AdjustEBOVGP-Dx

European & Developing Countries Clinical Trials Partnership (EDCTP)
Horizon 2020 "Research and Innovation Actions" DESCA
PI: Dr Dimitrios Vlachakis (AUA, Greece)

Biochemical Adjustments of native EBOV Glycoprotein in Patient Sample to Unmask target-Epitopes for Rapid Diagnostic Testing.

Ebolavirus and Marburgvirus are two genera of the negative sense RNA virus family Filoviridae, order mononegavirales. Filoviruses cause rare but fatal viral hemorrhagic fevers-VHF in equatorial Africa--with potential for regional and international urban spread. Both virus associated VHFs present with a similar prodrome; mimicking several tropical infectious diseases. Early detection is important for mobilizing swift response and control. Existing technologies for filovirus detection are not suited for point of care (POC) use, being expensive, not fast-enough and requiring laboratory facilities absent in many village where index cases occur. While 2 rapid diagnostic tests (RDTs) have recently emerged detecting EBOV at the point of care (POC), there are no pan-filovirus targeted RDTs. This project could yield the 1st ever prototypes of RDTs for the duo-detection of EBOV and MARV. Pan-filovirus RDTs are required to ensure early detection, response and control of the on-going and future outbreaks. Moreover, the mAbs presented are candidate therapeutics.


FrailSafe

Horizon 2020 "Research and Innovation Actions" frailsafe-project.eu (No 690140)
Researcher: Dr Dimitrios Vlachakis (WP4 Leader)

FrailSafe aims to better understand frailty and its relation to co-morbidities

FrailSafe is trying to identify quantitative and qualitative measures of frailty through advanced data mining approaches on multiparametric data and use them to predict short and long-term outcome and risk of frailty; to develop real life sensing (physical, cognitive, psychological, social) and intervention (guidelines, real-time feedback, Augmented Reality serious games) platform offering physiological reserve and external challenges; to provide a digital patient model of frailty sensitive to several dynamic parameters, including physiological, behavioural and contextual; this model being the key for developing and testing pharmaceutical and non-pharmaceutical interventions; to create “prevent-frailty” evidence-based recommendations for the elderly; to strengthen the motor, cognitive, and other “anti-frailty” activities through the delivery of personalised treatment programmes, monitoring alerts, guidance and education; and to achieve all with a safe, unobtrusive and acceptable system for the ageing population while reducing the cost of health care systems.




MS Azure for Genomics

Microsoft Cloud-Based Genomics Grant
PI: Dr Dimitrios Vlachakis

A scalable and secure secondary analysis of genomes, starting from raw reads and producing aligned reads and variant calls

Flaviviridae is a family of viruses that infect vertebrates. Distinct viral structures of this family are visible in thin sections of infected tissue. The size of virion has been estimated by filtration. Virions of the flaviviridae family are enveloped and slightly pleomorphic during their life cycle. They are spherical in shape and usually 40-60 nm in diameter. Their nucleocapsids are isometric and sometimes penetrated by stain. The usual size of the nucleocapsids is 25-30 nm in diameter and they have polyhedral symmetry. Virions of the flaviviridae family contain one molecule of linear positive-sense single stranded RNA. The total genome length is 9500-12500 nt. The 5' end of the genome has a cap, or a genome-linked protein (VPg). The 3' end regularly has no poly (A) tract (except some strains of tick-borne encephalitis complex of flaviviruses, which have a poly (A) tract). Their nucleic acid material is fully encapsidated and solely genomic. The genome of flaviviridae features a 5' end that encodes structural proteins, whereas the non-structural proteins including protease, helicase and polymerase, are encoded at the 3' end. To date neither specific antiviral treatments exist nor are there any vaccines available for either infection. Thus there is an urgent need for new therapies. Herein, an effort will be made to shed light to the genetic, evolutionary and structural features of the viral helicase enzymes towards the establishment of a versatile drug design platform of potent antiviral agents. The proposed project will involve full phylogenetic and biostatistical analysis of viral genomes and comparative/homology modeling of helicase enzymes. Eventually a drug design platform will be established and a series of drug-like inhibitors of the viral helicase enzyme will be in silico scored and evaluated.



AWS Amazon Cloud

AMAZON CLOUD COMPUTING AMBASSADOR for METAGENOMICS
PI: Dr Dimitrios Vlachakis

Teaching Undergraduate students cloud computing for professional development in modern in metagenomics

Statistical analysis of genomic data. Given the rapid advances in genomics and bioinformatics that have taken place in the past few years, there is a growing need for analysing vast amount of data and interpreting their results. Large-scale cancer genome studies have successfully applied some preliminary integration approaches. To this end, we aim to analyse multi-source data using association statistics to estimate pair wise as well as group dependencies in the data. Multivariate analysis and likelihood-based inference will be also employed to estimate data patterns. The ultimate goal is to establish a methodology to integrate different data which measure multiple genomic features and discover or validate findings that would not be discovered by analysing each data independently. Good programming skills are a pre-requisite (e.g. R statistical software, C/C++).




Radagast

ESPA Young Researchers Support / phase B' (No 47 - Life Sciences & Medicine)
PI: Dr Dimitrios Vlachakis for PhD students Eleni Papakonstantinou & Katerina Pierouli

Rational Drug Design of Novel Antiviral Agents against the Helicase Enzyme of the Yellow Fever Virus

The Flaviviridae family of viruses infects vertebrates and it is primarily spread through arthropod vectors. The Yellow Fever virus belongs to the Flaviviridae family. Although, there are very limited data regarding the Yellow Fever virus and its epidemiology, there have been reported few cases Yellow Fever infection in Greece. However, despite the severity of Flaviviridae causing diseases (e.g. Dengue fever, Classical swine fever, Japanese encephalitis), currently there is not any available anti-flaviviridae therapy. Thus, there is a need for the development of effective anti-Yellow Fever viral pharmaceutical strategies. It has been shown that RNA helicases, which are involved in duplex unwinding during viral RNA replication, represent promising antiviral targets. Therefore, the inhibition of the Yellow Fever viral helicase would be an effective approach of interrupting the life cycle of the Yellow Fever virus. The proposed research will be directed towards the computer-aided development of a series of drug-like low molecular weight compounds capable of inhibiting the helicase enzyme of Yellow Fever virus. Results derived from a repertoire of multi-disciplinary bioinformatics and statistical methods would enhance the understanding of the mechanism of action of the Yellow Fever viral helicase enzyme. The ultimate goal is to design a series of novel anti-helicase compounds as drug candidates against the Yellow Fever virus while the inhibitory activity of our novel compounds will be evaluated biologically.




CHARME COST Action

COST Action CA15110
MC sub: Dr Dimitrios Vlachakis (Greece)

Harmonising standardisation strategies to increase efficiency and competitiveness of European life-science research

Biotechnology is an enabling technology that alone, or in combination with cognate technologies, provides the capacity to spur huge leaps in the performance and capabilities of numerous sectors, such as healthcare and medicine, agricultural production, and industrial production. In this context, a prerequisite for modern R&D is a high quality of the research data. By enabling re-use of research assets, research is made considerably more efficient and economical. This can only be achieved reliably and efficiently if these data are generated according to standards and Standard Operating Procedures (SOPs). Standardisation and quality management are thus important drivers in the life sciences and biotechnology, as only data generated with minimum quality assurance can be easily implemented into industrial applications. Furthermore, standards assure and ensure that data become easily accessible, shareable and comparable along the value chain. The use of common standards may hence result in improved efficiency and competitiveness of European life-science research. Moreover, standardisation strategies are required or have gained in importance in the assessment of proposals in the new H2020 framework programme. It was logical then that measures were taken by several initiatives and institutions to develop and implement standards in the life sciences: one of these was set up by the International Organisation for Standardisation (ISO), which is seeking comprehensive agreement on standards in the life sciences, particularly in biotechnology and related fields. Under the auspices of the German Institute for Standardisation (DIN), an international committee (ISO/TC 276 Biotechnology) has been created that will endorse necessary standards and - if necessary - encourage the development of new norms and standards in a top-down approach. Unfortunately, current and new efforts remain fragmented and largely disconnected from each other.
The COST Action CHARME aims to bridge and combine the fragmented areas to achieve a breakthrough in standardisation efforts. CHARME will identify needs and gaps, teaming up with other initiatives and organisations to avoid duplication and overlap of standardisation activities. Only through a common, coordinated, long-term strategy, by active involvement of all stakeholders (from research, industry and policy), can standards be succesfully assimilated into the daily work-flow and thus increase efficiency and competitiveness of European life-science research.



ML4microbiome COST Action

COST Action CA18131
MC sub: Dr Dimitrios Vlachakis (Greece)

Automation opportunities for novel development of ML/Statistics methods targeting microbiome data

In recent years, the human microbiome has been characterised in great detail in several large-scale studies as a key player in intestinal and non-intestinal diseases, e.g. inflammatory bowel disease, diabetes and liver cirrhosis, along with brain development and behaviour. As more associations between microbiome and phenotypes are elucidated, research focus is now shifting towards causality and clinical use for diagnostics, prognostics and therapeutics, where some promising applications have recently been showcased. Microbiome data are inherently convoluted, noisy and highly variable, and non-standard analytical methodologies are therefore required to unlock its clinical and scientific potential. While a range of statistical modelling and Machine Learning (ML) methods are now available, sub-optimal implementation often leads to errors, over-fitting and misleading results, due to a lack of good analytical practices and ML expertise in the microbiome community. Thus, this COST Action network will create productive symbiosis between discovery-oriented microbiome researchers and data-driven ML experts, through regular meetings, workshops and training courses. Together, it will first optimise and then standardise the use of said techniques, following the creation of publicly available benchmark datasets. Correct usage of these approaches will allow for better identification of predictive and discriminatory ‘omics’ features, increase study repeatability, and provide mechanistic insights into possible causal or contributing roles of the microbiome. This Action will also investigate automation opportunities and define priority areas for novel development of ML/Statistics methods targeting microbiome data. Thus, this COST Action will open novel and exciting avenues within the fields of both ML/Statistics and microbiome research.