About

Our lab aims to identify the transcriptional mechanisms that control different biological processes including immune responses, cell differentiation, and viral pathogenesis. We tackle this overarching goal by integrating high-throughput screening approaches, bioinformatics analyses, and cell biology follow-up studies. Ultimately, the lessons learned from these studies will provide insights into disease mechanisms and provide a blueprint for the design of novel therapeutic approaches.

Research topics

• Biology
• Genetics
• Computational biology
• Cell biology
• Medicine

Selected publications

• Beyond motif recognition: Specificity of human transcription factors in yeast

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-29

  article

  Abstract Transcription factors (TFs) bind DNA through sequence-specific DNA-binding domains (DBDs), yet genome-wide analyses show that TFs occupy only a small fraction of their motif occurrences. This raises the question of how TFs distinguish specific targets from the many potential sites in the genome. To investigate determinants of binding specificity beyond the cognate motif and cofactor influences, we measured the binding of 60 human TFs across the budding yeast genome. Although human TFs robustly recognized their motifs, they displayed strong selectivity in site occupancy. Nucleosome abundance explained this selectivity only in part: among the 5-20% of motif sites that were bound, a substantial fraction remained nucleosome covered. Furthermore, TFs recognizing similar motif sequences independently localized to distinct subsets of sites within different promoters. Despite the absence of human-specific cofactors in yeast, both binding stability and genomic preferences depended on largely disordered non-DBD regions. These findings suggest intrinsically disordered regions (IDRs) may therefore direct genome binding TF target recognition across evolutionarily distant genomes. Graphical Abstract

  PublisherDOI

• Flux sampling and context-specific genome-scale metabolic models for biotechnological applications

  Trends in biotechnology · 2025-08-07 · 3 citations

  review
  PublisherDOI

• Widespread variation in molecular interactions and regulatory properties among transcription factor isoforms

  Molecular Cell · 2025-03-27 · 29 citations

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Global cis-regulatory landscape of double-stranded DNA viruses

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-07-20 · 1 citations

  preprintOpen accessSenior authorCorresponding

  Most double-stranded DNA (dsDNA) viruses use the host transcriptional machinery to express viral genes for replication and immune evasion. This is mediated by viral cis-regulatory elements (CREs) regulated by host and viral transcription factors (TFs). Although some viral CREs and their regulatory mechanisms have been determined, most remain unidentified. Here, we used massively parallel reporter assays to identify ~2,000 CREs across 27 dsDNA viruses from the Adenovirus, Herpesvirus, Polyomavirus and Papillomavirus families. Viral genomes have a higher CRE density than the human genome, with most viral CREs having promoter-like features and overlapping protein coding sequences. Using saturation mutagenesis and machine learning models, we report viral CRE regulators, including SP, ETS, bZIPs, and TFs acting downstream of signal-activated pathways. Altogether, we present a comprehensive functional CRE map of human-infecting dsDNA viruses that serves as a blueprint for further studies in viral regulation, reactivation, evolution, and viral vector design.

  PublisherOA PDFDOI

• MACAW: a method for semi-automatic detection of errors in genome-scale metabolic models

  Genome biology · 2025-03-28 · 4 citations

  articleOpen accessSenior author

  Genome-scale metabolic models (GSMMs) are used to predict metabolic fluxes, with applications ranging from identifying novel drug targets to engineering microbial metabolism. Erroneous or missing reactions, scattered throughout densely interconnected networks, are a limiting factor in these applications. We present Metabolic Accuracy Check and Analysis Workflow (MACAW), a suite of algorithms that helps to identify and visualize errors at the level of connected pathways, rather than individual reactions. We show how MACAW highlights inaccuracies of varying severity in manually curated and automatically generated GSMMs for humans, yeast, and bacteria and helps to identify systematic issues to be addressed in future model construction efforts.

  PublisherOA PDFDOI

• Systematic Discovery of Pathogen Effector Functions across Human Pathogens and Pathways

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-11-17 · 1 citations

  preprintOpen access

  SUMMARY Pathogens deploy effector proteins to exploit host cell biology, and most pathogen open reading frames (ORFs) are rapidly evolving and lack functional annotation. We developed the eORFeome, a scalable functional genomics platform encompassing 3,835 effector ORFs from diverse viruses, bacteria, and parasites. High-throughput barcoded screens across NFκB, apoptosis, p53, cGAS–STING and MHC-I pathways revealed functions for hundreds of uncharacterized eORFs, unexpected new activities for known effectors, and distinct pathway-specific functions encoded by single ORFs. Illustrating the power of the approach, we identify HHV6A U14 as a p53 antagonist, HHV7 U21 as a dual-function STING antagonist and MHC-I antigen display inhibitor, and adenoviral 13.6K/i-leader protein as a de novo evolved TAP inhibitor that suppresses MHC-I display. These results establish a general framework for systematic effector annotation, uncover new mechanisms of host–pathogen interaction across kingdoms, and highlight pathogen effectors as a versatile toolkit for rewiring and probing human cellular pathways.

  PublisherDOI

• Viral transcriptional regulators extensively rewire host pathways through diverse mechanisms

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-12-02 · 1 citations

  articleOpen accessSenior authorCorresponding

  Viral transcriptional regulators (vTRs) reprogram host gene regulatory networks to promote replication, persistence, and immune evasion. Despite the identification of hundreds of vTRs in human viruses, how they rewire host pathways remains unclear. Here, we systematically profiled 95 vTRs from diverse human viruses across multiple functional assays. vTRs perturb immune, cell proliferation/death, and signaling pathways through various mechanisms; some bind DNA directly, others cooperate or antagonize human transcription factors (hTFs), and some remodel chromatin. vTRs can act as activators or repressors and recruit similar but not identical repertoires of proteins as hTFs. These findings reveal vTRs as versatile transcriptional modulators that converge on conserved host "pressure points" while diversifying across pathways to promote viral replication and persistence. Notably, many vTR dysregulate genes within autoimmune, neurological, and cardiovascular risk loci, revealing mechanistic links to disease. Together, we provide a comprehensive resource for understanding and targeting viral control of human transcription.

  PublisherDOI

• Understanding the logic and grammar of cis-regulatory elements

  Nature Reviews Genetics · 2025-05-07 · 5 citations

  review1st authorCorresponding
  PublisherDOI

• A Roadmap for the Future of Systems Biology in Cancer Research

  Cancer Research · 2025-10-15 · 1 citations

  articleOpen access

  Cancer systems biology seeks to understand how cancer arises as a system of interconnected molecules, cells, and tissues, with the goal of understanding, predicting, and controlling the disease. In the last decade, the field has rapidly grown as advances in experimental, computational, and analytic technologies have improved our ability to capture and recapitulate the complexities of cancer at multiple scales. However, the field's promise to understand how specific molecular changes give rise to altered cancer outcomes remains incompletely fulfilled. Fortunately, an opportunity exists to accelerate progress by better coordinating modeling and data-gathering efforts across the cancer systems biology community. This will create the foundation for building accurate, multiscale cancer models that can better predict and identify improved therapeutic interventions. Here, we outline some of the current challenges in cancer systems biology research, how they can be addressed, and actions that the community can take to accelerate progress in the field. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .

  PublisherOA PDFDOI

• Applications of high-throughput reporter assays to gene regulation studies

  Current Opinion in Structural Biology · 2025-06-27 · 3 citations

  reviewOpen accessSenior authorCorresponding
  PublisherOA PDFDOI

Recent grants

• Structure and Function of Immune Gene Regulatory Networks

  NIH · $3.6M · 2018–2028

• Delineation of a cytokine gene regulatory network and rewiring in disease

  NIH · $498k · 2015–2019

• Rewiring of regulatory networks in breast cancer by transcription factor isoforms

  NIH · $4.1M · 2018–2024

Frequent coauthors

• María Laura Gabelloni

  Consejo Nacional de Investigaciones Científicas y Técnicas

  35 shared

• Jorge Geffner

  University of Buenos Aires

  30 shared

• Analía Trevani

  Academia Nacional de Medicina

  29 shared

• Albertha J.M. Walhout

  University of Massachusetts Chan Medical School

  20 shared

• Mónica Vermeulen

  Consejo Nacional de Investigaciones Científicas y Técnicas

  19 shared

• David E. Hill

  Dana-Farber Cancer Institute

  16 shared

• Clarissa Stephanie Santoso

  Boston University

  16 shared

• Marc Vidal

  16 shared

Labs

• Fuxman Bass labPI

Education

• Ph.D. in Biology

  University of Buenos Aires

  2011

• B.S. Biology

  University of Buenos Aires

  2006