Colleagues: Recently Tenured

SHANSHAN ZHAO, PH.D., NIEHS

Senior Investigator, Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences (NIEHS)

Education: Peking University, Beijing, China (B.S. in applied mathematics); University of Iowa, Iowa City, Iowa (M.S. in biostatistics); University of Washington, Seattle (Ph.D. in biostatistics)

Training: Postdoctoral Research Fellow at the Fred Hutchinson Cancer Research Center, Seattle (2012-2014)

Came to NIH: In 2015 as a Tenure-Track Investigator, NIEHS

Outside interests: Gardening; painting; spending time with family

Website: https://irp.nih.gov/pi/shanshan-zhao

Research interests: My main research interest is to develop novel statistical methods to discover how humans’ interaction with the physical and social environments influence their health and well-being. By developing and applying powerful statistical tools, my group aims to elucidate the etiology of various health outcomes through assessing risk factors and further building robust risk prediction models.

Toward achieving this overarching goal, we use a dynamic approach by integrating statistical methodological developments and collaborative population-based studies through two aspects. First, we develop general statistical methods for disease risk assessment, with a focus on methods for time-to-event outcomes. Many biological studies follow time-to-occurrences for multiple events, such as the onset of diseases or death, which motivated us to develop joint models for multivariate time-to-event outcomes (Lifetime Data Anal 24:3-27, 2018; J Am Stat Assoc 116:1330-1345, 2021). These methods enable researchers to take a lifetime history of related health outcomes into account for accurate prediction of future diseases.

Second, we develop statistical methods for cancer and environmental epidemiological studies, motivated by our collaborations with other NIEHS researchers. An area of special interest is developing statistical methods for various issues in environmental mixtures analysis to account for the synergy and interaction between components of high dimensionality and high correlation (J Expo Sci Environ Epidemiol 30:149-159, 2020).

JINWEI ZHANG, PH.D, NIDDK

Senior Investigator, Laboratory of Molecular Biology (LMB), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)

Education: Peking University, Beijing, China (B.S. in biochemistry and molecular biology); University of Wisconsin at Madison, Madison, Wisconsin (Ph.D. in biomolecular chemistry)

Training: Postdoctoral training in molecular biophysics, Howard Hughes Medical Institute, Chevy Chase, Maryland; the Fred Hutchinson Cancer Center, Seattle (2009-2011); and the National Heart, Lung, and Blood Institute (2011-2015)

Before coming to NIH: Research Associate, Howard Hughes Medical Institute, and Fred Hutchinson Cancer Center

Came to NIH: In 2011 as a Research Fellow at the National Heart, Lung, and Blood Institute, then in 2015 as a Stadtman Tenure-Track Investigator in the LMB, NIDDK

Outside interests: Traveling with family; table tennis

Website: https://www-mslmb.niddk.nih.gov/zhang/zhanglab.html

Research interests: My lab focuses on understanding the structure, recognition, and mechanisms of host and viral noncoding RNAs (ncRNAs). The COVID-19 pandemic occurred against the backdrop of a rapid expansion of our knowledge of ncRNAs—the “dark matter” of our genome. The extraordinary efficacy of the mRNA vaccines was in part enabled by decades of basic research into RNA structure, stability, modification, and translation, attesting to the critical importance of fundamental RNA research to public health.

However, wider application of ncRNAs is still severely limited by our rudimentary, largely descriptive understanding of these versatile molecules. Despite clear evidence of the importance of RNA structure to function, there is little 3D structural information available for most complex ncRNAs, which precludes our understanding of their mechanisms of action. My lab aims to bridge this critical gap in knowledge by elucidating the fundamental principles of RNA structure, dynamics, and interaction, using multidisciplinary approaches that combine atomic-resolution structural analyses with advanced biochemical, biophysical, and single-molecule methods.

Our recent work has uncovered how two ncRNA archetypes—the tRNA and double-stranded RNAs (dsRNAs)—mediate stress responses, viral replication, and antiviral immunity. For tRNAs, we first visualized and elucidated how widespread bacterial ncRNAs termed T-box riboswitches recognize the 3D structure and aminoacylation state of their tRNA partners using complex, form-fitting RNA-RNA interactions, to sense and respond to amino acid starvation (Nat Struct Mol Biol 26:1094–1105, 2019; Nat Struct Mol Biol 26:1114–1122, 2019). We uncovered and elucidated a novel form of host tRNA parasitism by retroviruses including HIV-1, where the major viral protein Gag directly and specifically binds host tRNAs to slow down Gag migration toward the plasma membrane, in order to optimize viral replication (Cell Host Microbe 29:1421-1436, 2021).

For dsRNAs, we revealed how adenovirus virus-associated RNAs function as decoys of dsRNAs to disable human antiviral protein PKR (Nat Commun 10:2871, 2019), and how the widely used S9.6 monoclonal antibody picks out DNA-RNA hybrids in R-loops from the abundant dsRNAs (Nat Commun 13:1641, 2022).

These research findings and our future ones will open new RNA-based avenues to treat bacterial and viral infections and autoimmune and inflammatory diseases as well as to spur novel biotechnological applications including RNA sensors, switches, and catalysts.

KATHERINE L. GRANTZ, M.D., M.S., NICHD

Senior Investigator, Epidemiology Branch, Division of Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD)

Education: Duke University, Durham, North Carolina (B.S. in biology); Virginia Commonwealth University, Richmond, Virginia (M.D.); NIH Clinical Research Training Program at the University of Pittsburgh, Pittsburgh (M.S. in Clinical Research)

Training: Obstetrics and Gynecology residency, University of North Carolina, Chapel Hill (2000-2004); Maternal-Fetal Medicine fellowship, Magee-Womens Hospital, University of Pittsburgh (2006-2009); IRTA postdoctoral fellowship, Epidemiology Branch, Division of Population Health Research, NICHD (2009-2011)

Before coming to NIH: Maternal-Fetal Fellow at Magee-Womens Hospital, University of Pittsburgh

Came to NIH: In 2009 as an IRTA postdoctoral fellow at NICHD, then as a Research Fellow in 2011

Outside interests: Running; going on nature walks with my husband and three daughters; attending our children’s sports games and dance recitals; traveling; reading

Website: https://irp.nih.gov/pi/katherine-grantz

Research interests: I am an obstetrician and maternal-fetal medicine specialist who leads a research program on clinical management of pregnancy complications, including aberrant fetal growth, when to deliver a high-risk pregnancy, and labor and delivery management. Findings from my research have informed over 28 national and international clinical guidelines with evidence-based practice recommendations. An expert in the field of fetal growth, I am responsible along with a multidisciplinary team for an effort that generated fetal-growth percentile charts in a diverse U.S. population for clinical practice (Am J Obstet Gynecol 213:449.e1-449.e41, 2015; Am J Obstet Gynecol 226:576-587, 2022). I led development of a first-ever fetal-growth velocity calculator (Am J Obstet Gynecol 219:285.e1-285.e36, 2018; Am J Obstet Gynecol 227:916-922, 2022) for clinical use as well as development of twin fetal-growth percentile charts (Am J Obstet Gynecol 215:221.e1-221.e16, 2016).

My work addresses the clinical challenge of differentiating constitutionally small-for-gestational-age from fetal-growth restriction, which is associated with increased morbidity and mortality. An emerging area uses 3D ultrasound that provides more detail than the standard 2D ultrasound in determining fetal fat and lean tissue volumes. My group is among the first to have accumulated a large collection of fetal 3D volumes from a racially and ethnically diverse pregnancy cohort with repeat ultrasounds spanning the length of gestation in the Fetal 3D Study (Am J Epidemiol, in press). Detection of fetal volume and body-composition changes in fetuses that are growth restricted or growing excessively has potential to inform clinical management, such as increased antenatal monitoring to prevent stillbirth or changes in maternal nutrition to prevent excess fetal fat accumulation.

My team is also addressing labor and delivery management to prevent medically unnecessary cesarean section deliveries, an issue declared as a national priority because cesarean section delivery is a risk factor for severe maternal morbidity and mortality (Obstet Gynecol 122:33-40, 2013; Obstet Gynecol 124:57-67, 2014; Obstet Gynecol 131:345-353, 2018). More recently, I lead a multicenter randomized clinical trial in the Study of Pregnancy And Neonatal Health (SPAN) to determine the optimal timing of delivery for uncontrolled gestational-diabetes-complicated pregnancies to prevent neonatal complications.