The document discusses the growing problem of antibiotic resistance around the world. It provides statistics on antibiotic resistance from the US, Europe, and Middle East. It describes the "superbug" NDM-1 found in India that is resistant to nearly all antibiotics. The document also discusses ongoing research into the evolution of antibiotic resistance and databases that track resistance genes. It summarizes projects by the ND4BB to address antibiotic resistance through collaboration between academics and industry. These projects aim to create a network of clinical sites, study how drugs enter and remain in bacteria, and facilitate drug discovery.

1. EVOLUTION OF ANTIBIOTIC
RESISTANCE
(TODAY’S WORLD vs ANTIBIOTIC RESISTANCE)
Ahmet VARIS
January 8th Thursday, 2015

2. OUTLINE
•Statistics from US
Europe
Middle east
•Superbug from India
•Ongoing researches about evolution of antibiotic
resistance
•Databases

3. “Consequently, we face the prospect of returning to a
preantibiotic era, where an increasing number of
infections can no longer be treated effectively with
our current arsenal of drugs”
(Moly K. Gibson et al. Improved annotation of
antibiotic resistance determinants reveals microbial
resistomes cluster by ecology, 2014)

4. (http://www.cdc.gov/drugresistance/threat-report-2013/pdf/ar-threats-2013-
508.pdf#page=13)

5. CDC (center for disease control and prevention) divide antibiotic
resistance 3 level
•Urgent: The highest risk for public health.
Ex: Clostridium difficile, Carbapenem-resistant Enterobacteriaceae
•Serious: These can be urgent if they effect public health.
Ex: Multidrug-resistant Acinetobacter, Fluconazole-resistant Candida
•Concerning: There are multiple treatment options for these
resistance.
Ex:Erythromycin-resistant Streptococcus Group A, Clindamycin-resistant
Streptococcus GroupB

7. WHAT ND4BB DOING?
•Every year 25 000 people die becaus of anti
biotic resistance in EU.
•Antibiotic resistance cost for EU is €1.5 billion.
•ND4BB is trying to collect acedemisions,
politics, companies.
•There are 3 projects for this aim
•COMBACTE, TRANSLOCATION, ENABLE

8. •COMBACTE:
• Creating a pan-European network of clinical sites
• It has a network of about 300 clnical sites from 34
countries
• Total cost: €250 million
•TRANSLOCATION:
• Getting drugs in to bugs and keeping them there
• 2/3 of deaths in EU coused by gram-negative
bacteria.
• Total cost: €29 million
•ENABLE:
• A Drug discovery platform
• This Project offer Access for universities and small

9. (From ND4BB report,
2013)

11. NDM-1 (NEW DELHI METALLO
ΒETA-LACTAMASE 1)
•Firstly investigate in india
•Most of scientist agreed that NDM-1 is the most
powerful resistance
•It is spread around the World and other bacteria
•Just two antibitics are cure for this resistance

12. (Figures are taken from Dvies&Davies, 2010)

13. Genome-wide analysis captures the
determinants of the antibiotic cross-
resistance interaction network
(Viktoria L. et.al 8 July 2014)Aims: To show
1) Convergent molecular evolution is
commen across antibiotic treatments
2) Resistance, gaining by mutations enhance
sensitivity to other drug
3) Antibiotic adaptation can partly be
achieved without gain of novel function

14. RESULTS OF THIS EXPERIMENT
These are antibiotics which was used at experiment

16. PREDICTION OF ANTIBIOTIC
RESISTANCE BY GENE EXPRESSION
PROFILES
(Shingo S. et. al, 17 December 2014)
Antibiotics name Abbreviation Class Cellular target
Cefoperazone ​CPZ Cephalosporin, β-lactam Cell wall
Cefixime ​CFIX Cephalosporin, β-lactam Cell wall
Amikacin ​AMK Aminoglycoside Protein synthesis, 30S
Neomycin NM Aminoglycoside Protein synthesis, 30S
​Doxycycline ​DOXY Tetracycline Protein synthesis, 30S
​Chloramphenicol ​CP Protein synthesis, 50S
​Azithromycin ​AZM Azalide, macrolide Protein synthesis, 50S
​Trimethoprim ​TP Folic acid synthesis
Enoxacin ​ENX Quinolone DNA gyrase
​Ciprofloxacin ​CPFX Quinolone DNA gyrase
​Colistin ​CL Peptide Cell membrane
These are antibiotics which was used at experiment

18. DATABASES FOR ANTIBIOTIC
RESISTANCE
ARDB - Antibiotic Resistance Genes Database

19. REFERENCES
• Prediction of antibiotic resistance by gene expression profiles, Shingo Suziki,
Takaaki Horinouchi & Chikara Furusawa,2014
• Improved annotation of antibiotic resistance determinants reveals microbial
resistomes cluster by ecology, Molly K Gibson, Kevin J Forsberg &
Gautam Dantas,2014
• Genome-wide analysis captures the determinants of the antibiotic cross-
resistance interaction network, Viktor lazar et.al, 2014
• Streptococcus pneumoniae R6 interspecies transformation: genetic analysis of
penicillin resistance determinants and genome-wide recombination events,Julia
Sauerbier, Patrick Maurer, Martin Rieger and Regine Hakenbeck,2012
• http://www.cdc.gov/drugresistance/about.html
• http://www.searo.who.int/entity/antimicrobial_resistance/documents/en/
• http://www.imi.europa.eu/content/home
• The Comprehensive Antibiotic Resistance Database, Andrew G. Mcarthur et.al,
2013

20. THANK YOU