Colleagues: Recently Tenured

JONAS S. ALMEIDA, PH.D., NCI-DCEG

Senior Investigator and Chief Data Scientist, Division of Cancer Epidemiology and Genetics, National Cancer Institute

Education: Faculty of Sciences, University of Lisbon, in Lisbon, Portugal (B.S. in plant biology); Faculty of Sciences and Technology, New University of Lisbon, Lisbon, Portugal (Ph.D. in biological engineering)

Training: Postdoctoral training in microbial ecology and in computational statistics and machine learning at University of Tennessee (Knoxville, Tennessee) and Oak Ridge National Laboratory (Oak Ridge, Tennessee)

Before coming to NIH: Professor (tenured) and Chief Technology Officer, Department of Biomedical Informatics, Stony Brook University (Stony Brook, New York)

Came to NIH: In 2019

Selected professional activities: Participation in the International Society for Computational Biology and the American Society for Clinical Pathology with a focus on hands-on conferences, hackathons, and integrative data science

Outside interests: Dragging the family outdoors; sailing and kayaking are his favorite sports

Website: https://dceg.cancer.gov/about/staff-directory/biographies/A-J/almeida-jonas (external link)

Research interests: To accelerate the investigation of the epidemiologic and genetic causes of cancer, I am developing innovative methods to advance the computational infrastructure that will allow researchers to conduct precision-prevention studies of cancer. I lead a multidisciplinary research program that combines systems biology, computational statistics, and software engineering for biomedical applications.

The intersection of computational biology and data sciences has become a new frontier for engineering-software ecosystems for precision medicine. I seek to identify consumer-facing solutions for cancer prevention that use cloud computing, web applications, and machine learning. I am exploring this interrelated computational ecosystem by developing portable software solutions that can migrate between data sources (from consumer genomics to wearable sensing devices) and different contexts of application (for patients, caregivers, and others). This system is a new realm for participative computation, one that changes individual behavior and scales collective cognition in a manner sometimes described as “The Planet of the Apps.”

As NCI-DCEG’s chief data scientist, I have the dual responsibilities of 1) leading efforts for the integrated creation, management, and analysis of data-intensive knowledge bases, establishing cost-effective scalable researcher-facing analytic infrastructure, and defining new infrastructure to extract, manage, and analyze data in a scalable way to support epidemiological research; and 2) conducting independent research to advance real-time analytics of cancer “Big Data.” My work provides support for the data and technology infrastructure used by a new prospective multicenter cohort study that will serve as an important DCEG-wide resource.

TODD S. MACFARLAN, PH.D., NICHD

Senior Investigator, Section on Mammalian Development and Evolution, National Institute of Child Health and Human Development

Education: Pennsylvania State University at State College, Pennsylvania (B.S. in biochemistry and molecular biology); University of Pennsylvania School of Medicine, Philadelphia (Ph.D. in cell and molecular biology)

Training: Postdoctoral training, The Salk Institute (La Jolla, California)

Before coming to NIH: Senior research associate, The Salk Institute

Came to NIH: In 2012 as an Earl Stadtman Investigator

Selected professional activities: Editor for Mobile DNA; class dean for the NIH Oxford-Cambridge Scholars Program for graduate students

Outside interests: Spending time with his wife and two children; playing soccer and basketball; coaching youth sports; running; hiking; listening to music

Website: https://irp.nih.gov/pi/todd-macfarlan

Research interests: At NICHD, our central mission is to ensure that every human is born healthy. Despite much progress in understanding the many ways a mother interacts with her fetus during development, we still know little about the molecular changes that promoted the emergence of placental mammals over 100 million years ago from our egg-laying relatives, or about the mechanisms that continue to drive phenotypic differences among mammals. One attractive hypothesis is that retroviruses and their endogenization into the genomes of our ancestors played an important role in eutherian evolution by providing protein-coding genes such as syncytins (derived from retroviral ENV genes that cause cell fusions in placental trophoblasts) and novel gene-regulatory nodes that altered expression networks to allow implantation and the emergence and continued evolution of the placenta.

The primary interest of my lab is to explore the impact of these endogenous retroviruses (ERVs), which account for about 10 percent of our genomic DNA, on embryonic development and on the evolution of new traits in mammals. This interest has led us to examine the rapidly evolving Kruppel-associated box zinc-finger protein (KZFP) family, the single largest family of transcription factors (TFs) in most, if not all, mammalian genomes. Our hypothesis is that KZFP gene expansion and diversification has been driven primarily by the constant onslaught of ERVs and other transposable elements (TEs) on the genomes of our ancestors as a means to transcriptionally repress them.

This hypothesis is supported by recent evidence demonstrating that most KZFPs bind TEs and that TEs and nearby genes are activated in KZFP-knockout mice. In the next several years we will continue to explore the impacts of the TE-KZFP arms race on the evolution of mammals. We will also begin a new phase exploring whether KZFPs play broader roles in genome regulation than just gene silencing and how these functions affect mammalian development.

ALISON ANNE MOTSINGER-REIF, PH.D., NIEHS

Senior Investigator and Chief, Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences

Education: Vanderbilt University, Nashville, Tennessee (B.S. in biological sciences; M.S. in applied statistics; and Ph.D. in human genetics)

Before coming to NIH: Professor, Department of Statistics, and Core Director, Bioinformatics Consulting and Service Core, North Carolina State University (Raleigh, North Carolina)

Came to NIH: In 2018

Selected professional activities: Statistical Board of Reviewing Editors, Science

Outside interests: Parenting two young boys at home

Website: https://www.niehs.nih.gov//research/atniehs/labs/bb/index.cfm (external link)

Research interests: I am interested in developing new analytic methods that can be applied to real data—such as large-scale genetics and genomics data—to find genetic factors that may predict complex disease as well as people’s responses to drugs and environmental chemicals. For example, in my research on the response to cancer chemotherapies, I use cell-line models of drug response to look for genetic factors that can predict who will respond. Being able to predict mechanisms of action helps us better understand the biology of drug response, points to biomarkers, and helps us move toward personalized medicine.

The Biostatistics and Computational Biology Branch, which I lead, carries out basic and applied research dealing with statistical issues of relevance to environmental health. We are developing new methodologies and applying existing techniques in novel ways to address environmental health problems. We also advise NIEHS intramural scientists on computational study design and on database and statistical issues through both long-term and short-term collaborations in research projects throughout the institute.

In particular, we focus on developing and applying 1) animal toxicology and carcinogenicity experiments and improved statistical methods to analyze our findings; 2) methodologies for epidemiological and clinical human studies; 3) new bioinformatics techniques for harvesting information from high-dimensional genomic, gene-expression, and proteomic data; 4) new design and analysis approaches in statistical genetics; and 5) broadly applicable statistical approaches.

PHILIP SHAW, B.M., B.CH. (SURGERY), PH.D., NHGRI

Senior Investigator and Head, Neurobehavioral Clinical Research Section, Social and Behavioral Research Branch, National Human Genome Research Institute

Education: Oxford University, Oxford, England (B.A. in experimental psychology. B.M. and B.Ch. in medicine and surgery); Kings College, London (Ph.D. in psychological medicine)

Training: Residency in internal medicine at three hospitals in London: National Hospital for Neurology and Neurosurgery (neurology), Hammersmith Hospital (clinical pharmacology, cardiology), and Royal Free Hospital (infectious diseases, HIV medicine); residency in psychiatry, Maudsley and King’s College Hospitals (London); fellowship in child psychiatry, New Children’s Hospital (Sydney, Australia)

Before coming to NIH: Lecturer in neuropsychiatry, Institute of Psychiatry (London)

Came to NIH: In 2004 as a clinical research fellow and staff clinician, Child Psychiatry Branch, National Institute of Mental Health; became an Earl Stadtman Investigator in 2011

Selected professional activities: Member of the American College of Neuropsychopharmacology

Outside interests: Doing CrossFit (half-heartedly)

Website: https://irp.nih.gov/pi/philip-shaw

Research interests: My main interest is in the genetic, social, and environmental factors that influence the development of brain and behavior, especially in attention-deficit-hyperactivity disorder (ADHD). The ultimate goal is to provide tools to aid diagnosis and prognosis, and to develop individualized treatments that reflect the diversity of the genetic, behavioral, and neurocognitive problems found in ADHD.

ADHD is a highly heritable disorder, but only a small fraction of the genes that are likely to contribute to this complex disorder have been identified. My group aims to further genomic discovery by focusing not only on the clinical features of ADHD but also on the underlying brain features. We focus on the brain’s structural and functional connections, particularly on how they develop over time. We have already identified the connections that are both heritable and associated with symptom severity and now use these features as targets for gene discovery and understanding.

Not all children with ADHD simply “grow out of the disorder” by adulthood; about a quarter of them have the full syndrome into adulthood, and many more have impairing symptoms. We have looked at the neural factors underpinning these variable adult outcomes. We found that among individuals who remitted from childhood ADHD by adulthood, the structure of cortical regions supporting attention and cognitive control veered toward typical dimensions, or “normalized,” over the course of development. By contrast, those who had persistent symptoms into adulthood showed fixed, nonprogressive deficits. We use multimodal imaging to map how adult outcomes of childhood ADHD are underpinned by changes in the brain’s structural and functional connections. We hope to translate these findings into clinically useful tools that can help predict the adolescent and adult outcome of a child’s ADHD. This work could help target clinical treatments toward those most at risk of poor outcomes.

Furthermore, in collaboration with the National Institute of Mental Health, we aim for novel interventions grounded in our understanding of the neural and genomic contributors to ADHD. For example, we are developing a study in which a child with ADHD can use real-time feedback on his or her brain underactivation during an attention-demanding task to “normalize” this activation, thus improving attentional skills and symptoms. Through this intervention we aim to greatly accelerate the normalization of brain activity that underpins remission.