Harshavardhan Doddapaneni

Biallelic loss of MUTYH causes MUTYH-associated polyposis (OMIM: 608456), characterized by the oxidative base-excision-repair mutational signature SBS18, but whether the roughly 1 in 100 individuals who carry a single pathogenic MUTYH allele acquire a mutational phenotype in normal tissue is unknown. Analysis of multi-tissue sequencing of 25 donors from the Somatic Mosaicism across Human Tissues (SMaHT) Network identified three heterozygous carriers of MUTYH p.Gly396Asp. One carrier showed SBS18 across multiple normal tissues, ranking first of 25 donors with three independent methods and remaining significant after adjustment for tissue, without a somatic, epigenetic or transcriptional second hit at the MUTYH locus. A single hypomorphic MUTYH allele can therefore leave a measurable oxidative mutational mark in healthy tissues, supporting tissue-level mutational readouts as an approach to cancer risk stratification within this common carrier state.

Summary: Human spaceflight has historically been led by government agencies, but the emergence of commercial organizations is enabling broader participation and new research opportunities. In this study, we present a comprehensive molecular characterization of early human responses to spaceflight, leveraging multi-omics data across the first three weeks of two commercial missions. Biospecimens from six individuals, four from the Axiom 2 mission (10 days) and two from Axiom 3 (21 days), were analyzed using single-cell and bulk RNA sequencing, alongside proteomic profiling. Individual and integrative analyses of these datasets reveal systemic changes in cell types, transcripts, and proteins related to immune regulation, osteoclast differentiation, NF-κB signaling, and blood homeostasis pathways. Importantly, several of the detected pathways align with physiological patterns observed in longer-duration missions. This work establishes a foundational resource for understanding early adaptation to spaceflight at the cellular and molecular levels, providing insights to reduce future space-travel health risks. Highlights: First integrated multi-omics analysis using single-cell, bulk RNA sequencing and proteomic profiling of early human spaceflight responses across two commercial missions (Ax-2 and Ax-3). Distinct PBMC clustering was observed across Ax-2 and Ax-3, and post-flight samples. It showed fewer monocytes, dendritic cells, and megakaryocytes with increased naïve CD4⁺ and cytotoxic T cells. Multi-omics integration identifies shared biological signatures, including osteoclast differentiation, metabolic stress, and coagulation changes. These findings lay the foundation for developing countermeasures to protect immune, skeletal, and vascular health during spaceflight.

Summary: Spaceflight introduces environmental stressors that can alter human microbiomes and immune responses. We analyzed 259 biospecimens from six astronauts across two commercial ISS missions: Axiom 2 (10-day mission) and Axiom 3 (21-day mission). Samples included saliva, stool, urine, and body swabs from 10 anatomical sites, profiled via 16S ribosomal RNA (rRNA) gene sequencing. Gut and oral microbiomes remained stable, while skin-associated communities exhibited transient diversity shifts post-flight. Taxonomic analysis revealed individual and site-specific patterns as well as a possible microbial acquisition from the ISS/space-flight environment and microbiome exclusivity. Cytokine profiling from single cell data indicated immune activation, with IL-32 and IL-16 elevated in Axiom 2 and Axiom 3, coinciding with microbial changes. These findings provide an integrated view of microbiome individuality, exclusivity and immune dynamics during two short-duration commercial spaceflights of three weeks, informing strategies for crew health on future long-duration missions.

Detecting somatic mutations in normal tissues is challenging due to sequencing errors and the low allele fractions of post-zygotic variants. Duplex sequencing greatly reduces errors and can detect mutations at any allele fraction, but systematic, cross-platform comparisons are lacking. We present a comprehensive benchmarking of six duplex sequencing technologies used by the SMaHT Network: CODEC, CompDuplex-seq, HiDEF-seq, NanoSeq, ppmSeq, and VISTA-seq. We evaluated their performance using cord blood DNA, a tumor-normal cell line mixture, and homogenates from six human tissues. Each method shows distinct profiles in genomic footprint, sensitivity, and cost. Despite differences in library construction and sequencing platforms, estimates of mutation rates and mutational signatures are highly concordant. Integration with ultra-deep whole-genome sequencing shows that duplex approaches sensitively capture mutations and signatures beyond embryonic or clonally expanded variants. These results provide a foundation for selecting duplex methods and interpreting their data, enabling scalable single-molecule analyses of somatic mutation landscapes.

Single-cell DNA sequencing offers a powerful means of studying somatic mosaicism but requires careful analysis to mitigate DNA amplification-related artifacts. We performed primary template-directed amplification (PTA) and sequencing of 102 nuclei from postmortem lung and colon tissues of a 74-year-old male. Single-cell mutation burdens and spectra were validated by duplex sequencing and revealed heterogeneity across organs and cells, including signatures of APOBEC activity and tobacco exposure. Cells from both tissues exhibited chromosomal aneuploidies, loss of chromosome Y, and chromosomal rearrangements including rearrangements of the T-cell receptor loci indicative of T-cells. Shared embryonic mutations between cells enabled reconstruction of cellular ancestries from the zygote, which were validated by bulk sequencing. Collectively, we demonstrate a comprehensive approach for single-cell genomics that yields an expansive view of diverse somatic mutation types from development through aging across diverse tissues—insights that are obscured in bulk sequencing and only partially captured by other single-cell methods.

Mosaic mutations in normal tissues occur at low variant allele fractions (VAFs), complicating detection. To benchmark strategies, the SMaHT Network created a cell-line mixture (1:49) and produced ultra-deep whole-genome sequencing using short and long reads (five centers, 180–500× each). We assembled a reference of 44,008 mosaic SNVs and 2,059 Indels, cross-validation between platforms to expose limits of short-read analysis. We also partitioned the genome by mappability to examine the impact of genomic context, added a negative reference set, and accounted for culture-derived mutations. When seven institutions applied eleven algorithms to mixture data, call sets were largely discordant across tools and replicates, partly reflecting stochastic presence of low-VAF mutations in biological replicants. For >2% VAF SNVs, sensitivity and precision approached ∼80% at ≥300×, with little gain from additional sequencing. This work provides a comprehensive framework for reliable detection of low-VAF mutations in non-cancer tissues and a valuable resource for the community.

To better characterize the potential biological mechanisms underlying insulin resistance (IR) and dementia, we derive cross-population and population specific polygenic scores [PSs] for fasting insulin and IR-related partitioned PSs [pPSs]. We conduct a cross-sectional study of the associations of these genetic scores with neurological outcomes in >17k participants (36% men, mean age 55 yrs) from the Trans-Omics for Precision Medicine (TOPMed) program (50% Non-Hispanic White, 23% Black/African American, 21% Hispanic/Latino American, and 4% Asian American). We report significant negative associations (P < 0.002) of the cross-population (P = 1.3 × 10^-5) and European (P_EA = 3.0 × 10^-8) fasting insulin PSs with total cranial volume, and of a metabolic syndrome European PS with general cognitive function (B_EA = -0.13, P_EA = 0.0002) and lateral ventricular volume (B_EA = 0.09, P_EA = 0.002). We identify suggestive negative associations (P < 0.007) of metabolic syndrome and obesity pPSs with general cognitive function, and of lipodystrophy pPSs with total cranial volume. A higher genetic predisposition to IR is associated with lower brain size, and a genetic predisposition to specific IR-related type 2 diabetes subtypes, such as metabolic syndrome and mechanisms of IR mediated through obesity and lipodystrophy, is potentially involved in cognitive decline.

Background: Respiratory syncytial virus (RSV) is a leading cause of severe lower respiratory tract infections in infants and poses significant risks to immuno-compromised individuals and the elderly. The COVID-19 pandemic disrupted typical RSV seasonality, leading to unusual patterns of viral circulation and resurgence. However, the impact of these disruptions on RSV evolutionary dynamics remains incompletely understood. This study aimed to investigate the evolutionary dynamics of RSV in Houston, Texas, across pre-pandemic (November 2015 – March 2020), pandemic (April 2021 – June 2023), and post-pandemic periods (July 2023 – December 2024), focusing on genetic diversity, lineage dynamics, and selective pressures. Methods: In one of the largest comprehensive RSV genomic evolutionary analyses, we analyzed 1,344 RSV-positive respiratory samples collected from children seeking outpatient care or hospitalized with acute respiratory infections between November 2015 to February 2024, which is one of the largest comprehensive RSV genomic evolutionary analyses. We successfully sequenced the whole genomes for 87.5% of the isolates (606 RSV/A and 570 RSV/B). To assess genetic diversity, lineage dynamics, and selective pressures, we conducted phylogenetic analysis, variant calling, and dN/dS ratio calculations. We clustered variant prevalence patterns using k-means and used Shannon entropy to quantify genetic variability. We performed statistical regression analyses to evaluate the accumulation of variants and their impact on gene expression. Results: RSV circulation in Houston was markedly disrupted during the pandemic, with an absence of cases in 2020-2021 followed by off-season surges from 2021 to 2023. Phylogenetic analysis revealed distinct lineage dynamics, with RSV/A showing remarkable persistence of specific lineages such as A.D.1 and emergence of new lineages including A.D.3. In contrast, RSV/B underwent a dramatic restructuring, dominated by the B.D.E.1 lineage in the pandemic and post-pandemic period. Bayesian molecular clock analysis estimated nucleotide substitution rates of 9.37 × 10 ⁻⁴ (95% HPD: 7.99 × 10 ⁻⁴ − 1.07 × 10 ⁻³ ) substitutions/site/year for RSV/A and 9.79 × 10 ⁻⁴ (95% HPD: 8.96 × 10 ⁻⁴ – 1.06 × 10 ⁻³ ) for RSV/B. The time to the most recent common ancestor (tMRCA) of the dominant RSV/A clade, A.D.1, is dated to approximately 2010 (95% HPD: 2008–2011), while for RSV/B the dominant clade- B.D.E.1 traces back to 2002 (95% HPD: 1997–2007). Despite reduced lineage diversity, RSV/B accumulated non-synonymous variants at nearly twice the rate of RSV/A, notably in the M2-2 gene, reflecting high within-lineage variability. The significant increase in non-synonymous mutations in M2-2 in RSV/B during the pandemic and post-pandemic time phases strongly correlated with increased transcriptional activity. Additionally, antigenic site mutations in the F protein, particularly in RSV/B, were also observed, with implications for immune evasion and effectiveness of the current monoclonal antibody immunoprophylaxis intervention. Conclusions: The COVID-19 pandemic significantly impacted RSV evolution, leading to reduced genetic diversity during the pandemic and the emergence of novel lineages post-pandemic. RSV/B exhibited more dynamic evolutionary changes, particularly in the M2-2 gene, suggesting potential adaptive advantages. Ongoing genomic surveillance coupled with functional studies is crucial for monitoring variants and assessing the clinical impact of these mutations on transmission, disease severity, fitness, and long-term effectiveness of RSV prevention and treatment strategies.

The Genome in a Bottle Consortium (GIAB), hosted by the National Institute of Standards and Technology (NIST), is developing new matched tumor-normal samples, the first explicitly consented for public dissemination of genomic data and cell lines. Here, we describe a comprehensive genomic dataset from the first individual, HG008, including DNA from an adherent, epithelial-like pancreatic ductal adenocarcinoma (PDAC) tumor cell line and matched normal cells from duodenal and pancreatic tissues. Data for the tumor-normal matched samples comes from seventeen distinct state-of-the-art whole genome measurement technologies, including high depth short and long-read bulk whole genome sequencing (WGS), single cell WGS, Hi-C, and karyotyping. These data will be used by the GIAB Consortium to develop matched tumor-normal benchmarks for somatic variant detection. We expect these data to facilitate innovation for whole genome measurement technologies, de novo assembly of tumor and normal genomes, and bioinformatic tools to identify small and structural somatic variants. This first-of-its-kind broadly consented open-access resource will facilitate further understanding of sequencing methods used for cancer biology.