Gunsalus and Piano Lab

Investigating the genetic and evolutionary mechanisms underlying early embryonic development
at NYU’s Center for Genomics and Systems Biology

The main focus of our research centers on the key broad question of how the genome directs early animal development. We have used a variety of genome-wide approaches to elucidate the genetic architecture of molecular networks underlying complex developmental programs. We are interested not only in the roles that specific genes play, but also how groups of genes and interactions of pathways work together to ensure that embryos develop properly.

Using the animal model C. elegans, we generated the first molecular map of early embryonic development (Gunsalus et al., Nature 2005). This work demonstrated a modular architecture of molecular machinery by identifying groups of genes that share phenotypes, interact with each other, and are co-expressed across a variety of conditions. In collaboration with the Oegema lab at UCSD, we extended the map to include an earlier developmental stage, the germline, which revealed how some of the same molecular components are reused for different cellular processes during different stages of development (Green et al., Cell 2011).

The integrated embryo network illustrated that interconnected molecular modules form the basic functional units within molecular networks and led us to shift our overall focus from individual genes to molecular modules and their functional relationships. An emergent theme from these early studies was the idea that the loss of function of most genes can be buffered through compensatory mechanisms. It is now clear that many diseases are caused by context-dependent genetic interactions rather than mutations in individual genes. As a result, small individual differences can lead to highly variable responses to treatments for diseases like cancer. Thus, gaining a global perspective on the functional dependencies within genetic networks is an important goal in understanding complex genetic systems, with direct relevance to human health.

Our lab is intercontinental.

The lab at NYU’s campus on Washington Square maintains close collaborations with our sister lab at NYU Abu Dhabi (NYUAD) in the United Arab Emirates. The Chemical and Functional Genomics lab at NYUAD leverages experiments on genetic networks in worms with responses to chemical treatments, using Abu Dhabi’s natural resources to identify novel anthelminthics and antimicrobials. Researchers use a high-content phenotypic approach on mammalian cells to understand responses to chemical treatments, utilizing chemical libraries and natural products from marine environments in the UAE.


Novel LOTUS-domain proteins are organizational hubs that recruit C. elegans Vasa to germ granules

In our latest paper, published in eLife, we describe MIP-1 and MIP-2, novel paralogous C. elegans germ granule components that interact with the intrinsically disordered MEG-3 protein.We propose that the MIP proteins serve as scaffolds and organizing centers for ribonucleoprotein networks within P granules that help recruit and balance essential RNA processing machinery to regulate key developmental transitions in the germ line.


Pheniqs 2.0: accurate, high-performance Bayesian decoding and confidence estimation for combinatorial barcode indexing

Systems biology increasingly relies on deep sequencing with combinatorial index tags to associate biological sequences with their sample, cell, or molecule of origin. Accurate data interpretation depends on the ability to classify sequences based on correct decoding of these combinatorial barcodes.We introduce a computationally efficient software that implements both probabilistic and minimum distance decoders and show that decoding barcodes using posterior probabilities is more accurate than available methods. Pheniqs allows fine-tuning of decoding sensitivity using intuitive confidence thresholds and is extensible with alternative decoders and new error models. Read more about Pheniqs in our BMC Bioinformatics paper.

Computer-Based Modeling Can Predict Mutation “Hotspots” and Antibody Escapers in the SARS-CoV-2 Spike Protein

Our study, published in the Journal of Molecular Biology, identifies the structural basis of spike protein mutations with stronger binding and antibody resistance, which may explain transmissibility of new COVID-19 variants.