Statistical Detection of Relatives Typed with Disjoint Forensic and Biomedical Loci.

In familial searching in forensic genetics, a query DNA profile is tested against a database to determine whether it represents a relative of a database entrant. We examine the potential for using linkage disequilibrium to identify pairs of profiles as belonging to relatives when the query and database rely on nonoverlapping genetic markers. Considering data on individuals genotyped with both microsatellites used in forensic applications and genome-wide SNPs, we find that ∼30%-32% of parent-offspring pairs and ∼35%-36% of sib pairs can be identified from the SNPs of one member of the pair and the microsatellites of the other. The method suggests the possibility of performing familial searches of microsatellite databases using query SNP profiles, or vice versa. It also reveals that privacy concerns arising from computations across multiple databases that share no genetic markers in common entail risks, not only for database entrants, but for their close relatives as well.

Scalable mixed model methods for set-based association studies on large-scale categorical data analysis and its application to exome-sequencing data in UK Biobank.

The ongoing release of large-scale sequencing data in the UK Biobank allows for the identification of associations between rare variants and complex traits. SAIGE-GENE+ is a valid approach to conducting set-based association tests for quantitative and binary traits. However, for ordinal categorical phenotypes, applying SAIGE-GENE+ with treating the trait as quantitative or binarizing the trait can cause inflated type I error rates or power loss. In this study, we propose a scalable and accurate method for rare-variant association tests, POLMM-GENE, in which we used a proportional odds logistic mixed model to characterize ordinal categorical phenotypes while adjusting for sample relatedness. POLMM-GENE fully utilizes the categorical nature of phenotypes and thus can well control type I error rates while remaining powerful. In the analyses of UK Biobank 450k whole-exome-sequencing data for five ordinal categorical traits, POLMM-GENE identified 54 gene-phenotype associations.

Bayesian mixed model inference for genetic association under related samples with brain network phenotype.

Genetic association studies for brain connectivity phenotypes have gained prominence due to advances in noninvasive imaging techniques and quantitative genetics. Brain connectivity traits, characterized by network configurations and unique biological structures, present distinct challenges compared to other quantitative phenotypes. Furthermore, the presence of sample relatedness in the most imaging genetics studies limits the feasibility of adopting existing network-response modeling. In this article, we fill this gap by proposing a Bayesian network-response mixed-effect model that considers a network-variate phenotype and incorporates population structures including pedigrees and unknown sample relatedness. To accommodate the inherent topological architecture associated with the genetic contributions to the phenotype, we model the effect components via a set of effect network configurations and impose an inter-network sparsity and intra-network shrinkage to dissect the phenotypic network configurations affected by the risk genetic variant. A Markov chain Monte Carlo (MCMC) algorithm is further developed to facilitate uncertainty quantification. We evaluate the performance of our model through extensive simulations. By further applying the method to study, the genetic bases for brain structural connectivity using data from the Human Connectome Project with excessive family structures, we obtain plausible and interpretable results. Beyond brain connectivity genetic studies, our proposed model also provides a general linear mixed-effect regression framework for network-variate outcomes.

Kin selection for cooperation in natural bacterial populations.

Bacteria produce a range of molecules that are secreted from the cell and can provide a benefit to the local population of cells. Laboratory experiments have suggested that these "public goods" molecules represent a form of cooperation, favored because they benefit closely related cells (kin selection). However, there is a relative lack of data demonstrating kin selection for cooperation in natural populations of bacteria. We used molecular population genetics to test for signatures of kin selection at the genomic level in natural populations of the opportunistic pathogen Pseudomonas aeruginosa We found consistent evidence from multiple traits that genes controlling putatively cooperative traits have higher polymorphism and greater divergence and are more likely to harbor deleterious mutations relative to genes controlling putatively private traits, which are expressed at similar rates. These patterns suggest that cooperative traits are controlled by kin selection, and we estimate that the relatedness for social interactions in P. aeruginosa is r = 0.84. More generally, our results demonstrate how molecular population genetics can be used to study the evolution of cooperation in natural populations.

An exploration of linkage fine-mapping on sequences from case-control studies.

Linkage analysis maps genetic loci for a heritable trait by identifying genomic regions with excess relatedness among individuals with similar trait values. Analysis may be conducted on related individuals from families, or on samples of unrelated individuals from a population. For allelically heterogeneous traits, population-based linkage analysis can be more powerful than genotypic-association analysis. Here, we focus on linkage analysis in a population sample, but use sequences rather than individuals as our unit of observation. Earlier investigations of sequence-based linkage mapping relied on known sequence relatedness, whereas we infer relatedness from the sequence data. We propose two ways to associate similarity in relatedness of sequences with similarity in their trait values and compare the resulting linkage methods to two genotypic-association methods. We also introduce a procedure to label case sequences as potential carriers or noncarriers of causal variants after an association has been found. This post hoc labeling of case sequences is based on inferred relatedness to other case sequences. Our simulation results indicate that methods based on sequence relatedness improve localization and perform as well as genotypic-association methods for detecting rare causal variants. Sequence-based linkage analysis therefore has potential to fine-map allelically heterogeneous disease traits.

Phylogenomic analyses and reclassification of the Mesorhizobium complex: proposal for 9 novel genera and reclassification of 15 species.

BACKGROUD: The genus Mesorhizobium is shown by phylogenomics to be paraphyletic and forms part of a complex that includes the genera Aminobacter, Aquamicrobium, Pseudaminobacter and Tianweitania. The relationships for type strains belong to these genera need to be carefully re-evaluated. RESULTS: The relationships of Mesorhizobium complex are evaluated based on phylogenomic analyses and overall genome relatedness indices (OGRIs) of 61 type strains. According to the maximum likelihood phylogenetic tree based on concatenated sequences of 539 core proteins and the tree constructed using the bac120 bacterial marker set from Genome Taxonomy Database, 65 type strains were grouped into 9 clusters. Moreover, 10 subclusters were identified based on the OGRIs including average nucleotide identity (ANI), average amino acid identity (AAI) and core-proteome average amino acid identity (cAAI), with AAI and cAAI showing a clear intra- and inter-(sub)cluster gaps of 77.40-80.91% and 83.98-86.16%, respectively. Combined with the phylogenetic trees and OGRIs, the type strains were reclassified into 15 genera. This list includes five defined genera Mesorhizobium, Aquamicrobium, Pseudaminobacter, Aminobacterand Tianweitania, among which 40/41 Mesorhizobium species and one Aminobacter species are canonical legume microsymbionts. The other nine (sub)clusters are classified as novel genera. Cluster III, comprising symbiotic M. alhagi and M. camelthorni, is classified as Allomesorhizobium gen. nov. Cluster VI harbored a single symbiotic species M. albiziae and is classified as Neomesorhizobium gen. nov. The remaining seven non-symbiotic members were proposed as: Neoaquamicrobium gen. nov., Manganibacter gen. nov., Ollibium gen. nov., Terribium gen. nov., Kumtagia gen. nov., Borborobacter gen. nov., Aerobium gen. nov.. Furthermore, the genus Corticibacterium is restored and two species in Subcluster IX-1 are reclassified as the member of this genus. CONCLUSION: The Mesorhizobium complex are classified into 15 genera based on phylogenomic analyses and OGRIs of 65 type strains. This study resolved previously non-monophyletic genera in the Mesorhizobium complex.

Phylogeographic Analysis of Mycobacterium kansasii Isolates from Patients with M. kansasii Lung Disease in Industrialized City, Taiwan.

Little is known about environmental transmission of Mycobacterium kansasii. We retrospectively investigated potential environmental acquisition, primarily water sources, of M. kansasii among 216 patients with pulmonary disease from an industrial city in Taiwan during 2015-2017. We analyzed sputum mycobacterial cultures using whole-genome sequencing and used hierarchical Bayesian spatial network methods to evaluate risk factors for genetic relatedness of M. kansasii strains. The mean age of participants was 67 years; 24.1% had previously had tuberculosis. We found that persons from districts served by 2 water purification plants were at higher risk of being infected with genetically related M. kansasii isolates. The adjusted odds ratios were 1.81 (1.25-2.60) for the Weng Park plant and 1.39 (1.12-1.71) for the Fongshan plant. Those findings unveiled the association between water purification plants and M. kansasii pulmonary disease, highlighting the need for further environmental investigations to evaluate the risk for M. kansasii transmission.

Distinguishing pedigree relationships via multi-way identity by descent sharing and sex-specific genetic maps.

The proportion of samples with one or more close relatives in a genetic dataset increases rapidly with sample size, necessitating relatedness modeling and enabling pedigree-based analyses. Despite this, relatives are generally unreported and current inference methods typically detect only the degree of relatedness of sample pairs and not pedigree relationships. We developed CREST, an accurate and fast method that identifies the pedigree relationships of close relatives. CREST utilizes identity by descent (IBD) segments shared between a pair of samples and their mutual relatives, leveraging the fact that sharing rates among these individuals differ across pedigree configurations. Furthermore, CREST exploits the profound differences in sex-specific genetic maps to classify pairs as maternally or paternally related-e.g., paternal half-siblings-using the locations of autosomal IBD segments shared between the pair. In simulated data, CREST correctly classifies 91.5%-100% of grandparent-grandchild (GP) pairs, 80.0%-97.5% of avuncular (AV) pairs, and 75.5%-98.5% of half-siblings (HS) pairs compared to PADRE's rates of 38.5%-76.0% of GP, 60.5%-92.0% of AV, 73.0%-95.0% of HS pairs. Turning to the real 20,032 sample Generation Scotland (GS) dataset, CREST identified seven pedigrees with incorrect relationship types or maternal/paternal parent sexes, five of which we confirmed as mistakes, and two with uncertain relationships. After correcting these, CREST correctly determines relationship types for 93.5% of GP, 97.7% of AV, and 92.2% of HS pairs that have sufficient mutual relative data; the parent sex in 100% of HS and 99.6% of GP pairs; and it completes this analysis in 2.8 h including IBD detection in eight threads.

A novel hybridization capture method for direct whole genome sequencing of clinical specimens to inform Legionnaires' disease investigations.

The unprecedented precision and resolution of whole genome sequencing (WGS) can provide definitive identification of infectious agents for epidemiological outbreak tracking. WGS approaches, however, are frequently impeded by low pathogen DNA recovery from available primary specimens or unculturable samples. A cost-effective hybrid capture assay for Legionella pneumophila WGS analysis directly on primary specimens was developed. DNA from a diverse range of sputum and autopsy specimens PCR-positive for L. pneumophila serogroup 1 (LPSG1) was enriched with this method, and WGS was performed. All tested specimens were determined to be enriched for Legionella reads (up to 209,000-fold), significantly improving the discriminatory power to compare relatedness when no clinical isolate was available. We found the WGS data from some enriched specimens to differ by less than five single-nucleotide polymorphisms (SNPs) when compared to the WGS data of a matched culture isolate. This testing and analysis retrospectively provided previously unconfirmed links to environmental sources for clinical specimens of sputum and autopsy lung tissue. The latter provided the additional information needed to identify the source of these culture-negative cases associated with the South Bronx 2015 Legionnaires' disease (LD) investigation in New York City. This new method provides a proof of concept for future direct clinical specimen hybrid capture enrichment combined with WGS and bioinformatic analysis during outbreak investigations.IMPORTANCELegionnaires' disease (LD) is a severe and potentially fatal type of pneumonia primarily caused by inhalation of Legionella-contaminated aerosols from man-made water or cooling systems. LD remains extremely underdiagnosed as it is an uncommon form of pneumonia and relies on clinicians including it in the differential and requesting specialized testing. Additionally, it is challenging to obtain clinical lower respiratory specimens from cases with LD, and when available, culture requires specialized media and growth conditions, which are not available in all microbiology laboratories. In the current study, a method for Legionella pneumophila using hybrid capture by RNA baiting was developed, which allowed us to generate sufficient genome resolution from L. pneumophila serogroup 1 PCR-positive clinical specimens. This new approach offers an additional tool for surveillance of future LD outbreaks where isolation of Legionella is not possible and may help solve previously unanswered questions from past LD investigations.

Social and genetic connectivity despite ecological variation in a killer whale network.

Philopatric kin-based societies encourage a narrow breadth of conservative behaviours owing to individuals primarily learning from close kin, promoting behavioural homogeneity. However, weaker social ties beyond kin, and across a behaviourally diverse social landscape, could be sufficient to induce variation and a greater ecological niche breadth. We investigated a network of 457 photo-identified killer whales from Norway (548 encounters in 2008-2021) with diet data available (46 mixed-diet individuals feeding on both fish and mammals, and 411 exclusive fish-eaters) to quantify patterns of association within and between diet groups, and to identify underlying correlates. We genotyped a subset of 106 whales to assess patterns of genetic differentiation. Our results suggested kinship as main driver of social bonds within and among cohesive social units, while diet was most likely a consequence reflective of cultural diffusion, rather than a driver. Flexible associations within and between ecologically diverse social units led to a highly connected network, reducing social and genetic differentiation between diet groups. Our study points to a role of social connectivity, in combination with individual behavioural variation, in influencing population ecology in killer whales.

Relatives reproduce in synchrony: kinship and individual condition shape intraspecific variation in masting phenotype.

Masting (synchronous and interannually variable seed production) is frequently called a reproductive strategy; yet it is unclear whether the reproductive behaviour of individuals has a heritable component. To address this, we used 22 years of annual fruit production data from 110 Sorbus aucuparia L. trees to examine the contributions of genetic factors to the reproductive phenotype of individuals, while controlling for environmental variation. Trees sharing close genetic relationships and experiencing similar habitat conditions exhibited similar levels of reproductive synchrony. Trees of comparable sizes displayed similar levels of year-to-year variation in fruiting, with relatedness contributing to this variation. External factors, such as shading, influenced the time intervals between years with abundant fruit production. The effects of genetic relatedness on the synchrony of reproduction among trees and on interannual variation provide long-awaited evidence that the masting phenotype is heritable, and can respond to natural selection.

No evidence for a role of MHC class II genotype in the chemical encoding of heterozygosity and relatedness in Antarctic fur seals.

Despite decades of research, surprisingly little is known about the mechanism(s) by which an individual's genotype is encoded in odour. Many studies have focused on the role of the major histocompatibility complex (MHC) owing to its importance for survival and mate choice. However, the salience of MHC-mediated odours compared to chemicals influenced by the rest of the genome remains unclear, especially in wild populations where it is challenging to quantify and control for the effects of the genomic background. We addressed this issue in Antarctic fur seals by analysing skin swabs together with full-length MHC DQB II exon 2 sequences and data from 41 genome-wide distributed microsatellites. We did not find any effects of MHC relatedness on chemical similarity and there was also no relationship between MHC heterozygosity and chemical diversity. However, multilocus heterozygosity showed a significant positive association with chemical diversity, even after controlling for MHC heterozygosity. Our results appear to rule out a dominant role of the MHC in the chemical encoding of genetic information in a wild vertebrate population and highlight the need for genome-wide approaches to elucidate the mechanism(s) and specific genes underlying genotype-odour associations.

Pathologic Assessment and Staging of Multiple Non-Small Cell Lung Carcinomas: A Paradigm Shift with the Emerging Role of Molecular Methods.

Non-small cell lung carcinomas (NSCLCs) commonly present as 2 or more separate tumors. Biologically, this encompasses 2 distinct processes: separate primary lung carcinomas (SPLCs), representing independently arising tumors, and intrapulmonary metastases (IPMs), representing intrapulmonary spread of a single tumor. The advent of computed tomography imaging has substantially increased the detection of multifocal NSCLCs. The strategies and approaches for distinguishing between SPLCs and IPMs have evolved significantly over the years. Recently, genomic sequencing of somatic mutations has been widely adopted to identify targetable alterations in NSCLC. These molecular techniques have enabled pathologists to reliably discern clonal relationships among multiple NSCLCs in clinical practice. However, a standardized approach to evaluating and staging multiple NSCLCs using molecular methods is still lacking. Here, we reviewed the historical context and provided an update on the growing applications of genomic testing as a clinically relevant benchmark for determining clonal relationships in multiple NSCLCs, a practice we have designated "comparative molecular profiling." We examined the strengths and limitations of the morphology-based distinction of SPLCs vs IPMs and highlighted pivotal clinical and pathologic insights that have emerged from studying multiple NSCLCs using genomic approaches as a gold standard. Lastly, we suggest a practical approach for evaluating multiple NSCLCs in the clinical setting, considering the varying availability of molecular techniques.

Privacy-aware estimation of relatedness in admixed populations.

BACKGROUND: Estimation of genetic relatedness, or kinship, is used occasionally for recreational purposes and in forensic applications. While numerous methods were developed to estimate kinship, they suffer from high computational requirements and often make an untenable assumption of homogeneous population ancestry of the samples. Moreover, genetic privacy is generally overlooked in the usage of kinship estimation methods. There can be ethical concerns about finding unknown familial relationships in third-party databases. Similar ethical concerns may arise while estimating and reporting sensitive population-level statistics such as inbreeding coefficients for the concerns around marginalization and stigmatization. RESULTS: Here, we present SIGFRIED, which makes use of existing reference panels with a projection-based approach that simplifies kinship estimation in the admixed populations. We use simulated and real datasets to demonstrate the accuracy and efficiency of kinship estimation. We present a secure federated kinship estimation framework and implement a secure kinship estimator using homomorphic encryption-based primitives for computing relatedness between samples in two different sites while genotype data are kept confidential. Source code and documentation for our methods can be found at https://doi.org/10.5281/zenodo.7053352. CONCLUSIONS: Analysis of relatedness is fundamentally important for identifying relatives, in association studies, and for estimation of population-level estimates of inbreeding. As the awareness of individual and group genomic privacy is growing, privacy-preserving methods for the estimation of relatedness are needed. Presented methods alleviate the ethical and privacy concerns in the analysis of relatedness in admixed, historically isolated and underrepresented populations. SHORT ABSTRACT: Genetic relatedness is a central quantity used for finding relatives in databases, correcting biases in genome wide association studies and for estimating population-level statistics. Methods for estimating genetic relatedness have high computational requirements, and occasionally do not consider individuals from admixed ancestries. Furthermore, the ethical concerns around using genetic data and calculating relatedness are not considered. We present a projection-based approach that can efficiently and accurately estimate kinship. We implement our method using encryption-based techniques that provide provable security guarantees to protect genetic data while kinship statistics are computed among multiple sites.

Dissecting the genetic architecture of quantitative traits using genome-wide identity-by-descent sharing.

Additive and dominance genetic variances underlying the expression of quantitative traits are important quantities for predicting short-term responses to selection, but they are notoriously challenging to estimate in most non-model wild populations. Specifically, large-sized or panmictic populations may be characterized by low variance in genetic relatedness among individuals which, in turn, can prevent accurate estimation of quantitative genetic parameters. We used estimates of genome-wide identity-by-descent (IBD) sharing from autosomal SNP loci to estimate quantitative genetic parameters for ecologically important traits in nine-spined sticklebacks (Pungitius pungitius) from a large, outbred population. Using empirical and simulated datasets, with varying sample sizes and pedigree complexity, we assessed the performance of different crossing schemes in estimating additive genetic variance and heritability for all traits. We found that low variance in relatedness characteristic of wild outbred populations with high migration rate can impair the estimation of quantitative genetic parameters and bias heritability estimates downwards. On the other hand, the use of a half-sib/full-sib design allowed precise estimation of genetic variance components and revealed significant additive variance and heritability for all measured traits, with negligible dominance contributions. Genome-partitioning and QTL mapping analyses revealed that most traits had a polygenic basis and were controlled by genes at multiple chromosomes. Furthermore, different QTL contributed to variation in the same traits in different populations suggesting heterogeneous underpinnings of parallel evolution at the phenotypic level. Our results provide important guidelines for future studies aimed at estimating adaptive potential in the wild, particularly for those conducted in outbred large-sized populations.

Population genomics of harbour seal Phoca vitulina from northern British Columbia through California and comparison to the Atlantic subspecies.

The harbour seal Phoca vitulina is a ubiquitous pinniped species found throughout coastal waters of the Northern Hemisphere. Harbour seal impacts on ecosystem dynamics may be significant due to their high abundance and food web position. Two subspecies exist in North America, P. v. richardii in the Pacific Ocean and P. v. vitulina in the Atlantic. Strong natal philopatry of harbour seals can result in fine-scale genetic structure and isolation by distance. Management of harbour seals is expected to benefit from improved resolution of seal population structure and dynamics. Here, we use genotyping-by-sequencing to genotype 146 harbour seals from the eastern Pacific Ocean (i.e. British Columbia (BC), Oregon and California) and the western Atlantic Ocean (i.e. Québec, Newfoundland and Labrador). Using 12,742 identified variants, we confirm the recently identified elevated genetic diversity in the eastern Pacific relative to the western Atlantic and greatest differentiation between the subspecies. Further, we demonstrate that this is independent of reference genome bias or other potential technical artefacts. Coast-specific analyses with 8933 and 3828 variants in Pacific and Atlantic subspecies, respectively, identify divergence between BC and Oregon-California, and between Québec and Newfoundland-Labrador. Unexpected PCA outlier clusters were observed in two populations due to cryptic relatedness of individuals; subsequently, closely related samples were removed. Admixture analysis indicates an isolation-by-distance signature where Oregon seals contained some of the BC signature, whereas California did not. Additional sampling is needed in the central and north coast of BC to determine whether a discrete separation of populations exists within the region.

Discrimination of relatedness drives rice flowering and reproduction in cultivar mixtures.

The improvement of performance and yield in both cultivar and species mixtures has been well established. Despite the clear benefits of crop mixtures to agriculture, identifying the critical mechanisms behind performance increases are largely lacking. We experimentally demonstrated that the benefits of rice cultivar mixtures were linked to relatedness-mediated intraspecific neighbour recognition and discrimination under both field and controlled conditions. We then tested biochemical mechanisms of responses in incubation experiments involving the addition of root exudates and a root-secreted signal, (-)-loliolide, followed by transcriptome analysis. We found that closely related cultivar mixtures increased grain yields by modifying root behaviour and accelerating flowering over distantly related mixtures. Importantly, these responses were accompanied by altered concentration of signalling (-)-loliolide that affected rice transcriptome profiling, directly regulating root growth and flowering gene expression. These findings suggest that beneficial crop combinations may be generated a-priori by manipulating neighbour genetic relatedness in rice cultivar mixtures and that root-secreted (-)-loliolide functions as a key mediator of genetic relatedness interactions. The ability of relatedness discrimination to regulate rice flowering and yield raises an intriguing possibility to increase crop production.

Spatial patterns and climatic drivers of phylogenetic structure of regional liverwort assemblages in China.

BACKGROUND AND AIMS: Latitudinal diversity gradients have been intimately linked to the tropical niche conservatism hypothesis, which posits that there has been a strong filter due to the challenges faced by ancestral tropical lineages to adapt to low temperatures and colonize extra-tropical regions. In liverworts, species richness is higher towards the tropics, but the centres of diversity of the basal lineages are distributed across extra-tropical regions, pointing to the colonization of tropical regions by phylogenetically clustered assemblages of species of temperate origin. Here, we test this hypothesis through analyses of the relationship between macroclimatic variation and phylogenetic diversity in Chinese liverworts. METHODS: Phylogenetic diversity metrics and their standardized effect sizes for liverworts in each of the 28 regional floras at the province level in China were related to latitude and six climate variables using regression analysis. We conducted variation partitioning analyses to determine the relative importance of each group of climatic variables. KEY RESULTS: We find that the number of species decreases with latitude, whereas phylogenetic diversity shows the reverse pattern, and that phylogenetic diversity is more strongly correlated with temperature-related variables compared with precipitation-related variables. CONCLUSIONS: We interpret the opposite patterns observed in phylogenetic diversity and species richness in terms of a more recent origin of tropical diversity coupled with higher extinctions in temperate regions.

Genome-based reclassification of Williamsia marianensis as a later heterotypic synonym of Williamsia muralis.

The present study used whole-genome data to clarify the taxonomic assignment of two closely related Williamsia species. Genomic information for 10 type strains was available at the time of conducting this analysis. One group of type strains was found to be conspecific, namely Williamsia muralis Kämpfer et al. 1999 and Williamsia marianensis Pathom-aree et al. 2006. The 16S rRNA gene sequences showed 99 % similarity between these type strains. Whole-genome-based comparisons showed that Williamsia muralis DSM 44343T and Williamsia marianensis DSM 44944T shared 98.07 % average nucleotide identity, 98.29 % average amino acid identity and 84.80 % digital DNA-DNA hybridization values. These values exceeded the threshold values for bacterial species delineation. Further, phylogenomic analysis based on the core genomes of the strains under study confirmed that Williamsia muralis DSM 44343T and Williamsia marianensis DSM 44944T formed a monophyletic clade. Based on this evidence, we propose the reclassification of Williamsia marianensis Pathom-aree et al. 2006 as a later heterotypic synonym of Williamsia muralis Kämpfer et al. 1999.

Keguizhuia sedimenti gen. nov., sp. nov., isolated from river sediment represents a novel genus of the family Oxalobacteraceae.

A Gram-stain-negative bacterium, designated as R-40T, was isolated from sediment of the Mulong river in Mianyang city, Sichuan province, PR China. The cells of strain R-40T were aerobic non-motile and formed translucent white colonies on R2A agar. Growth occurred at 15-37 °C (optimum 30 °C), pH 5.0-9.0 (optimum 7.0) and salinities of 0-3.0 % (w/v, optimum 0 %). R-40T showed 95.2-96.6 % 16S rRNA gene sequence similarities with the type strains of species of the genera Oxalicibacterium, Herminiimonas, Lacisediminimonas, Paucimonas, Herbaspirillum and Noviherbaspirillum in the family Oxalobacteraceae. The results of phylogenetic analysis based on genome sequences indicated that the strain was clustered with type strains of species of the genera Oxalicibacterium and Herminiimonas in the family Oxalobacteraceae but formed a distinct lineage. The average nucleotide identity (ANI), digital DNA-DNA hybridization (dDDH) and average amino acid identity (AAI) values between R-40T and type strains of species of the genera Oxalicibacterium, Herminiimonas, Lacisediminimonas, Paucimonas, Herbaspirillum and Noviherbaspirillum ranged from 69.3 to 74.1 %, from 18.2 to 21.4 % and from 60.1 to 67.4 %, respectively. The major cellular fatty acids were C16 : 0, C17 : 0 cyclo and summed feature 3 (C16 : 1ω7c and/or C16 : 1ω6c). The major quinone was ubiquinone-8 (Q-8). The polar lipid profile consisted of diphosphatidylglycerol, phosphatidylethanolamine, phosphatidylglycerol, phospholipid and small amounts of glycophospholipids. The genome size of R-40T was 5.1 Mbp with 54.0 % DNA G+C content. On the basis of the evidence presented in this study, strain R-40T represents a novel species of a novel genus in the family Oxalobacteraceae, for which the name Keguizhuia sedimenti gen. nov., sp. nov. (type strain R-40T=MCCC 1K08818T=KCTC 8137T) is proposed.