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.

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.

Canonical correlation analysis for multi-omics: Application to cross-cohort analysis.

Integrative approaches that simultaneously model multi-omics data have gained increasing popularity because they provide holistic system biology views of multiple or all components in a biological system of interest. Canonical correlation analysis (CCA) is a correlation-based integrative method designed to extract latent features shared between multiple assays by finding the linear combinations of features-referred to as canonical variables (CVs)-within each assay that achieve maximal across-assay correlation. Although widely acknowledged as a powerful approach for multi-omics data, CCA has not been systematically applied to multi-omics data in large cohort studies, which has only recently become available. Here, we adapted sparse multiple CCA (SMCCA), a widely-used derivative of CCA, to proteomics and methylomics data from the Multi-Ethnic Study of Atherosclerosis (MESA) and Jackson Heart Study (JHS). To tackle challenges encountered when applying SMCCA to MESA and JHS, our adaptations include the incorporation of the Gram-Schmidt (GS) algorithm with SMCCA to improve orthogonality among CVs, and the development of Sparse Supervised Multiple CCA (SSMCCA) to allow supervised integration analysis for more than two assays. Effective application of SMCCA to the two real datasets reveals important findings. Applying our SMCCA-GS to MESA and JHS, we identified strong associations between blood cell counts and protein abundance, suggesting that adjustment of blood cell composition should be considered in protein-based association studies. Importantly, CVs obtained from two independent cohorts also demonstrate transferability across the cohorts. For example, proteomic CVs learned from JHS, when transferred to MESA, explain similar amounts of blood cell count phenotypic variance in MESA, explaining 39.0% ~ 50.0% variation in JHS and 38.9% ~ 49.1% in MESA. Similar transferability was observed for other omics-CV-trait pairs. This suggests that biologically meaningful and cohort-agnostic variation is captured by CVs. We anticipate that applying our SMCCA-GS and SSMCCA on various cohorts would help identify cohort-agnostic biologically meaningful relationships between multi-omics data and phenotypic traits.

MagicalRsq: Machine-learning-based genotype imputation quality calibration.

Whole-genome sequencing (WGS) is the gold standard for fully characterizing genetic variation but is still prohibitively expensive for large samples. To reduce costs, many studies sequence only a subset of individuals or genomic regions, and genotype imputation is used to infer genotypes for the remaining individuals or regions without sequencing data. However, not all variants can be well imputed, and the current state-of-the-art imputation quality metric, denoted as standard Rsq, is poorly calibrated for lower-frequency variants. Here, we propose MagicalRsq, a machine-learning-based method that integrates variant-level imputation and population genetics statistics, to provide a better calibrated imputation quality metric. Leveraging WGS data from the Cystic Fibrosis Genome Project (CFGP), and whole-exome sequence data from UK BioBank (UKB), we performed comprehensive experiments to evaluate the performance of MagicalRsq compared to standard Rsq for partially sequenced studies. We found that MagicalRsq aligns better with true R2 than standard Rsq in almost every situation evaluated, for both European and African ancestry samples. For example, when applying models trained from 1,992 CFGP sequenced samples to an independent 3,103 samples with no sequencing but TOPMed imputation from array genotypes, MagicalRsq, compared to standard Rsq, achieved net gains of 1.4 million rare, 117k low-frequency, and 18k common variants, where net gains were gained numbers of correctly distinguished variants by MagicalRsq over standard Rsq. MagicalRsq can serve as an improved post-imputation quality metric and will benefit downstream analysis by better distinguishing well-imputed variants from those poorly imputed. MagicalRsq is freely available on GitHub.

Bacteroides propionicigenes sp. nov., isolated from human faeces.

An anaerobic bacterial strain, designated as NSJ-90T, was isolated from the faeces of a healthy adult in China. Cells of strain NSJ-90T were Gram-stain-negative, non-motile, non-spore-forming and rod-shaped. Based on 16S rRNA gene sequence analysis, strain NSJ-90T belonged to the genus Bacteroides and was phylogenetically closely related to Bacteroides clarus YIT 12056T (16S rRNA gene identity was 97.04 %). The DNA G+C content of strain NSJ-90T was 44.85 mol% (calculated from the genome). The average nucleotide identity between strain NSJ-90T and B. clarus YIT 12056T was 87.60 %. The major cellular fatty acids (>10 %) of strain NSJ-90T were iso-C15 : 0, anteiso-C15 : 0 and iso-C17 : 0 3-OH. Menaquinone-10 was detected as the respiratory quinone. The major products of glucose fermentation were acetic, propionic and isovaleric acids. Based on its phylogenetic, phenotypic and chemotaxonomic characteristics, we propose that strain NSJ-90T represents a novel species of the genus Bacteroides, for which the name Bacteroides propionicigenes sp. nov. is proposed. The type strain is NSJ-90T (=CGMCC 1.17886T=KCTC 25305T).

Submerged macrophytes recruit unique microbial communities and drive functional zonation in an aquatic system.

Aquatic and wetland systems are widely used for landscapes and water regeneration. Microbiomes and submerged macrophytes (hydrophytes) play essential roles in conversions of organic and inorganic compounds in those ecosystems. The systems were extensively investigated for microbial diversities and compositions. However, little is known about how hydrophytes recruited diverse microbiota and affected functional zonation in aquatic systems. To address this issue, epiphytic leaf and root, sediment, and surrounding water samples were collected from the dragon-shape aquatic system in Beijing Olympic Park. Metagenomic DNAs were extracted and subjected to sequencing. Results showed that epiphytic leaf and root microbiomes and metabolic marker genes were remarkably different from that of surrounding environment. Twenty indicator bacterial genera for epiphytic microbiomes were identified and 50 metabolic marker genes were applied to evaluate the function of epiphytic leaf and root, water, and sediment microbiomes. Co-occurrence analysis revealed highly modularized pattern of metabolic marker genes and indicator bacterial genera related to metabolic functions. These results suggested that hydrophytes shaped microbiomes and drove functional zonation in aquatic systems. KEY POINTS: • Microbiomes of hydrophytes and their surrounding environments were investigated. • Twenty indicator bacterial genera highly specific to epiphytic biofilms were identified. • Epiphytes recruited unique microbiomes and drove functional zonation in aquatic systems.

Mucilaginibacter xinganensis sp. nov., a phenanthrene-degrading bacterium isolated from wetland soil.

An aerobic, Gram-stain negative, rod-shaped and non-motile strain, BJC16-A31T, was isolated from the wetland soil sample taken from Daxing'anling, Heilongjiang, People's Republic of China. Strain BJC16-A31T was found to be oxidase- and catalase-positive, and produced light orange colonies on modified R2A agar. Phylogenetic analysis based on 16S rRNA gene sequences showed that strain BJC16-A31T is closely related to Mucilaginibacter gotjawali SA3-7T with 96.54% sequence similarity and it formed a separate lineage in the genus Mucilaginibacter. Strain BJC16-A31T contained menaquinone-7 (MK-7) as the predominant isoprenoid quinine. Anteiso-C15:0, C16:0 and anteiso-C15:0 were the major fatty acids. The major polar lipids were phosphatidylethanolamine, six unidentified polar lipid, two unidentified aminophospholipids and one unidentified aminolipid. The genome is composed of a circular 5,301,339 bp chromosome with average G + C percentage of 42.25%. The Average Nucleotide Identity (ANI) between strain BJC16-A31T and M. gotjawali SA3-7T was 77.51%. Combined phenotypic, chemotaxonomic, phylogenetic and genomic characteristics support the conclusion that strain BJC16-A31T represents a novel species of the genus Mucilaginibacter, for which the name Mucilaginibacter xinganensis sp. nov. is proposed. The type strain is BJC16-A31T (= CGMCC 1.12728T = NBRC 110384T).

Gut microbial interactions based on network construction and bacterial pairwise cultivation.

Association networks are widely applied for the prediction of bacterial interactions in studies of human gut microbiomes. However, the experimental validation of the predicted interactions is challenging due to the complexity of gut microbiomes and the limited number of cultivated bacteria. In this study, we addressed this challenge by integrating in vitro time series network (TSN) associations and co-cultivation of TSN taxon pairs. Fecal samples were collected and used for cultivation and enrichment of gut microbiome on YCFA agar plates for 13 days. Enriched cells were harvested for DNA extraction and metagenomic sequencing. A total of 198 metagenome-assembled genomes (MAGs) were recovered. Temporal dynamics of bacteria growing on the YCFA agar were used to infer microbial association networks. To experimentally validate the interactions of taxon pairs in networks, we selected 24 and 19 bacterial strains from this study and from the previously established human gut microbial biobank, respectively, for pairwise co-cultures. The co-culture experiments revealed that most of the interactions between taxa in networks were identified as neutralism (51.67%), followed by commensalism (21.67%), amensalism (18.33%), competition (5%) and exploitation (3.33%). Genome-centric analysis further revealed that the commensal gut bacteria (helpers and beneficiaries) might interact with each other via the exchanges of amino acids with high biosynthetic costs, short-chain fatty acids, and/or vitamins. We also validated 12 beneficiaries by adding 16 additives into the basic YCFA medium and found that the growth of 66.7% of these strains was significantly promoted. This approach provides new insights into the gut microbiome complexity and microbial interactions in association networks. Our work highlights that the positive relationships in gut microbial communities tend to be overestimated, and that amino acids, short-chain fatty acids, and vitamins are contributed to the positive relationships.

Christensenella minuta interacts with multiple gut bacteria.

Introduction: Gut microbes form complex networks that significantly influence host health and disease treatment. Interventions with the probiotic bacteria on the gut microbiota have been demonstrated to improve host well-being. As a representative of next-generation probiotics, Christensenella minuta (C. minuta) plays a critical role in regulating energy balance and metabolic homeostasis in human bodies, showing potential in treating metabolic disorders and reducing inflammation. However, interactions of C. minuta with the members of the networked gut microbiota have rarely been explored. Methods: In this study, we investigated the impact of C. minuta on fecal microbiota via metagenomic sequencing, focusing on retrieving bacterial strains and coculture assays of C. minuta with associated microbial partners. Results: Our results showed that C. minuta intervention significantly reduced the diversity of fecal microorganisms, but specifically enhanced some groups of bacteria, such as Lactobacillaceae. C. minuta selectively enriched bacterial pathways that compensated for its metabolic defects on vitamin B1, B12, serine, and glutamate synthesis. Meanwhile, C. minuta cross-feeds Faecalibacterium prausnitzii and other bacteria via the production of arginine, branched-chain amino acids, fumaric acids and short-chain fatty acids (SCFAs), such as acetic. Both metagenomic data analysis and culture experiments revealed that C. minuta negatively correlated with Klebsiella pneumoniae and 14 other bacterial taxa, while positively correlated with F. prausnitzii. Our results advance our comprehension of C. minuta's in modulating the gut microbial network. Conclusions: C. minuta disrupts the composition of the fecal microbiota. This disturbance is manifested through cross-feeding, nutritional competition, and supplementation of its own metabolic deficiencies, resulting in the specific enrichment or inhibition of the growth of certain bacteria. This study will shed light on the application of C. minuta as a probiotic for effective interventions on gut microbiomes and improvement of host health.

Gut commensal Christensenella minuta modulates host metabolism via acylated secondary bile acids.

A strong correlation between gut microbes and host health has been observed in numerous gut metagenomic cohort studies. However, the underlying mechanisms governing host-microbe interactions in the gut remain largely unknown. Here we report that the gut commensal Christensenella minuta modulates host metabolism by generating a previously undescribed class of secondary bile acids with 3-O-acylation substitution that inhibit the intestinal farnesoid X receptor. Administration of C. minuta alleviated features of metabolic disease in high fat diet-induced obese mice associated with a significant increase in these acylated bile acids, which we refer to as 3-O-acyl-cholic acids. Specific knockout of intestinal farnesoid X receptor in mice counteracted the beneficial effects observed in their wild-type counterparts. Finally, we showed that 3-O-acyl-CAs were prevalent in healthy humans but significantly depleted in patients with type 2 diabetes. Our findings indicate a role for C. minuta and acylated bile acids in metabolic diseases.

Functional characterization of Alzheimer's disease genetic variants in microglia.

Candidate cis-regulatory elements (cCREs) in microglia demonstrate the most substantial enrichment for Alzheimer's disease (AD) heritability compared to other brain cell types. However, whether and how these genome-wide association studies (GWAS) variants contribute to AD remain elusive. Here we prioritize 308 previously unreported AD risk variants at 181 cCREs by integrating genetic information with microglia-specific 3D epigenome annotation. We further establish the link between functional variants and target genes by single-cell CRISPRi screening in microglia. In addition, we show that AD variants exhibit allelic imbalance on target gene expression. In particular, rs7922621 is the effective variant in controlling TSPAN14 expression among other nominated variants in the same cCRE and exerts multiple physiological effects including reduced cell surface ADAM10 and altered soluble TREM2 (sTREM2) shedding. Our work represents a systematic approach to prioritize and characterize AD-associated variants and provides a roadmap for advancing genetic association to experimentally validated cell-type-specific phenotypes and mechanisms.

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. 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.

Droplet microfluidics-based high-throughput bacterial cultivation for validation of taxon pairs in microbial co-occurrence networks.

Co-occurrence networks inferred from the abundance data of microbial communities are widely applied to predict microbial interactions. However, the high workloads of bacterial isolation and the complexity of the networks themselves constrained experimental demonstrations of the predicted microbial associations and interactions. Here, we integrate droplet microfluidics and bar-coding logistics for high-throughput bacterial isolation and cultivation from environmental samples, and experimentally investigate the relationships between taxon pairs inferred from microbial co-occurrence networks. We collected Potamogeton perfoliatus plants (including roots) and associated sediments from Beijing Olympic Park wetland. Droplets of series diluted homogenates of wetland samples were inoculated into 126 96-well plates containing R2A and TSB media. After 10 days of cultivation, 65 plates with > 30% wells showed microbial growth were selected for the inference of microbial co-occurrence networks. We cultivated 129 bacterial isolates belonging to 15 species that could represent the zero-level OTUs (Zotus) in the inferred co-occurrence networks. The co-cultivations of bacterial isolates corresponding to the prevalent Zotus pairs in networks were performed on agar plates and in broth. Results suggested that positively associated Zotu pairs in the co-occurrence network implied complicated relations including neutralism, competition, and mutualism, depending on bacterial isolate combination and cultivation time.

Whole genome sequencing identifies structural variants contributing to hematologic traits in the NHLBI TOPMed program.

Genome-wide association studies have identified thousands of single nucleotide variants and small indels that contribute to variation in hematologic traits. While structural variants are known to cause rare blood or hematopoietic disorders, the genome-wide contribution of structural variants to quantitative blood cell trait variation is unknown. Here we utilized whole genome sequencing data in ancestrally diverse participants of the NHLBI Trans Omics for Precision Medicine program (N = 50,675) to detect structural variants associated with hematologic traits. Using single variant tests, we assessed the association of common and rare structural variants with red cell-, white cell-, and platelet-related quantitative traits and observed 21 independent signals (12 common and 9 rare) reaching genome-wide significance. The majority of these associations (N = 18) replicated in independent datasets. In genome-editing experiments, we provide evidence that a deletion associated with lower monocyte counts leads to disruption of an S1PR3 monocyte enhancer and decreased S1PR3 expression.

Expression, Purification, and Bioactivity of a Soluble Recombinant Ovine Interferon-tau in Escherichia Coli.

INTRODUCTION: Ovine interferon-tau (oIFN-τ) is a newly discovered type I interferon. This study used biochemical techniques to transform the oIFN-τ gene into Escherichia coli to obtain the mass and soluble expression of the recombinant protein. MATERIAL AND METHODS: First, total RNA was extracted from fresh sheep embryonic tissues with TRIzol reagent and then used as a template to reverse transcribe and amplify the mature oIFN-τ gene with RT-PCR. The amplified product was next digested with the HindIII and XhoI restriction enzymes and inserted into the pET-32a(+) vector to construct the prokaryotic expression plasmid. The corrected in-frame recombinant plasmid, pET-32a(+)-oIFN-τ, was transformed into E. coli Rosetta (DE3) competent cells. After induction with isopropyl-beta-D-thiogalactopyranoside (IPTG), the recombinant protein was detected in bacteria. Finally, the bacteria were lysed by sonication, and the recombinant protein was purified by nickel affinity chromatography and DEAE anion exchange chromatography. RESULTS: The protein was confirmed to be oIFN-τ, which mainly existed in the soluble lysate fraction, as proven by SDS-PAGE and Western blot assays. CONCLUSION: Purified IFN-τ exists mostly in a soluble form, and its anti-vesicular stomatitis virus (VSV) activity reached 7.08×10(6)IU/mL.

Acute and Sub-chronic Toxicity Study of Recombinant Bovine Interferon Alpha in Rodents.

INTRODUCTION: Recombinant bovine interferon alpha (rBoIFN-α) has been demonstrated to have antiviral activity. However, no conduct of acute or chronic toxicity tests has been reported. MATERIAL AND METHODS: Specific pathogen-free Sprague Dawley rats were administered doses at different concentrations through intraperitoneal or intravenous injection. After the administration (single for an acute toxicity test over 14 days or daily for a sub-chronic toxicity test over 30 days), the rats' behaviour and other indicators and the degree of toxic reaction were continuously monitored. Blood was collected for haematological and serum biochemical examinations. At the end of the experiments, the rats were sacrificed for necropsy and histopathological tissue analysis. RESULTS: The external performance, behaviour characteristics, and changes in body temperature and body weight of the rats in each subgroup were comparable to the normal control subgroup. Except for a few cases, there were no lesions in the viscera's pathological structures, and the blood parameters and biochemical indicators were not noticeably different from those of the control subgroup. CONCLUSION: This study suggests that rBoIFN-α seems to be safe for rats, and its use may foster the development of the cattle industry in China by protecting livestock health.

Enlightening the taxonomy darkness of human gut microbiomes with a cultured biobank.

BACKGROUND: In gut microbiome studies, the cultured gut microbial resource plays essential roles, such as helping to unravel gut microbial functions and host-microbe interactions. Although several major studies have been performed to elucidate the cultured human gut microbiota, up to 70% of the Unified Human Gastrointestinal Genome species have not been cultured to date. Large-scale gut microbial isolation and identification as well as availability to the public are imperative for gut microbial studies and further characterizing human gut microbial functions. RESULTS: In this study, we constructed a human Gut Microbial Biobank (hGMB; homepage: hgmb.nmdc.cn ) through the cultivation of 10,558 isolates from 31 sample mixtures of 239 fresh fecal samples from healthy Chinese volunteers, and deposited 1170 strains representing 400 different species in culture collections of the International Depository Authority for long-term preservation and public access worldwide. Following the rules of the International Code of Nomenclature of Prokaryotes, 102 new species were characterized and denominated, while 28 new genera and 3 new families were proposed. hGMB represented over 80% of the common and dominant human gut microbial genera and species characterized from global human gut 16S rRNA gene amplicon data (n = 11,647) and cultured 24 "most-wanted" and "medium priority" taxa proposed by the Human Microbiome Project. We in total sequenced 115 genomes representing 102 novel taxa and 13 previously known species. Further in silico analysis revealed that the newly sequenced hGMB genomes represented 22 previously uncultured species in the Unified Human Gastrointestinal Genome (UHGG) and contributed 24 representatives of potentially "dark taxa" that had not been discovered by UHGG. The nonredundant gene catalogs generated from the hGMB genomes covered over 50% of the functionally known genes (KEGG orthologs) in the largest global human gut gene catalogs and approximately 10% of the "most wanted" functionally unknown proteins in the FUnkFams database. CONCLUSIONS: A publicly accessible human Gut Microbial Biobank (hGMB) was established that contained 1170 strains and represents 400 human gut microbial species. hGMB expands the gut microbial resources and genomic repository by adding 102 novel species, 28 new genera, 3 new families, and 115 new genomes of human gut microbes. Video abstract.