Colorectal cancer

There are 1.4 million people in the US with a history of colorectal cancer (CRC). Although the mortality rate has declined in recent decades, incidence rates are expected to rise due to the aging population and increasing occurrence of CRC in younger individuals. Cancerous or precancerous cells in the colon form lesions which are typically detected via colonoscopy, but the technique is invasive, expensive, and only 39% of patients return for subsequent screening. There is a need for improved non-invasive screening methods. We are using an innovative source to detect CRC: the human microbiome. Human microbiome can directly contribute to the development of CRC. We try to identify microbial biomarkers associated with CRC and to develop computational models that improve the non-invasive detection of CRC.

  1. Sze MA, Topcuoglu BD, Lesniak NA, IV Ruffin MT, Schloss PD. 2019. Fecal short-chain fatty acids are not predictive of colonic tumor status and cannot be predicted based on bacterial community structure. mBio. 10: e01454-19. DOI: 10.1128/mBio.01454-19.
  2. Hannigan GD, Duhaime MB, Ruffin IV MT, Koumpouras CC, Schloss PD. 2018. The Diagnostic Potential and Interactive Dynamics of the Colorectal Cancer Virome. mBio. 9: e02248-18. DOI: 10.1128/mBio.02248-18.
  3. Baxter NT, Ruffin MT 4th, Rogers MA, Schloss PD. 2016. Microbiota-based model improves the sensitivity of fecal immunochemical test for detecting colonic lesions. Genome Medicine. 8: 1. DOI: 10.1186/s13073-016-0290-3.

Clostridium difficile Infection

Clostridium difficile infection (CDI) following therapeutic antibiotic treatment represents a considerable threat to human health, each year causing as many as half a million infections, 29,000 deaths, and a healthcare burden of $4.8 million. CDI can cause severe abdominal pain and diarrhea, and can develop the life-threatening conditions, which it accomplishes through secretion of protein toxins. Infections in healthy individuals are uncommon, as the combination of the innate immune system and gut microbiome prevent colonization under ordinary circumstances. However, disruption of the native gut bacterial communities during antibiotic therapy, often for unrelated illness, provides opportunity for C. difficile to establish infection. Subsequent treatment of CDI with antibiotics is typically effective, but recurrence of disease is common and may be increasing in prevalence. This, in combination with increased prevalence of infection, the emergence of more virulent forms of the pathogen, and the ever-present threat of antibiotic resistance highlight the need to better understand the mechanisms by which the gut immune system and the resident microbiota prevent initial colonization and subsequent recurrence by C. difficile. We use a combination of 16S rRNA gene sequencing, metagenomics, metatranscriptomics, and metabolomics in a mouse model for CDI and in infected patients to identify microbial functions that are important for colonization resistance and clearance of C. difficile.

  1. Jenior ML, Leslie JL, Young VB, Schloss PD. 2018. Clostridium difficile differentially alters the structure and metabolism of distinct cecal microbiomes to promote persistent colonization during infection. mSphere. 3: e00261-18. DOI: 10.1128/mSphere.00261-18.
  2. Jenior ML, Leslie JL, Young VB, Schloss PD. 2017. Clostridium difficile colonizes alternative nutrient niches during infection across distinct murine gut environments. mSystems. 2: e00063-17. DOI: 10.1128/mSystems.00063-17.
  3. Schubert AM, Sinani H, Schloss PD. 2015. Antibiotic-induced alterations of the murine gut microbiota and subsequent effects on colonization resistance against Clostridium difficile. mBio. 6: e00974-15. DOI: 10.1128/mBio.00974-15.

Bioinformatic tool development

  1. Schloss PD. 2018. The Riffomonas Reproducible Research Tutorial Series. The Journal of Open Source Education. 1: 13. DOI: 10.21105/jose.00013.
  2. Westcott SL, Schloss PD. 2017. OptiClust, an Improved Method for Assigning Amplicon-Based Sequence Data to Operational Taxonomic Units. mSphere. 2: e00073-17. DOI: 10.1128/mSphereDirect.00073-17.
  3. Schloss PD. 2016. Application of a Database-Independent Approach To Assess the Quality of Operational Taxonomic Unit Picking Methods. mSystems. 1: e00027-16. DOI: 10.1128/mSystems.00027-16.

Scientific culture

  1. Schloss PD. 2018. In Defense of an Academic Career in Microbiology. mSphere. 3: e00575-17. DOI: 10.1128/mSphere.00575-17.
  2. Schloss PD. 2018. Identifying and Overcoming Threats to Reproducibility, Replicability, Robustness, and Generalizability in Microbiome Research. mBio. 9: e00525-18. DOI: 10.1128/mBio.00525-18.
  3. Schloss PD, Johnston M, Casadevall A. 2017. Support Science by Publishing in Scientific Society Journals. mBio. 8: e01633-17. DOI: 10.1128/mBio.01633-17.