Computational Biology for Infection Research

The Department of “Computational Biology for Infection Research” studies the human microbiome, viral and bacterial pathogens, and human cell lineages within individual patients by analysis of large-scale biological and epidemiological data sets with computational techniques. Focusing on high throughput meta’omics, population genomic and single cell sequencing data, we produce testable hypotheses, such as sets of key sites or relevant genes associated with the presence of a disease, of antibiotic resistance or pathogenic evasion of immune defense. We interact with experimental collaborators to verify our findings and to promote their translation into medical treatment or diagnosis procedures. To achieve its research goals, the department also develops novel algorithms and software.

Leader

Host (epi)genomics

High-throughput technologies such as microarray chips and next-generation sequencing have enabled screening of a wide range of patients in genome-wide association studies, revealing unprecedented insights into genomic, transcriptomic and plenty of different epigenomic drivers of disease. 

Transcriptional regulation is a multistep process involving not only the transcription machinery and transcription factors, but is also defined by histone modifications, nuclear chromatin organization and availability of cis-regulatory elements (for example enhancers). We study the composition of such factors with respect to transcriptional output in immune development and infectious diseases 1. To this end, the incorporation of Hi-C data (3D chromatin information) with ChIP-seq of histone modifications and chromatin regulators has enabled us to predict the chromatin environment of leukemia and how targeted drug therapies affect this complex network 2. Furthermore, more recent techniques such as HiChIP require advanced computational approaches not yet fully developed, and have been used to identify the involvement of Klf4 in cellular reprogramming 3.

However, the complexity of protein expression and regulation does not end at gene transcription. Posttranscriptional regulation ranges from controlling mRNA stability to translational rate and more, and can be studied by specific sequencing approaches 4. We study the effects of RNA-binding proteins (RBPs) by projecting miRNA expression and binding onto mRNA expression regulation 5 with the help of PAR-CLIP. Or, study the direct effects of RPBs on mRNA binding and stability, and how such regulators affect downstream cellular processes in disease 6. As in any computational biology field, the development of new approaches for gaining deeper insights into the underlying biology is key 7, and we continue to develop computational methods and integrative pipelines for various purposes and sequencing approaches.

Our research focuses on

  • How do chromatin regulators, histone modifications and chromatin organization orchestrate the complex transcription machinery in immune cells? Do these features hold predispositions of their own for infectious diseases?
  • Integration of RNA-binding protein information with RNA-seq or miRNA-seq to achieve a more directed way of posttranscriptional gene regulation by individual RBPs
  • How can novel machine learning or deep learning approaches support integrative tasks of genomics and transcriptomics data? To this end, we develop computational approaches to deal with novel and custom sequencing data, and to integrate complex data from various sources in order to achieve a complete view of (post-)transcriptional gene regulation. 

Selected publications

  1. ​​​​​​A. Kloetgen, P. Thandapani et al., 3D Chromosomal Landscapes in Hematopoiesis and Immunity. Trends Immunol. 2019 40: 809
  2. A. Kloetgen, P. Thandapani, P. Ntziachristos et al., Three-dimensional chromatin landscapes in T cell acute lymphoblastic leukemia. Nat Genet. 2020 52: 388
  3. D. C. Di Giammartino, A. Kloetgen, A. Polyzos A et al., KLF4 is involved in the organization and regulation of pluripotency-associated three-dimensional enhancer networks. Nat Cell Biol. 2019 21: 1179
  4. A. Kloetgen et al., Biochemical and bioinformatic methods for elucidating the role of RNA-protein interactions in posttranscriptional regulation. Brief Funct Genomics. 2015 14 :102
  5. K. Hezaveh, A. Kloetgen, S. H. Bernhart et al., Alterations of microRNA and microRNA-regulated messenger RNA expression in germinal center B-cell lymphomas determined by integrative sequencing analysis. Haematologica. 2016 101: 1380
  6. S.  Duggimpudi, A. Kloetgen, S. K. Maney et al., Transcriptome-wide analysis uncovers the targets of the RNA-binding protein MSI2 and effects of MSI2's RNA-binding activity on IL-6 signaling. J Biol Chem. 2018 293: 15359
  7. A. Kloetgen et al., The PARA-suite: PAR-CLIP specific sequence read simulation and processing. PeerJ. 2016 4:e2619

Researchers

  • Dr. Andreas Kloetgen

Collaborators

  • Abel Viejo-Borbolla, MHH, Hannover, Deutschland
  • Effie Apostolou, Weill Cornell Medicine, New York, USA
  • Ari Melnick, Weill Cornell Medicine, New York, USA
  • Tracy McGaha, Princess Margaret Cancer Centre, Toronto, Kanada
  • Iannis Aifantis, NYU Langone Health, New York, USA
PrintSend per emailShare