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Archive for the ‘Anticancer 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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Inhibitory CD161 receptor recognized as a potential immunotherapy target in glioma-infiltrating T cells by single-cell analysis

Reporter: Dr. Premalata Pati, Ph.D., Postdoc

 

Brain tumors, especially the diffused Gliomas are of the most devastating forms of cancer and have so-far been resistant to immunotherapy. It is comprehended that T cells can penetrate the glioma cells, but it still remains unknown why infiltrating cells miscarry to mount a resistant reaction or stop the tumor development.

Gliomas are brain tumors that begin from neuroglial begetter cells. The conventional therapeutic methods including, surgery, chemotherapy, and radiotherapy, have accomplished restricted changes inside glioma patients. Immunotherapy, a compliance in cancer treatment, has introduced a promising strategy with the capacity to penetrate the blood-brain barrier. This has been recognized since the spearheading revelation of lymphatics within the central nervous system. Glioma is not generally carcinogenic. As observed in a number of cases, the tumor cells viably reproduce and assault the adjoining tissues, by and large, gliomas are malignant in nature and tend to metastasize. There are four grades in glioma, and each grade has distinctive cell features and different treatment strategies. Glioblastoma is a grade IV glioma, which is the crucial aggravated form. This infers that all glioblastomas are gliomas, however, not all gliomas are glioblastomas.

Decades of investigations on infiltrating gliomas still take off vital questions with respect to the etiology, cellular lineage, and function of various cell types inside glial malignancies. In spite of the available treatment options such as surgical resection, radiotherapy, and chemotherapy, the average survival rate for high-grade glioma patients remains 1–3 years (1).

A recent in vitro study performed by the researchers of Dana-Farber Cancer Institute, Massachusetts General Hospital, and the Broad Institute of MIT and Harvard, USA, has recognized that CD161 is identified as a potential new target for immunotherapy of malignant brain tumors. The scientific team depicted their work in the Cell Journal, in a paper entitled, “Inhibitory CD161 receptor recognized in glioma-infiltrating T cells by single-cell analysis.” on 15th February 2021.

To further expand their research and findings, Dr. Kai Wucherpfennig, MD, PhD, Chief of the Center for Cancer Immunotherapy, at Dana-Farber stated that their research is additionally important in a number of other major human cancer types such as 

  • melanoma,
  • lung,
  • colon, and
  • liver cancer.

Dr. Wucherpfennig has praised the other authors of the report Mario Suva, MD, PhD, of Massachusetts Common Clinic; Aviv Regev, PhD, of the Klarman Cell Observatory at Broad Institute of MIT and Harvard, and David Reardon, MD, clinical executive of the Center for Neuro-Oncology at Dana-Farber.

Hence, this new study elaborates the effectiveness of the potential effectors of anti-tumor immunity in subsets of T cells that co-express cytotoxic programs and several natural killer (NK) cell genes.

The Study-

IMAGE SOURCE: Experimental Strategy (Mathewson et al., 2021)

 

The group utilized single-cell RNA sequencing (RNA-seq) to mull over gene expression and the clonal picture of tumor-infiltrating T cells. It involved the participation of 31 patients suffering from diffused gliomas and glioblastoma. Their work illustrated that the ligand molecule CLEC2D activates CD161, which is an immune cell surface receptor that restrains the development of cancer combating activity of immune T cells and tumor cells in the brain. The study reveals that the activation of CD161 weakens the T cell response against tumor cells.

Based on the study, the facts suggest that the analysis of clonally expanded tumor-infiltrating T cells further identifies the NK gene KLRB1 that codes for CD161 as a candidate inhibitory receptor. This was followed by the use of 

  • CRISPR/Cas9 gene-editing technology to inactivate the KLRB1 gene in T cells and showed that CD161 inhibits the tumor cell-killing function of T cells. Accordingly,
  • genetic inactivation of KLRB1 or
  • antibody-mediated CD161 blockade

enhances T cell-mediated killing of glioma cells in vitro and their anti-tumor function in vivo. KLRB1 and its associated transcriptional program are also expressed by substantial T cell populations in other forms of human cancers. The work provides an atlas of T cells in gliomas and highlights CD161 and other NK cell receptors as immune checkpoint targets.

Further, it has been identified that many cancer patients are being treated with immunotherapy drugs that disable their “immune checkpoints” and their molecular brakes are exploited by the cancer cells to suppress the body’s defensive response induced by T cells against tumors. Disabling these checkpoints lead the immune system to attack the cancer cells. One of the most frequently targeted checkpoints is PD-1. However, recent trials of drugs that target PD-1 in glioblastomas have failed to benefit the patients.

In the current study, the researchers found that fewer T cells from gliomas contained PD-1 than CD161. As a result, they said, “CD161 may represent an attractive target, as it is a cell surface molecule expressed by both CD8 and CD4 T cell subsets [the two types of T cells engaged in response against tumor cells] and a larger fraction of T cells express CD161 than the PD-1 protein.”

However, potential side effects of antibody-mediated blockade of the CLEC2D-CD161 pathway remain unknown and will need to be examined in a non-human primate model. The group hopes to use this finding in their future work by

utilizing their outline by expression of KLRB1 gene in tumor-infiltrating T cells in diffuse gliomas to make a remarkable contribution in therapeutics related to immunosuppression in brain tumors along with four other common human cancers ( Viz. melanoma, non-small cell lung cancer (NSCLC), hepatocellular carcinoma, and colorectal cancer) and how this may be manipulated for prevalent survival of the patients.

References

(1) Anders I. Persson, QiWen Fan, Joanna J. Phillips, William A. Weiss, 39 – Glioma, Editor(s): Sid Gilman, Neurobiology of Disease, Academic Press, 2007, Pages 433-444, ISBN 9780120885923, https://doi.org/10.1016/B978-012088592-3/50041-4.

Main Source

Mathewson ND, Ashenberg O, Tirosh I, Gritsch S, Perez EM, Marx S, et al. 2021. Inhibitory CD161 receptor identified in glioma-infiltrating T cells by single-cell analysis. Cell.https://www.cell.com/cell/fulltext/S0092-8674(21)00065-9?elqTrackId=c3dd8ff1d51f4aea87edd0153b4f2dc7

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Gamma Linolenic Acid (GLA) as a Therapeutic tool in the Management of Glioblastoma

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Cancer Genomics: Multiomic Analysis of Single Cells and Tumor Heterogeneity

Curator: Stephen J. Williams, PhD

4.3.7

4.3.7 Cancer Genomics: Multiomic Analysis of Single Cells and Tumor Heterogeneity, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 4: Single Cell Genomics

scTrio-seq identifies colon cancer lineages

Single-cell multiomics sequencing and analyses of human colorectal cancer. Shuhui Bian et al. Science  30 Nov 2018:Vol. 362, Issue 6418, pp. 1060-1063

To better design treatments for cancer, it is important to understand the heterogeneity in tumors and how this contributes to metastasis. To examine this process, Bian et al. used a single-cell triple omics sequencing (scTrio-seq) technique to examine the mutations, transcriptome, and methylome within colorectal cancer tumors and metastases from 10 individual patients. The analysis provided insights into tumor evolution, linked DNA methylation to genetic lineages, and showed that DNA methylation levels are consistent within lineages but can differ substantially among clones.

Science, this issue p. 1060

Abstract

Although genomic instability, epigenetic abnormality, and gene expression dysregulation are hallmarks of colorectal cancer, these features have not been simultaneously analyzed at single-cell resolution. Using optimized single-cell multiomics sequencing together with multiregional sampling of the primary tumor and lymphatic and distant metastases, we developed insights beyond intratumoral heterogeneity. Genome-wide DNA methylation levels were relatively consistent within a single genetic sublineage. The genome-wide DNA demethylation patterns of cancer cells were consistent in all 10 patients whose DNA we sequenced. The cancer cells’ DNA demethylation degrees clearly correlated with the densities of the heterochromatin-associated histone modification H3K9me3 of normal tissue and those of repetitive element long interspersed nuclear element 1. Our work demonstrates the feasibility of reconstructing genetic lineages and tracing their epigenomic and transcriptomic dynamics with single-cell multiomics sequencing.

Fig. 1 Reconstruction of genetic lineages with scTrio-seq2.

Global SCNA patterns (250-kb resolution) of CRC01. Each row represents an individual cell. The subclonal SCNAs used for identifying genetic sublineages were marked and indexed; for details, see fig. S6B. On the top of the heatmap, the amplification or deletion frequency of each genomic bin (250 kb) of the non-hypermutated CRC samples from the TCGA Project and patient CRC01’s cancer cells are shown.

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Fig. 1 Reconstruction of genetic lineages with scTrio-seq2.

Global SCNA patterns (250-kb resolution) of CRC01. Each row represents an individual cell. The subclonal SCNAs used for identifying genetic sublineages were marked and indexed; for details, see fig. S6B. On the top of the heatmap, the amplification or deletion frequency of each genomic bin (250 kb) of the non-hypermutated CRC samples

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Gender affects the prevalence of the cancer type, 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)

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

 

Gender of a person can affect the kinds of cancer-causing mutations they develop, according to a genomic analysis spanning nearly 2,000 tumours and 28 types of cancer. The results show striking differences in the cancer-causing mutations found in people who are biologically male versus those who are biologically female — not only in the number of mutations lurking in their tumours, but also in the kinds of mutations found there.

 

Liver tumours from women were more likely to carry mutations caused by a faulty system of DNA mending called mismatch repair, for instance. And men with any type of cancer were more likely to exhibit DNA changes thought to be linked to a process that the body uses to repair DNA with two broken strands. These biases could point researchers to key biological differences in how tumours develop and evolve across sexes.

 

The data add to a growing realization that sex is important in cancer, and not only because of lifestyle differences. Lung and liver cancer, for example, are more common in men than in women — even after researchers control for disparities in smoking or alcohol consumption. The source of that bias, however, has remained unclear.

In 2014, the US National Institutes of Health began encouraging researchers to consider sex differences in preclinical research by, for example, including female animals and cell lines from women in their studies. And some studies have since found sex-linked biases in the frequency of mutations in protein-coding genes in certain cancer types, including some brain cancers and advanced melanoma.

 

But the present study is the most comprehensive study of sex differences in tumour genomes so far. It looks at mutations not only in genes that code for proteins, but also in the vast expanses of DNA that have other functions, such as controlling when genes are turned on or off. The study also compares male and female genomes across many different cancers, which can allow researchers to pick up on additional patterns of DNA mutations, in part by increasing the sample sizes.

 

Researchers analysed full genome sequences gathered by the International Cancer Genome Consortium. They looked at differences in the frequency of 174 mutations known to drive cancer, and found that some of these mutations occurred more frequently in men than in women, and vice versa. When they looked more broadly at the loss or duplication of DNA segments in the genome, they found 4,285 sex-biased genes spread across 15 chromosomes.

 

There were also differences found when some mutations seemed to arise during tumour development, suggesting that some cancers follow different evolutionary paths in men and women. Researchers also looked at particular patterns of DNA changes. Such patterns can, in some cases, reflect the source of the mutation. Tobacco smoke, for example, leaves behind a particular signature in the DNA.

 

Taken together, the results highlight the importance of accounting for sex, not only in clinical trials but also in preclinical studies. This could eventually allow researchers to pin down the sources of many of the differences found in this study. Liver cancer is roughly three times as common in men as in women in some populations, and its incidence is increasing in some countries. A better understanding of its aetiology may turn out to be really important for prevention strategies and treatments.

 

References:

 

https://www.nature.com/articles/d41586-019-00562-7?utm_source=Nature+Briefing

 

https://www.nature.com/news/policy-nih-to-balance-sex-in-cell-and-animal-studies-1.15195

 

https://www.ncbi.nlm.nih.gov/pubmed/26296643

 

https://www.biorxiv.org/content/10.1101/507939v1

 

https://www.ncbi.nlm.nih.gov/pubmed/25985759

 

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Pancreatic cancer survival is determined by ratio of two enzymes, 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)

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

 

Protein kinase C (PKC) isozymes function as tumor suppressors in increasing contexts. These enzymes are crucial for a number of cellular activities, including cell survival, proliferation and migration — functions that must be carefully controlled if cells get out of control and form a tumor. In contrast to oncogenic kinases, whose function is acutely regulated by transient phosphorylation, PKC is constitutively phosphorylated following biosynthesis to yield a stable, autoinhibited enzyme that is reversibly activated by second messengers. Researchers at University of California San Diego School of Medicine found that another enzyme, called PHLPP1, acts as a “proofreader” to keep careful tabs on PKC.

 

The researchers discovered that in pancreatic cancer high PHLPP1 levels lead to low PKC levels, which is associated with poor patient survival. They reported that the phosphatase PHLPP1 opposes PKC phosphorylation during maturation, leading to the degradation of aberrantly active species that do not become autoinhibited. They discovered that any time an over-active PKC is inadvertently produced, the PHLPP1 “proofreader” tags it for destruction. That means the amount of PHLPP1 in patient’s cells determines his amount of PKC and it turns out those enzyme levels are especially important in pancreatic cancer.

 

This team of researchers reversed a 30-year paradigm when they reported evidence that PKC actually suppresses, rather than promotes, tumors. For decades before this revelation, many researchers had attempted to develop drugs that inhibit PKC as a means to treat cancer. Their study implied that anti-cancer drugs would actually need to do the opposite — boost PKC activity. This study sets the stage for clinicians to one day use a pancreatic cancer patient’s PHLPP1/PKC levels as a predictor for prognosis, and for researchers to develop new therapeutic drugs that inhibit PHLPP1 and boost PKC as a means to treat the disease.

 

The ratio — high PHLPP1/low PKC — correlated with poor prognoses: no pancreatic patient with low PKC in the database survived longer than five-and-a-half years. On the flip side, 50 percent of the patients with low PHLPP1/high PKC survived longer than that. While still in the earliest stages, the researchers hope that this information might one day aid pancreatic diagnostics and treatment. The researchers are next planning to screen chemical compounds to find those that inhibit PHLPP1 and restore PKC levels in low-PKC-pancreatic cancer cells in the lab. These might form the basis of a new therapeutic drug for pancreatic cancer.

 

References:

 

https://health.ucsd.edu/news/releases/Pages/2019-03-20-two-enzymes-linked-to-pancreatic-cancer-survival.aspx?elqTrackId=b6864b278958402787f61dd7b7624666

 

https://www.ncbi.nlm.nih.gov/pubmed/30904392

 

https://www.ncbi.nlm.nih.gov/pubmed/29513138

 

https://www.ncbi.nlm.nih.gov/pubmed/18511290

 

https://www.ncbi.nlm.nih.gov/pubmed/28476658

 

https://www.ncbi.nlm.nih.gov/pubmed/28283201

 

https://www.ncbi.nlm.nih.gov/pubmed/24231509

 

https://www.ncbi.nlm.nih.gov/pubmed/28112438

 

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Immunoediting can be a constant defense in the cancer landscape

Immuno-editing can be a constant defense in the cancer landscape, 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)

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

 

There are many considerations in the cancer immunoediting landscape of defense and regulation in the cancer hallmark biology. The cancer hallmark biology in concert with key controls of the HLA compatibility affinity mechanisms are pivotal in architecting a unique patient-centric therapeutic application. Selection of random immune products including neoantigens, antigens, antibodies and other vital immune elements creates a high level of uncertainty and risk of undesirable immune reactions. Immunoediting is a constant process. The human innate and adaptive forces can either trigger favorable or unfavorable immunoediting features. Cancer is a multi-disease entity. There are multi-factorial initiators in a certain disease process. Namely, environmental exposures, viral and / or microbiome exposure disequilibrium, direct harm to DNA, poor immune adaptability, inherent risk and an individual’s own vibration rhythm in life.

 

When a human single cell is crippled (Deranged DNA) with mixed up molecular behavior that is the initiator of the problem. A once normal cell now transitioned into full threatening molecular time bomb. In the modeling and creation of a tumor it all begins with the singular molecular crisis and crippling of a normal human cell. At this point it is either chop suey (mixed bit responses) or a productive defensive and regulation response and posture of the immune system. Mixed bits of normal DNA, cancer-laden DNA, circulating tumor DNA, circulating normal cells, circulating tumor cells, circulating immune defense cells, circulating immune inflammatory cells forming a moiety of normal and a moiety of mess. The challenge is to scavenge the mess and amplify the normal.

 

Immunoediting is a primary push-button feature that is definitely required to be hit when it comes to initiating immune defenses against cancer and an adaptation in favor of regression. As mentioned before that the tumor microenvironment is a “mixed bit” moiety, which includes elements of the immune system that can defend against circulating cancer cells and tumor growth. Personalized (Precision-Based) cancer vaccines must become the primary form of treatment in this case. Current treatment regimens in conventional therapy destroy immune defenses and regulation and create more serious complications observed in tumor progression, metastasis and survival. Commonly resistance to chemotherapeutic agents is observed. These personalized treatments will be developed in concert with cancer hallmark analytics and immunocentrics affinity and selection mapping. This mapping will demonstrate molecular pathway interface and HLA compatibility and adaptation with patientcentricity.

References:

 

https://www.linkedin.com/pulse/immunoediting-cancer-landscape-john-catanzaro/

 

https://www.cell.com/cell/fulltext/S0092-8674(16)31609-9

 

https://www.researchgate.net/publication/309432057_Circulating_tumor_cell_clusters_What_we_know_and_what_we_expect_Review

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4190561/

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5840207/

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5593672/

 

https://www.frontiersin.org/articles/10.3389/fimmu.2018.00414/full

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5593672/

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4190561/

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4388310/

 

https://www.linkedin.com/pulse/cancer-hallmark-analytics-omics-data-pathway-studio-review-catanzaro/

 

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Immunotherapy may help in glioblastoma survival

Immunotherapy may help in glioblastoma survival, 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)

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

 

Glioblastoma is the most common primary malignant brain tumor in adults and is associated with poor survival. But, in a glimmer of hope, a recent study found that a drug designed to unleash the immune system helped some patients live longer. Glioblastoma powerfully suppresses the immune system, both at the site of the cancer and throughout the body, which has made it difficult to find effective treatments. Such tumors are complex and differ widely in their behavior and characteristics.

 

A small randomized, multi-institution clinical trial was conducted and led by researchers at the University of California at Los Angeles involved patients who had a recurrence of glioblastoma, the most common central nervous system cancer. The aim was to evaluate immune responses and survival following neoadjuvant and/or adjuvant therapy with pembrolizumab (checkpoint inhibitor) in 35 patients with recurrent, surgically resectable glioblastoma. Patients who were randomized to receive neoadjuvant pembrolizumab, with continued adjuvant therapy following surgery, had significantly extended overall survival compared to patients that were randomized to receive adjuvant, post-surgical programmed cell death protein 1 (PD-1) blockade alone.

 

Neoadjuvant PD-1 blockade was associated with upregulation of T cell– and interferon-γ-related gene expression, but downregulation of cell-cycle-related gene expression within the tumor, which was not seen in patients that received adjuvant therapy alone. Focal induction of programmed death-ligand 1 in the tumor microenvironment, enhanced clonal expansion of T cells, decreased PD-1 expression on peripheral blood T cells and a decreasing monocytic population was observed more frequently in the neoadjuvant group than in patients treated only in the adjuvant setting. These findings suggest that the neoadjuvant administration of PD-1 blockade enhanced both the local and systemic antitumor immune response and may represent a more efficacious approach to the treatment of this uniformly lethal brain tumor.

 

Immunotherapy has not proved to be effective against glioblastoma. This small clinical trial explored the effect of PD-1 blockade on recurrent glioblastoma in relation to the timing of administration. A total of 35 patients undergoing resection of recurrent disease were randomized to either neoadjuvant or adjuvant pembrolizumab, and surgical specimens were compared between the two groups. Interestingly, the tumoral gene expression signature varied between the two groups, such that those who received neoadjuvant pembrolizumab displayed an INF-γ gene signature suggestive of T-cell activation as well as suppression of cell-cycle signaling, possibly consistent with growth arrest. Although the study was not powered for efficacy, the group found an increase in overall survival in patients receiving neoadjuvant pembrolizumab compared with adjuvant pembrolizumab of 13.7 months versus 7.5 months, respectively.

 

In this small pilot study, neoadjuvant PD-1 blockade followed by surgical resection was associated with intratumoral T-cell activation and inhibition of tumor growth as well as longer survival. How the drug works in glioblastoma has not been totally established. The researchers speculated that giving the drug before surgery prompted T-cells within the tumor, which had been impaired, to attack the cancer and extend lives. The drug didn’t spur such anti-cancer activity after the surgery because those T-cells were removed along with the tumor. The results are very important and very promising but would need to be validated in much larger trials.

 

References:

 

https://www.washingtonpost.com/health/2019/02/11/immunotherapy-may-help-patients-with-kind-cancer-that-killed-john-mccain/?noredirect=on&utm_term=.e1b2e6fffccc

 

https://www.ncbi.nlm.nih.gov/pubmed/30742122

 

https://www.practiceupdate.com/content/neoadjuvant-anti-pd-1-immunotherapy-promotes-immune-responses-in-recurrent-gbm/79742/37/12/1

 

https://www.esmo.org/Oncology-News/Neoadjuvant-PD-1-Blockade-in-Glioblastoma

 

https://neurosciencenews.com/immunotherapy-glioblastoma-cancer-10722/

 

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Lesson 9 Cell Signaling:  Curations and Articles of reference as supplemental information for lecture section on WNTs: #TUBiol3373

Stephen J. Wiilliams, Ph.D: Curator

UPDATED 4/23/2019

This has an updated lesson on WNT signaling.  Please click on the following and look at the slides labeled under lesson 10

cell motility 9b lesson_2018_sjw

Remember our lessons on the importance of signal termination.  The CANONICAL WNT signaling (that is the β-catenin dependent signaling)

is terminated by the APC-driven degradation complex.  This leads to the signal messenger  β-catenin being degraded by the proteosome.  Other examples of growth factor signaling that is terminated by a proteosome-directed include the Hedgehog signaling system, which is involved in growth and differentiation as well as WNTs and is implicated in various cancers.

A good article on the Hedgehog signaling pathway is found here:

The Voice of a Pathologist, Cancer Expert: Scientific Interpretation of Images: Cancer Signaling Pathways and Tumor Progression

All images in use for this article are under copyrights with Shutterstock.com

Cancer is expressed through a series of transformations equally involving metabolic enzymes and glucose, fat, and protein metabolism, and gene transcription, as a result of altered gene regulatory and transcription pathways, and also as a result of changes in cell-cell interactions.  These are embodied in the following series of graphics.

Figure 1: Sonic_hedgehog_pathwaySonic_hedgehog_pathway

The Voice of Dr. Larry

The figure shows a modification of nuclear translocation by Sonic hedgehog pathway. The hedgehog proteins have since been implicated in the development of internal organs, midline neurological structures, and the hematopoietic system in humans. The Hh signaling pathway consists of three main components: the receptor patched 1 (PTCH1), the seven transmembrane G-protein coupled receptor smoothened (SMO), and the intracellular glioma-associated oncogene homolog (GLI) family of transcription factors.5The GLI family is composed of three members, including GLI1 (gene activating), GLI2 (gene activating and repressive), and GLI3 (gene repressive).6 In the absence of an activating signal from either Shh, Ihh or Dhh, PTCH1 exerts an inhibitory effect on the signal transducer SMO, preventing any downstream signaling from occurring.7 When Hh ligands bind and activate PTCH1, the inhibition on SMO is released, allowing the translocation of SMO into the cytoplasm and its subsequent activation of the GLI family of transcription factors.

 

And from the review of  Elaine Y. C. HsiaYirui Gui, and Xiaoyan Zheng   Regulation of Hedgehog Signaling by Ubiquitination  Front Biol (Beijing). 2015 Jun; 10(3): 203–220.

the authors state:

Finally, termination of Hh signaling is also important for controlling the duration of pathway activity. Hh induced ubiquitination and degradation of Ci/Gli is the most well-established mechanism for limiting signal duration, and inhibiting this process can lead to cell patterning disruption and excessive cell proliferation (). In addition to Ci/Gli, a growing body of evidence suggests that ubiquitination also plays critical roles in regulating other Hh signaling components including Ptc, Smo, and Sufu. Thus, ubiquitination serves as a general mechanism in the dynamic regulation of the Hh pathway.

Overview of Hedgehog signaling showing the signal termination by ubiquitnation and subsequent degradation of the Gli transcriptional factors. obtained from Oncotarget 5(10):2881-911 · May 2014. GSK-3B as a Therapeutic Intervention in Cancer

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Note that in absence of Hedgehog ligands Ptch inhibits Smo accumulation and activation but upon binding of Hedgehog ligands (by an autocrine or paracrine fashion) Ptch is now unable to inhibit Smo (evidence exists that Ptch is now targeted for degradation) and Smo can now inhibit Sufu-dependent and GSK-3B dependent induced degradation of Gli factors Gli1 and Gli2.  Also note the Gli1 and Gli2 are transcriptional activators while Gli3 is a transcriptional repressor.

UPDATED 4/16/2019

Please click on the following links for the Powerpoint presentation for lesson 9.  In addition click on the mp4 links to download the movies so you can view them in Powerpoint slide 22:

cell motility 9 lesson_SJW 2019

movie file 1:

Tumorigenic but noninvasive MCF-7 cells motility on an extracellular matrix derived from normal (3DCntrol) or tumor associated (TA) fibroblasts.  Note that TA ECM is “soft” and not organized and tumor cells appear to move randomly if  much at all.

Movie 2:

 

Note that these tumorigenic and invasive MDA-MB-231 breast cancer cells move in organized patterns on organized ECM derived from Tumor Associated (TA) fibroblasts than from the ‘soft’ or unorganized ECM derived from normal  (3DCntrl) fibroblasts

 

The following contain curations of scientific articles from the site https://pharmaceuticalintelligence.com  intended as additional reference material  to supplement material presented in the lecture.

Wnts are a family of lipid-modified secreted glycoproteins which are involved in:

Normal physiological processes including

A. Development:

– Osteogenesis and adipogenesis (Loss of wnt/β‐catenin signaling causes cell fate shift of preosteoblasts from osteoblasts to adipocytes)

  – embryogenesis including body axis patterning, cell fate specification, cell proliferation and cell migration

B. tissue regeneration in adult tissue

read: Wnt signaling in the intestinal epithelium: from endoderm to cancer

And in pathologic processes such as oncogenesis (refer to Wnt/β-catenin Signaling [7.10]) and to your Powerpoint presentation

 

The curation Wnt/β-catenin Signaling is a comprehensive review of canonical and noncanonical Wnt signaling pathways

 

To review:

 

 

 

 

 

 

 

 

 

 

 

Activating the canonical Wnt pathway frees B-catenin from the degradation complex, resulting in B-catenin translocating to the nucleus and resultant transcription of B-catenin/TCF/LEF target genes.

Fig. 1 Canonical Wnt/FZD signaling pathway. (A) In the absence of Wnt signaling, soluble β-catenin is phosphorylated by a degradation complex consisting of the kinases GSK3β and CK1α and the scaffolding proteins APC and Axin1. Phosphorylated β-catenin is targeted for proteasomal degradation after ubiquitination by the SCF protein complex. In the nucleus and in the absence of β-catenin, TCF/LEF transcription factor activity is repressed by TLE-1; (B) activation of the canonical Wnt/FZD signaling leads to phosphorylation of Dvl/Dsh, which in turn recruits Axin1 and GSK3β adjacent to the plasma membrane, thus preventing the formation of the degradation complex. As a result, β-catenin accumulates in the cytoplasm and translocates into the nucleus, where it promotes the expression of target genes via interaction with TCF/LEF transcription factors and other proteins such as CBP, Bcl9, and Pygo.

NOTE: In the canonical signaling, the Wnt signal is transmitted via the Frizzled/LRP5/6 activated receptor to INACTIVATE the degradation complex thus allowing free B-catenin to act as the ultimate transducer of the signal.

Remember, as we discussed, the most frequent cancer-related mutations of WNT pathway constituents is in APC.

This shows how important the degradation complex is in controlling canonical WNT signaling.

Other cell signaling systems are controlled by protein degradation:

A.  The Forkhead family of transcription factors

Read: Regulation of FoxO protein stability via ubiquitination and proteasome degradation

B. Tumor necrosis factor α/NF κB signaling

Read: NF-κB, the first quarter-century: remarkable progress and outstanding questions

1.            Question: In cell involving G-proteins, the signal can be terminated by desensitization mechanisms.  How is both the canonical and noncanonical Wnt signal eventually terminated/desensitized?

We also discussed the noncanonical Wnt signaling pathway (independent of B-catenin induced transcriptional activity).  Note that the canonical and noncanonical involve different transducers of the signal.

Noncanonical WNT Signaling

Note: In noncanonical signaling the transducer is a G-protein and second messenger system is IP3/DAG/Ca++ and/or kinases such as MAPK, JNK.

Depending on the different combinations of WNT ligands and the receptors, WNT signaling activates several different intracellular pathways  (i.e. canonical versus noncanonical)

 

In addition different Wnt ligands are expressed at different times (temporally) and different cell types in development and in the process of oncogenesis. 

The following paper on Wnt signaling in ovarian oncogenesis shows how certain Wnt ligands are expressed in normal epithelial cells but the Wnt expression pattern changes upon transformation and ovarian oncogenesis. In addition, differential expression of canonical versus noncanonical WNT ligands occur during the process of oncogenesis (for example below the authors describe the noncanonical WNT5a is expressed in normal ovarian  epithelia yet WNT5a expression in ovarian cancer is lower than the underlying normal epithelium. However the canonical WNT10a, overexpressed in ovarian cancer cells, serves as an oncogene, promoting oncogenesis and tumor growth.

Wnt5a Suppresses Epithelial Ovarian Cancer by Promoting Cellular Senescence

Benjamin G. Bitler,1 Jasmine P. Nicodemus,1 Hua Li,1 Qi Cai,2 Hong Wu,3 Xiang Hua,4 Tianyu Li,5 Michael J. Birrer,6Andrew K. Godwin,7 Paul Cairns,8 and Rugang Zhang1,*

A.           Abstract

Epithelial ovarian cancer (EOC) remains the most lethal gynecological malignancy in the US. Thus, there is an urgent need to develop novel therapeutics for this disease. Cellular senescence is an important tumor suppression mechanism that has recently been suggested as a novel mechanism to target for developing cancer therapeutics. Wnt5a is a non-canonical Wnt ligand that plays a context-dependent role in human cancers. Here, we investigate the role of Wnt5a in regulating senescence of EOC cells. We demonstrate that Wnt5a is expressed at significantly lower levels in human EOC cell lines and in primary human EOCs (n = 130) compared with either normal ovarian surface epithelium (n = 31; p = 0.039) or fallopian tube epithelium (n = 28; p < 0.001). Notably, a lower level of Wnt5a expression correlates with tumor stage (p = 0.003) and predicts shorter overall survival in EOC patients (p = 0.003). Significantly, restoration of Wnt5a expression inhibits the proliferation of human EOC cells both in vitro and in vivo in an orthotopic EOC mouse model. Mechanistically, Wnt5a antagonizes canonical Wnt/β-catenin signaling and induces cellular senescence by activating the histone repressor A (HIRA)/promyelocytic leukemia (PML) senescence pathway. In summary, we show that loss of Wnt5a predicts poor outcome in EOC patients and Wnt5a suppresses the growth of EOC cells by triggering cellular senescence. We suggest that strategies to drive senescence in EOC cells by reconstituting Wnt5a signaling may offer an effective new strategy for EOC therapy.

Oncol Lett. 2017 Dec;14(6):6611-6617. doi: 10.3892/ol.2017.7062. Epub 2017 Sep 26.

Clinical significance and biological role of Wnt10a in ovarian cancer. 

Li P1Liu W1Xu Q1Wang C1.

Ovarian cancer is one of the five most malignant types of cancer in females, and the only currently effective therapy is surgical resection combined with chemotherapy. Wnt family member 10A (Wnt10a) has previously been identified to serve an oncogenic function in several tumor types, and was revealed to have clinical significance in renal cell carcinoma; however, there is still only limited information regarding the function of Wnt10a in the carcinogenesis of ovarian cancer. The present study identified increased expression levels of Wnt10a in two cell lines, SKOV3 and A2780, using reverse transcription-polymerase chain reaction. Functional analysis indicated that the viability rate and migratory ability of SKOV3 cells was significantly inhibited following Wnt10a knockdown using short interfering RNA (siRNA) technology. The viability rate of SKOV3 cells decreased by ~60% compared with the control and the migratory ability was only ~30% of that in the control. Furthermore, the expression levels of β-catenin, transcription factor 4, lymphoid enhancer binding factor 1 and cyclin D1 were significantly downregulated in SKOV3 cells treated with Wnt10a-siRNA3 or LGK-974, a specific inhibitor of the canonical Wnt signaling pathway. However, there were no synergistic effects observed between Wnt10a siRNA3 and LGK-974, which indicated that Wnt10a activated the Wnt/β-catenin signaling pathway in SKOV3 cells. In addition, using quantitative PCR, Wnt10a was overexpressed in the tumor tissue samples obtained from 86 patients with ovarian cancer when compared with matching paratumoral tissues. Clinicopathological association analysis revealed that Wnt10a was significantly associated with high-grade (grade III, P=0.031) and late-stage (T4, P=0.008) ovarian cancer. Furthermore, the estimated 5-year survival rate was 18.4% for patients with low Wnt10a expression levels (n=38), whereas for patients with high Wnt10a expression (n=48) the rate was 6.3%. The results of the present study suggested that Wnt10a serves an oncogenic role during the carcinogenesis and progression of ovarian cancer via the Wnt/β-catenin signaling pathway.

Targeting the Wnt Pathway includes curations of articles related to the clinical development of Wnt signaling inhibitors as a therapeutic target in various cancers including hepatocellular carcinoma, colon, breast and potentially ovarian cancer.

 

2.         Question: Given that different Wnt ligands and receptors activate different signaling pathways, AND  WNT ligands  can be deferentially and temporally expressed  in various tumor types and the process of oncogenesis, how would you approach a personalized therapy targeting the WNT signaling pathway?

3.         Question: What are the potential mechanisms of either intrinsic or acquired resistance to Wnt ligand antagonists being developed?

 

Other related articles published in this Open Access Online Scientific Journal include the following:

Targeting the Wnt Pathway [7.11]

Wnt/β-catenin Signaling [7.10]

Cancer Signaling Pathways and Tumor Progression: Images of Biological Processes in the Voice of a Pathologist Cancer Expert

e-Scientific Publishing: The Competitive Advantage of a Powerhouse for Curation of Scientific Findings and Methodology Development for e-Scientific Publishing – LPBI Group, A Case in Point 

Electronic Scientific AGORA: Comment Exchanges by Global Scientists on Articles published in the Open Access Journal @pharmaceuticalintelligence.com – Four Case Studies

 

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Knowing the genetic vulnerability of bladder cancer for therapeutic intervention, 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)

Knowing the genetic vulnerability of bladder cancer for therapeutic intervention

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

 

A mutated gene called RAS gives rise to a signalling protein Ral which is involved in tumour growth in the bladder. Many researchers tried and failed to target and stop this wayward gene. Signalling proteins such as Ral usually shift between active and inactive states.

 

So, researchers next tried to stop Ral to get into active state. In inacvtive state Ral exposes a pocket which gets closed when active. After five years, the researchers found a small molecule dubbed BQU57 that can wedge itself into the pocket to prevent Ral from closing and becoming active. Now, BQU57 has been licensed for further development.

 

Researchers have a growing genetic data on bladder cancer, some of which threaten to overturn the supposed causes of bladder cancer. Genetics has also allowed bladder cancer to be reclassified from two categories into five distinct subtypes, each with different characteristics and weak spots. All these advances bode well for drug development and for improved diagnosis and prognosis.

 

Among the groups studying the genetics of bladder cancer are two large international teams: Uromol (named for urology and molecular biology), which is based at Aarhus University Hospital in Denmark, and The Cancer Genome Atlas (TCGA), based at institutions in Texas and Boston. Each team tackled a different type of cancer, based on the traditional classification of whether or not a tumour has grown into the muscle wall of the bladder. Uromol worked on the more common, earlier form, non-muscle-invasive bladder cancer, whereas TCGA is looking at muscle-invasive bladder cancer, which has a lower survival rate.

 

The Uromol team sought to identify people whose non-invasive tumours might return after treatment, becoming invasive or even metastatic. Bladder cancer has a high risk of recurrence, so people whose non-invasive cancer has been treated need to be monitored for many years, undergoing cystoscopy every few months. They looked for predictive genetic footprints in the transcriptome of the cancer, which contains all of a cell’s RNA and can tell researchers which genes are turned on or off.

 

They found three subgroups with distinct basal and luminal features, as proposed by other groups, each with different clinical outcomes in early-stage bladder cancer. These features sort bladder cancer into genetic categories that can help predict whether the cancer will return. The researchers also identified mutations that are linked to tumour progression. Mutations in the so-called APOBEC genes, which code for enzymes that modify RNA or DNA molecules. This effect could lead to cancer and cause it to be aggressive.

 

The second major research group, TCGA, led by the National Cancer Institute and the National Human Genome Research Institute, that involves thousands of researchers across USA. The project has already mapped genomic changes in 33 cancer types, including breast, skin and lung cancers. The TCGA researchers, who study muscle-invasive bladder cancer, have looked at tumours that were already identified as fast-growing and invasive.

 

The work by Uromol, TCGA and other labs has provided a clearer view of the genetic landscape of early- and late-stage bladder cancer. There are five subtypes for the muscle-invasive form: luminal, luminal–papillary, luminal–infiltrated, basal–squamous, and neuronal, each of which is genetically distinct and might require different therapeutic approaches.

 

Bladder cancer has the third-highest mutation rate of any cancer, behind only lung cancer and melanoma. The TCGA team has confirmed Uromol research showing that most bladder-cancer mutations occur in the APOBEC genes. It is not yet clear why APOBEC mutations are so common in bladder cancer, but studies of the mutations have yielded one startling implication. The APOBEC enzyme causes mutations early during the development of bladder cancer, and independent of cigarette smoke or other known exposures.

 

The TCGA researchers found a subset of bladder-cancer patients, those with the greatest number of APOBEC mutations, had an extremely high five-year survival rate of about 75%. Other patients with fewer APOBEC mutations fared less well which is pretty surprising.

 

This detailed knowledge of bladder-cancer genetics may help to pinpoint the specific vulnerabilities of cancer cells in different people. Over the past decade, Broad Institute researchers have identified more than 760 genes that cancer needs to grow and survive. Their genetic map might take another ten years to finish, but it will list every genetic vulnerability that can be exploited. The goal of cancer precision medicine is to take the patient’s tumour and decode the genetics, so the clinician can make a decision based on that information.

 

References:

 

https://www.ncbi.nlm.nih.gov/pubmed/29117162

 

https://www.ncbi.nlm.nih.gov/pubmed/27321955

 

https://www.ncbi.nlm.nih.gov/pubmed/28583312

 

https://www.ncbi.nlm.nih.gov/pubmed/24476821

 

https://www.ncbi.nlm.nih.gov/pubmed/28988769

 

https://www.ncbi.nlm.nih.gov/pubmed/28753430

 

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Insights into the Metabolome

Curator: Larry H. Bernstein, MD, FCAP

FCAP

 

Updated 6/3/2016

 

Tapping the Metabolome

Genes, Transcripts, Proteins—All Have Come into Their “-Ome”     GEN May 15, 2016 (Vol. 36, No. 10)

http://www.genengnews.com/gen-articles/tapping-the-metabolome/5770/

 

 

http://www.genengnews.com/Media/images/Article/NationalEyeInst_Weiss_RetinitisPigmentosa1714323106.jpg

The retina is responsible for capturing images from the visual field. Retinitis pigmentosa, which refers to a group of inherited diseases that cause retinal degeneration, causes a gradual decline in vision because retinal photoreceptor cells (rods and cones) die. Images on the left are courtesy of the National Eye Institute, NIH; image on the right is courtesy of Robert Fariss, Ph.D., and Ann Milam, Ph.D., National Eye Institute, NIH.

Metabolomics, the comprehensive evaluation of the products of cellular processes, can provide new findings and insight in a vast array of diseases and dysfunctions. Though promising, metabolomics lacks the standing of genomics or proteomics. It is, in a manner of speaking, the new kid on the “omics” block.

Even though metabolomics is still an emerging discipline, at least some quarters are giving it a warm welcome. For example, metabolomics is being advanced by the Common Fund, an initiate of the National Institutes of Health (NIH). The Common Fund has established six national metabolomics cores. In addition, individual agencies within NIH, such as the National Institute of Environmental Health Sciences (NIEHS), are releasing solicitations focused on growing more detailed metabolomics programs.

Whether metabolomic studies are undertaken with or without public support, they share certain characteristics and challenges. Untargeted or broad-spectrum studies are used for hypotheses generation, whereas targeted studies probe specific compounds or pathways. Reproducibility is a major challenge in the field; many studies cannot be reproduced in larger cohorts. Carefully defined guidance and standard operating procedures for sample collection and processing are needed.

While these challenges are being addressed, researchers are patiently amassing metabolomic insights in several areas, such as retinal diseases, neurodegenerative diseases, and autoimmune diseases. In addition, metabolomic sleuths are availing themselves of a growing selection of investigative tools.

A Metabolomic Eye on Retinal Degeneration

The retina has one of the highest metabolic activities of any tissue in the body and is composed of multiple cell types. This fact suggests that metabolomics might be helpful in understanding retinal degeneration. At least, that’s what occurred to Ellen Weiss, Ph.D., a professor of cell biology and physiology at the University of North Carolina School of Medicine at Chapel Hill. To explore this possibility, Dr. Weiss began collaborating with Susan Sumner, Ph.D., director of systems and translational sciences at RTI International.

Retinal degeneration is often studied through the use of genetic-mouse models that mimic the disease in humans. In the model used by Dr. Weiss, cells with a disease-causing mutation are the major light-sensing cells that degenerate during the disease. Individuals with the same or a similar genetic mutation will initially lose dim-light vision then, ultimately, bright-light vision and color vision.

Wild-type and mutant phenotypes, as well as dark- and light-raised animals, were compared, since retinal degeneration is exacerbated by light in this genetic model. Retinas were collected as early as day 18, prior to symptomatic disease, and analyzed. Although data analysis is ongoing, distinct differences have emerged between the phenotypes as well as between dark- and light-raised animals.

“There is a clear increase in oxidative stress in both light-raised groups but to a larger extent in the mutant phenotype,” reports Dr. Weiss. “There are global changes in metabolites that suggest mitochondrial dysfunction, and dramatic changes in lipid profiles. Now we need to understand how these metabolites are involved in this eye disease and the relevance of these perturbations.”

For example, the glial cells in the retina that upregulate a number of proteins in response to stress to attempt to save the retina are as likely as the light-receptive neurons to undergo metabolic changes.

“One of the challenges in metabolomics studies is assigning the signals that represent the metabolites or compounds in the samples,” notes Dr. Sumner. “Signals may be ‘unknown unknowns,’ compounds that have never been identified before, or ‘known unknowns,’ compounds that are known but that have not yet been assigned in the biological matrix.”

Internal and external libraries, such as the Human Metabolome Dictionary, are used to match signals. Whether or not a match exists, fragmentation patterns are used to characterize the metabolite, and when possible a standard is obtained to confirm identity. To assist with this process, the NIH Common Fund supports Metabolite Standard Synthesis Cores (MSSCs). RTI International holds an MSSC contract in addition to being a NIH-designated metabolomics core.

Mitochondrial Dysfunction in Alzheimer’s Disease     

Alzheimer’s disease (AD) is difficult to diagnose early due to its asymptomatic phase; accurate diagnosis occurs only in postmortem brain tissue. To evaluate familial AD, a rare inherited form of the disease, the laboratory of Eugenia Trushina, Ph.D., associate professor of neurology and associate professor of pharmacology at the Mayo Clinic, uses mouse models to study the disease’s early molecular mechanisms.

Synaptic loss underlies cognitive dysfunction. The length of neurons dictates that mitochondria move within the cell to provide energy at the site of the synapses. An initial finding was that very early on mitochondrial trafficking was affected reducing energy supply to synapses and distant parts of the cell.

During energy production, the major mitochondrial metabolite is ATP, but the organelle also produces many other metabolites, molecules that are implicated in many pathways. One can assume that changes in energy utilization, production, and delivery are associated with some disturbance.

“Our goal,” explains Dr. Trushina, “was to get a proof of concept that we could detect in the blood of AD patients early changes of mitochondria dysfunction or other changes that could be informative of the disease over time.”

A Mayo Clinic aging study involves a cohort of patients, from healthy to those with mild cognitive impairment (MCI) through AD. Patients undergo an annual battery of tests including cognitive function along with blood and cerebrospinal fluid sampling. Metabolic signatures in plasma and cerebrospinal fluid of normal versus various disease stages were compared, and affected mitochondrial and lipid pathways identified in MCI patients that progressed to AD.

“Last year we published on a new compound that goes through the blood/brain barrier, gets into mitochondria, and very specifically, partially inhibits mitochondrial complex I activity, making the cell resistant to oxidative damage,” details Dr. Trushina. “The compound was able to either prevent or slow the disease in the animal familial models.

“Treatment not only reduced levels of amyloid plaques and phosphorylated tau, it also restored mitochondrial transport in neurons. Now we have additional compounds undergoing investigation for safety in humans, and target selectivity and engagement.”

“Mitochondria play a huge role in every aspect of our lives,” Dr. Trushina continues. “The discovery seems counterintuitive, but if mitochondria function is at the heart of AD, it may provide insight into the major sporadic form of the disease.”

Distinguishing Types of Asthma

In children, asthma generally manifests as allergy-induced asthma, or allergic asthma. And allergic asthma has commonalities with allergic dermatitis/eczema, food allergies, and allergic rhinitis. In adults, asthma is more heterogeneous, and distinct and varied subpopulations emerge. Some have nonallergic asthma; some have adult-onset asthma; and some have obesity-, occupational-, or exercise-induced asthma.

Adult asthmatics may have markers of TH2 high verus TH2 low asthma (T helper 2 cell cytokines) and they may respond to various triggers—environmental antigens, occupational antigens, irritants such as perfumes and chlorine, and seasonal allergens. Exercise, too, can trigger asthma.

One measure that can phenotype asthmatics is nitric oxide, an exhaled breath biomarker. Nitric oxide is a smooth muscle relaxant, vasodilator, and bronchodilator that can have anti-inflammatory properties. There is a wide range of values in asthmatics, and a number of values are needed to understand the trend in a particular patient. L-arginine is the amino acid that produces nitric oxide when converted to L-citrulline, a nonessential amino acid.

According to Nicholas Kenyon, M.D., a pulmonary and critical care specialist who is co-director of the University of California, Davis Asthma Network (UCAN), some metabolomic studies suggest that there is a state of L-arginine depletion during asthma attacks or in severe asthma suggesting a lack of substrate to produce nitric oxide. Dr. Kenyon is conducting clinical work on L-arginine supplementation in a double-blind cross-over  intervention trial of L-arginine versus placebo. The 50-subject study in severe asthmatics should be concluded in early 2017.

Many new biologic therapies are coming to market to treat asthma; it will be challenging to determine which advanced therapy to provide to which patient. Therapeutics mostly target severe asthma populations and are for patients with evidence of higher numbers of eosinophils in the blood and lung, which include anti-IL-5 and (soon) anti-IL-13, among others.

Tools Development 

Waters is developing metabolomics applications that use multivariate statistical methods to highlight compounds of interest. Typically these applications combine separation procedures, accomplished by means of liquid chromatography or gas chromatography (LC or GC), with detection methods that rely on mass spectrometry (MS). To support the identification, quantification, and analysis of LC-MS data, the company provides bioinformatics software. For example, Progenesis QI software can interrogate publicly available databases and process information about isotopic patterns, retention times, and collision cross-sections.

Mass spectrometry (MS) is the gold standard in metabolomics and lipidomics. But there is a limit to what accurate mass and resolution can achieve. For example, neither isobaric nor isomeric species are resolvable solely by MS. New orthogonal analytical tools will allow more confident identifications.

To improve metabolomics separations before MS detection, a post-ionization separation tool, like ion mobility, which is currently used to support traditional UPLC-MS and MS imaging metabolomics protocols, becomes useful. The collision-cross section (CCS), which measures the shape of molecules, can be derived, and it can be used as an additional identification coordinate.

Other new chromatographic tools are under development, such as microflow devices and UltraPerformance Convergence Chromatography (UPC2), which uses liquid CO2 as its mobile phase, to enable new ways of separating chiral metabolites. Both UPC2 and microflow technologies have decreased solvent consumption and waste disposal while maintaining UPLC-quality performance in terms of chromatographic resolution, robustness, and reproducibility.

Informatics tools are also improving. In the latest versions of Waters’ Progenesis software, typical metabolomics identification problems are resolved by allowing interrogation of publicly available databases and scoring according to accurate mass, isotopic pattern, retention time, CCS, and either theoretical or experimental fragments.

MS imaging techniques, such as MALDI and DESI, provide spatial information about the metabolite composition in tissues. These approaches can be used to support and confirm traditional analyses without sample extraction, and they allow image generation without the use of antibodies, similar to immunohistochemistry.

“Ion-mobility tools will soon be implemented for routine use, and the use of extended CCS databases will help with metabolite identification,” comments Giuseppe Astarita Ph.D., principal scientist, Waters. “More applications of ambient ionization MS will emerge, and they will allow direct-sampling analyses at atmospheric pressure with little or no sample preparation, generating real-time molecular fingerprints that can be used to discriminate among phenotypes.”

Microflow Technology   

Microflow technology offers sensitivity and robustness. For example, at the Proteomics and Metabolomics Facility, Colorado State University, peptide analysis was typically performed using nanoflow chromatography; however, nanoflow chromatography is slow and technically challenging. Moving to microflow offered significant improvements in robustness and ease-of-use and resulted in improved chromatography without sacrificing sensitivity.

Conversely, small molecule applications were typically performed with analytical-scale chromatography. While this flow regime is extremely robust and fast, it can sometimes be limited in sensitivity. Moving to microflow offered significant improvements in sensitivity, 5- to 10-fold depending on the compound, without sacrificing robustness.

But broad-scale microflow adoption is hampered by a lack of available column chemistries and legacy HPLC or UPLC infrastructure that is not conducive to low-flow operation.

“We utilize microflow technology on all of our tandem quadrupole instruments for targeted quantitative assays,” says Jessica Prenni, Ph.D., director, Proteomics and Metabolomics Facility, Colorado State University. “All of our peptide quantitation is exclusively performed with microflow technology, and many of our small molecule assays. Application examples include endocannabinoids, bile acids and plant phytohormone panels.”

Compound annotation and comparability and transparency in data processing and reporting is a challenge in metabolomics research. Multiple groups are actively working on developing new tools and strategies; common best practices need to be adopted.

The continued growth of open-source spectral databases and new tools for spectral prediction from compound databases will dramatically impact the ability for metabolomics to result in novel discoveries. The move to a systems-level understanding through the combination of various omics data also will have a huge influence and be enabled by the continued development of open-source and user-friendly pathway-analysis tools.

 Where Trackless Terrain Once Challenged Biomarker Development, Clearer Paths Are Emerging

http://www.genengnews.com/gen-articles/paving-the-road-for-clinical-biomarkers/5757/

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Fusion detection can be carried out with traditional opposing primer-based library preparation methods, which require target- and fusion-specific primers that define the region to be sequenced. With these methods, primers are needed that flank the target region and the fusion partner, so only known fusions can be detected. An alternative method, ArcherDX’ Anchored Multiplex PCR (AMP), can be used to detect the target of interest, plus any known and unknown fusion partners. This is because AMP uses target-specific unidirectional primers, along with reverse primers, that hybridize to the sequencing adapter that is ligated to each fragment prior to amplification.

In time, the narrow, tortuous paths followed by pioneers become wider and straighter, whether the pioneers are looking to settle new land or bring new biomarkers to the clinic.

In the case of biomarkers, we’re still at the stage where pioneers need to consult guides and outfitters or, in modern parlance, consultants and technology providers. These hardy souls tend to congregate at events like the Biomarker Conference, which was held recently in San Diego.

At this event, biomarker experts discussed ways to avoid unfortunate detours on the trail from discovery and development to clinical application and regulatory approval. Of particular interest were topics such as the identification of accurate biomarkers, the explication of disease mechanisms, the stratification of patient groups, and the development of standard protocols and assay platforms. In each of these areas, presenters reported progress.

Another crucial subject is the integration of techniques such as next-generation sequencing (NGS). This particular technique has been instrumental in advancing clinical cancer genomics and continues to be the most feasible way of simultaneously interrogating multiple genes for driver mutations.

Enriching nucleic acid libraries for target genes of interest prior to NGS greatly enhances the sensitivity of detecting mutations, as the enriched regions are sequenced multiple times. This is particularly useful when analyzing clinical samples, which generate low amounts of poor-quality nucleic acids.

Most target-enrichment strategies require prior knowledge of both ends of the target region to be sequenced. Therefore, only gene fusions with known partners can be amplified for downstream NGS assays.

Archer’s Anchored Multiplex PCR (AMP™) technology overcomes this limitation, as it can enrich for novel fusions, while only requiring knowledge of one end of the fusion pair. At the heart of the AMP chemistry are unique Molecular Barcode (MBC) adapters, ligated to the 5′ ends of DNA fragments prior to amplification. The MBCs contain universal primer binding sites for PCR and a molecular barcode for identifying unique molecules. When combined with 3′ gene-specific primers, MBCs enable amplification of target regions with unknown 5′ ends.

“Tagging each molecule of input nucleic acid with a unique molecular barcode allows for de-duplication, error correction, and quantitative analysis, resulting in high sequencing consensus. With its low error rate and low limits of detection, AMP is revolutionizing the field of cancer genomics.”

In a proof-of-concept study, a single-tube 23-plex panel was designed to amplify the kinase domains of ALK, RET, ROS1, and MUSK genes by AMP. This enrichment strategy enabled identification of gene fusions with multiple partners and alternative splicing events in lung cancer, thyroid cancer, and glioblastoma specimens by NGS.

Over the last decade, the Biomarker/Translational Research Laboratory has focused on developing clinical genotyping and fluorescent in situ hybridization (FISH) assays for rapid personalized genomic testing.

“Initially, we analyzed the most prevalent hotspot mutations, about 160 in 25 cancer genes,” continued Dr. Borger. “However, this approach revealed mutations in only half of our patients. With the advent of NGS, we are able to sequence 190 exons in 39 cancer genes and obtain significantly richer genetic fingerprints, finding genetic aberrations in 92% of our cancer patients.”

Using multiplexed approaches, Dr. Borger’s team within the larger Center for Integrated Diagnostics (CID) program at MGH has established high-throughput genotyping service as an important component of routine care. While only a few susceptible molecular alterations may currently have a corresponding drug, the NGS-driven analysis may supply new information for inclusion of patients into ongoing clinical trials, or bank the result for future research and development.

“A significant impediment to discovery of clinically relevant genomic signatures is our current inability to interconnect the data,” explained Dr. Borger. “On the local level, we are striving to compile the data from clinical observations, including responses to therapy and genotyping. Globally, it is imperative that comprehensive public databases become available to the research community.”

http://www.genengnews.com/Media/images/Article/MassGeneral_FISH_Analysis_GElJunction1495512021.jpg

This image, from the Massachusetts General Hospital Cancer Center, shows multicolor fluorescence in situ hybridization (FISH) analysis of cells from a patient with esophagogastric cancer. Remarkably, the FISH analysis revealed that co-amplification of the MET gene (red signal) and the EGFR gene (green signal) existed simultaneously in the same tumor cells. A chromosome 7 control probe is shown in blue.

Tumor profiling at MGH have already yielded significant discoveries. Dr. Borger’s lab, in collaboration with oncologists at the MGH Cancer Center, found significant correlations between mutations in the genes encoding the metabolic enzymes isocitrate dehydrogenase (IDH1 and IDH2) and certain types of cancers, such as cholangiocarcinoma and acute myelogenous leukemia (AML).

Historically, cancer signatures largely focus on signaling proteins. Discovery of a correlative metabolic enzyme offered a promise of diagnostics based on metabolic byproducts that may be easily identified in blood. Indeed, the metabolite 2-hydroxyglutarate accumulates to high levels in the tissues of patients carrying IDH1 and IDH2 mutations. They have reported that circulating 2-hydroxyglutarate as measured in the blood correlates with tumor burden, and could serve as an important surrogate marker of treatment response.  …..

 

Researchers Uncover How ‘Silent’ Genetic Changes Drive Cancer

Fri, 06/03/2016 – 8:41amby Rockeller University

http://www.dddmag.com/news/2016/06/researchers-uncover-how-silent-genetic-changes-drive-cancer

“Traditionally, it has been hard to use standard methods to quantify the amount of tRNA in the cell,” says Tavazoie. The lead authors of the article, Hani Goodarzi, formerly a postdoc in the lab and now a new assistant professor at UCSF, and research assistant Hoang Nguyen, devised and applied a new method that utilizes state-of-the-art genomic sequencing technology to measure the amount of tRNAs in different cell types.

The team chose to compare breast tissue from healthy individuals with tumor samples taken from breast cancer patients–including both primary tumors that had not spread from the breast to other body sites, and highly aggressive, metastatic tumors.

They found that the levels of two specific tRNAs were significantly higher in metastatic cells and metastatic tumors than in primary tumors that did not metastasize or healthy samples. “There are four different ways to encode for the protein building block arginine,” explains Tavazoie. “Yet only one of those–the tRNA that recognizes the codon CGG–was associated with increased metastasis.”

The tRNA that recognizes the codon GAA and encodes for a building block known as glutamic acid was also elevated in metastatic samples.

The team hypothesized that the elevated levels of these tRNAs may in fact drive metastasis. Working in mouse models of primary, non-metastatic tumors, the researchers increased the production of the tRNAs, and found that these cells became much more invasive and metastatic.

They also did the inverse experiment, with the anticipated results: reducing the levels of these tRNAs in metastatic cells decreased the incidence of metastases in the animals.

How do two tRNAs drive metastasis? The researchers teamed up with members of the Rockefeller University proteomics facility to see how protein expression changes in cells with elevated levels of these two tRNAs.

“We found global increases in many dozens of genes,” says Tavazoie, “so we analyzed their sequences and found that the majority of them had significantly increased numbers of these two specific codons.”

According to the researchers, two genes stood out among the list. Known as EXOSC2 and GRIPAP1, these genes were strongly and directly induced by elevated levels of the specific glutamic acid tRNA.

“When we mutated the GAA codons to GAG– a “silent” mutation because they both spell out the protein building block glutamic acid–we found that increasing the amount of tRNA no longer increased protein levels,” explains Tavazoie. These proteins were found to drive breast cancer metastasis.

The work challenges previous assumptions about how tRNAs function and suggests that tRNAs can modulate gene expression, according to the researchers. Tavazoie points out that “it is remarkable that within a single cell type, synonymous changes in genetic sequence can dramatically affect the levels of specific proteins, their transcripts, and the way a cell behaves.”

 

Testing Blood Metabolites Could Help Tailor Cancer Treatment

6/03/2016 1 Comment by Institute of Cancer Research
http://www.dddmag.com/news/2016/06/testing-blood-metabolites-could-help-tailor-cancer-treatment

Scientists have found that measuring how cancer treatment affects the levels of metabolites – the building blocks of fats and proteins – can be used to assess whether the drug is hitting its intended target.

This new way of monitoring cancer therapy could speed up the development of new targeted drugs – which exploit specific genetic weaknesses in cancer cells – and help in tailoring treatment for patients.

Scientists at The Institute of Cancer Research, London, measured the levels of 180 blood markers in 41 patients with advanced cancers in a phase I clinical trial conducted with The Royal Marsden NHS Foundation Trust.

They found that investigating the mix of metabolic markers could accurately assess how cancers were responding to the targeted drug pictilisib.

Their study was funded by the Wellcome Trust, Cancer Research UK and the pharmaceutical company Roche, and is published in the journal Molecular Cancer Therapeutics.

Pictilisib is designed to specifically target a molecular pathway in cancer cells, called PI3 kinase, which has key a role in cell metabolism and is defective in a range of cancer types.

As cancers with PI3K defects grow, they can cause a decrease in the levels of metabolites in the bloodstream.

The new study is the first to show that blood metabolites are testable indicators of whether or not a new cancer treatment is hitting the correct target, both in preclinical mouse models and also in a trial of patients.

Using a sensitive technique called mass spectrometry, scientists at The Institute of Cancer Research (ICR) initially analysed the metabolite levels in the blood of mice with cancers that had defects in the PI3K pathway.

They found that the blood levels of 26 different metabolites, which were low prior to therapy, had risen considerably following treatment with pictilisib. Their findings indicated that the drug was hitting its target, and reversing the effects of the cancer on mouse metabolites.

Similarly, in humans the ICR researchers found that almost all of the metabolites – 22 out of the initial 26 – once again rose in response to pictilisib treatment, as seen in the mice.

Blood levels of the metabolites began to increase after a single dose of pictilisib, and were seen to drop again when treatment was stopped, suggesting that the effect was directly related to the drug treatment.

Metabolites vary naturally depending on the time of day or how much food a patient has eaten. But the researchers were able to provide the first strong evidence that despite this variation metabolites can be used to test if a drug is working, and could help guide decisions about treatment.

 

New Metabolic Pathway Reveals Aspirin-Like Compound’s Anti-Cancer Properties

http://www.genengnews.com/gen-news-highlights/new-metabolic-pathway-reveals-aspirin-like-compound-s-anti-cancer-properties/81252777/

Researchers at the Gladstone Institutes say they have found a new pathway by which salicylic acid, a key compound in the nonsteroidal anti-inflammatory drugs aspirin and diflunisal, stops inflammation and cancer.

In a study (“Salicylate, Diflunisal and Their Metabolites Inhibit CBP/p300 and Exhibit Anticancer Activity”) published in eLife, the investigators discovered that both salicylic acid and diflunisal suppress two key proteins that help control gene expression throughout the body. These sister proteins, p300 and CREB-binding protein (CBP), are epigenetic regulators that control the levels of proteins that cause inflammation or are involved in cell growth.

By inhibiting p300 and CBP, salicylic acid and diflunisal block the activation of these proteins and prevent cellular damage caused by inflammation. This study provides the first concrete demonstration that both p300 and CBP can be targeted by drugs and may have important clinical implications, according to Eric Verdin, M.D., associate director of the Gladstone Institute of Virology and Immunology .

“Salicylic acid is one of the oldest drugs on the planet, dating back to the Egyptians and the Greeks, but we’re still discovering new things about it,” he said. “Uncovering this pathway of inflammation that salicylic acid acts upon opens up a host of new clinical possibilities for these drugs.”

Earlier research conducted in the laboratory of co-author Stephen D. Nimer, M.D., director of Sylvester Comprehensive Cancer Center at the University of Miami Miller School of Medicine, and a collaborator of Verdin’s, established a link between p300 and the leukemia-promoting protein AML1-ETO. In the current study, scientists at Gladstone and Sylvester worked together to test whether suppressing p300 with diflunisal would suppress leukemia growth in mice. As predicted, diflunisal stopped cancer progression and shrunk the tumors in the mouse model of leukemia. ……

 

Novel Protein Agent Targets Cancer and Host of Other Diseases

http://www.genengnews.com/gen-news-highlights/new-protein-agent-targets-cancer-and-host-of-other-diseases/81252780/

http://www.genengnews.com/Media/images/Article/MassGeneral_FISH_Analysis_GElJunction1495512021.jpg

Researchers at Georgia State University have designed a new protein compound that can effectively target the cell surface receptor integrin v3, mutations in which have been linked to a number of diseases. Initial results using this new molecule show its potential as a therapeutic treatment for an array of illnesses, including cancer.

The novel protein molecule targets integrin v3 at a novel site that has not been targeted by other scientists. The researchers found that the molecule induces apoptosis, or programmed cell death, of cells that express integrin v3. This integrin has been a focus for drug development because abnormal expression of v3 is linked to the development and progression of various diseases.

“This integrin pair, v3, is not expressed in high levels in normal tissue,” explained senior study author Zhi-Ren Liu, Ph.D., professor in the department of biology at Georgia State. “In most cases, it’s associated with a number of different pathological conditions. Therefore, it constitutes a very good target for multiple disease treatment.”

“Here we use a rational design approach to develop a therapeutic protein, which we call ProAgio, which binds to integrin αvβ3 outside the classical ligand-binding site,” the authors wrote. “We show ProAgio induces apoptosis of integrin αvβ3-expressing cells by recruiting and activating caspase 8 to the cytoplasmic domain of integrin αvβ3.”

The findings from this study were published recently in Nature Communications in an article entitled “Rational Design of a Protein That Binds Integrin αvβ3 Outside the Ligand Binding Site.”   …..

“We took a unique angle,” Dr. Lui noted. “We designed a protein that binds to a different site. Once the protein binds to the site, it directly triggers cell death. When we’re able to kill pathological cells, then we’re able to kill the disease.”

The investigators performed extensive cell and molecular testing that confirmed ProAgio interacts and binds well with integrin v3. Interestingly, they found that ProAgio induces apoptosis by recruiting caspase 8—an enzyme that plays an essential role in programmed cell death—to the cytoplasmic area of integrin v3. ProAgio was much more effective in inducing cell death than other agents tested.

 

Noncoding RNAs Not So Noncoding

Bits of the transcriptome once believed to function as RNA molecules are in fact translated into small proteins.

By Ruth Williams | June 1, 2016

http://www.the-scientist.com/?articles.view/articleNo/46150/title/Noncoding-RNAs-Not-So-Noncoding

In 2002, a group of plant researchers studying legumes at the Max Planck Institute for Plant Breeding Research in Cologne, Germany, discovered that a 679-nucleotide RNA believed to function in a noncoding capacity was in fact a protein-coding messenger RNA (mRNA).1 It had been classified as a long (or large) noncoding RNA (lncRNA) by virtue of being more than 200 nucleotides in length. The RNA, transcribed from a gene called early nodulin 40 (ENOD40), contained short open reading frames (ORFs)—putative protein-coding sequences bookended by start and stop codons—but the ORFs were so short that they had previously been overlooked. When the Cologne collaborators examined the RNA more closely, however, they found that two of the ORFs did indeed encode tiny peptides: one of 12 and one of 24 amino acids. Sampling the legumes confirmed that these micropeptides were made in the plant, where they interacted with a sucrose-synthesizing enzyme.

Five years later, another ORF-containing mRNA that had been posing as a lncRNA was discovered inDrosophila.2,3 After performing a screen of fly embryos to find lncRNAs, Yuji Kageyama, then of the National Institute for Basic Biology in Okazaki, Japan, suppressed each transcript’s expression. “Only one showed a clear phenotype,” says Kageyama, now at Kobe University. Because embryos missing this particular RNA lacked certain cuticle features, giving them the appearance of smooth rice grains, the researchers named the RNA “polished rice” (pri).

Turning his attention to how the RNA functioned, Kageyama thought he should first rule out the possibility that it encoded proteins. But he couldn’t. “We actually found it was a protein-coding gene,” he says. “It was an accident—we are RNA people!” The pri gene turned out to encode four tiny peptides—three of 11 amino acids and one of 32—that Kageyama and colleagues showed are important for activating a key developmental transcription factor.4

Since then, a handful of other lncRNAs have switched to the mRNA ranks after being found to harbor micropeptide-encoding short ORFs (sORFs)—those less than 300 nucleotides in length. And given the vast number of documented lncRNAs—most of which have no known function—the chance of finding others that contain micropeptide codes seems high.

Overlooked ORFs

From the late 1990s into the 21st century, as species after species had their genomes sequenced and deposited in databases, the search for novel genes and their associated mRNAs duly followed. With millions or even billions of nucleotides to sift through, researchers devised computational shortcuts to hunt for canonical gene and mRNA features, such as promoter regions, exon/intron splice sites, and, of course, ORFs.

ORFs can exist in practically any stretch of RNA sequence by chance, but many do not encode actual proteins. Because the chance that an ORF encodes a protein increases with its length, most ORF-finding algorithms had a size cut-off of 300 nucleotides—translating to 100 amino acids. This allowed researchers to “filter out garbage—that is, meaningless ORFs that exist randomly in RNAs,” says Eric Olsonof the University of Texas Southwestern Medical Center in Dallas.

Of course, by excluding all ORFs less than 300 nucleotides in length, such algorithms inevitably missed those encoding genuine small peptides. “I’m sure that the people who came up with [the cut-off] understood that this rule would have to miss anything that was shorter than 100 amino acids,” saysNicholas Ingolia of the University of California, Berkeley. “As people applied this rule more and more, they sort of lost track of that caveat.” Essentially, sORFs were thrown out with the computational trash and forgotten.

Aside from statistical practicality and human oversight, there were also technical reasons that contributed to sORFs and their encoded micropeptides being missed. Because of their small size, sORFs in model organisms such as mice, flies, and fish are less likely to be hit in random mutagenesis screens than larger ORFs, meaning their functions are less likely to be revealed. Also, many important proteins are identified based on their conservation across species, says Andrea Pauli of the Research Institute of Molecular Pathology in Vienna, but “the shorter [the ORF], the harder it gets to find and align this region to other genomes and to know that this is actually conserved.”

As for the proteins themselves, the standard practice of using electrophoresis to separate peptides by size often meant micropeptides would be lost, notes Doug Anderson, a postdoc in Olson’s lab. “A lot of times we run the smaller things off the bottom of our gels,” he says. Standard protein mass spectrometry was also problematic for identifying small peptides, says Gerben Menschaert of Ghent University in Belgium, because “there is a washout step in the protocol so that only larger proteins are retained.”

But as researchers take a deeper dive into the function of the thousands of lncRNAs believed to exist in genomes, they continue to uncover surprise micropeptides. In February 2014, for example, Pauli, then a postdoc in Alex Schier’s lab at Harvard University, discovered a hidden code in a zebrafish lncRNA. She had been hunting for lncRNAs involved in zebrafish development because “we hadn’t really anticipated that there would be any coding regions out there that had not been discovered—at least not something that is essential,” she says. But one lncRNA she identified actually encoded a 58-amino-acid micropeptide, which she called Toddler, that functioned as a signaling protein necessary for cell movements that shape the early embryo.5

Then, last year, Anderson and his colleagues reported another. Since joining Olson’s lab in 2010, Anderson had been searching for lncRNAs expressed in the heart and skeletal muscles of mouse embryos. He discovered a number of candidates, but one stood out for its high level of sequence conservation—suggesting to Anderson that it might have an important function. He was right, the RNA was important, but for a reason that neither Anderson nor Olson had considered: it was in fact an mRNA encoding a 46-amino-acid-long micropeptide.6

“When we zeroed in on the conserved region [of the gene], Doug found that it began with an ATG [start] codon and it terminated with a stop codon,” Olson says. “That’s when he looked at whether it might encode a peptide and found that indeed it did.” The researchers dubbed the peptide myoregulin, and found that it functioned as a critical calcium pump regulator for muscle relaxation.

With more and more overlooked peptides now being revealed, the big question is how many are left to be discovered. “Were there going to be dozens of [micropeptides]? Were there going to be hundreds, like there are hundreds of microRNAs?” says Ingolia. “We just didn’t know.”

see more at  http://www.the-scientist.com/?articles.view/articleNo/46150/title/Noncoding-RNAs-Not-So-Noncoding

Research at Micro- and Nanoscales

From whole cells to genes, closer examination continues to surprise.

By Mary Beth Aberlin | June 1, 2016

http://www.the-scientist.com/?articles.view/articleNo/46129/title/Research-at-Micro–and-Nanoscales

Little things mean a lot. To any biologist, this time-worn maxim is old news. But it’s worth revisiting. As several articles in this issue of The Scientist illustrate, how researchers define and examine the “little things” does mean a lot.

Consider this month’s cover story, “Noncoding RNAs Not So Noncoding,” by TS correspondent Ruth Williams. Combing the human genome for open reading frames (ORFs), sequences bracketed by start and stop codons, yielded a protein-coding count somewhere in the neighborhood of 24,000. That left a lot of the genome relegated to the category of junk—or, later, to the tens of thousands of mostly mysterious long noncoding RNAs (lncRNAs). But because they had only been looking for ORFs that were 300 nucleotides or longer (i.e., coding for proteins at least 100 amino acids long), genome probers missed so-called short ORFs (sORFs), which encode small peptides. “Their diminutive size may have caused these peptides to be overlooked, their sORFs to be buried in statistical noise, and their RNAs to be miscategorized, but it does not prevent them from serving important, often essential functions, as the micropeptides characterized to date demonstrate,” writes Williams.

How little things work definitely informs another field of life science research: synthetic biology. As the functions of genes and gene networks are sussed out, bioengineers are using the information to design small, synthetic gene circuits that enable them to better understand natural networks. In “Synthetic Biology Comes into Its Own,” Richard Muscat summarizes the strides made by synthetic biologists over the last 15 years and offers an optimistic view of how such networks may be put to use in the future. And to prove him right, just as we go to press, a collaborative group led by one of syn bio’s founding fathers, MIT’s James Collins, has devised a paper-based test for Zika virus exposure that relies on a freeze-dried synthetic gene circuit that changes color upon detection of RNAs in the viral genome. The results are ready in a matter of hours, not the days or weeks current testing takes, and the test can distinguish Zika from dengue virus. “What’s really exciting here is you can leverage all this expertise that synthetic biologists are gaining in constructing genetic networks and use it in a real-world application that is important and can potentially transform how we do diagnostics,” commented one researcher about the test.

Moving around little things is the name of the game when it comes to delivering a package of drugs to a specific target or to operating on minuscule individual cells. Mini-scale delivery of biocompatible drug payloads often needs some kind of boost to overcome fluid forces or size restrictions that interfere with fine-scale manipulation. To that end, ingenious solutions that motorize delivery by harnessing osmotic changes, magnets, ultrasound, and even bacterial flagella are reviewed in “Making Micromotors Biocompatible.”

….  http://www.the-scientist.com/?articles.view/articleNo/46129/title/Research-at-Micro–and-Nanoscales

Cilengitide: The First Anti-Angiogenic Small Molecule Drug Candidate. Design, Synthesis and Clinical Evaluation

Anticancer Agents Med Chem. 2010 Dec; 10(10): 753–768.
doi:  10.2174/187152010794728639

Cilengitide, a cyclic RGD pentapeptide, is currently in clinical phase III for treatment of glioblastomas and in phase II for several other tumors. This drug is the first anti-angiogenic small molecule targeting the integrins αvβ3, αvβ5 and α5β1. It was developed by us in the early 90s by a novel procedure, the spatial screening. This strategy resulted in c(RGDfV), the first superactive αvβ3 inhibitor (100 to 1000 times increased activity over the linear reference peptides), which in addition exhibited high selectivity against the platelet receptor αIIbβ3. This cyclic peptide was later modified by N-methylation of one peptide bond to yield an even greater antagonistic activity in c(RGDf(NMe)V). This peptide was then dubbed Cilengitide and is currently developed as drug by the company Merck-Serono (Germany).

This article describes the chemical development of Cilengitide, the biochemical background of its activity and a short review about the present clinical trials. The positive anti-angiogenic effects in cancer treatment can be further increased by combination with “classical” anti-cancer therapies. Several clinical trials in this direction are under investigation.

Integrins are heterodimeric receptors that are important for cell-cell and cell-extracellular matrix (ECM) interactions and are composed of one α and one β-subunit [1, 2]. These cell adhesion molecules act as transmembrane linkers between their extracellular ligands and the cytoskeleton, and modulate various signaling pathways essential in the biological functions of most cells. Integrins play a crucial role in processes such as cell migration, differentiation, and survival during embryogenesis, angiogenesis, wound healing, immune and non-immune defense mechanisms, hemostasis and oncogenic transformation [1]. The fact that many integrins are also linked with pathological conditions has converted them into very promising therapeutic targets [3]. In particular, integrins αvβ3, αvβ5 and α5β1 are involved in angiogenesis and metastasis of solid tumors, being excellent candidates for cancer therapy [47].

There are a number of different integrin subtypes which recognize and bind to the tripeptide sequence RGD (arginine, glycine, aspartic acid), which represents the most prominent recognition motif involved in cell adhesion. For example, the pro-angiogenic αvβ3 integrin binds various RGD-containing proteins, including fibronectin (Fn), fibrinogen (Fg), vitronectin (Vn) and osteopontin [8]. It is therefore not surprising that this integrin has been targeted for cancer therapy and that RGD-containing peptides and peptidomimetics have been designed and synthesized aiming to selectively inhibit this receptor [9, 10].

One classical strategy used in drug design is based on the knowledge about the structure of the receptor-binding pocket, preferably in complex with the natural ligand. However, this strategy, the so-called “rational structure-based design”, could not be applied in the field of integrin ligands since the first structures of integrin’s extracellular head groups were not described until 2001 for αvβ3 [11] (one year later, in 2002 the structure of this integrin in complex with Cilengitide was also reported [12]) and 2004 for αIIbβ3 [13]. Therefore, initial efforts in this field focused on a “ligand-oriented design”, which concentrated on optimizing RGD peptides by means of different chemical approaches in order to establish structure-activity relationships and identify suitable ligands.

We focused our interest in finding ligands for αvβ3 and based our approach on three chemical strategies pioneered in our group: 1) Reduction of the conformational space by cyclization; 2) Spatial screening of cyclic peptides; and 3)N-Methyl scan.

The combination of these strategies lead to the discovery of the cyclic peptidec(RGDf(NMe)V) in 1995. This peptide showed subnanomolar antagonistic activity for the αvβ3 receptor, nanomolar affinities for the closely related integrins αvβ5 and α5β1, and high selectivity towards the platelet receptor αIIbβ3. The peptide was patented together with Merck in 1997 (patent application submitted in 15.9.1995, opened in 20.3.1997) [14] and first presented with Merck’s agreement at the European Peptide Symposium in Edinburgh (September 1996) [15]. The synthesis and activity of this molecule was finally published in 1999 [16]. This peptide is now developed by Merck-Serono, (Darmstadt, Germany) under the name “Cilengitide” and has recently entered Phase III clinical trials for treating glioblastoma [17].  …..

The discovery 30 years ago of the RGD motif in Fn was a major breakthrough in science. This tripeptide sequence was also identified in other ECM proteins and was soon described as the most prominent recognition motif involved in cell adhesion. Extensive research in this direction allowed the description of a number of bidirectional proteins, the integrins, which were able to recognize and bind to the RGD sequence. Integrins are key players in the biological function of most cells and therefore the inhibition of RGD-mediated integrin-ECM interactions became an attractive target for the scientific community.

However, the lack of selectivity of linear RGD peptides represented a major pitfall which precluded any clinical application of RGD-based inhibitors. The control of the molecule’s conformation by cyclization and further spatial screening overcame these limitations, showing that it is possible to obtain privileged bioactive structures, which enhance the biological activity of linear peptides and significantly improve their receptor selectivity. Steric control imposed in RGD peptides together with their biological evaluation and extensive structural studies yielded the cyclic peptide c(RGDfV), the first small selective anti-angiogenic molecule described. N-Methylation of this cyclic peptide yielded the much potentc(RGDf(NMe)V), nowadays known as Cilengitide.

The fact that brain tumors, which are highly angiogenic, are more susceptible to the treatment with integrin antagonists, and the positive synergy observed for Cilengitide in combination with radio-chemotherapy in preclinical studies, encouraged subsequent clinical trials. Cilengitide is currently in phase III for GBM patients and in phase II for other types of cancers, with to date a promising therapeutic outcome. In addition, the absence of significant toxicity and excellent tolerance of this drug allows its combination with classical therapies such as RT or cytotoxic agents. The controlled phase III study CENTRIC was launched in 2008, with primary outcome measures due on September 2012. The results of this and other clinical studies are expected with great hope and interest.

Integrin Targeted Therapeutics

Integrins are heterodimeric, transmembrane receptors that function as mechanosensors, adhesion molecules and signal transduction platforms in a multitude of biological processes. As such, integrins are central to the etiology and pathology of many disease states. Therefore, pharmacological inhibition of integrins is of great interest for the treatment and prevention of disease. In the last two decades several integrin-targeted drugs have made their way into clinical use, many others are in clinical trials and still more are showing promise as they advance through preclinical development. Herein, this review examines and evaluates the various drugs and compounds targeting integrins and the disease states in which they are implicated.
Integrins are heterodimeric cell surface receptors found in nearly all metazoan cell types, composed of non-covalently linked α and β subunits. In mammals, eighteen α-subunits and eight β-subunits have been identified to date 1. From this pool, 24 distinct heterodimer combinations have been observed in vivo that confer cell-to-cell and cell-to-ligand specificity relevant to the host cell and the environment in which it functions 2. Integrin-mediated interactions with the extracellular matrix (ECM) are required for the attachment, cytoskeletal organization, mechanosensing, migration, proliferation, differentiation and survival of cells in the context of a multitude of biological processes including fertilization, implantation and embryonic development, immune response, bone resorption and platelet aggregation. Integrins also function in pathological processes such as inflammation, wound healing, angiogenesis, and tumor metastasis. In addition, integrin binding has been identified as a means of viral entry into cells 3. ….

Combination of cilengitide and radiation therapy and temozolomide. The addition of cilengitide to radiotherapy and temozolomide based treatment regimens has shown promising preliminary results in ongoing Phase II trials in both newly diagnosed and progressive glioblastoma multiforme 139140. In addition to the Phase II objectives sought, these trials are significant in that they represent progress that has made in determining tumor drug uptake and in identifying a subset of patients that may benefit from treatment. In a Phase II trial enrolling 52 patients with newly diagnosed glioblastoma multiforme receiving 500 mg cilengitide twice weekly during radiotherapy and in combination with temozolomide for 6 monthly cycles following radiotherapy, 69% achieved 6 months progression free survival compared to 54 % of patients receiving radiotherapy followed by temozolomide alone. The one-year overall survival was 67 and 62 % of patients for the cilengitide combination group and the radiotherapy and temozolomide group, respectively. Non-hematological grade 3-4 toxcities were limited, and included symptoms of fatigue, asthenia, anorexia, elevated liver function tests, deep vein thrombosis and pulmonary embolism in across a total of 5.7% of the patients. Grade 3-4 hematological malignancies were more common and included lymphopenia (53.8%), thrombocytopenia (13.4%) and neutropenia (9.6%). This trial is significant in the fact that is has provided the first evidence correlating a molecular biomarker with response to treatment. Decreased methylguanine methyltransferase (MGMT) expression was associated with favorable outcome. Patients harboring increased MGMT promoter methylation appeared to benefit more from combined treatment with cilengitide than did patients lacking promoter methylation. The significance of the MGMT promoter methylation in predicting response is likely due to inclusion of temozolomide in the treatment combination.

A similar Phase II study evaluating safety and differences in overall survival among newly diagnosed glioblastoma multiforme patients receiving radiation therapy combined with temozolomide and varying doses of cilengitide is nearing completion. Preliminary reports specify that initial safety run-in studies in 18 patients receiving doses 500, 1000 and 2000 mg cilengitide found no dose limiting toxicities. Subsequently 94 patients were randomized to receive standard therapy plus 500 or 2000 mg cilengitide. Median survival time in both cohorts was 18.9 months. At 12 months the overall survival was 79.5 % (89/112 patients).

In the last two decades great progress has been made in the discovery and development of integrin targeted therapeutics. Years of intense research into integrin function has provided an understanding of the potential applications for the treatment of disease. Advances in structural characterization of integrin-ligand interactions has proved beneficial in the design and development of potent, selective inhibitors for a number of integrins involved in platelet aggregation, inflammatory responses, angiongenesis, neovascularization and tumor growth.

The αIIbβ3 integrin antagonists were the first inhibitors to make their way into clinical use and have proven to be effective and safe drugs, contributing to the reduction of mortality and morbidity associated with acute coronary syndromes. Interestingly, the prolonged administration of small molecules targeting this integrin for long-term prevention of thrombosis related complications have not been successful, for reasons that are not yet fully understood. This suggests that modulating the intensity, duration and temporal aspects of integrin function may be more effective than simply shutting off integrin signaling in some instances. Further research into the dynamics of platelet activation and thrombosis formation may elucidate the mechanisms by which integrin activation is modulated.

The introduction of α4 targeted therapies held great promise for the treatment of inflammatory diseases. The development of Natalizumab greatly improved the quality of life for multiple sclerosis patients and those suffering with Crohn’s Disease compared to previous treatments, but the role in asthma related inflammation could not be validated. Unfortunately for MS and Crohn’s patients, immune surveillance in the central nervous system was also compromised as a direct effect α4β7 antagonism, with potentially lethal effects. Thus Natalizumab and related α4β7 targeting drugs are now limited to patients refractory to standard therapies. The design and development of α4β1 antagonists for the treatment of Crohn’s Disease may offer benefit with decreased risks. The involvement of these integrins in fetal development also raises concerns for widespread clinical use.

Integrin antagonists that target angiogenesis are progressing through clinical trials. Cilengitide has shown promising results for the treatment of glioblastomas and recurrent gliomas, cancers with notoriously low survival and cure rates. The greatest challenge facing the development of anti-angiogenic integrin targeted therapies is the overall lack of biomarkers by which to measure treatment efficacy.

 

Mapping the ligand-binding pocket of integrin α5β1 using a gain-of-function approach

Biochem J. 2009 Nov 11; 424(2): 179–189. doi:  10.1042/BJ20090992
Integrin α5β1 is a key receptor for the extracellular matrix protein fibronectin. Antagonists of human α5β1 have therapeutic potential as anti-angiogenic agents in cancer and diseases of the eye. However, the structure of the integrin is unsolved and the atomic basis of fibronectin and antagonist binding by α5β1 is poorly understood. Here we demonstrate that zebrafish α5β1 integrins do not interact with human fibronectin or the human α5β1 antagonists JSM6427 and cyclic peptide CRRETAWAC. Zebrafish α5β1 integrins do bind zebrafish fibronectin-1, and mutagenesis of residues on the upper surface and side of the zebrafish α5 subunit β-propeller domain shows that these residues are important for the recognition of RGD and synergy sites in fibronectin. Using a gain-of-function analysis involving swapping regions of the zebrafish α5 subunit with the corresponding regions of human α5 we show that blades 1-4 of the β-propeller are required for human fibronectin recognition, suggesting that fibronectin binding involves a broad interface on the side and upper face of the β-propeller domain. We find that the loop connecting blades 2 and 3 of the β-propeller (D3-A3 loop) contains residues critical for antagonist recognition, with a minor role played by residues in neighbouring loops. A new homology model of human α5β1 supports an important function for D3-A3 loop residues Trp-157 and Ala-158 in the binding of antagonists. These results will aid the development of reagents that block α5β1 functions in vivo.
Structural Basis of Integrin Regulation and Signaling
Integrins are cell adhesion molecules that mediate cell-cell, cell-extracellular matrix, and cellpathogen interactions. They play critical roles for the immune system in leukocyte trafficking and migration, immunological synapse formation, costimulation, and phagocytosis. Integrin adhesiveness can be dynamically regulated through a process termed inside-out signaling. In addition, ligand binding transduces signals from the extracellular domain to the cytoplasm in the classical outside-in direction. Recent structural, biochemical, and biophysical studies have greatly advanced our understanding of the mechanisms of integrin bidirectional signaling across the plasma membrane. Large-scale reorientations of the ectodomain of up to 200 Å couple to conformational change in ligand-binding sites and are linked to changes in α and β subunit transmembrane domain association. In this review, we focus on integrin structure as it relates to affinity modulation, ligand binding, outside-in signaling, and cell surface distribution dynamics.
The immune system relies heavily on integrins for (a) adhesion during leukocyte trafficking from the bloodstream, migration within tissues, immune synapse formation, and phagocytosis; and (b) signaling during costimulation and cell polarization. Integrins are so named because they integrate the extracellular and intracellular environments by binding to ligands outside the cell and cytoskeletal components and signaling molecules inside the cell. Integrins are noncovalently associated heterodimeric cell surface adhesion molecules. In vertebrates, 18 α subunits and 8 β subunits form 24 known αβ pairs (Figure 1). This diversity in subunit composition contributes to diversity in ligand recognition, binding to cytoskeletal components and coupling to downstream signaling pathways. Immune cells express at least 10 members of the integrin family belonging to the β2, β7, and β1 subfamilies (Table 1). The β2 and β7 integrins are exclusively expressed on leukocytes, whereas the β1 integrins are expressed on a wide variety of cells throughout the body. Distribution and ligand-binding properties of the integrins on leukocytes are summarized in Table 1. For reviews, see References 1 and 2. Mutations that block expression of the β2 integrin subfamily lead to leukocyte adhesion deficiency, a disease associated with severe immunodeficiency (3).
As adhesion molecules, integrins are unique in that their adhesiveness can be dynamically regulated through a process termed inside-out signaling or priming. Thus, stimuli received by cell surface receptors for chemokines, cytokines, and foreign antigens initiate intracellular signals that impinge on integrin cytoplasmic domains and alter adhesiveness for extracellular ligands. In addition, ligand binding transduces signals from the extracellular domain to the cytoplasm in the classical outside-in direction (outside-in signaling). These dynamic properties of integrins are central to their proper function in the immune system. Indeed, mutations or small molecules that stabilize either the inactive state or the active adhesive state—and thereby block the adhesive dynamics of leukocyte integrins—inhibit leukocyte migration and normal immune responses.

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