Rationale, application and clinical qualification for NT-proBNP as a surrogate end point in pivotal clinical trials in patients with AL amyloidosis

Amyloid light-chain (LC) amyloidosis (AL amyloidosis) is a rare and fatal disease for which there are no approved therapies. In patients with AL amyloidosis, LC aggregates progressively accumulate in organs, resulting in organ failure that is particularly lethal when the heart is involved. A significant obstacle in the development of treatments for patients with AL amyloidosis, as well as for those with any disease that is rare, severe and heterogeneous, has been satisfying traditional clinical trial end points (for example, overall survival or progression-free survival). It is for this reason that many organizations, including the United States Food and Drug Administration through its Safety and Innovation Act Accelerated Approval pathway, have recognized the need for biomarkers as surrogate end points. The international AL amyloidosis expert community is in agreement that the N-terminal fragment of the pro-brain natriuretic peptide (NT-proBNP) is analytically validated and clinically qualified as a biomarker for use as a surrogate end point for survival in patients with AL amyloidosis. Underlying this consensus is the demonstration that NT-proBNP is an indicator of cardiac response in all interventional studies in which it has been assessed, despite differences in patient population, treatment type and treatment schedule. Furthermore, NT-proBNP expression is directly modulated by amyloidogenic LC-elicited signal transduction pathways in cardiomyocytes. The use of NT-proBNP will greatly facilitate the development of targeted therapies for AL amyloidosis. Here, we review the data supporting the use of NT-proBNP, a biomarker that is analytically validated, clinically qualified, directly modulated by LC and universally accepted by AL amyloidosis specialists, as a surrogate end point for survival.Leukemia advance online publication, 2 August 2016; doi:10.1038/leu.2016.191

Impact of actin filament stabilization on adult hippocampal and olfactory bulb neurogenesis

Rearrangement of the actin cytoskeleton is essential for dynamic cellular processes. Decreased actin turnover and rigidity of cytoskeletal structures have been associated with aging and cell death. Gelsolin is a Ca(2+)-activated actin-severing protein that is widely expressed throughout the adult mammalian brain. Here, we used gelsolin-deficient (Gsn(-/-)) mice as a model system for actin filament stabilization. In Gsn(-/-) mice, emigration of newly generated cells from the subventricular zone into the olfactory bulb was slowed. In vitro, gelsolin deficiency did not affect proliferation or neuronal differentiation of adult neural progenitors cells (NPCs) but resulted in retarded migration. Surprisingly, hippocampal neurogenesis was robustly induced by gelsolin deficiency. The ability of NPCs to intrinsically sense excitatory activity and thereby implement coupling between network activity and neurogenesis has recently been established. Depolarization-induced [Ca(2+)](i) increases and exocytotic neurotransmitter release were enhanced in Gsn(-/-) synaptosomes. Importantly, treatment of Gsn(-/-) synaptosomes with mycotoxin cytochalasin D, which, like gelsolin, produces actin disassembly, decreased enhanced Ca(2+) influx and subsequent exocytotic norepinephrine release to wild-type levels. Similarly, depolarization-induced glutamate release from Gsn(-/-) brain slices was increased. Furthermore, increased hippocampal neurogenesis in Gsn(-/-) mice was associated with a special microenvironment characterized by enhanced density of perfused vessels, increased regional cerebral blood flow, and increased endothelial nitric oxide synthase (NOS-III) expression in hippocampus. Together, reduced filamentous actin turnover in presynaptic terminals causes increased Ca(2+) influx and, subsequently, elevated exocytotic neurotransmitter release acting on neural progenitors. Increased neurogenesis in Gsn(-/-) hippocampus is associated with a special vascular niche for neurogenesis

A Novel Unsupervised Method to Identify Genes Important in the Anti-viral Response: Application to Interferon/Ribavirin in Hepatitis C Patients

Background: Treating hepatitis C with interferon/ribavirin results in a varied response in terms of decrease in viral titer and ultimate outcome. Marked responders have a sharp decline in viral titer within a few days of treatment initiation, whereas in other patients there is no effect on the virus (poor responders). Previous studies have shown that combination therapy modifies expression of hundreds of genes in vitro and in vivo. However, identifying which, if any, of these genes have a role in viral clearance remains challenging. Aims: The goal of this paper is to link viral levels with gene expression and thereby identify genes that may be responsible for early decrease in viral titer. Methods: Microarrays were performed on RNA isolated from PBMC of patients undergoing interferon/ribavirin therapy. Samples were collected at pre-treatment (day 0), and 1, 2, 7, 14 and 28 days after initiating treatment. A novel method was applied to identify genes that are linked to a decrease in viral titer during interferon/ribavirin treatment. The method uses the relationship between inter-patient gene expression based proximities and inter-patient viral titer based proximities to define the association between microarray gene expression measurements of each gene and viral-titer measurements. Results: We detected 36 unique genes whose expressions provide a clustering of patients that resembles viral titer based clustering of patients. These genes include IRF7, MX1, OASL and OAS2, viperin and many ISG's of unknown function. Conclusion: The genes identified by this method appear to play a major role in the reduction of hepatitis C virus during the early phase of treatment. The method has broad utility and can be used to analyze response to any group of factors influencing biological outcome such as antiviral drugs or anti-cancer agents where microarray data are available. © 2007 Brodsky et al

A self-organized model for cell-differentiation based on variations of molecular decay rates

Systemic properties of living cells are the result of molecular dynamics governed by so-called genetic regulatory networks (GRN). These networks capture all possible features of cells and are responsible for the immense levels of adaptation characteristic to living systems. At any point in time only small subsets of these networks are active. Any active subset of the GRN leads to the expression of particular sets of molecules (expression modes). The subsets of active networks change over time, leading to the observed complex dynamics of expression patterns. Understanding of this dynamics becomes increasingly important in systems biology and medicine. While the importance of transcription rates and catalytic interactions has been widely recognized in modeling genetic regulatory systems, the understanding of the role of degradation of biochemical agents (mRNA, protein) in regulatory dynamics remains limited. Recent experimental data suggests that there exists a functional relation between mRNA and protein decay rates and expression modes. In this paper we propose a model for the dynamics of successions of sequences of active subnetworks of the GRN. The model is able to reproduce key characteristics of molecular dynamics, including homeostasis, multi-stability, periodic dynamics, alternating activity, differentiability, and self-organized critical dynamics. Moreover the model allows to naturally understand the mechanism behind the relation between decay rates and expression modes. The model explains recent experimental observations that decay-rates (or turnovers) vary between differentiated tissue-classes at a general systemic level and highlights the role of intracellular decay rate control mechanisms in cell differentiation.Comment: 16 pages, 5 figure

Effect of promoter architecture on the cell-to-cell variability in gene expression

According to recent experimental evidence, the architecture of a promoter, defined as the number, strength and regulatory role of the operators that control the promoter, plays a major role in determining the level of cell-to-cell variability in gene expression. These quantitative experiments call for a corresponding modeling effort that addresses the question of how changes in promoter architecture affect noise in gene expression in a systematic rather than case-by-case fashion. In this article, we make such a systematic investigation, based on a simple microscopic model of gene regulation that incorporates stochastic effects. In particular, we show how operator strength and operator multiplicity affect this variability. We examine different modes of transcription factor binding to complex promoters (cooperative, independent, simultaneous) and how each of these affects the level of variability in transcription product from cell-to-cell. We propose that direct comparison between in vivo single-cell experiments and theoretical predictions for the moments of the probability distribution of mRNA number per cell can discriminate between different kinetic models of gene regulation.Comment: 35 pages, 6 figures, Submitte

YEASTRACT: providing a programmatic access to curated transcriptional regulatory associations in Saccharomyces cerevisiae through a web services interface

The YEAst Search for Transcriptional Regulators And Consensus Tracking (YEASTRACT) information system (http://www.yeastract.com) was developed to support the analysis of transcription regulatory associations in Saccharomyces cerevisiae. Last updated in June 2010, this database contains over 48 200 regulatory associations between transcription factors (TFs) and target genes, including 298 specific DNA-binding sites for 110 characterized TFs. All regulatory associations stored in the database were revisited and detailed information on the experimental evidences that sustain those associations was added and classified as direct or indirect evidences. The inclusion of this new data, gathered in response to the requests of YEASTRACT users, allows the user to restrict its queries to subsets of the data based on the existence or not of experimental evidences for the direct action of the TFs in the promoter region of their target genes. Another new feature of this release is the availability of all data through a machine readable web-service interface. Users are no longer restricted to the set of available queries made available through the existing web interface, and can use the web service interface to query, retrieve and exploit the YEASTRACT data using their own implementation of additional functionalities. The YEASTRACT information system is further complemented with several computational tools that facilitate the use of the curated data when answering a number of important biological questions. Since its first release in 2006, YEASTRACT has been extensively used by hundreds of researchers from all over the world. We expect that by making the new data and services available, the system will continue to be instrumental for yeast biologists and systems biology researchers

Phylogenetic tree information aids supervised learning for predicting protein-protein interaction based on distance matrices

BACKGROUND: Protein-protein interactions are critical for cellular functions. Recently developed computational approaches for predicting protein-protein interactions utilize co-evolutionary information of the interacting partners, e.g., correlations between distance matrices, where each matrix stores the pairwise distances between a protein and its orthologs from a group of reference genomes. RESULTS: We proposed a novel, simple method to account for some of the intra-matrix correlations in improving the prediction accuracy. Specifically, the phylogenetic species tree of the reference genomes is used as a guide tree for hierarchical clustering of the orthologous proteins. The distances between these clusters, derived from the original pairwise distance matrix using the Neighbor Joining algorithm, form intermediate distance matrices, which are then transformed and concatenated into a super phylogenetic vector. A support vector machine is trained and tested on pairs of proteins, represented as super phylogenetic vectors, whose interactions are known. The performance, measured as ROC score in cross validation experiments, shows significant improvement of our method (ROC score 0.8446) over that of using Pearson correlations (0.6587). CONCLUSION: We have shown that the phylogenetic tree can be used as a guide to extract intra-matrix correlations in the distance matrices of orthologous proteins, where these correlations are represented as intermediate distance matrices of the ancestral orthologous proteins. Both the unsupervised and supervised learning paradigms benefit from the explicit inclusion of these intermediate distance matrices, and particularly so in the latter case, which offers a better balance between sensitivity and specificity in the prediction of protein-protein interactions

Redeployment-based drug screening identifies the anti-helminthic niclosamide as anti-myeloma therapy that also reduces free light chain production

Despite recent therapeutic advancements, multiple myeloma (MM) remains incurable and new therapies are needed, especially for the treatment of elderly and relapsed/refractory patients. We have screened a panel of 100 off-patent licensed oral drugs for anti-myeloma activity and identified niclosamide, an anti-helminthic. Niclosamide, at clinically achievable non-toxic concentrations, killed MM cell lines and primary MM cells as efficiently as or better than standard chemotherapy and existing anti-myeloma drugs individually or in combinations, with little impact on normal donor cells. Cell death was associated with markers of both apoptosis and autophagy. Importantly, niclosamide rapidly reduced free light chain (FLC) production by MM cell lines and primary MM. FLCs are a major cause of renal impairment in MM patients and light chain amyloid and FLC reduction is associated with reversal of tissue damage. Our data indicate that niclosamides anti-MM activity was mediated through the mitochondria with rapid loss of mitochondrial membrane potential, uncoupling of oxidative phosphorylation and production of mitochondrial superoxide. Niclosamide also modulated the nuclear factor-κB and STAT3 pathways in MM cells. In conclusion, our data indicate that MM cells can be selectively targeted using niclosamide while also reducing FLC secretion. Importantly, niclosamide is widely used at these concentrations with minimal toxicity

Identifying Cognate Binding Pairs among a Large Set of Paralogs: The Case of PE/PPE Proteins of Mycobacterium tuberculosis

We consider the problem of how to detect cognate pairs of proteins that bind when each belongs to a large family of paralogs. To illustrate the problem, we have undertaken a genomewide analysis of interactions of members of the PE and PPE protein families of Mycobacterium tuberculosis. Our computational method uses structural information, operon organization, and protein coevolution to infer the interaction of PE and PPE proteins. Some 289 PE/PPE complexes were predicted out of a possible 5,590 PE/PPE pairs genomewide. Thirty-five of these predicted complexes were also found to have correlated mRNA expression, providing additional evidence for these interactions. We show that our method is applicable to other protein families, by analyzing interactions of the Esx family of proteins. Our resulting set of predictions is a starting point for genomewide experimental interaction screens of the PE and PPE families, and our method may be generally useful for detecting interactions of proteins within families having many paralogs

Molecular epidemiology of pneumococci obtained from Gambian children aged 2–29 months with invasive pneumococcal disease during a trial of a 9-valent pneumococcal conjugate vaccine

BACKGROUND: The study describes the molecular epidemiology of Streptococcus pneumoniae causing invasive disease in Gambian children METHODS: One hundred and thirty-two S. pneumoniae isolates were recovered from children aged 2-29 months during the course of a pneumococcal conjugate vaccine trial conducted in The Gambia of which 131 were characterized by serotyping, antibiotic susceptibility, BOX-PCR and MLST. RESULTS: Twenty-nine different serotypes were identified; serotypes 14, 19A, 12F, 5, 23F, and 1 were common and accounted for 58.3% of all serotypes overall. MLST analysis showed 72 sequence types (STs) of which 46 are novel. eBURST analysis using the stringent 6/7 identical loci definition, grouped the isolates into 17 clonal complexes and 32 singletons. The population structure of the 8 serotype 1 isolates obtained from 4 vaccinated and 2 unvaccinated children were the same (ST 618) except that one (ST3336) of the isolates from an unvaccinated child had a novel ST which is a single locus variant of ST 618. CONCLUSION: We provide the first background data on the genetic structure of S. pneumoniae causing IPD prior to PC7V use in The Gambia. This data will be important for assessing the impact of PC7V in post-vaccine surveillance from The Gambia