Today is the launch of American Society of Nephrology’s (ASN) annual kidney week. Our team will be joining over 10,000 kidney professional from all over the world to engage in discussions, exchange knowledge, and learn about the latest advances in science and medicine. We also have the opportunity to share our work alongside these outstanding professionals. Here’s where you can find us at #kidneywk.

Last week our team attended the American Society of Human Genetics (ASHG) Annual Conference in Los Angeles to learn about the latest research in human genetics. We attended several interesting lectures where we were able to gain knowledge from our genomics colleagues around the world. We had a great time and are looking forward to next year.

Michelle McNulty and Matt Sampson were psyched to contribute to this paper on APOL1 regulation via RNA-editing led by Cristian Riella, David Friedman, and Martin Pollak. We analyzed APOL1 RNA-editing in the glomeruli of NEPTUNE patients to support their meticulous, in vitro insights.

We couldn't be prouder of Ana Onuchic-Whitford, MD who received the NKI-NEPTUNE Pilot Project Award and Grant from NephCure Kidney International for her work on "Discovering the landscape of allele-specific expression in the human kidney” at Boston Children’s Hospital and Brigham and Women’s Hospital Nephrology. Congratulations Ana!

At the Sampson Lab, we love our community and we want to keep you updated on all the great things our team is doing. We’re excited to announce that in addition to news features on our website and Matt’s @kidneyomicsamps Twitter, we are also launching three new social media accounts on Facebook, Instagram, and Twitter! We plan on sharing educational information about the kidney, highlights of our team members, and updates on our research and the BIGKiDs study.

We hope you consider joining us on your preferred social media and encourage you to share this with your friends, family, and co-workers. We look forward to connecting with you and seeing our community grow!

Facebook | Instagram | Twitter

We are so excited to introduce the newest member of our team, Gina Kalkar. She recently graduated from the University of Minnesota where she worked with Dr. James Luby researching apple breeding and genetics. In the Sampson Lab, Gina will be contributing to our kidneyomics efforts as a Research Assistant in the wet lab. Stay tuned to hear from Gina in the upcoming weeks!

Pediatric steroid-sensitive nephrotic syndrome (pSSNS) is the most common childhood glomerular disease. Previous genome-wide association studies (GWAS) identified a risk locus in the HLA Class II region and three additional signals. But the genetic architecture of pSSNS, and its genetically driven pathobiology, is largely unknown. We conducted a multi-population GWAS meta-analysis in 38,463 participants (2,440 cases) and population specific GWAS, discovering twelve significant associations (eight novel).

A special thank you to our collaborators at Boston Children’s Hospital, Harvard Medical School, Broad Institute, and Brigham and Women’s Hospital for helping make our research and presentation possible.

Thank you to the International Pediatric Nephrology Association (IPNA) for having us present at the 2022 IPNA Congress, which seeks to better equip participants, “to recognize, assess, investigate, and diagnose children presenting with kidney disease and then be better able to plan and manage their treatment.”

As the Summer draws to an end, the time has come to say goodbye to Catherine and Eric, who have spent this summer working as Summer Research Assistants in our lab. We are so grateful for the time that you’ve spent with us. Not only have you grown as researchers, but you have left an impact on our team and kidneyomics efforts. Have a great academic year and we hope to see you again soon!

Although it is bittersweet to see her go, we are so proud of Alex for starting her Masters in Computational Biology at the Harvard T.H. Chan School of Public Health. Alex has made significant contributions to the Sampson Lab during her time here and working on the computational team. She has a bright future ahead and we look forward to seeing what she accomplishes. Best of luck Alex!

Apolipoprotein L1 (APOL1)-associated focal segmental glomerulosclerosis (FSGS) is the dominant form of FSGS in Black individuals. There are no targeted therapies for this condition, in part because the molecular mechanisms underlying APOL1's pathogenic contribution to FSGS are incompletely understood. Studying the transcriptomic landscape of APOL1 FSGS in patient kidneys is an important way to discover genes and molecular behaviors that are unique or most relevant to the human disease.

Expression quantitative trait locus (eQTL) studies illuminate genomic variants that regulate specific genes and contribute to fine-mapped loci discovered via genome-wide association studies (GWAS). Efforts to maximize their accuracy are ongoing. Using 240 glomerular (GLOM) and 311 tubulointerstitial (TUBE) micro-dissected samples from human kidney biopsies, we discovered 5,371 GLOM and 9,787 TUBE eQTLs by incorporating kidney single-nucleus open chromatin data and transcription start site distance as an “integrative prior” for Bayesian statistical fine mapping.

We are so excited to be hosting our inaugural Summer Research Assistants at the Sampson Lab, Catherine Channell and Eric Sakkas. We are looking forward to having these outstanding students in our lab as well as seeing how they grow and are able contribute to our research efforts. Get to know more about Catherine and Eric.

Chromatin accessibility assays are central to the genome-wide identification of gene regulatory elements associated with transcriptional regulation. However, the data have highly variable quality arising from several biological and technical factors. To surmount this problem, we use the predictability of open-chromatin peaks from DNA sequence-based machine-learning models to evaluate and refine chromatin accessibility data. Our framework, gapped k-mer SVM quality check (gkmQC), provides the quality metrics for a sample based on the prediction accuracy of the trained models.