Biology Department Faculty

We work on microbial systems biology and try to understand a bacterium as a complete system by applying a combination of high-throughput robotics, next generation sequencing and computational biology. The goal of the lab is to develop new antimicrobial strategies, rapidly detect antibiotic resistant bacteria, develop predictive models and develop new high throughput tools.

Antibiotics

Current understanding of how antibiotics induce bacterial cell death is centered on the essential bacterial cell function that is inhibited. However, antibiotic-mediated cell death is a complex, multi-factorial process that begins with the physical interaction between a drug molecule and its specific target, and involves alterations to the affected bacterium at the biochemical, molecular, regulatory and structural levels. A deeper understanding of the complexity of interactions between the drug, the target and the rest of the genome, and thus of the specific underlying mechanisms that lead to antibiotic resistance, is essential for the successful development of new treatment strategies to kill multi-drug resistant bacteria as well as strategies to prevent the emergence and spread of antibiotic resistance.

We utilize cutting edge genome-wide, experimental and bioinformatics systems approaches, of which we have recently developed several ourselves, in order to construct drug/gene interaction networks that mediate the bacterial antibiotic responses. These networks are subsequently used to direct the development of new therapeutic treatments.

Bacterial Sensing

Microbes are extremists, being found on the most inhospitable places on earth; they live on the slopes of the highest mountains; the edges of volcanoes; in deep-sea ocean vents; and they can even survive solitarily deep under ground.  Living on and inside the human body they outnumber human cells 10:1, raising the philosophical question of what defines a human being. The robustness and impressive evolutionary potential of bacteria gives them the amazing ability to deal with almost any environment they are confronted with. In the lab we uncover how bacteria overcome stress in their environment, which include drugs and the host immune system. By mapping-out stress on several different organizational levels by means of different 'omics' approaches, including RNA-Seq, Tn-Seq, Metabolomics and Experimental Evolution we create network models with the objective to predict the survival outcome of an infection, and thus whether a bacterial pathogen will survive and cause disease or will be eradicated.

Genome-wide strategies

An important goal in modern biology is to understand the relationship between genotype and phenotype; what constitutes a phenotype, which genes are involved and how do they interact to provide an efficient yet robust response to environmental change. With respect to pathogenic microorganisms, the goal of uncovering genotype-phenotype relationships is especially relevant, because the lack of understanding about the function of a significant part of the (pan-)genome currently hampering the design of novel strategies to battle infectious diseases. Developing high-throughput approaches for non-model (pathogenic) organisms that can match genotypes to phenotypes under in vitro and in vivo (infection) conditions is therefore crucial.

We developed the now widely used massively parallel sequencing technique, Tn-Seq (van Opijnen et al., 2009), and have drawn up a detailed roadmap to link genotypes to phenotypes (Van Opijnen and Camilli 2012). New work in the lab includes the development of strategies that automate the discovery of genotype-phenotype links and the placement of genes in their pathways, different microfluidics approaches, and high-throughput genome-wide genetic interaction mapping tools.

Sensing bacteria

The misuse and overuse of antibacterial agents is one of the most vexing issues facing modern medicine. There are at least two underlying reasons for this mis/overuse: 1) Rapid identification of the infectious agent is limited, often leading to prescription that are based on deductive reasoning: 2) It is mostly not possible to determine what state an infections is in, e.g. whether it will get worse and needs treatment or whether it would resolve itself. To solve the latter problem we integrate our experimental work into computational/network models (as described above). To solve the first problem we collaborate with chemists, physicists and computer scientists, both within Boston College (e.g. Drs. Ken Burch, Jianmin Gao and José Bento) and outside, to develop exciting new tools and strategies including a graphene/microfluidics-based sensing device and bacterial strain specific chemical probes.

·  Hannah M Rowe, Erik Karlsson, Haley Echlin, Ti-Cheng Chang, Lei Wang, Tim van Opijnen, Stanley B. Pounds, Stacey Schultz-Cherry, Jason W. Rosch. 2019. Bacterial Factors Required for Transmission of Streptococcus pneumoniae in Mammalian Hosts. Cell Host & Microbe in press.

·  Wood S., Zhu Z., Surujon D., Rosconi F., Ortiz-Marquez J.C. and van Opijnen T. 2019 A pan-genomic perspective on the emergence, maintenance and predictability of antibiotic resistance. Nature springer

·  Geisinger E., Vargas-Cuebas G., Mortman N.J., Syal S., Wainwright W., Lazinski D.W., Wood S., Zhu Z., Anthony J.S., van Opijnen T., Isberg R.R. 2018. The landscape of intrinsic and evolved fluoroquinolone resistance in Acinetobacter baumannii includes suppression of drug-induced prophage replication (bioRxiv doi.org/10.1101/442681)

·  Thibault D., Wood S., Jensen P. and van Opijnen T. 2018. droplet-Tn-Seq combines microfluidics with Tn-Seq identifying complex single-cell phenotypes. https://www.biorxiv.org/content/early/2018/08/13/391045

·  Zhu Z., Surujon D., Pavao A., Bento J., and van Opijnen T. 2018. Forecasting bacterial survival-success and adaptive evolution through multi-omics stress response-mapping, network analyses and machine learning. under review and available at: https://www.biorxiv.org/content/early/2018/08/09/387910

·  McCarthy K.A., Kelly M.A., Li K., Cambray S., Hosseini A.S., van Opijnen T., Gao J. 2018. Phage Display of Dynamic Covalent Binding Motifs Enables Facile Development of Targeted Antiobitics. JACS 140 (19), pp 6137–6145, doi: 10.1021/jacs.8b02461.

·  Warrier I., Ram-Mohan N., Zhu Z., Hazery A., Meyer M.M., van Opijnen T. 2018. The Transcriptional landscape of Streptococcus pneumoniae reveals a complex operon architecture and abundant riboregulation critical for growth and virulence. bioRxiv 286344; doi: https://doi.org/10.1101/286344 | PLOS Pathogens https://doi.org/10.1371/journal.ppat.1007461.

·  Jensen, P., Zhu Z. and van Opijnen T. 2017. Antibiotics disrupt coordination between transcriptional and phenotypic stress responses in pathogenic bacteria. Cell Reports, volume 20, Issue 7, 1705-1716

·  McCoy K.M., Antonio M.L. and van Opijnen T. 2017. MAGenTA; a Galaxy implemented tool for complete Tn-Seq analysis and data visualization. Bioinformatics May 11. doi: 10.1093/bioinformatics/btx320 - scripts and manual available at https://vanopijnenlab.github.io/MAGenTA/

·  van Opijnen T., Dedrick S. and Bento J. 2016. Strain dependent genetic networks for antibiotic-sensitivity in a bacterial pathogen with a large pan-genome. PLoS Pathogens. September 8, 2016, http://dx.doi.org/10.1371/journal.ppat.1005869

·  van Opijnen T., Lazinski, D and Camilli A. 2015. Genome-Wide Fitness and Genetic Interactions Determined by Tn-seq, a High-Throughput Massively Parallel Sequencing Method for Microorganisms. Current protocols in Molecular Biology Feb 2; 36: 1E.3.1-1E.3.24. PMID: 25641100.

·  *Carter R., *Wolf J., *van Opijnen T., Muller M., Obert C., Burnham C., Mann B., Li Y., Hayden R.T., Pestina T., Persons D., Camilli A., Flynn P.M.,Tuomanen E.I., Rosch J.W. 2014. Genomic analysis of pneumococci from children with sickle cell disease reveals disease-specific bacterial adaptations and deficits in current clinical interventions. Cell Host and Microbe, 15(5):587-99. PMID: 248432453 (* equal 1st author contribution).

·  van Opijnen T., Lazinski D.W., and Camilli A. 2014. Genome-wide fitness and genetic interactions determined by Tn-seq, a high throughput massively parallel sequencing method for microorganisms. Current protocols in Molecular Biology, 106:7.16.1-7.16.24.

·  van Opijnen T., and Camilli A. 2013. Transposon insertion sequencing: a new tool for systems-level analysis of microorganisms. Nature Reviews Microbiology July 11: 435-442.

·  van Opijnen T., and Camilli A. 2012. A fine scale phenotype-genotype virulence map of a bacterial pathogen. Genome Research, 22 : 2541-2551.

·  *Mann B., *van Opijnen T., Wang J., Obert C., Wang Y.D., Carter R., McGoldrick D.J., Ridout G., Camilli A., Tuomanen E.I., Rosch J.W. 2012. Control of virulence by small RNAs in Streptococcus pneumoniae. Plos Pathogens Jul;8(7):e1002788. (* equal 1st author contribution).

·  van Opijnen T., and Camilli A. 2010. Genome-wide fitness and genetic interactions determined by Tn-seq, a high throughput massively parallel sequencing method for microorganisms. Current protocols in Microbiology, Chapter 1: Unit1E.3. PMID: 21053251.

·  van Opijnen T., Bodi K.L., and Camili A. 2009. Tn-seq: high-throughput parallel sequencing for fitness and genetic interaction studies in microorganisms. Nature Methods 6(10): 767-772. PMID: 19767758.