Using next-generation sequencing to identify hemorrhagic fever viruses in bat populations of central Africa and Asia
Outbreaks of hemorrhagic fever (e.g., Ebola , Marburg virus) in Africa occur when people are infected by viruses from the Filoviridae family. Outbreaks commonly are characterized by person-to-person spread and high fatality rates. No licensed vaccines or therapies exist to prevent infection or treat the illnesses caused by these viruses.
Outbreaks are controlled by identifying and isolating sick people. Rapid diagnostic tests help in this process. But because known filoviruses are genetically diverse—up to 50% at the nucleotide level—currently available sensitive sequence-based tests, such as real-time polymerase chain reaction tests, could miss a new virus. For example, Bundibugyo virus (species Bundibugyo ebolavirus) remained undetected until 2007, yet it caused two large outbreaks in Africa over the last decade. For these reasons, it is important to identify and characterize filovirus diversity in nature.
The natural reservoirs for ebolaviruses are largely unknown, but scientists believe they are various species of bats. This conclusion is based on reports of various bat species having ebolavirus-specific RNA or antibodies. Recently, virus isolation, PCR, and antibody data have shown a specific species of cave-dwelling fruit bat to be the natural reservoir for the related marburgviruses.
Therefore, to identify the natural reservoir(s) for the known ebolaviruses and discover previously undetected filoviruses, CDC will use a next-generation sequencing approach to screen thousands of bat specimens from areas of Africa known to have had filovirus activity. This project will expand the current database of filovirus genetic diversity, supporting development of improved diagnostic tests and the expansion of surveillance for these emerging threats.
Building advanced molecular detection infrastructure to combat healthcare-associated infections
Untreatable infections threaten a return to the time when simple infections were deadly. Hospitals and other healthcare settings battle to protect their patients from these drug-resistant organisms and prevent their spread to other patients. Predicting how these bacteria will become resistant is a challenge.
When investigating unfamiliar territory, such as the evolution of germs, the use of proven standardized investigative methods decreases confusion as multiple groups work to understand more about a pathogen. It is critical for partners to measure and investigate specific resistant bacteria in the same way, using a common vocabulary and accepted criteria. The study will decode the building blocks of genetic material to reveal how specific genes change and develop over time. This will create detailed family trees for two high-threat germs - Clostridium difficile—a germ that causes life-threatening diarrhea—and carbapenem -resistant Enterobacteriaceae (CRE)—a family of germs that have become resistant to all or nearly all the antibiotics we have today.
This study will help CDC establish a new, more robust standard to determine reliably the genetic history of drug-resistant bacteria. Understanding the genetic likeness will help in the development of protocols and procedures for testing and analysis. Ultimately, understanding how C. difficile and CRE have changed over time and how they spread will protect more people and reduce infections in healthcare settings.
Expanding data collection and curation for CDC’s reference database for identification of infectious pathogens (MicrobeNet)
CDC’s MicrobeNet is an online tool that scientists use to find information on the microorganisms that make people sick. Designed as a massive database, MicrobeNet makes the information CDC’s laboratories collect on microbes available to state and local health laboratories. This system provides access to crucial information that can identify disease outbreaks, track new and emerging diseases, and develop new ways of responding to illnesses.
Laboratory scientists use MicrobeNet to find a complete portrait of different species of bacteria or fungi. This includes images, information on antibiotic resistance, or the better ways to test for and identify them. Many of the searches performed in MicrobeNet are for rare or unusual pathogens, which are often difficult to grow and identify.
Since 2013, CDC has collected detailed information on approximately 500 species of microorganisms.
However, information about more than 4,000 organisms still needs to be added to MicrobeNet. To do this, CDC scientists must grow the organism, perform DNA sequence analysis and many other tests, and process each species.
Expanding MicrobeNet will allow public health laboratories anywhere in the world to run diagnostic tests and match results against CDC’s unique collection of pathogens, making it faster and easier for them to identify and respond to dangerous diseases.
Integrating data to understand better the transmission networks involved in the spread of infectious disease and drug resistance
HIV, other sexually transmitted diseases, hepatitis, and tuberculosis (TB) affect millions of people in the United States. These infections can be spread in many ways, including sexual contact, contact with bodily fluids of an infected person, and via air droplets. Even though scientists know that certain groups—such as gay and bisexual men and persons who inject drugs—are more likely to be affected, less is known about the connections between people that result in the spread of these infections. In addition, using epidemiological data alone may not always identify links between infected persons. Understanding these connections is critical to stopping the spread of disease.
CDC scientists are using genetic information (sequence data, including next generation sequencing) for the viruses and bacteria that cause these diseases, together with demographic, geographic, and clinical data from infected persons—such as risk group, age, location, and health status—to understand more about how infected people are connected. Combined, this information helps scientists to identify more precisely how these diseases are spreading so that outbreaks can be stopped.
These new tools, help CDC characterize transmission networks more quickly and easily to better target rapid responses to stop the spread of infection.
By improving the tools that are currently available, CDC can learn how diseases are spreading. With this knowledge, scientists can focus additional prevention tools to help protect health and reduce infections.
Transforming public health microbiology with whole genome sequencing for foodborne diseases
Bacteria such as Salmonella, Shiga toxin-producing Escherichia coli (STEC), and Listeria monocytogenes are among the bugs that cause the most outbreaks and severe illness from food. In order to understand the occurrence of foodborne diseases, the bugs are routinely identified, and scientists monitor their ability to cause disease (virulence) and antimicrobial resistance. During outbreaks, these bacteria must also be subtyped and compared in real time to identify clusters of infected patients that are likely to be infected from the same source.
PulseNet is an integrated network of over 85 U.S. public health, regulatory, and agriculture laboratories that uses a standardized DNA “fingerprinting” subtyping method on disease-causing bacteria obtained from sick patients, food, animals, and the environment. Using common tools and uploading data onto state and national databases, it is possible to match cases of illness, detect outbreaks, and identify sources of contamination. The methods currently used to identify, characterize and subtype foodborne bacteria are unique to each bug and require many time-consuming steps. Building on experience from an ongoing listeriosis project, CDC will consolidate most foodborne pathogen identification and characterization activities to a single, fast, and efficient whole genome sequencing process. Scientists also will create a standardized, national database and analysis platform.
This project will enhance significantly the ability of CDC and its state and local partners to determine what types of foods and activities make people ill from foodborne diseases. This information will provide more reliable information about the bugs that cause most illness associated with food and increase the ability of CDC, industry, and regulatory agencies to identify and stop outbreaks when they are small. This information will further increase the safety of the United States and the global food supply.
Transforming surveillance and research methods in the Emerging Infections Program (EIP) through next-generation sequencing
CDC’s Emerging Infections Program (EIP) will help transform public health practice by applying a range of advanced molecular detection tools and exploring how they affect surveillance and research activities across the EIP network.
For nearly 20 years, the EIP partnership between CDC, state health departments, academic institutions, local health departments, infection control practitioners, and other federal agencies has assessed how emerging infections affect public health. Through such services as FoodNet , Active Bacterial Core Surveillance (invasive bacterial diseases including pneumococcal disease), influenza hospital surveillance and vaccine evaluations, Healthcare-associated Infections-Community Interface, and TickNET, EIP also has evaluated methods to prevent and control these emerging infections.
EIPs rely heavily on culture-based methods. With AMD laboratory and bioinformatics support, the program will transform its current surveillance methodology for several infectious diseases. EIPs will validate new methods of pathogen identification and characterization for such pathogens as those that cause meningitis, pneumonia, healthcare-associated infections, and severe diarrhea. EIP projects will explore the genetic determinants of antimicrobial resistance; genetic elements associated with disease severity and vaccine failure, and best practices for modern day molecular epidemiology.
The first projects will emphasize whole genome sequencing. EIP also will begin using other AMD-related methods—such as meta-genomics, which studies genomes from a mixed community of organisms—to explore and develop their practical applications in public health. As a result, these EIP projects will guide the broader implementation of AMD in public health.
Using targeted and metagenomic sequencing to identify and characterize pathogens in unknown respiratory disease outbreaks
Coughing? It could be whooping cough, the flu, pneumonia, or a slew of other respiratory illnesses. Sometimes during outbreaks it’s not so easy for medical or public health professionals to diagnose respiratory illnesses quickly. This is why CDC brings together highly skilled epidemiology and laboratory respiratory experts to form the Unexplained Respiratory Disease Outbreaks (URDO) work group. CDC epidemiologists look at the characteristics of the disease and patterns of an outbreak. CDC laboratory scientists help solve the mystery by identifying the pathogen causing an outbreak. But, there is a problem. Current diagnostic techniques slow down the laboratory work, wasting time needed to control the outbreak and protect the public’s health.
CDC is developing a new tool to help laboratory scientists quickly identify which germ, including new, emerging or rare ones, is causing an outbreak. With targeted sequencing analysis, scientists will identify and characterize pathogens in respiratory specimens from URDO responses. They will also be able to determine the specific strain responsible for an outbreak and whether or not that strain is resistant to antibiotics.
By using a single and quick analytic tool, CDC will be able to identify the cause of a respiratory outbreak faster. This means that effective prevention and control strategies can be implemented more quickly, which protect people’s health.