A new family of diverse skin peptides from the microhylid frog genus phrynomantis

A wide range of frogs produce skin poisons composed of bioactive peptides for defence against pathogens, parasites and predators. While several frog families have been thoroughly screened for skin-secreted peptides, others, like the Microhylidae, have remained mostly unexplored. Previous studies of microhylids found no evidence of peptide secretion, suggesting that this defence adaptation was evolutionarily lost. We conducted transcriptome analyses of the skins of Phrynomantis bifasciatus and Phrynomantis microps, two African microhylid species long suspected to be poisonous. Our analyses reveal 17 evolutionary related transcripts that diversified from to those of cytolytic peptides found in other frog families. The 19 peptides predicted to be processed from these transcripts, named phrynomantins, show a striking structural diversity that is distinct from any previously identified frog skin peptide. Functional analyses of five phrynomantins confirm the loss of a cytolytic function and the absence of insecticidal or proinflammatory activity, suggesting that they represent an evolutionary transition to a new, yet unknown function. Our study shows that peptides have been retained in the defence poison of at least one microhylid lineage and encourages research on similarly understudied taxa to further elucidate the diversity and evolution of skin defence molecules

identification of molecular targets in the human genome

The purpose of the present study is to contribute to the field of functional genomics by developing, testing and applying computational methods to the problem of the evaluation of the effects of environmental and pharmacological molecules on genome expression. Original results are described in four independent sections The first two sections are devoted to the investigation of the coding potential of alternative splicing products in the human genome. Sections three and four are devoted to the application of computational techniques to investigate the molecular targets and the effects on their expression of molecules known to interfere with the physiological functions of a cell. In particular these techniques were applied on a class of compounds (tocotrienols) constituents of the Vitamin E

A systematic and prospectively validated approach for identifying synergistic drug combinations against malaria.

BACKGROUND: Nearly half of the world's population (3.2 billion people) were at risk of malaria in 2015, and resistance to current therapies is a major concern. While the standard of care includes drug combinations, there is a pressing need to identify new combinations that can bypass current resistance mechanisms. In the work presented here, a combined transcriptional drug repositioning/discovery and machine learning approach is proposed. METHODS: The integrated approach utilizes gene expression data from patient-derived samples, in combination with large-scale anti-malarial combination screening data, to predict synergistic compound combinations for three Plasmodium falciparum strains (3D7, DD2 and HB3). Both single compounds and combinations predicted to be active were prospectively tested in experiment. RESULTS: One of the predicted single agents, apicidin, was active with the AC50 values of 74.9, 84.1 and 74.9 nM in 3D7, DD2 and HB3 P. falciparum strains while its maximal safe plasma concentration in human is 547.6 ± 136.6 nM. Apicidin at the safe dose of 500 nM kills on average 97% of the parasite. The synergy prediction algorithm exhibited overall precision and recall of 83.5 and 65.1% for mild-to-strong, 48.8 and 75.5% for moderate-to-strong and 12.0 and 62.7% for strong synergies. Some of the prospectively predicted combinations, such as tacrolimus-hydroxyzine and raloxifene-thioridazine, exhibited significant synergy across the three P. falciparum strains included in the study. CONCLUSIONS: Systematic approaches can play an important role in accelerating discovering novel combinational therapies for malaria as it enables selecting novel synergistic compound pairs in a more informed and cost-effective manner

Polar Microalgae: New Approaches towards Understanding Adaptations to an Extreme and Changing Environment

Polar Regions are unique and highly prolific ecosystems characterized by extreme environmental gradients. Photosynthetic autotrophs, the base of the food web, have had to adapt physiological mechanisms to maintain growth, reproduction and metabolic activity despite environmental conditions that would shut-down cellular processes in most organisms. High latitudes are characterized by temperatures below the freezing point, complete darkness in winter and continuous light and high UV in the summer. Additionally, sea-ice, an ecological niche exploited by microbes during the long winter seasons when the ocean and land freezes over, is characterized by large salinity fluctuations, limited gas exchange, and highly oxic conditions. The last decade has been an exciting period of insights into the molecular mechanisms behind adaptation of microalgae to the cryosphere facilitated by the advancement of new scientific tools, particularly “omics” techniques. We review recent insights derived from genomics, transcriptomics, and proteomics studies. Genes, proteins and pathways identified from these highly adaptable polar microbes have far-reaching biotechnological applications. Furthermore, they may provide insights into life outside this planet, as well as glimpses into the past. High latitude regions also have disproportionately large inputs into global biogeochemical cycles and are the region most sensitive to climate change

Novel venom peptides from the cone snail Conus pulicarius discovered through next-generation sequencing of its venom duct transcriptome

ManuscriptThe venom peptides (i.e., conotoxins or conopeptides) that species in the genus Conus collectively produce are remarkably diverse, estimated to be around 50,000 to 140,000, but the pace of discovery and characterization of these peptides have been rather slow. To date, only a minor fraction have been identified and studied. However, the advent of next generation DNA sequencing technologies has opened up opportunities for expediting the exploration of this diversity. The whole transcriptome of a venom duct from the vermivorous marine snail C. pulicarius was sequenced using the 454 sequencing technology. Analysis of the data set resulted in the identification of over eighty unique putative conopeptide sequences, the highest number discovered so far from a Conus venom duct transcriptome. More importantly, majority of the sequences are potentially novel, many with unexpected structural features, hinting at the vastness of the diversity of Conus venom peptides that remains to be explored. The sequences can be classified into at least 14 major superfamilies/types (disulfide- and non-disulfide-rich), indicating the structural and functional diversity of conotoxins in the venom of C. pulicarius. In addition, the contryphans were surprisingly more diverse than what is currently known. Comparative analysis of the O-superfamily sequences also revealed insights into the complexity of the processes that drive the evolution and diversification of conotoxins

Multiplexed combinatorial drug screening using droplet-based microfluidics

The therapy of most cancers has greatly benefited from the use of targeted drugs. However, their effects are often short-lived since many tumors develop resistance against these drugs. Resistance of tumor cells against drugs can be adaptive or acquired and is often caused by genetic or non-genetic heterogeneity between tumor cells. A potential solution to overcome drug resistance is the use of drug combinations addressing multiple targets at once. Finding potent drug combinations against heterogeneous tumors is challenging. One reason is the high number of possible combinations. Another reason is the possibility of inter-patient heterogeneity in drug responses, making patient tailored treatments necessary. These require screens on patient material, which would drastically benefit from miniaturization, as it is the case in droplet-based microfluidics. However, drug screens in droplets against primary tumor cells have so far only been performed at a modest chemical complexity (55 treatment conditions) and with low content readouts. In this thesis we aimed at developing a droplet-based microfluidic workflow that allows the generation of high numbers of drug combinations in picolitre-sized droplets and their multiplexed analysis. To this end, we have established a pipeline to produce up to 420 drug combinations in droplets. We were able to significantly increase the number of possible combinations by building a microfluidic setup that comprises valve and micro-titer plate based injection of drugs into microfluidic devices for droplet generation Furthermore, we integrated a DNA-based barcoding approach to encode each treatment condition, enabling their multiplexed analyses since all droplets can be stored and processed together, which highly increases the throughput. With the established approach we can perform barcoding of each cells’ transcriptome according to the drugs it was exposed to in the droplet. Thereby, the effects of drug combinations on gene expression can be studied in a highly multiplexed way using RNA-Sequencing. We applied the developed approach to run combinatorial drug screens in droplets and analysed the effects of in total 630 drug combinations on gene expression in K562 cells. The low number of cells needed (max. 2 million cells) for such screens, could enable their application directly on tumor biopsies, thus paving the way for personalized therapy approaches. Since the established workflow is compatible with single cell readouts, we also envision its application to analyse drug resistances in heterogeneous tumor samples on the single cell level

Predicting function from sequence in a large multifunctional toxin family

Venoms contain active substances with highly specific physiological effects and are increasingly being used as sources of novel diagnostic, research and treatment tools for human disease. Experimental characterisation of individual toxin activities is a severe rate-limiting step in the discovery process, and in-silico tools which allow function to be predicted from sequence information are essential. Toxins are typically members of large multifunctional families of structurally similar proteins that can have different biological activities, and minor sequence divergence can have significant consequences. Thus, existing predictive tools tend to have low accuracy. We investigated a classification model based on physico-chemical attributes that can easily be calculated from amino-acid sequences, using over 250 (mostly novel) viperid phospholipase A2 toxins. We also clustered proteins by sequence profiles, and carried out in-vitro tests for four major activities on a selection of isolated novel toxins, or crude venoms known to contain them. The majority of detected activities were consistent with predictions, in contrast to poor performance of a number of tested existing predictive methods. Our results provide a framework for comparison of active sites among different functional sub-groups of toxins that will allow a more targeted approach for identification of potential drug leads in the future

Predicting selective drug targets in cancer through metabolic networks

The authors develop a genome-scale model of cancer metabolism and use it to predict genes that are essential for cancer cell growth. An array of target combinations are then identified that could potentially provide novel selective treatments for specific cancers