecular Science and Biotechnology Institute at The University of Melbourne.

    Malaria is caused by the protozoan parasite from the genus Plasmodium. Five different species are known to cause malaria in humans, with P. falciparum infection leading to the most deaths. The parasite is transmitted through the bite of the female mosquito and ultimately ends up residing within the host’s red blood cells (RBCs)—replicating and then bursting forth to invade more RBCs in a recurrently timed cycle.

    “This penetration/replication/breakout cycle is rapid—every 48 hours—providing the opportunity for large numbers of mutations that can produce drug resistance,” said senior study author Matthew Bogyo, Ph.D., professor in the department of pathology at Stanford Medical School. “Consequently, several generations of antimalarial drugs have long since been rendered useless.”

    The compound that investigators developed targets the parasites proteasome—a protein degradation pathway that removes surplus or damaged proteins through a cascade of proteolytic reactions.

    “The parasite’s proteasome is like a shredder that chews up damaged or used-up proteins. Malaria parasites generate a lot of damaged proteins as they switch from one life stage to another and are very reliant on their proteasome, making it an excellent drug target,” Dr. Tilley noted.

    The scientists purified the proteasome from the malaria parasite and examined its activity against hundreds of different peptide sequences. From this, they were able to design inhibitors that selectively targeted the parasite proteasome while sparing the human host enzymes.

    The findings from this study were published recently in Nature through an article titled “Structure- and function-based design of Plasmodium-selective proteasome inhibitors.”

    Additionally, scientists at the MRC used a new technique called Single-Particle Cryo-Electron Microscopy to generate a three-dimensional, high-resolution structure of a protein, based on thousands composite images.

    The researchers tested the new drug in red blood cells infected with parasites and found that it was as effective at killing the artemisinin resistant parasites as it was for the sensitive parasites.

    “The compounds we’ve derived can kill artemisinin-resistant parasites because those parasites have an increased need for highly efficient proteasomes,” Dr. Bogyo commented. “So, combining the proteasome inhibitor with artemisinin should make it possible to block the onset of resistance. That will, in turn, allow the continued use of that front-line malaria treatment, which has been so effective up until now.”

    “The new proteasome inhibitors actually complement artemisinin drugs,” Dr. Tilley added. “Artemisinins cause protein damage and proteasome inhibitors prevent the repair of protein damage. A combination of the two provides a double whammy and could rescue the artemisinins as antimalarials, restoring their activity against resistant parasites.”

    The scientists were excited by their results, as they may provide a much-needed strategy to combat the growing levels of resistance for this deadly pathogen. However, the researchers tempered their exuberance by noting that many more drug libraries needed to be screened before clinical trials can begin.

    “The current drug is a good start, but it’s not yet suitable for humans. It needs to be able to be administered orally and needs to last a long time in the blood stream,” Dr. Tilley concluded.

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Quantum dots target infections

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Photoactivated QDs Kill Antibiotic-Resistant ‘Superbugs’

BOULDER, Colo., Jan. 20, 2016 — A technique for treating bacterial infections has successfully used light-activated quantum dots (QDs) to kill multiple multidrug-resistant strains.

http://www.photonics.com/Article.aspx?AID=58218

e coli

http://www.photonics.com/images/Web/Articles/2016/1/20/PIC_QD2.jpg

Modified atomic force micrograph of multidrug-resistant E. coli. Courtesy of the Nagpal Group/University of Colorado Boulder.

 

The approach is adaptive to constantly evolving drug-resistant bacteria and avoids damage to surrounding cells, an issue encountered in earlier attempts that deployed metal nanoparticles such as gold and silver to combat bacteria.

“By shrinking these [QD] semiconductors down to the nanoscale, we’re able to create highly specific interactions within the cellular environment that only target the infection,” said professor Prashant Nagpal of the University of Colorado Boulder.

The QDs — which are inactive in darkness — were tailored to target particular infections thanks to their light-activated properties. The researchers said that by modifying the wavelength of light applied, they could activate the QDs to alter and kill infected cells with specificity.

Napgal and his team tested the QD therapy on mammalian tissue containing bacterial cells in mono- and cocultures. The bacteria under investigation were ethicillin-resistant Staphylococcus aureus, carbapenem-resistant E. coli, and extended-spectrum ß-lactamase-producing Klebsiella pneumoniae and Salmonella typhimurium.

They reported 92 percent of bacterial cells were killed, while leaving mammalian cells intact. The QDs could also be tuned to increase bacterial proliferation.

qd

http://www.photonics.com/images/Web/Articles/2016/1/20/PIC_QD1.jpg

Plated antibiotic resistant ‘superbugs’ before and after treatment with nanoparticles. Courtesy of the Nagpal Group/University of Colorado Boulder.

The team said the killing effect was independent of the QD material used; rather, it was controlled by the redox potentials of the photogenerated charge carriers, which selectively altered cellular redox states. Photoexcited QDs could be used in the study of the effect of redox states on living systems, and lead to clinical phototherapy for the treatment of infections, the researchers said.

The specificity of the treatment could help reduce or eliminate the potential side effects of other treatment methods, as well as provide a path forward for future development and clinical trials.

“Antibiotics are not just a baseline treatment for bacterial infections, but HIV and cancer as well,” said professor Anushree Chatterjee. “Failure to develop effective treatments for drug-resistant strains is not an option, and that’s what this technology moves closer to solving.”

Nagpal and Chatterjee are the cofounders of Praan Biosciences Inc., a startup that can sequence genetic profiles using a single molecule, and have filed a patent on the QD therapy technology.

The research was published in Nature Materials (doi: 10.1038/nmat4542).

 

Photoexcited quantum dots for killing multidrug-resistant bacteria

Colleen M. CourtneySamuel M. GoodmanJessica A. McDanielNancy E. MadingerAnushree ChatterjeePrashant Nagpal

Nature Materials(2016)      http://dx.doi.org:/10.1038/nmat4542

Multidrug-resistant bacterial infections are an ever-growing threat because of the shrinking arsenal of efficacious antibiotics1, 2, 3, 4. Metal nanoparticles can induce cell death, yet the toxicity effect is typically nonspecific5, 6, 7, 8. Here, we show that photoexcited quantum dots (QDs) can kill a wide range of multidrug-resistant bacterial clinical isolates, including methicillin-resistant Staphylococcus aureus, carbapenem-resistant Escherichia coli, and extended-spectrum β-lactamase-producingKlebsiella pneumoniae and Salmonella typhimurium. The killing effect is independent of material and controlled by the redox potentials of the photogenerated charge carriers, which selectively alter the cellular redox state. We also show that the QDs can be tailored to kill 92% of bacterial cells in a monoculture, and in a co-culture of E. coli and HEK 293T cells, while leaving the mammalian cells intact, or to increase bacterial proliferation. Photoexcited QDs could be used in the study of the effect of redox states on living systems, and lead to clinical phototherapy for the treatment of infections.

Figure 2: The effect of CdTe-2.4 is specific to the reduction and oxidation potentials.close

The effect of CdTe-2.4 is specific to the reduction and oxidation potentials.

a, Absorbance spectra for CdTe and CdSe of several sizes. Insets show transmission electron microscopy (TEM) images with colour-coded scale bars (50nm except for CdTe-2.4, which is 25nm). b, Scanning tunnelling spectroscopy (STS) meas…

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