Phylogenetic signatures reveal multilevel selection and fitness costs in SARS-CoV-2.

Background: Large-scale sequencing of SARS-CoV-2 has enabled the study of viral evolution during the COVID-19 pandemic. Some viral mutations may be advantageous to viral replication within hosts but detrimental to transmission, thus carrying a transient fitness advantage. By affecting the number of descendants, persistence times and growth rates of associated clades, these mutations generate localised imbalance in phylogenies. Quantifying these features in closely-related clades with and without recurring mutations can elucidate the tradeoffs between within-host replication and between-host transmission. Methods: We implemented a novel phylogenetic clustering algorithm ( mlscluster, https://github.com/mrc-ide/mlscluster) to systematically explore time-scaled phylogenies for mutations under transient/multilevel selection. We applied this method to a SARS-CoV-2 time-calibrated phylogeny with >1.2 million sequences from England, and characterised these recurrent mutations that may influence transmission fitness across PANGO-lineages and genomic regions using Poisson regressions and summary statistics. Results: We found no major differences across two epidemic stages (before and after Omicron), PANGO-lineages, and genomic regions. However, spike, nucleocapsid, and ORF3a were proportionally more enriched for transmission fitness polymorphisms (TFP)-homoplasies than other proteins. We provide a catalog of SARS-CoV-2 sites under multilevel selection, which can guide experimental investigations within and beyond the spike protein. Conclusions: This study provides empirical evidence for the existence of important tradeoffs between within-host replication and between-host transmission shaping the fitness landscape of SARS-CoV-2. This method may be used as a fast and scalable means to shortlist large sequence databases for sites under putative multilevel selection which may warrant subsequent confirmatory analyses and experimental confirmation.

Viral mutations can potentially carry a transient advantage, being simultaneously favourable for replication within hosts (e.g. by evading host immune responses) and deleterious to transmission (e.g. by having reduced cell binding). To identify such mutations, called transmission fitness polymorphisms (TFPs), we developed a clustering algorithm entitled mlscluster that computes clade-level statistics based on the number of descendants, persistence times, and growth rates of clades carrying a specific mutation in comparison with their immediate sisters without the mutation, which usually are different than expected in the presence of such TFPs. We then applied it to a representative SARS-CoV-2 time-scaled tree with >1 million whole-genome sequences from England. Our statistical analysis suggested approximately constant levels of transient selection across waves driven by very distinct variants. It also showed that genomic regions of known functional significance such as spike, nucleocapsid, and ORF3a were enriched for TFPs. This is the one of the first studies to characterise SARS-CoV-2 recurrent mutations potentially under multilevel selection, providing empirical evidence for the existence of important tradeoffs in selection between intrahost replication and inter-host transmission. Therefore, it provides target mutations for realistic coalescent-based modelling and laboratory-based investigations of their impacts and mechanisms of interaction with human cells.

Genetic Clustering of Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy Patients of Colorectal Origin: KRAS and Not TP53 Cluster Alterations are Associated with Poor Outcomes.

BACKGROUND: The prognostic impact of genetic mutations for patients who undergo cytoreductive surgery (CRS) with hyperthermic intraperitoneal chemotherapy (HIPEC) of colorectal origin (CRC) is not well defined. OBJECTIVE: We aimed to describe the genetic classifications in an unsupervised fashion, and the outcomes of this patient population. METHODS: A retrospective, bi-institutional study was performed on patients who underwent CRS-HIPEC with targeted mutation data with a median follow-up time of 61 months. Functional link analysis was performed using STRING v11.5. Genes with similar functional significance were clustered using unsupervised k-means clustering. Chi-square, Kaplan-Meier, and the log-rank test were used for comparative statistics. RESULTS: Sixty-four patients with peritoneal carcinomatosis from CRC origin underwent CRS-HIPEC between 2007 and 2022 and genetic mutation data were extracted. We identified 19 unique altered genes, with KRAS (56%), TP53 (33%), and APC (22%) being the most commonly altered; 12.5% had co-altered KRAS/TP53. After creating an interactome map, k-means clustering revealed three functional clusters. Reactome Pathway analysis on three clusters showed unique pathways (1): Ras/FGFR3 signaling; (2) p53 signaling; and (3): NOTCH signaling. Seventy-one percent of patients in cluster 1 had KRAS mutations and a median overall survival of 52.3 months (p < 0.05). CONCLUSIONS: Patients with peritoneal carcinomatosis (PC) of CRC origin who underwent CRS-HIPEC and with tumors that harbored mutations in cluster 1 (Ras/FGFR3 signaling) had worse outcomes. Pathway disruption and a cluster-centric perspective may affect prognosis more than individual genetic alterations in patients with PC of CRC origin.

Population structure and antimicrobial resistance patterns of Salmonella Typhi and Paratyphi A amid a phased municipal vaccination campaign in Navi Mumbai, India.

We performed whole-genome sequencing of 174 Salmonella Typhi and 54 Salmonella Paratyphi A isolates collected through prospective surveillance in the context of a phased typhoid conjugate vaccine introduction in Navi Mumbai, India. We investigate the temporal and geographical patterns of emergence and spread of antimicrobial resistance. We evaluated the relationship between the spatial distance between households and genetic clustering of isolates. Most isolates were non-susceptible to fluoroquinolones, with nearly 20% containing ≥3 quinolone resistance-determining region mutations. Two H58 isolates carried an IncX3 plasmid containing blaSHV-12, associated with ceftriaxone resistance, suggesting that the ceftriaxone-resistant isolates from India independently evolved on multiple occasions. Among S. Typhi, we identified two main clades circulating (2.2 and 4.3.1 [H58]); 2.2 isolates were closely related following a single introduction around 2007, whereas H58 isolates had been introduced multiple times to the city. Increasing geographic distance between isolates was strongly associated with genetic clustering (odds ratio [OR] = 0.72 per km; 95% credible interval [CrI]: 0.66-0.79). This effect was seen for distances up to 5 km (OR = 0.65 per km; 95% CrI: 0.59-0.73) but not seen for distances beyond 5 km (OR = 1.02 per km; 95% CrI: 0.83-1.26). There was a non-significant reduction in odds of clustering for pairs of isolates in vaccination communities compared with non-vaccination communities or mixed pairs compared with non-vaccination communities. Our findings indicate that S. Typhi was repeatedly introduced into Navi Mumbai and then spread locally, with strong evidence of spatial genetic clustering. In addition to vaccination, local interventions to improve water and sanitation will be critical to interrupt transmission. IMPORTANCE Enteric fever remains a major public health concern in many low- and middle-income countries, as antimicrobial resistance (AMR) continues to emerge. Geographical patterns of typhoidal Salmonella spread, critical to monitoring AMR and planning interventions, are poorly understood. We performed whole-genome sequencing of S. Typhi and S. Paratyphi A isolates collected in Navi Mumbai, India before and after a typhoid conjugate vaccine introduction. From timed phylogenies, we found two dominant circulating lineages of S. Typhi in Navi Mumbai-lineage 2.2, which expanded following a single introduction a decade prior, and 4.3.1 (H58), which had been introduced repeatedly from other parts of India, frequently containing "triple mutations" conferring high-level ciprofloxacin resistance. Using Bayesian hierarchical statistical models, we found that spatial distance between cases was strongly associated with genetic clustering at a fine scale (<5 km). Together, these findings suggest that antimicrobial-resistant S. Typhi frequently flows between cities and then spreads highly locally, which may inform surveillance and prevention strategies.

Development of a targeted amplicon sequencing method for genotyping Cyclospora cayetanensis from fresh produce and clinical samples with enhanced genomic resolution and sensitivity.

Outbreaks of cyclosporiasis, an enteric illness caused by the parasite Cyclospora cayetanensis, have been associated with consumption of various types of fresh produce. Although a method is in use for genotyping C. cayetanensis from clinical specimens, the very low abundance of C. cayetanensis in food and environmental samples presents a greater challenge. To complement epidemiological investigations, a molecular surveillance tool is needed for use in genetic linkage of food vehicles to cyclosporiasis illnesses, estimation of the scope of outbreaks or clusters of illness, and determination of geographical areas involved. We developed a targeted amplicon sequencing (TAS) assay that incorporates a further enrichment step to gain the requisite sensitivity for genotyping C. cayetanensis contaminating fresh produce samples. The TAS assay targets 52 loci, 49 of which are located in the nuclear genome, and encompasses 396 currently known SNP sites. The performance of the TAS assay was evaluated using lettuce, basil, cilantro, salad mix, and blackberries inoculated with C. cayetanensis oocysts. A minimum of 24 markers were haplotyped even at low contamination levels of 10 oocysts in 25 g leafy greens. The artificially contaminated fresh produce samples were included in a genetic distance analysis based on haplotype presence/absence with publicly available C. cayetanensis whole genome sequence assemblies. Oocysts from two different sources were used for inoculation, and samples receiving the same oocyst preparation clustered together, but separately from the other group, demonstrating the utility of the assay for genetically linking samples. Clinical fecal samples with low parasite loads were also successfully genotyped. This work represents a significant advance in the ability to genotype C. cayetanensis contaminating fresh produce along with greatly expanding the genomic diversity included for genetic clustering of clinical specimens.

ReporTree: a surveillance-oriented tool to strengthen the linkage between pathogen genetic clusters and epidemiological data.

BACKGROUND: Genomics-informed pathogen surveillance strengthens public health decision-making, playing an important role in infectious diseases' prevention and control. A pivotal outcome of genomics surveillance is the identification of pathogen genetic clusters and their characterization in terms of geotemporal spread or linkage to clinical and demographic data. This task often consists of the visual exploration of (large) phylogenetic trees and associated metadata, being time-consuming and difficult to reproduce. RESULTS: We developed ReporTree, a flexible bioinformatics pipeline that allows diving into the complexity of pathogen diversity to rapidly identify genetic clusters at any (or all) distance threshold(s) or cluster stability regions and to generate surveillance-oriented reports based on the available metadata, such as timespan, geography, or vaccination/clinical status. ReporTree is able to maintain cluster nomenclature in subsequent analyses and to generate a nomenclature code combining cluster information at different hierarchical levels, thus facilitating the active surveillance of clusters of interest. By handling several input formats and clustering methods, ReporTree is applicable to multiple pathogens, constituting a flexible resource that can be smoothly deployed in routine surveillance bioinformatics workflows with negligible computational and time costs. This is demonstrated through a comprehensive benchmarking of (i) the cg/wgMLST workflow with large datasets of four foodborne bacterial pathogens and (ii) the alignment-based SNP workflow with a large dataset of Mycobacterium tuberculosis. To further validate this tool, we reproduced a previous large-scale study on Neisseria gonorrhoeae, demonstrating how ReporTree is able to rapidly identify the main species genogroups and characterize them with key surveillance metadata, such as antibiotic resistance data. By providing examples for SARS-CoV-2 and the foodborne bacterial pathogen Listeria monocytogenes, we show how this tool is currently a useful asset in genomics-informed routine surveillance and outbreak detection of a wide variety of species. CONCLUSIONS: In summary, ReporTree is a pan-pathogen tool for automated and reproducible identification and characterization of genetic clusters that contributes to a sustainable and efficient public health genomics-informed pathogen surveillance. ReporTree is implemented in python 3.8 and is freely available at https://github.com/insapathogenomics/ReporTree .

A deep learning framework for characterization of genotype data.

Dimensionality reduction is a data transformation technique widely used in various fields of genomics research. The application of dimensionality reduction to genotype data is known to capture genetic similarity between individuals, and is used for visualization of genetic variation, identification of population structure as well as ancestry mapping. Among frequently used methods are principal component analysis, which is a linear transform that often misses more fine-scale structures, and neighbor-graph based methods which focus on local relationships rather than large-scale patterns. Deep learning models are a type of nonlinear machine learning method in which the features used in data transformation are decided by the model in a data-driven manner, rather than by the researcher, and have been shown to present a promising alternative to traditional statistical methods for various applications in omics research. In this study, we propose a deep learning model based on a convolutional autoencoder architecture for dimensionality reduction of genotype data. Using a highly diverse cohort of human samples, we demonstrate that the model can identify population clusters and provide richer visual information in comparison to principal component analysis, while preserving global geometry to a higher extent than t-SNE and UMAP, yielding results that are comparable to an alternative deep learning approach based on variational autoencoders. We also discuss the use of the methodology for more general characterization of genotype data, showing that it preserves spatial properties in the form of decay of linkage disequilibrium with distance along the genome and demonstrating its use as a genetic clustering method, comparing results to the ADMIXTURE software frequently used in population genetic studies.

Molecular identification, characterization, and antagonistic activity profiling of Bacillus cereus LOCK 1002 along with the in-silico analysis of its presumptive bacteriocins.

Objectives: This research aimed to isolate, identify, and characterize a new strain of Bacillus cereus through different molecular biology approaches so that it could be further studied for therapeutic purposes against selective enteric pathogens. Materials and Methods: Pure isolates of B. cereus were prepared from buffalo yogurt samples in REMBA medium. Initially, the morphological, physiological, and biochemical properties were studied accordingly. Following the tests, the molecular identification for the strain identification was conducted through plasmid DNA extraction, PCR, agarose gel electrophoresis, and 16S rRNA sequencing up to 1.37 kb. Afterward, the antibiotic sensitivity [Epsilometer test (E-Test)] and antifungal activity were tested considering different concentrations. Being classified from the aforementioned tests, a comprehensive antimicrobial activity test was conducted using the cell-free-supernatant (CFS) of the test strain against selective enteric pathogens in humans in vitro. Besides, the different clusters of genes were identified and characterized for understanding the presumptive bacteriocins present in the CFS of the strain in silico, where molecular string properties were calculated. Finally, the evolutionary relationship among diversified bacteriocins synthesized by different Bacillus strains was studied to predict the CFS-containing bacteriocins of the new strain. Results: Purified isolates of B. cereus were Gram-positive rods and showed significant tolerance (p < 0.0001) to different concentrations of pH, phenol, bile salt, and NaCl. 16S rRNA revealed the strain as LOCK 1002, which was strongly sensitive to all the antibiotics used and resistant to selective antifungal agents. The CFS of B. cereus LOCK 1002 was found to be a very promising antagonist to all the enteric pathogens used in the culture condition. Two gene clusters were predicted to be interconnected and responsible for different presumptive bacteriocins. Conclusion: The newly identified LOCK 1002 can be a very potent strain of B. cereus in use as an antimicrobial agent for having different bacteriocin coding gene clusters.

Diversity, Dispersal and Mode of Reproduction of Amanita exitialis in Southern China.

Amanita exitialis is a poisonous mushroom and has caused many deaths in southern China. In this study, we collected 118 fruiting bodies of A. exitialis from seven different sites in Guangdong Province in southern China and investigated their genetic relationships using 14 polymorphic molecular markers. These 14 markers grouped the 118 fruiting bodies into 20 multilocus genotypes. Among these 20 genotypes, eight were each found only once while the remaining 12 were each represented by two to 54 fruiting bodies. Interestingly, among the 12 shared genotypes, four were shared between/among local populations that were separated by as far as over 80 km, a result consistent with secondary homothallic reproduction and long-distance spore dispersal. Despite the observed gene flow, significant genetic differentiations were found among the local populations, primarily due to the over-representation of certain genotypes within individual local populations. STRUCTURE analyses revealed that the 118 fruiting bodies belonged to three genetic clusters, consistent with divergence within this species in this geographic region. Interestingly, we found an excess of heterozygous individuals at both the local and the total sample level, suggesting potential inbreeding depression and heterozygous advantage in these populations of A. exitialis. We discuss the implications of our results for understanding the life cycle, dispersal, and evolution of this poisonous mushroom.

Characteristics and growth of the genetic HIV transmission network of Mexico City during 2020.

INTRODUCTION: Molecular surveillance systems could provide public health benefits to focus strategies to improve the HIV care continuum. Here, we infer the HIV genetic network of Mexico City in 2020, and identify actively growing clusters that could represent relevant targets for intervention. METHODS: All new diagnoses, referrals from other institutions, as well as persons returning to care, enrolling at the largest HIV clinic in Mexico City were invited to participate in the study. The network was inferred from HIV pol sequences, using pairwise genetic distance methods, with a locally hosted, secure version of the HIV-TRACE tool: Seguro HIV-TRACE. Socio-demographic, clinical and behavioural metadata were overlaid across the network to design focused prevention interventions. RESULTS: A total of 3168 HIV sequences from unique individuals were included. One thousand and one-hundred and fifty (36%) sequences formed 1361 links within 386 transmission clusters in the network. Cluster size varied from 2 to 14 (63% were dyads). After adjustment for covariates, lower age (adjusted odds ratio [aOR]: 0.37, p<0.001; >34 vs. <24 years), being a man who has sex with men (MSM) (aOR: 2.47, p = 0.004; MSM vs. cisgender women), having higher viral load (aOR: 1.28, p<0.001) and higher CD4+ T cell count (aOR: 1.80, p<0.001; ≥500 vs. <200 cells/mm3 ) remained associated with higher odds of clustering. Compared to MSM, cisgender women and heterosexual men had significantly lower education (none or any elementary: 59.1% and 54.2% vs. 16.6%, p<0.001) and socio-economic status (low income: 36.4% and 29.0% vs. 18.6%, p = 0.03) than MSM. We identified 10 (2.6%) clusters with constant growth, for prioritized intervention, that included intersecting sexual risk groups, highly connected nodes and bridge nodes between possible sub-clusters with high growth potential. CONCLUSIONS: HIV transmission in Mexico City is strongly driven by young MSM with higher education level and recent infection. Nevertheless, leveraging network inference, we identified actively growing clusters that could be prioritized for focused intervention with demographic and risk characteristics that do not necessarily reflect the ones observed in the overall clustering population. Further studies evaluating different models to predict growing clusters are warranted. Focused interventions will have to consider structural and risk disparities between the MSM and the heterosexual populations.

Extensive Genetic Diversity and Widespread Azole Resistance in Greenhouse Populations of Aspergillus fumigatus in Yunnan, China.

Aspergillus fumigatus is the main cause of invasive aspergillosis (IA) with a high annual global incidence and mortality rate. Recent studies have indicated an increasing prevalence of azole-resistant A. fumigatus (ARAF) strains, with agricultural use of azole fungicides as a potential contributor. China has an extensive agricultural production system and uses a wide array of fungicides for crop production, including in modern growth facilities such as greenhouses. Soils in greenhouses are among the most intensively cultivated. However, little is known about the occurrence and distribution of ARAF in greenhouse soils. Here, we investigated genetic variation and triazole drug susceptibility in A. fumigatus from greenhouses around metropolitan Kunming in Yunnan, southwest China. Abundant allelic and genotypic variations were found among 233 A. fumigatus strains isolated from nine greenhouses in this region. Significantly, ∼80% of the strains were resistant to at least one medical triazole drug, with >30% showing cross-resistance to both itraconazole and voriconazole. Several previously reported mutations associated with triazole resistance in the triazole target gene cyp51A were also found in our strains, with a strong positive correlation between the frequency of mutations at the cyp51A promoter and that of voriconazole resistance. Phylogenetic analyses of cyp51A gene sequences showed evidence for multiple independent origins of azole-resistant genotypes of A. fumigatus in these greenhouses. Evidence for multiple origins of azole resistance and the widespread distributions of genetically very diverse triazole-resistant strains of A. fumigatus in greenhouses calls for significant attention from public health agencies.IMPORTANCE The origin and prevalence of azole-resistant Aspergillus fumigatus have been attracting increasing attention from biologists, clinicians, and public health agencies. Current evidence suggests agricultural fungicide use as a major cause. In southwest China, greenhouses are used to produce large amounts of fruits, flowers, and vegetables for consumers throughout China as well as those in other countries, primarily in southeast Asia. Here, we found a very high frequency (∼80%) of triazole-resistant A. fumigatus in our sample, the highest reported so far, with a significant proportion of these strains resistant to both tested agricultural fungicides and medical triazole drugs. In addition, we found novel allelic and genotypic diversities and evidence for multiple independent origins of azole-resistant genotypes of A. fumigatus in greenhouse populations in this region. Our study calls for a systematic evaluation of the effects of azole fungicide usage in greenhouses on human health.

Enhancing genomics-based outbreak detection of endemic Salmonella enterica serovar Typhimurium using dynamic thresholds.

Salmonella enterica serovar Typhimurium is the leading cause of salmonellosis in Australia, and the ability to identify outbreaks and their sources is vital to public health. Here, we examined the utility of whole-genome sequencing (WGS), including complete genome sequencing with Oxford Nanopore technologies, in examining 105 isolates from an endemic multi-locus variable number tandem repeat analysis (MLVA) type over 5 years. The MLVA type was very homogeneous, with 90 % of the isolates falling into groups with a five SNP cut-off. We developed a new two-step approach for outbreak detection using WGS. The first clustering at a zero single nucleotide polymorphism (SNP) cut-off was used to detect outbreak clusters that each occurred within a 4 week window and then a second clustering with dynamically increased SNP cut-offs were used to generate outbreak investigation clusters capable of identifying all outbreak cases. This approach offered optimal specificity and sensitivity for outbreak detection and investigation, in particular of those caused by endemic MLVA types or clones with low genetic diversity. We further showed that inclusion of complete genome sequences detected no additional mutational events for genomic outbreak surveillance. Phylogenetic analysis found that the MLVA type was likely to have been derived recently from a single source that persisted over 5 years, and seeded numerous sporadic infections and outbreaks. Our findings suggest that SNP cut-offs for outbreak cluster detection and public-health surveillance should be based on the local diversity of the relevant strains over time. These findings have general applicability to outbreak detection of bacterial pathogens.

Public health in genetic spaces: a statistical framework to optimize cluster-based outbreak detection.

Genetic clustering is a popular method for characterizing variation in transmission rates for rapidly evolving viruses, and could potentially be used to detect outbreaks in 'near real time'. However, the statistical properties of clustering are poorly understood in this context, and there are no objective guidelines for setting clustering criteria. Here, we develop a new statistical framework to optimize a genetic clustering method based on the ability to forecast new cases. We analysed the pairwise Tamura-Nei (TN93) genetic distances for anonymized HIV-1 subtype B pol sequences from Seattle (n = 1,653) and Middle Tennessee, USA (n = 2,779), and northern Alberta, Canada (n = 809). Under varying TN93 thresholds, we fit two models to the distributions of new cases relative to clusters of known cases: 1, a null model that assumes cluster growth is strictly proportional to cluster size, i.e. no variation in transmission rates among individuals; and 2, a weighted model that incorporates individual-level covariates, such as recency of diagnosis. The optimal threshold maximizes the difference in information loss between models, where covariates are used most effectively. Optimal TN93 thresholds varied substantially between data sets, e.g. 0.0104 in Alberta and 0.016 in Seattle and Tennessee, such that the optimum for one population would potentially misdirect prevention efforts in another. For a given population, the range of thresholds where the weighted model conferred greater predictive accuracy tended to be narrow (±0.005 units), and the optimal threshold tended to be stable over time. Our framework also indicated that variation in the recency of HIV diagnosis among clusters was significantly more predictive of new cases than sample collection dates (ΔAIC > 50). These results suggest that one cannot rely on historical precedence or convention to configure genetic clustering methods for public health applications, especially when translating methods between settings of low-level and generalized epidemics. Our framework not only enables investigators to calibrate a clustering method to a specific public health setting, but also provides a variable selection procedure to evaluate different predictive models of cluster growth.

Intercontinental long-distance seed dispersal across the Mediterranean Basin explains population genetic structure of a bird-dispersed shrub.

Long-distance dispersal (LDD) is a pivotal process for plants determining their range of distribution and promoting gene flow among distant populations. Most fleshy-fruited species rely on frugivorous vertebrates to disperse their seeds across the landscape. While LDD events are difficult to record, a few ecological studies have shown that birds move a sizeable number of ingested seeds across geographic barriers, such as sea straits. The foraging movements of migrant frugivores across distant populations, including those separated by geographic barriers, creates a constant flow of propagules that in turn shapes the spatial distributions of the genetic variation in populations. Here, we have analysed the genetic diversity and structure of 74 populations of Pistacia lentiscus, a fleshy-fruited shrub widely distributed in the Mediterranean Basin, to elucidate whether the Mediterranean Sea acts as a geographic barrier or alternatively whether migratory frugivorous birds promote gene flow among populations located on both sides of the sea. Our results show reduced genetic distances among populations, including intercontinental populations, and they show a significant genetic structure across an eastern-western axis. These findings are consistent with known bird migratory routes that connect the European and African continents following a north-southwards direction during the fruiting season of many fleshy-fruited plants. Further, approximate Bayesian analysis failed to explain the observed patterns as a result of historical population migrations at the end of Last Glacial Maximum. Therefore, anthropic and/or climatic changes that would disrupt the migratory routes of frugivorous birds might have genetic consequences for the plant species they feed upon.

Combining Bayesian genetic clustering and ecological niche modeling: Insights into wolf intraspecific genetic structure.

The distribution of intraspecific genetic variation and how it relates to environmental factors is of increasing interest to researchers in macroecology and biogeography. Recent studies investigated the relationships between the environment and patterns of intraspecific genetic variation across species ranges but only few rigorously tested the relation between genetic groups and their ecological niches. We quantified the relationship of genetic differentiation (F ST) and the overlap of ecological niches (as measured by n-dimensional hypervolumes) among genetic groups resulting from spatial Bayesian genetic clustering in the wolf (Canis lupus) in the Italian peninsula. Within the Italian wolf population, four genetic clusters were detected, and these clusters showed different ecological niches. Moreover, different wolf clusters were significantly related to differences in land cover and human disturbance features. Such differences in the ecological niches of genetic clusters should be interpreted in light of neutral processes that hinder movement, dispersal, and gene flow among the genetic clusters, in order to not prematurely assume any selective or adaptive processes. In the present study, we found that both the plasticity of wolves-a habitat generalist-to cope with different environmental conditions and the occurrence of barriers that limit gene flow lead to the formation of genetic intraspecific genetic clusters and their distinct ecological niches.

A fast likelihood solution to the genetic clustering problem.

The investigation of genetic clusters in natural populations is an ubiquitous problem in a range of fields relying on the analysis of genetic data, such as molecular ecology, conservation biology and microbiology. Typically, genetic clusters are defined as distinct panmictic populations, or parental groups in the context of hybridisation. Two types of methods have been developed for identifying such clusters: model-based methods, which are usually computer-intensive but yield results which can be interpreted in the light of an explicit population genetic model, and geometric approaches, which are less interpretable but remarkably faster.Here, we introduce snapclust, a fast maximum-likelihood solution to the genetic clustering problem, which allies the advantages of both model-based and geometric approaches. Our method relies on maximising the likelihood of a fixed number of panmictic populations, using a combination of geometric approach and fast likelihood optimisation, using the Expectation-Maximisation (EM) algorithm. It can be used for assigning genotypes to populations and optionally identify various types of hybrids between two parental populations. Several goodness-of-fit statistics can also be used to guide the choice of the retained number of clusters.Using extensive simulations, we show that snapclust performs comparably to current gold standards for genetic clustering as well as hybrid detection, with some advantages for identifying hybrids after several backcrosses, while being orders of magnitude faster than other model-based methods. We also illustrate how snapclust can be used for identifying the optimal number of clusters, and subsequently assign individuals to various hybrid classes simulated from an empirical microsatellite dataset. snapclust is implemented in the package adegenet for the free software R, and is therefore easily integrated into existing pipelines for genetic data analysis. It can be applied to any kind of co-dominant markers, and can easily be extended to more complex models including, for instance, varying ploidy levels. Given its flexibility and computer-efficiency, it provides a useful complement to the existing toolbox for the study of genetic diversity in natural populations.

Lingual Gyrus Surface Area Is Associated with Anxiety-Depression Severity in Young Adults: A Genetic Clustering Approach.

Here we aimed to identify cortical endophenotypes for anxiety-depression. Our data-driven approach used vertex-wise genetic correlations (estimated from a twin sample: 157 monozygotic and 194 dizygotic twin pairs) to parcellate cortical thickness (CT) and surface area (SA) into genetically homogeneous regions (Chen et al., 2013). In an overlapping twin and sibling sample (n = 834; aged 15-29, 66% female), in those with anxiety-depression Somatic and Psychological Health Report (SPHERE) scores (Hickie et al., 2001) above median, we found a reduction of SA in an occipito-temporal cluster, which comprised part of the right lingual, fusiform and parahippocampal gyrii. A similar reduction was observed in the Human Connectome Project (HCP) sample (n = 890, age 22-37, 56.5% female) in those with Adult Self Report (ASR) DSM-oriented scores (Achenbach et al., 2005) in the 25-95% quantiles. A post hoc vertex-wise analysis identified the right lingual and, to a lesser extent the fusiform gyrus. Overall, the surface reduction explained by the anxiety-depression scores was modest (r = -0.10, 3rd order spline, and r = -0.040, 1st order spline in the HCP). The discordant results in the top 5% of the anxiety-depression scores may be explained by differences in recruitment between the studies. However, we could not conclude whether this cortical region was an endophenotype for anxiety-depression as the genetic correlations did not reach significance, which we attribute to the modest effect size (post hoc statistical power <10%).

Biased phylodynamic inferences from analysing clusters of viral sequences.

Phylogenetic methods are being increasingly used to help understand the transmission dynamics of measurably evolving viruses, including HIV. Clusters of highly similar sequences are often observed, which appear to follow a 'power law' behaviour, with a small number of very large clusters. These clusters may help to identify subpopulations in an epidemic, and inform where intervention strategies should be implemented. However, clustering of samples does not necessarily imply the presence of a subpopulation with high transmission rates, as groups of closely related viruses can also occur due to non-epidemiological effects such as over-sampling. It is important to ensure that observed phylogenetic clustering reflects true heterogeneity in the transmitting population, and is not being driven by non-epidemiological effects. We qualify the effect of using a falsely identified 'transmission cluster' of sequences to estimate phylodynamic parameters including the effective population size and exponential growth rate under several demographic scenarios. Our simulation studies show that taking the maximum size cluster to re-estimate parameters from trees simulated under a randomly mixing, constant population size coalescent process systematically underestimates the overall effective population size. In addition, the transmission cluster wrongly resembles an exponential or logistic growth model 99% of the time. We also illustrate the consequences of false clusters in exponentially growing coalescent and birth-death trees, where again, the growth rate is skewed upwards. This has clear implications for identifying clusters in large viral databases, where a false cluster could result in wasted intervention resources.

Range-wide genetic structure and demographic history in the bat ectoparasite Cimex adjunctus.

BACKGROUND: Evolutionary histories of parasite and host populations are intimately linked such that their spatial genetic structures may be correlated. While these processes have been relatively well studied in specialist parasites and their hosts, less is known about the ecological and evolutionary consequences of relationships between generalist ectoparasites and their hosts. The aim of this study was to investigate the genetic structure and demographic history of a bat ectoparasite, Cimex adjunctus, whose host affinity is weak but the biology of the potential hosts have been well studied. This ectoparasite has been hypothesized to rely on its hosts for dispersal due to its low inherent dispersal potential. Here we describe genetic diversity and demographic history in C. adjunctus through most of its range in North America. We investigated variation at the cytochrome c oxidase 1 mitochondrial gene and nine microsatellite markers, and tested the prediction that genetic diversity in C. adjunctus is spatially structured. We also tested the prediction that demographic history in C. adjunctus is characterized by range and demographic expansion as a consequence of post-Pleistocene climate warming. RESULTS: We found stronger spatial structuring of genetic diversity in C. adjunctus than has been quantified in two of its hosts, but contrast in amount of variation explained by host association with different genetic markers (i.e., nuclear vs mitochondrial DNA). Also, C. adjunctus' history is not primarily characterized by demographic and range expansion, as is the case with two of its key hosts. CONCLUSIONS: Our study shows different patterns of genetic structure and demographic history in C. adjunctus than have been detected in two of its key hosts. Our results suggest an effect of a loose parasite-host relationship and anti-parasitism strategies on genetic structure and post-Pleistocene recovery of population size.

Impacts and shortcomings of genetic clustering methods for infectious disease outbreaks.

For infectious diseases, a genetic cluster is a group of closely related infections that is usually interpreted as representing a recent outbreak of transmission. Genetic clustering methods are becoming increasingly popular for molecular epidemiology, especially in the context of HIV where there is now considerable interest in applying these methods to prioritize groups for public health resources such as pre-exposure prophylaxis. To date, genetic clustering has generally been performed with ad hoc algorithms, only some of which have since been encoded and distributed as free software. These algorithms have seldom been validated on simulated data where clusters are known, and their interpretation and similarities are not transparent to users outside of the field. Here, I provide a brief overview on the development and inter-relationships of genetic clustering methods, and an evaluation of six methods on data simulated under an epidemic model in a risk-structured population. The simulation analysis demonstrates that the majority of clustering methods are systematically biased to detect variation in sampling rates among subpopulations, not variation in transmission rates. I discuss these results in the context of previous work and the implications for public health applications of genetic clustering.

Genetic polymorphisms and skin aging: the identification of population genotypic groups holds potential for personalized treatments.

INTRODUCTION: Skin changes are among the most visible signs of aging. Skin properties such as hydration, elasticity, and antioxidant capacity play a key role in the skin aging process. Skin aging is a complex process influenced by heritable and environmental factors. Recent studies on twins have revealed that up to 60% of the skin aging variation between individuals can be attributed to genetic factors, while the remaining 40% is due to non-genetic factors. Recent advances in genomics and bioinformatics approaches have led to the association of certain single nucleotide polymorphisms (SNPs) to skin properties. Our aim was to classify individuals based on an ensemble of multiple polymorphisms associated with certain properties of the skin for providing personalized skin care and anti-aging therapies. METHODS AND RESULTS: We identified the key proteins and SNPs associated with certain properties of the skin that contribute to skin aging. We selected a set of 13 SNPs in gene coding for these proteins which are potentially associated with skin aging. Finally, we classified a sample of 120 female volunteers into ten clusters exhibiting different skin properties according to their genotypic signature. CONCLUSION: This is the first study that describes the actual frequency of genetic polymorphisms and their distribution in clusters involved in skin aging in a Caucasian population. Individuals can be divided into genetic clusters defined by genotypic variables. These genotypic variables are linked with polymorphisms in one or more genes associated with certain properties of the skin that contribute to a person's perceived age. Therefore, by using this classification, it is possible to characterize human skin care and anti-aging needs on the basis of an individual's genetic signature, thus opening the door to personalized treatments addressed at specific populations. This is part of an ongoing effort towards personalized anti-aging therapies combining genetic signatures with environmental and life style evaluations.