Longitudinal multi-omics analysis of host microbiome architecture and immune responses during short-term spaceflight.

Maintenance of astronaut health during spaceflight will require monitoring and potentially modulating their microbiomes. However, documenting microbial shifts during spaceflight has been difficult due to mission constraints that lead to limited sampling and profiling. Here we executed a six-month longitudinal study to quantify the high-resolution human microbiome response to three days in orbit for four individuals. Using paired metagenomics and metatranscriptomics alongside single-nuclei immune cell profiling, we characterized time-dependent, multikingdom microbiome changes across 750 samples and 10 body sites before, during and after spaceflight at eight timepoints. We found that most alterations were transient across body sites; for example, viruses increased in skin sites mostly during flight. However, longer-term shifts were observed in the oral microbiome, including increased plaque-associated bacteria (for example, Fusobacteriota), which correlated with immune cell gene expression. Further, microbial genes associated with phage activity, toxin-antitoxin systems and stress response were enriched across multiple body sites. In total, this study reveals in-depth characterization of microbiome and immune response shifts experienced by astronauts during short-term spaceflight and the associated changes to the living environment, which can help guide future missions, spacecraft design and space habitat planning.

Genome and clonal hematopoiesis stability contrasts with immune, cfDNA, mitochondrial, and telomere length changes during short duration spaceflight.

Background: The Inspiration4 (I4) mission, the first all-civilian orbital flight mission, investigated the physiological effects of short-duration spaceflight through a multi-omic approach. Despite advances, there remains much to learn about human adaptation to spaceflight's unique challenges, including microgravity, immune system perturbations, and radiation exposure. Methods: To provide a detailed genetics analysis of the mission, we collected dried blood spots pre-, during, and post-flight for DNA extraction. Telomere length was measured by quantitative PCR, while whole genome and cfDNA sequencing provided insight into genomic stability and immune adaptations. A robust bioinformatic pipeline was used for data analysis, including variant calling to assess mutational burden. Result: Telomere elongation occurred during spaceflight and shortened after return to Earth. Cell-free DNA analysis revealed increased immune cell signatures post-flight. No significant clonal hematopoiesis of indeterminate potential (CHIP) or whole-genome instability was observed. The long-term gene expression changes across immune cells suggested cellular adaptations to the space environment persisting months post-flight. Conclusion: Our findings provide valuable insights into the physiological consequences of short-duration spaceflight, with telomere dynamics and immune cell gene expression adapting to spaceflight and persisting after return to Earth. CHIP sequencing data will serve as a reference point for studying the early development of CHIP in astronauts, an understudied phenomenon as previous studies have focused on career astronauts. This study will serve as a reference point for future commercial and non-commercial spaceflight, low Earth orbit (LEO) missions, and deep-space exploration.

ArCH: improving the performance of clonal hematopoiesis variant calling and interpretation.

MOTIVATION: The acquisition of somatic mutations in hematopoietic stem and progenitor stem cells with resultant clonal expansion, termed clonal hematopoiesis (CH), is associated with increased risk of hematologic malignancies and other adverse outcomes. CH is generally present at low allelic fractions, but clonal expansion and acquisition of additional mutations leads to hematologic cancers in a small proportion of individuals. With high depth and high sensitivity sequencing, CH can be detected in most adults and its clonal trajectory mapped over time. However, accurate CH variant calling is challenging due to the difficulty in distinguishing low frequency CH mutations from sequencing artifacts. The lack of well-validated bioinformatic pipelines for CH calling may contribute to lack of reproducibility in studies of CH. RESULTS: Here, we developed ArCH, an Artifact filtering Clonal Hematopoiesis variant calling pipeline for detecting single nucleotide variants and short insertions/deletions by combining the output of four variant calling tools and filtering based on variant characteristics and sequencing error rate estimation. ArCH is an end-to-end cloud-based pipeline optimized to accept a variety of inputs with customizable parameters adaptable to multiple sequencing technologies, research questions, and datasets. Using deep targeted sequencing data generated from six acute myeloid leukemia patient tumor: normal dilutions, 31 blood samples with orthogonal validation, and 26 blood samples with technical replicates, we show that ArCH improves the sensitivity and positive predictive value of CH variant detection at low allele frequencies compared to standard application of commonly used variant calling approaches. AVAILABILITY AND IMPLEMENTATION: The code for this workflow is available at: https://github.com/kbolton-lab/ArCH.

Sequencing by avidity enables high accuracy with low reagent consumption.

We present avidity sequencing, a sequencing chemistry that separately optimizes the processes of stepping along a DNA template and that of identifying each nucleotide within the template. Nucleotide identification uses multivalent nucleotide ligands on dye-labeled cores to form polymerase-polymer-nucleotide complexes bound to clonal copies of DNA targets. These polymer-nucleotide substrates, termed avidites, decrease the required concentration of reporting nucleotides from micromolar to nanomolar and yield negligible dissociation rates. Avidity sequencing achieves high accuracy, with 96.2% and 85.4% of base calls having an average of one error per 1,000 and 10,000 base pairs, respectively. We show that the average error rate of avidity sequencing remained stable following a long homopolymer.

Enhancer-driven 3D chromatin domain folding modulates transcription in human mammary tumor cells.

The genome is organized in functional compartments and structural domains at the sub-megabase scale. How within these domains interactions between numerous cis-acting enhancers and promoters regulate transcription remains an open question. Here, we determined chromatin folding and composition over several hundred kb around estrogen-responsive genes in human breast cancer cell lines after hormone stimulation. Modeling of 5C data at 1.8 kb resolution was combined with quantitative 3D analysis of multicolor FISH measurements at 100 nm resolution and integrated with ChIP-seq data on transcription factor binding and histone modifications. We found that rapid estradiol induction of the progesterone gene expression occurs in the context of preexisting, cell type-specific chromosomal architectures encompassing the 90 kb progesterone gene coding region and an enhancer-spiked 5' 300 kb upstream genomic region. In response to estradiol, interactions between estrogen receptor α (ERα) bound regulatory elements are reinforced. Whereas initial enhancer-gene contacts coincide with RNA Pol 2 binding and transcription initiation, sustained hormone stimulation promotes ERα accumulation creating a regulatory hub stimulating transcript synthesis. In addition to implications for estrogen receptor signaling, we uncover that preestablished chromatin architectures efficiently regulate gene expression upon stimulation without the need for de novo extensive rewiring of long-range chromatin interactions.

Viral activation and ecological restructuring characterize a microbiome axis of spaceflight-associated immune activation.

Maintenance of astronaut health during spaceflight will require monitoring and potentially modulating their microbiomes, which play a role in some space-derived health disorders. However, documenting the response of microbiota to spaceflight has been difficult thus far due to mission constraints that lead to limited sampling. Here, we executed a six-month longitudinal study centered on a three-day flight to quantify the high-resolution microbiome response to spaceflight. Via paired metagenomics and metatranscriptomics alongside single immune profiling, we resolved a microbiome "architecture" of spaceflight characterized by time-dependent and taxonomically divergent microbiome alterations across 750 samples and ten body sites. We observed pan-phyletic viral activation and signs of persistent changes that, in the oral microbiome, yielded plaque-associated pathobionts with strong associations to immune cell gene expression. Further, we found enrichments of microbial genes associated with antibiotic production, toxin-antitoxin systems, and stress response enriched universally across the body sites. We also used strain-level tracking to measure the potential propagation of microbial species from the crew members to each other and the environment, identifying microbes that were prone to seed the capsule surface and move between the crew. Finally, we identified associations between microbiome and host immune cell shifts, proposing both a microbiome axis of immune changes during flight as well as the sources of some of those changes. In summary, these datasets and methods reveal connections between crew immunology, the microbiome, and their likely drivers and lay the groundwork for future microbiome studies of spaceflight.

An automated 13.5 hour system for scalable diagnosis and acute management guidance for genetic diseases.

While many genetic diseases have effective treatments, they frequently progress rapidly to severe morbidity or mortality if those treatments are not implemented immediately. Since front-line physicians frequently lack familiarity with these diseases, timely molecular diagnosis may not improve outcomes. Herein we describe Genome-to-Treatment, an automated, virtual system for genetic disease diagnosis and acute management guidance. Diagnosis is achieved in 13.5 h by expedited whole genome sequencing, with superior analytic performance for structural and copy number variants. An expert panel adjudicated the indications, contraindications, efficacy, and evidence-of-efficacy of 9911 drug, device, dietary, and surgical interventions for 563 severe, childhood, genetic diseases. The 421 (75%) diseases and 1527 (15%) effective interventions retained are integrated with 13 genetic disease information resources and appended to diagnostic reports ( https://gtrx.radygenomiclab.com ). This system provided correct diagnoses in four retrospectively and two prospectively tested infants. The Genome-to-Treatment system facilitates optimal outcomes in children with rapidly progressive genetic diseases.

ExpansionHunter Denovo: a computational method for locating known and novel repeat expansions in short-read sequencing data.

Repeat expansions are responsible for over 40 monogenic disorders, and undoubtedly more pathogenic repeat expansions remain to be discovered. Existing methods for detecting repeat expansions in short-read sequencing data require predefined repeat catalogs. Recent discoveries emphasize the need for methods that do not require pre-specified candidate repeats. To address this need, we introduce ExpansionHunter Denovo, an efficient catalog-free method for genome-wide repeat expansion detection. Analysis of real and simulated data shows that our method can identify large expansions of 41 out of 44 pathogenic repeats, including nine recently reported non-reference repeat expansions not discoverable via existing methods.

Genome sequencing in persistently unsolved white matter disorders.

Genetic white matter disorders have heterogeneous etiologies and overlapping clinical presentations. We performed a study of the diagnostic efficacy of genome sequencing in 41 unsolved cases with prior exome sequencing, resolving an additional 14 from an historical cohort (n = 191). Reanalysis in the context of novel disease-associated genes and improved variant curation and annotation resolved 64% of cases. The remaining diagnoses were directly attributable to genome sequencing, including cases with small and large copy number variants (CNVs) and variants in deep intronic and technically difficult regions. Genome sequencing, in combination with other methodologies, achieved a diagnostic yield of 85% in this retrospective cohort.

The genome-wide multi-layered architecture of chromosome pairing in early Drosophila embryos.

Genome organization involves cis and trans chromosomal interactions, both implicated in gene regulation, development, and disease. Here, we focus on trans interactions in Drosophila, where homologous chromosomes are paired in somatic cells from embryogenesis through adulthood. We first address long-standing questions regarding the structure of embryonic homolog pairing and, to this end, develop a haplotype-resolved Hi-C approach to minimize homolog misassignment and thus robustly distinguish trans-homolog from cis contacts. This computational approach, which we call Ohm, reveals pairing to be surprisingly structured genome-wide, with trans-homolog domains, compartments, and interaction peaks, many coinciding with analogous cis features. We also find a significant genome-wide correlation between pairing, transcription during zygotic genome activation, and binding of the pioneer factor Zelda. Our findings reveal a complex, highly structured organization underlying homolog pairing, first discovered a century ago in Drosophila. Finally, we demonstrate the versatility of our haplotype-resolved approach by applying it to mammalian embryos.

Highly structured homolog pairing reflects functional organization of the Drosophila genome.

Trans-homolog interactions have been studied extensively in Drosophila, where homologs are paired in somatic cells and transvection is prevalent. Nevertheless, the detailed structure of pairing and its functional impact have not been thoroughly investigated. Accordingly, we generated a diploid cell line from divergent parents and applied haplotype-resolved Hi-C, showing that homologs pair with varying precision genome-wide, in addition to establishing trans-homolog domains and compartments. We also elucidate the structure of pairing with unprecedented detail, observing significant variation across the genome and revealing at least two forms of pairing: tight pairing, spanning contiguous small domains, and loose pairing, consisting of single larger domains. Strikingly, active genomic regions (A-type compartments, active chromatin, expressed genes) correlated with tight pairing, suggesting that pairing has a functional implication genome-wide. Finally, using RNAi and haplotype-resolved Hi-C, we show that disruption of pairing-promoting factors results in global changes in pairing, including the disruption of some interaction peaks.

Publisher Correction: Heterochromatin drives compartmentalization of inverted and conventional nuclei.

An Amendment to this paper has been published and can be accessed via a link at the top of the paper.

Heterochromatin drives compartmentalization of inverted and conventional nuclei.

The nucleus of mammalian cells displays a distinct spatial segregation of active euchromatic and inactive heterochromatic regions of the genome1,2. In conventional nuclei, microscopy shows that euchromatin is localized in the nuclear interior and heterochromatin at the nuclear periphery1,2. Genome-wide chromosome conformation capture (Hi-C) analyses show this segregation as a plaid pattern of contact enrichment within euchromatin and heterochromatin compartments3, and depletion between them. Many mechanisms for the formation of compartments have been proposed, such as attraction of heterochromatin to the nuclear lamina2,4, preferential attraction of similar chromatin to each other1,4-12, higher levels of chromatin mobility in active chromatin13-15 and transcription-related clustering of euchromatin16,17. However, these hypotheses have remained inconclusive, owing to the difficulty of disentangling intra-chromatin and chromatin-lamina interactions in conventional nuclei18. The marked reorganization of interphase chromosomes in the inverted nuclei of rods in nocturnal mammals19,20 provides an opportunity to elucidate the mechanisms that underlie spatial compartmentalization. Here we combine Hi-C analysis of inverted rod nuclei with microscopy and polymer simulations. We find that attractions between heterochromatic regions are crucial for establishing both compartmentalization and the concentric shells of pericentromeric heterochromatin, facultative heterochromatin and euchromatin in the inverted nucleus. When interactions between heterochromatin and the lamina are added, the same model recreates the conventional nuclear organization. In addition, our models allow us to rule out mechanisms of compartmentalization that involve strong euchromatin interactions. Together, our experiments and modelling suggest that attractions between heterochromatic regions are essential for the phase separation of the active and inactive genome in inverted and conventional nuclei, whereas interactions of the chromatin with the lamina are necessary to build the conventional architecture from these segregated phases.

ExpansionHunter: a sequence-graph-based tool to analyze variation in short tandem repeat regions.

SUMMARY: We describe a novel computational method for genotyping repeats using sequence graphs. This method addresses the long-standing need to accurately genotype medically important loci containing repeats adjacent to other variants or imperfect DNA repeats such as polyalanine repeats. Here we introduce a new version of our repeat genotyping software, ExpansionHunter, that uses this method to perform targeted genotyping of a broad class of such loci. AVAILABILITY AND IMPLEMENTATION: ExpansionHunter is implemented in C++ and is available under the Apache License Version 2.0. The source code, documentation, and Linux/macOS binaries are available at https://github.com/Illumina/ExpansionHunter/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bi-allelic CSF1R Mutations Cause Skeletal Dysplasia of Dysosteosclerosis-Pyle Disease Spectrum and Degenerative Encephalopathy with Brain Malformation.

Colony stimulating factor 1 receptor (CSF1R) plays key roles in regulating development and function of the monocyte/macrophage lineage, including microglia and osteoclasts. Mono-allelic mutations of CSF1R are known to cause hereditary diffuse leukoencephalopathy with spheroids (HDLS), an adult-onset progressive neurodegenerative disorder. Here, we report seven affected individuals from three unrelated families who had bi-allelic CSF1R mutations. In addition to early-onset HDLS-like neurological disorders, they had brain malformations and skeletal dysplasia compatible to dysosteosclerosis (DOS) or Pyle disease. We identified five CSF1R mutations that were homozygous or compound heterozygous in these affected individuals. Two of them were deep intronic mutations resulting in abnormal inclusion of intron sequences in the mRNA. Compared with Csf1r-null mice, the skeletal and neural phenotypes of the affected individuals appeared milder and variable, suggesting that at least one of the mutations in each affected individual is hypomorphic. Our results characterized a unique human skeletal phenotype caused by CSF1R deficiency and implied that bi-allelic CSF1R mutations cause a spectrum of neurological and skeletal disorders, probably depending on the residual CSF1R function.

Measuring the reproducibility and quality of Hi-C data.

BACKGROUND: Hi-C is currently the most widely used assay to investigate the 3D organization of the genome and to study its role in gene regulation, DNA replication, and disease. However, Hi-C experiments are costly to perform and involve multiple complex experimental steps; thus, accurate methods for measuring the quality and reproducibility of Hi-C data are essential to determine whether the output should be used further in a study. RESULTS: Using real and simulated data, we profile the performance of several recently proposed methods for assessing reproducibility of population Hi-C data, including HiCRep, GenomeDISCO, HiC-Spector, and QuASAR-Rep. By explicitly controlling noise and sparsity through simulations, we demonstrate the deficiencies of performing simple correlation analysis on pairs of matrices, and we show that methods developed specifically for Hi-C data produce better measures of reproducibility. We also show how to use established measures, such as the ratio of intra- to interchromosomal interactions, and novel ones, such as QuASAR-QC, to identify low-quality experiments. CONCLUSIONS: In this work, we assess reproducibility and quality measures by varying sequencing depth, resolution and noise levels in Hi-C data from 13 cell lines, with two biological replicates each, as well as 176 simulated matrices. Through this extensive validation and benchmarking of Hi-C data, we describe best practices for reproducibility and quality assessment of Hi-C experiments. We make all software publicly available at http://github.com/kundajelab/3DChromatin_ReplicateQC to facilitate adoption in the community.

Copy-number variants in clinical genome sequencing: deployment and interpretation for rare and undiagnosed disease.

PURPOSE: Current diagnostic testing for genetic disorders involves serial use of specialized assays spanning multiple technologies. In principle, genome sequencing (GS) can detect all genomic pathogenic variant types on a single platform. Here we evaluate copy-number variant (CNV) calling as part of a clinically accredited GS test. METHODS: We performed analytical validation of CNV calling on 17 reference samples, compared the sensitivity of GS-based variants with those from a clinical microarray, and set a bound on precision using orthogonal technologies. We developed a protocol for family-based analysis of GS-based CNV calls, and deployed this across a clinical cohort of 79 rare and undiagnosed cases. RESULTS: We found that CNV calls from GS are at least as sensitive as those from microarrays, while only creating a modest increase in the number of variants interpreted (~10 CNVs per case). We identified clinically significant CNVs in 15% of the first 79 cases analyzed, all of which were confirmed by an orthogonal approach. The pipeline also enabled discovery of a uniparental disomy (UPD) and a 50% mosaic trisomy 14. Directed analysis of select CNVs enabled breakpoint level resolution of genomic rearrangements and phasing of de novo CNVs. CONCLUSION: Robust identification of CNVs by GS is possible within a clinical testing environment.

Higher-Order Organization Principles of Pre-translational mRNPs.

Compared to noncoding RNAs (ncRNAs), such as rRNAs and ribozymes, for which high-resolution structures abound, little is known about the tertiary structures of mRNAs. In eukaryotic cells, newly made mRNAs are packaged with proteins in highly compacted mRNA particles (mRNPs), but the manner of this mRNA compaction is unknown. Here, we developed and implemented RIPPLiT (RNA immunoprecipitation and proximity ligation in tandem), a transcriptome-wide method for probing the 3D conformations of RNAs stably associated with defined proteins, in this case, exon junction complex (EJC) core factors. EJCs multimerize with other mRNP components to form megadalton-sized complexes that protect large swaths of newly synthesized mRNAs from endonuclease digestion. Unlike ncRNPs, wherein strong locus-specific structures predominate, mRNPs behave more like flexible polymers. Polymer analysis of proximity ligation data for hundreds of mRNA species demonstrates that nascent and pre-translational mammalian mRNAs are compacted by their associated proteins into linear rod-like structures.

Integrative detection and analysis of structural variation in cancer genomes.

Structural variants (SVs) can contribute to oncogenesis through a variety of mechanisms. Despite their importance, the identification of SVs in cancer genomes remains challenging. Here, we present a framework that integrates optical mapping, high-throughput chromosome conformation capture (Hi-C), and whole-genome sequencing to systematically detect SVs in a variety of normal or cancer samples and cell lines. We identify the unique strengths of each method and demonstrate that only integrative approaches can comprehensively identify SVs in the genome. By combining Hi-C and optical mapping, we resolve complex SVs and phase multiple SV events to a single haplotype. Furthermore, we observe widespread structural variation events affecting the functions of noncoding sequences, including the deletion of distal regulatory sequences, alteration of DNA replication timing, and the creation of novel three-dimensional chromatin structural domains. Our results indicate that noncoding SVs may be underappreciated mutational drivers in cancer genomes.