The human microbiome has become a very interesting and intriguing area of biomedical research. The completion of phase I of the NIH-funded Human Microbiome Project (HMP) has provided the biomedical community with a nearly complete snapshot of the millions of bacteria that live on and within the human body. Accurate and robust bioinformatic tools are needed to facilitate the analysis of such data. Many analytical tools are available specifically for the analysis of microbiome sequencing data however these tools are designed mainly for the Illumina MiSeq sequencing platform. Over the last few years, an analytical pipeline was developed for the Ion Torrent Personal Genome Machine (PGM) sequencing platform. This pipeline was published in 2016 and has been used for other sequencing studies in collaboration with multiple NIH institutes. In collaboration with the Clinical Center Nursing Department (CCND), two manuscripts were submitted in late 2017 and early 2018. Both manuscripts utilized the data processing pipeline specific for Ion Torrent Personal Genome Machine data. The first manuscript, submitted to PlosOne October 2017, describes a whole stool sampling method. This work is currently being revised where more sequencing data will be added in order to strengthen the findings. The second manuscript, submitted April 2018 describes the changes of the oral microbiome of Severe Aplastic Anemia Patients during treatment. This manuscript will be resubmitted by October 2018. In a joint collaboration with CCND and NIAAA, stool and oral specimens were collected from alcohol use disorder (AUD) patients during a 28-day in-patient detoxification process.
The aim of this study is to examine the changes of the oral and gut microbiome during the process of detoxification. Specimens from 23 patients were collected over a 28-day period and a maximum of 10 samples per patient were taken. All stool samples have been sequenced using the PGM over 9 sequencing runs. The Ion 16s Metagenomics kit containing 12 sequencing primers was used for library construction. The analysis pipeline mentioned above will be slightly adapted now that the primer sequences have been provided. An initial challenge of this data is that the primer sequences were proprietary and were unpublished. Recently, the company ThermoFisher provided one person with the primer sequences through a nondisclosure agreement (NDA) therefore the pipeline will need to be modified slightly to account for this. To date, the stool sequencing data has been analyzed using the original pipeline. We hope to have the stool data and oral data analyzed with the modified pipeline by the middle of September 2018. In a collaboration with the NIAAA, roughly about 300 clinical variables were collected from approximately 1,000 patients with alcohol use disorder (AUD). With the use of Alternative Decision Trees and machine learning using a tool called Weka (Frank E 2016), we were able to predict Treatment (Tx) and non-Treatment (Non-Tx) seeking individuals with about 85% accuracy. Some of the clinical variables collected and used for these people include drug, alcohol and smoking use, clinical diagnostics, personal characteristics, family history, psychological, emotional and social traits. We were able to narrow down the most important variables that are predictive for treatment seeking. We submitted the manuscript earlier this year and it is currently in the revision process for resubmission. The goal of this work is to devise a patient-specific treatment plan based on whether a patient seeks treatment or does not. Being able to reduce the number of highly predictive variables will greatly improve patient classification for applied treatment plans and will be an important contribution to the field of mental health and drug abuse disorders. In another collaboration jointly with NIAAA and NIDA, data of 339 people was investigated in order to study and understand how the decision making process in people with addiction differs from that of normal controls. This work is underway and is currently being investigated in collaboration with another MSCL member, Dr. Philip McQueen.
