Genetics of Infectious and Inflammatory Diseases: Overlapping Discoveries from Association and Exome-Sequencing Studies.

Genome technologies have defined a complex genetic architecture in major infectious, inflammatory, and autoimmune disorders. High density marker arrays and Immunochips have powered genome-wide association studies (GWAS) that have mapped nearly 450 genetic risk loci in 22 major inflammatory diseases, including a core of common genes that play a central role in pathological inflammation. Whole-exome and whole-genome sequencing have identified more than 265 genes in which mutations cause primary immunodeficiencies and rare forms of severe inflammatory bowel disease. Combined analysis of inflammatory disease GWAS and primary immunodeficiencies point to shared proteins and pathways that are required for immune cell development and protection against infections and are also associated with pathological inflammation. Finally, sequencing of chromatin immunoprecipitates containing specific transcription factors, with parallel RNA sequencing, has charted epigenetic regulation of gene expression by proinflammatory transcription factors in immune cells, providing complementary information to characterize morbid genes at infectious and inflammatory disease loci.

Looping the Genome with SMC Complexes.

SMC (structural maintenance of chromosomes) protein complexes are an evolutionarily conserved family of motor proteins that hold sister chromatids together and fold genomes throughout the cell cycle by DNA loop extrusion. These complexes play a key role in a variety of functions in the packaging and regulation of chromosomes, and they have been intensely studied in recent years. Despite their importance, the detailed molecular mechanism for DNA loop extrusion by SMC complexes remains unresolved. Here, we describe the roles of SMCs in chromosome biology and particularly review in vitro single-molecule studies that have recently advanced our understanding of SMC proteins. We describe the mechanistic biophysical aspects of loop extrusion that govern genome organization and its consequences.

Neutralizing immunity in vaccine breakthrough infections from the SARS-CoV-2 Omicron and Delta variants.

Virus-like particle (VLP) and live virus assays were used to investigate neutralizing immunity against Delta and Omicron SARS-CoV-2 variants in 259 samples from 128 vaccinated individuals. Following Delta breakthrough infection, titers against WT rose 57-fold and 3.1-fold compared with uninfected boosted and unboosted individuals, respectively, versus only a 5.8-fold increase and 3.1-fold decrease for Omicron breakthrough infection. Among immunocompetent, unboosted patients, Delta breakthrough infections induced 10.8-fold higher titers against WT compared with Omicron (p = 0.037). Decreased antibody responses in Omicron breakthrough infections relative to Delta were potentially related to a higher proportion of asymptomatic or mild breakthrough infections (55.0% versus 28.6%, respectively), which exhibited 12.3-fold lower titers against WT compared with moderate to severe infections (p = 0.020). Following either Delta or Omicron breakthrough infection, limited variant-specific cross-neutralizing immunity was observed. These results suggest that Omicron breakthrough infections are less immunogenic than Delta, thus providing reduced protection against reinfection or infection from future variants.

Transmission, infectivity, and neutralization of a spike L452R SARS-CoV-2 variant.

We identified an emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant by viral whole-genome sequencing of 2,172 nasal/nasopharyngeal swab samples from 44 counties in California, a state in the western United States. Named B.1.427/B.1.429 to denote its two lineages, the variant emerged in May 2020 and increased from 0% to >50% of sequenced cases from September 2020 to January 2021, showing 18.6%-24% increased transmissibility relative to wild-type circulating strains. The variant carries three mutations in the spike protein, including an L452R substitution. We found 2-fold increased B.1.427/B.1.429 viral shedding in vivo and increased L452R pseudovirus infection of cell cultures and lung organoids, albeit decreased relative to pseudoviruses carrying the N501Y mutation common to variants B.1.1.7, B.1.351, and P.1. Antibody neutralization assays revealed 4.0- to 6.7-fold and 2.0-fold decreases in neutralizing titers from convalescent patients and vaccine recipients, respectively. The increased prevalence of a more transmissible variant in California exhibiting decreased antibody neutralization warrants further investigation.

Biophysical Techniques in Structural Biology.

Over the past six decades, steadily increasing progress in the application of the principles and techniques of the physical sciences to the study of biological systems has led to remarkable insights into the molecular basis of life. Of particular significance has been the way in which the determination of the structures and dynamical properties of proteins and nucleic acids has so often led directly to a profound understanding of the nature and mechanism of their functional roles. The increasing number and power of experimental and theoretical techniques that can be applied successfully to living systems is now ushering in a new era of structural biology that is leading to fundamentally new information about the maintenance of health, the origins of disease, and the development of effective strategies for therapeutic intervention. This article provides a brief overview of some of the most powerful biophysical methods in use today, along with references that provide more detailed information about recent applications of each of them. In addition, this article acts as an introduction to four authoritative reviews in this volume. The first shows the ways that a multiplicity of biophysical methods can be combined with computational techniques to define the architectures of complex biological systems, such as those involving weak interactions within ensembles of molecular components. The second illustrates one aspect of this general approach by describing how recent advances in mass spectrometry, particularly in combination with other techniques, can generate fundamentally new insights into the properties of membrane proteins and their functional interactions with lipid molecules. The third reviewdemonstrates the increasing power of rapidly evolving diffraction techniques, employing the very short bursts of X-rays of extremely high intensity that are now accessible as a result of the construction of free-electron lasers, in particular to carry out time-resolved studies of biochemical reactions. The fourth describes in detail the application of such approaches to probe the mechanism of the light-induced changes associated with bacteriorhodopsin's ability to convert light energy into chemical energy.

Intra- and Inter-cellular Rewiring of the Human Colon during Ulcerative Colitis.

Genome-wide association studies (GWAS) have revealed risk alleles for ulcerative colitis (UC). To understand their cell type specificities and pathways of action, we generate an atlas of 366,650 cells from the colon mucosa of 18 UC patients and 12 healthy individuals, revealing 51 epithelial, stromal, and immune cell subsets, including BEST4+ enterocytes, microfold-like cells, and IL13RA2+IL11+ inflammatory fibroblasts, which we associate with resistance to anti-TNF treatment. Inflammatory fibroblasts, inflammatory monocytes, microfold-like cells, and T cells that co-express CD8 and IL-17 expand with disease, forming intercellular interaction hubs. Many UC risk genes are cell type specific and co-regulated within relatively few gene modules, suggesting convergence onto limited sets of cell types and pathways. Using this observation, we nominate and infer functions for specific risk genes across GWAS loci. Our work provides a framework for interrogating complex human diseases and mapping risk variants to cell types and pathways.

Implementing Genome-Driven Oncology.

Early successes in identifying and targeting individual oncogenic drivers, together with the increasing feasibility of sequencing tumor genomes, have brought forth the promise of genome-driven oncology care. As we expand the breadth and depth of genomic analyses, the biological and clinical complexity of its implementation will be unparalleled. Challenges include target credentialing and validation, implementing drug combinations, clinical trial designs, targeting tumor heterogeneity, and deploying technologies beyond DNA sequencing, among others. We review how contemporary approaches are tackling these challenges and will ultimately serve as an engine for biological discovery and increase our insight into cancer and its treatment.

Scalable Functional Assays for the Interpretation of Human Genetic Variation.

Scalable sequence-function studies have enabled the systematic analysis and cataloging of hundreds of thousands of coding and noncoding genetic variants in the human genome. This has improved clinical variant interpretation and provided insights into the molecular, biophysical, and cellular effects of genetic variants at an astonishing scale and resolution across the spectrum of allele frequencies. In this review, we explore current applications and prospects for the field and outline the principles underlying scalable functional assay design, with a focus on the study of single-nucleotide coding and noncoding variants.

From genetic associations to genes: methods, applications, and challenges.

Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.

Finding genes and pathways that underlie coral adaptation.

Mass coral bleaching is one of the clearest threats of climate change to the persistence of marine biodiversity. Despite the negative impacts of bleaching on coral health and survival, some corals may be able to rapidly adapt to warming ocean temperatures. Thus, a significant focus in coral research is identifying the genes and pathways underlying coral heat adaptation. Here, we review state-of-the-art methods that may enable the discovery of heat-adaptive loci in corals and identify four main knowledge gaps. To fill these gaps, we describe an experimental approach combining seascape genomics with CRISPR/Cas9 gene editing to discover and validate heat-adaptive loci. Finally, we discuss how information on adaptive genotypes could be used in coral reef conservation and management strategies.

The genetic landscape of age-related hearing loss.

Age-related hearing loss (ARHL) is a prevalent concern in the elderly population. Recent genome-wide and phenome-wide association studies (GWASs and PheWASs) have delved into the identification of causative variants and the understanding of pleiotropy, highlighting the polygenic intricacies of this complex condition. While recent large-scale GWASs have pinpointed significant SNPs and risk variants associated with ARHL, the detailed mechanisms, encompassing both genetic and epigenetic modifications, remain to be fully elucidated. This review presents the latest advances in association studies, integrating findings from both human studies and model organisms. By juxtaposing historical perspectives with contemporary genomics, we aim to catalyze innovative research and foster the development of novel therapeutic strategies for ARHL.

Evaluation of polygenic scoring methods in five biobanks shows larger variation between biobanks than methods and finds benefits of ensemble learning.

Methods of estimating polygenic scores (PGSs) from genome-wide association studies are increasingly utilized. However, independent method evaluation is lacking, and method comparisons are often limited. Here, we evaluate polygenic scores derived via seven methods in five biobank studies (totaling about 1.2 million participants) across 16 diseases and quantitative traits, building on a reference-standardized framework. We conducted meta-analyses to quantify the effects of method choice, hyperparameter tuning, method ensembling, and the target biobank on PGS performance. We found that no single method consistently outperformed all others. PGS effect sizes were more variable between biobanks than between methods within biobanks when methods were well tuned. Differences between methods were largest for the two investigated autoimmune diseases, seropositive rheumatoid arthritis and type 1 diabetes. For most methods, cross-validation was more reliable for tuning hyperparameters than automatic tuning (without the use of target data). For a given target phenotype, elastic net models combining PGS across methods (ensemble PGS) tuned in the UK Biobank provided consistent, high, and cross-biobank transferable performance, increasing PGS effect sizes (ß coefficients) by a median of 5.0% relative to LDpred2 and MegaPRS (the two best-performing single methods when tuned with cross-validation). Our interactively browsable online-results and open-source workflow prspipe provide a rich resource and reference for the analysis of polygenic scoring methods across biobanks.

A multi-tissue, splicing-based joint transcriptome-wide association study identifies susceptibility genes for breast cancer.

Splicing-based transcriptome-wide association studies (splicing-TWASs) of breast cancer have the potential to identify susceptibility genes. However, existing splicing-TWASs test the association of individual excised introns in breast tissue only and thus have limited power to detect susceptibility genes. In this study, we performed a multi-tissue joint splicing-TWAS that integrated splicing-TWAS signals of multiple excised introns in each gene across 11 tissues that are potentially relevant to breast cancer risk. We utilized summary statistics from a meta-analysis that combined genome-wide association study (GWAS) results of 424,650 women of European ancestry. Splicing-level prediction models were trained in GTEx (v.8) data. We identified 240 genes by the multi-tissue joint splicing-TWAS at the Bonferroni-corrected significance level; in the tissue-specific splicing-TWAS that combined TWAS signals of excised introns in genes in breast tissue only, we identified nine additional significant genes. Of these 249 genes, 88 genes in 62 loci have not been reported by previous TWASs, and 17 genes in seven loci are at least 1 Mb away from published GWAS index variants. By comparing the results of our splicing-TWASs with previous gene-expression-based TWASs that used the same summary statistics and expression prediction models trained in the same reference panel, we found that 110 genes in 70 loci that are identified only by the splicing-TWASs. Our results showed that for many genes, expression quantitative trait loci (eQTL) did not show a significant impact on breast cancer risk, whereas splicing quantitative trait loci (sQTL) showed a strong impact through intron excision events.

MagicalRsq-X: A cross-cohort transferable genotype imputation quality metric.

Since genotype imputation was introduced, researchers have been relying on the estimated imputation quality from imputation software to perform post-imputation quality control (QC). However, this quality estimate (denoted as Rsq) performs less well for lower-frequency variants. We recently published MagicalRsq, a machine-learning-based imputation quality calibration, which leverages additional typed markers from the same cohort and outperforms Rsq as a QC metric. In this work, we extended the original MagicalRsq to allow cross-cohort model training and named the new model MagicalRsq-X. We removed the cohort-specific estimated minor allele frequency and included linkage disequilibrium scores and recombination rates as additional features. Leveraging whole-genome sequencing data from TOPMed, specifically participants in the BioMe, JHS, WHI, and MESA studies, we performed comprehensive cross-cohort evaluations for predominantly European and African ancestral individuals based on their inferred global ancestry with the 1000 Genomes and Human Genome Diversity Project data as reference. Our results suggest MagicalRsq-X outperforms Rsq in almost every setting, with 7.3%-14.4% improvement in squared Pearson correlation with true R2, corresponding to 85-218 K variant gains. We further developed a metric to quantify the genetic distances of a target cohort relative to a reference cohort and showed that such metric largely explained the performance of MagicalRsq-X models. Finally, we found MagicalRsq-X saved up to 53 known genome-wide significant variants in one of the largest blood cell trait GWASs that would be missed using the original Rsq for QC. In conclusion, MagicalRsq-X shows superiority for post-imputation QC and benefits genetic studies by distinguishing well and poorly imputed lower-frequency variants.

The Transcription Factor Ets1 Suppresses T Follicular Helper Type 2 Cell Differentiation to Halt the Onset of Systemic Lupus Erythematosus.

Single-nucleotide polymorphisms in ETS1 are associated with systemic lupus erythematosus (SLE). Ets1-/- mice develop SLE-like symptoms, suggesting that dysregulation of this transcription factor is important to the onset or progression of SLE. We used conditional deletion approaches to examine the impact of Ets1 expression in different immune cell types. Ets1 deletion on CD4+ T cells, but not B cells or dendritic cells, resulted in the SLE autoimmunity, and this was associated with the spontaneous expansion of T follicular helper type 2 (Tfh2) cells. Ets1-/- Tfh2 cells exhibited increased expression of GATA-3 and interleukin-4 (IL-4), which induced IgE isotype switching in B cells. Neutralization of IL-4 reduced Tfh2 cell frequencies and ameliorated disease parameters. Mechanistically, Ets1 suppressed signature Tfh and Th2 cell genes, including Cxcr5, Bcl6, and Il4ra, thus curbing the terminal Tfh2 cell differentiation process. Tfh2 cell frequencies in SLE patients correlated with disease parameters, providing evidence for the relevance of these findings to human disease.

Contribution of protein conformational heterogeneity to NMR lineshapes at cryogenic temperatures.

While low-temperature Nuclear Magnetic Resonance (NMR) holds great promise for the analysis of unstable samples and for sensitizing NMR detection, spectral broadening in frozen protein samples is a common experimental challenge. One hypothesis explaining the additional linewidth is that a variety of conformations are in rapid equilibrium at room temperature and become frozen, creating an inhomogeneous distribution at cryogenic temperatures. Here, we investigate conformational heterogeneity by measuring the backbone torsion angle (Ψ) in Escherichia coli Dihydrofolate Reductase (DHFR) at 105 K. Motivated by the particularly broad N chemical shift distribution in this and other examples, we modified an established NCCN Ψ experiment to correlate the chemical shift of Ni+1 to Ψi. With selective 15N and 13C enrichment of Ile, only the unique I60-I61 pair was expected to be detected in 13C'-15N correlation spectrum. For this unique amide, we detected three different conformation basins based on dispersed chemical shifts. Backbone torsion angles Ψ were determined for each basin: 114 ± 7° for the major peak and 150 ± 8° and 164 ± 16° for the minor peaks as contrasted with 118° for the X-ray crystal structure (and 118° to 130° for various previously reported structures). These studies support the hypothesis that inhomogeneous distributions of protein backbone torsion angles contribute to the lineshape broadening in low-temperature NMR spectra.

Extracellular Vesicles: Translational Agenda Questions for Three Protozoan Parasites.

The protozoan parasites Plasmodium falciparum, Leishmania spp. and Trypanosoma cruzi continue to exert a significant toll on the disease landscape of the human population in sub-Saharan Africa and Latin America. Control measures have helped reduce the burden of their respective diseases-malaria, leishmaniasis and Chagas disease-in endemic regions. However, the need for new drugs, innovative vaccination strategies and molecular markers of disease severity and outcomes has emerged because of developing antimicrobial drug resistance, comparatively inadequate or absent vaccines, and a lack of trustworthy markers of morbid outcomes. Extracellular vesicles (EVs) have been widely reported to play a role in the biology and pathogenicity of P. falciparum, Leishmania spp. and T. cruzi ever since they were discovered. EVs are secreted by a yet to be fully understood mechanism in protozoans into the extracellular milieu and carry a cargo of diverse molecules that reflect the originator cell's metabolic state. Although our understanding of the biogenesis and function of EVs continues to deepen, the question of how EVs in P. falciparum, Leishmania spp. and T. cruzi can serve as targets for a translational agenda into clinical and public health interventions is yet to be fully explored. Here, as a consortium of protozoan researchers, we outline a plan for future researchers and pose three questions to direct an EV's translational agenda in P. falciparum, Leishmania spp. and T. cruzi. We opine that in the long term, executing this blueprint will help bridge the current unmet needs of these medically important protozoan diseases in sub-Saharan Africa and Latin America.

Functional characterization of HNF4A gene variants identify promoter and cell line specific transactivation effects.

Hepatocyte nuclear factor-4 alpha (HNF-4A) regulates genes with roles in glucose metabolism and ß-cell development. Although pathogenic HNF4A variants are commonly associated with maturity-onset diabetes of the young (MODY1; HNF4A-MODY), rare phenotypes also include hyperinsulinemic hypoglycemia, renal Fanconi syndrome and liver disease. While the association of rare functionally damaging HNF1A variants with HNF1A-MODY and type 2 diabetes is well established owing to robust functional assays, the impact of HNF4A variants on HNF-4A transactivation in tissues including the liver and kidney is less known, due to lack of similar assays. Our aim was to investigate the functional effects of seven HNF4A variants, located in the HNF-4A DNA binding domain and associated with different clinical phenotypes, by various functional assays and cell lines (transactivation, DNA binding, protein expression, nuclear localization) and in silico protein structure analyses. Variants R85W, S87N and R89W demonstrated reduced DNA binding to the consensus HNF-4A binding elements in the HNF1A promoter (35, 13 and 9%, respectively) and the G6PC promoter (R85W ~10%). While reduced transactivation on the G6PC promoter in HepG2 cells was shown for S87N (33%), R89W (65%) and R136W (35%), increased transactivation by R85W and R85Q was confirmed using several combinations of target promoters and cell lines. R89W showed reduced nuclear levels. In silico analyses supported variant induced structural impact. Our study indicates that cell line specific functional investigations are important to better understand HNF4A-MODY genotype-phenotype correlations, as our data supports ACMG/AMP interpretations of loss-of-function variants and propose assay-specific HNF4A control variants for future functional investigations.

Whole genome sequencing based analysis of inflammation biomarkers in the Trans-Omics for Precision Medicine (TOPMed) consortium.

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38 465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program (with varying sample size by trait, where the minimum sample size was n = 737 for MMP-1). We identified 22 distinct single-variant associations across 6 traits-E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin-that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.

Variant-to-gene, gene-to-trait: finding new sleep genes through GWAS.

Distilling insomnia genome-wide association study (GWAS) variants, Palermo and colleagues identified several genes that participate in sleep regulation in two different model organisms. This workflow sets off an innovative strategy to extract biological relevance from large human genomic databases.