Selection Events Research Articles

The Legacy of Infectious Disease Exposure on the Genomic Diversity of Indigenous Southern Mexicans.

To characterize host risk factors for infectious disease in Mesoamerican populations, we interrogated 857,481 SNPs assayed using the Affymetrix 6.0 genotyping array for signatures of natural selection in immune response genes. We applied three statistical tests to identify signatures of natural selection: locus-specific branch length (LSBL), the cross-population extended haplotype homozygosity (XP-EHH), and the integrated haplotype score (iHS). Each of the haplotype tests (XP-EHH and iHS) were paired with LSBL and significance was determined at the 1% level. For the paired analyses, we identified 95 statistically significant windows for XP-EHH/LSBL and 63 statistically significant windows for iHS/LSBL. Among our top immune response loci, we found evidence of recent directional selection associated with the major histocompatibility complex (MHC) and the peroxisome proliferator-activated receptor gamma (PPAR-γ) signaling pathway. These findings illustrate that Mesoamerican populations' immunity has been shaped by exposure to infectious disease. As targets of selection, these variants are likely to encode phenotypes that manifest themselves physiologically and therefore may contribute to population-level variation in immune response. Our results shed light on past selective events influencing the host response to modern diseases, both pathogenic infection as well as autoimmune disorders.

Genomic evidence for adaptation to tuberculosis in the Andes before European contact

Most studies focusing on human high-altitude adaptation in the Andean highlands have thus far been focused on Peruvian populations. We present high-coverage whole genomes from Indigenous people living in the Ecuadorian highlands and perform multi-method scans to detect positive natural selection. We identified regions of the genome that show signals of strong selection to both cardiovascular and hypoxia pathways, which are distinct from those uncovered in Peruvian populations. However, the strongest signals of selection were related to regions of the genome that are involved in immune function related to tuberculosis. Given our estimated timing of this selection event, the Indigenous people of Ecuador may have adapted to Mycobacterium tuberculosis thousands of years before the arrival of Europeans. Furthermore, we detect a population collapse that coincides with the arrival of Europeans, which is more severe than other regions of the Andes, suggesting differing effects of contact across high-altitude populations.

Estimating Temporally Variable Selection Intensity from Ancient DNA Data.

Novel technologies for recovering DNA information from archaeological and historical specimens have made available an ever-increasing amount of temporally spaced genetic samples from natural populations. These genetic time series permit the direct assessment of patterns of temporal changes in allele frequencies and hold the promise of improving power for the inference of selection. Increased time resolution can further facilitate testing hypotheses regarding the drivers of past selection events such as the incidence of plant and animal domestication. However, studying past selection processes through ancient DNA (aDNA) still involves considerable obstacles such as postmortem damage, high fragmentation, low coverage, and small samples. To circumvent these challenges, we introduce a novel Bayesian framework for the inference of temporally variable selection based on genotype likelihoods instead of allele frequencies, thereby enabling us to model sample uncertainties resulting from the damage and fragmentation of aDNA molecules. Also, our approach permits the reconstruction of the underlying allele frequency trajectories of the population through time, which allows for a better understanding of the drivers of selection. We evaluate its performance through extensive simulations and demonstrate its utility with an application to the ancient horse samples genotyped at the loci for coat coloration. Our results reveal that incorporating sample uncertainties can further improve the inference of selection.

Highly differentiated loci resolve phylogenetic relationships in the Bean Goose complex

BackgroundReconstructing phylogenetic relationships with genomic data remains a challenging endeavor. Numerous phylogenomic studies have reported incongruent gene trees when analyzing different genomic regions, complicating the search for a ‘true’ species tree. Some authors have argued that genomic regions of increased divergence (i.e. differentiation islands) reflect the species tree, although other studies have shown that these regions might produce misleading topologies due to species-specific selective sweeps or ancient introgression events. In this study, we tested the extent to which highly differentiated loci can resolve phylogenetic relationships in the Bean Goose complex, a group of goose taxa that includes the Taiga Bean Goose (Anser fabalis), the Tundra Bean Goose (Anser serrirostris) and the Pink-footed Goose (Anser brachyrhynchus).ResultsFirst, we show that a random selection of genomic loci—which mainly samples the undifferentiated regions of the genome—results in an unresolved species complex with a monophyletic A. brachyrhynchus embedded within a paraphyletic cluster of A. fabalis and A. serrirostris. Next, phylogenetic analyses of differentiation islands converged upon a topology of three monophyletic clades in which A. brachyrhynchus is sister to A. fabalis, and A. serrirostris is sister to the clade uniting these two species. Close inspection of the locus trees within the differentiated regions revealed that this topology was consistently supported over other phylogenetic arrangements. As it seems unlikely that selection or introgression events have impacted all differentiation islands in the same way, we are convinced that this topology reflects the ‘true’ species tree. Additional analyses, based on D-statistics, revealed extensive introgression between A. fabalis and A. serrirostris, which partly explains the failure to resolve the species complex with a random selection of genomic loci. Recent introgression between these taxa has probably erased the phylogenetic branching pattern across a large section of the genome, whereas differentiation islands were unaffected by the homogenizing gene flow and maintained the phylogenetic patterns that reflect the species tree.ConclusionsThe evolution of the Bean Goose complex can be depicted as a simple bifurcating tree, but this would ignore the impact of introgressive hybridization. Hence, we advocate that the evolutionary relationships between these taxa are best represented as a phylogenetic network.

Zanubrutinib in patients with treatment-naïve or relapsed/refractory Waldenström macroglobulinemia: An expanded-access study of 50 patients in the United States.

Zanubrutinib in patients with treatment-naïve or relapsed/refractory Waldenström macroglobulinemia: An expanded-access study of 50 patients in the United States.

Anomaly detection under coordinate transformations

There is a growing need for machine learning-based anomaly detection strategies to broaden the search for Beyond-the-Standard-Model (BSM) physics at the Large Hadron Collider (LHC) and elsewhere. The first step of any anomaly detection approach is to specify observables and then use them to decide on a set of anomalous events. One common choice is to select events that have low probability density. It is a well-known fact that probability densities are not invariant under coordinate transformations, so the sensitivity can depend on the initial choice of coordinates. The broader machine learning community has recently connected coordinate sensitivity with anomaly detection and our goal is to bring awareness of this issue to the growing high energy physics literature on anomaly detection. In addition to analytical explanations, we provide numerical examples from simple random variables and from the LHC Olympics Dataset that show how using probability density as an anomaly score can lead to events being classified as anomalous or not depending on the coordinate frame.

Electron transfer catalysis mediated by 3d complexes of redox non-innocent ligands possessing an azo function: a perspective.

It was first reported almost two decades ago that ligands with azo functions are capable of accepting electron(s) upon coordination to produce azo-anion radical complexes, thereby exhibiting redox non-innocence. Over the past two decades, there have been numerous reports of such complexes along with their structures and diverse characteristics. The ability of a coordinated azo function to accept one or more electron(s), thereby acting as an electron reservoir, is currently employed to carry out electron transfer catalysis since they can undergo redox transformation at mild potentials due to the presence of energetically accessible energy levels. The cooperative involvement of redox non-innocent ligand(s) containing an azo group and the coordinated metal centre can adjust and modulate the Lewis acidity of the latter through selective ligand-centred redox events, thereby manipulating the capacity of the metal centre to bind to the substrate. We have summarized the list of first row transition metal complexes of iron, cobalt, nickel, copper and zinc with redox non-innocent ligands incorporating an azo function that have been exploited as electron transfer catalysts to effectuate sustainable synthesis of a wide variety of useful chemicals. These include ketazines, pyrimidines, benzothiazole, benzoxazoles, N-acyl hydrazones, quinazoline-4(3)H-ones, C-3 alkylated indoles, N-alkylated anilines and N-alkylated heteroamines. The reaction pathways, as demonstrated by catalytic loops, reveal that the azo function of a coordinated ligand can act as an electron sink in the initial steps to bring about alcohol oxidation and thereafter, they serve as an electron pool to produce the final products either via HAT or PCET processes.

Wildfires in the western United States are mobilizing PM2.5-associated nutrients and may be contributing to downwind cyanobacteria blooms.

Wildfire activity is increasing in the continental U.S. and can be linked to climate change effects, including rising temperatures and more frequent drought conditions. Wildfire emissions and large fire frequency have increased in the western U.S., impacting human health and ecosystems. We linked 15 years (2006-2020) of particulate matter (PM2.5) chemical speciation data with smoke plume analysis to identify PM2.5-associated nutrients elevated in air samples on smoke-impacted days. Most macro- and micro-nutrients analyzed (phosphorus, calcium, potassium, sodium, silicon, aluminum, iron, manganese, and magnesium) were significantly elevated on smoke days across all years analyzed. The largest percent increase was observed for phosphorus. With the exception of ammonium, all other nutrients (nitrate, copper, and zinc), although not statistically significant, had higher median values across all years on smoke vs. non-smoke days. Not surprisingly, there was high variation between smoke impacted days, with some nutrients episodically elevated >10 000% during select fire events. Beyond nutrients, we also explored instances where algal blooms occurred in multiple lakes downwind from high-nutrient fires. In these cases, remotely sensed cyanobacteria indices in downwind lakes increased two to seven days following the occurrence of wildfire smoke above the lake. This suggests that elevated nutrients in wildfire smoke may contribute to downwind algal blooms. Since cyanobacteria blooms can be associated with the production of cyanotoxins and wildfire activity is increasing due to climate change, this finding has implications for drinking water reservoirs in the western United States, and for lake ecology, particularly alpine lakes with otherwise limited nutrient inputs.

Negeteroseksualnye uliki: rassledovanie sobstvennoy seksualnosti v biograficheskikh narrativakh

The article is devoted to how non-heterosexual people look for perceived signs of their sexual otherness in the past and present them in biographical narratives. This search for clues is like an investigation: informants select events and details from childhood and adolescence that are assumed to be related to their current sexual identity. These clues include, among other things, recollections of childhood sexualized games, child gender otherness, and childhood interest or indifference to the topic of sexuality. In biographical interviews and written autobiographies, the author considers rhetorical strategies for the self-description of non-heterosexual people. The evidence paradigm of K. Ginzburg is used as a conceptualization tool, but, in this case, participants look for clues, not the researcher. The methodological basis of the work is a biographical approach and narrative analysis. With the help of non-heterosexual clues found in the past, participants simultaneously confirm the sustainability of their sexuality and connect elements of their biography into an orderly story. A coherent biographical narrative allows them to normalize their non-heterosexuality by offering an explanation and background to it. At the same time, it is built on opposition to the heterosexual norm.

Population pharmacokinetics and exposure-response analyses of daratumumab plus pomalidomide/dexamethasone in relapsed or refractory multiple myeloma.

A population pharmacokinetic (PPK) model was developed to characterize pharmacokinetics (PK) of subcutaneous or intravenous daratumumab administration in a new indication (i.e., combination with pomalidomide and dexamethasone [D-Pd] in patients with relapsed or refractory multiple myeloma [RRMM]). Analyses were conducted to explore exposure-response (E-R) relationships for efficacy and select treatment-emergent adverse events (TEAEs). The PPK analysis included pooled data from the D-Pd cohorts of the phase 3 APOLLO and phase 1b EQUULEUS studies. Covariates were evaluated in the PPK model. Model-predicted exposures to daratumumab were compared between covariate subgroups of interest and used to investigate relationships between daratumumab exposure and efficacy and safety in APOLLO. The PPK analysis included 1146 daratumumab PK samples from 239 patients (APOLLO, n = 140; EQUULEUS, n = 99). Observed concentration-time data of daratumumab were well described by a two-compartment PPK model with first-order absorption and parallel linear and nonlinear elimination pathways. Treatment with D-Pd provided similar daratumumab PK characteristics versus historical daratumumab monotherapy. The E-R dataset contained data from 290 APOLLO patients (D-Pd, n = 140; Pd, n = 150). The PK-efficacy relationship of daratumumab supported improved progression-free survival for patients in the D-Pd group vs. the Pd group. Additionally, TEAEs did not increase with increasing PK exposure in the D-Pd group. The PPK and E-R analyses support the daratumumab subcutaneous 1800 mg dosing regimen in combination with Pd for treatment of patients with RRMM. No dose adjustment is recommended in this indication for any of the investigated factors, none of which had clinically relevant effects on daratumumab PK.

Resolving drug selection and migration in an inbred South American Plasmodium falciparum population with identity-by-descent analysis.

The human malaria parasite Plasmodium falciparum is globally widespread, but its prevalence varies significantly between and even within countries. Most population genetic studies in P. falciparum focus on regions of high transmission where parasite populations are large and genetically diverse, such as sub-Saharan Africa. Understanding population dynamics in low transmission settings, however, is of particular importance as these are often where drug resistance first evolves. Here, we use the Pacific Coast of Colombia and Ecuador as a model for understanding the population structure and evolution of Plasmodium parasites in small populations harboring less genetic diversity. The combination of low transmission and a high proportion of monoclonal infections means there are few outcrossing events and clonal lineages persist for long periods of time. Yet despite this, the population is evolutionarily labile and has successfully adapted to changes in drug regime. Using newly sequenced whole genomes, we measure relatedness between 166 parasites, calculated as identity by descent (IBD), and find 17 distinct but highly related clonal lineages, six of which have persisted in the region for at least a decade. This inbred population structure is captured in more detail with IBD than with other common population structure analyses like PCA, ADMIXTURE, and distance-based trees. We additionally use patterns of intra-chromosomal IBD and an analysis of haplotypic variation to explore past selection events in the region. Two genes associated with chloroquine resistance, crt and aat1, show evidence of hard selective sweeps, while selection appears soft and/or incomplete at three other key resistance loci (dhps, mdr1, and dhfr). Overall, this work highlights the strength of IBD analyses for studying parasite population structure and resistance evolution in regions of low transmission, and emphasizes that drug resistance can evolve and spread in small populations, as will occur in any region nearing malaria elimination.

1117. COVID-19 Monoclonal Antibodies: A Single Center Real World Experience

Abstract Background COVID-19 monoclonal antibodies (mAb) have been shown to reduce risk of ED visit and hospitalization when administered early in disease course. However, there has been little data evaluating the challenges and barriers to delivering an outpatient mAb infusion. Methods This was a retrospective cohort review of patients referred 12/7/20-5/20/21 to evaluate factors associated with selection and treatment with mAb. Inclusion criteria included symptom onset within 10 days of referral, high-risk for severe disease, not hospitalized or meeting hospitalization criteria, and followed by in-network primary care provider. We collected data on demographics, high risk comorbidities, illness course, infusion selection, and adverse events. Using unadjusted regression models, we estimated effect sizes (mean difference or percentage point difference) and 95% confidence intervals to estimate baseline factors associated with infusion. Results Of the 230 eligible subjects, 179 (78%) were selected for mAb treatment and 119 (66%) were infused. Those with a Social Vulnerability Index > 0.5 were more likely to be selected (86% vs 71%) but among those selected were less likely to be infused (59% vs 74%). Other predictors for selection (Fig 1) included Spanish language preference, 3+ symptoms at the time of referral, shortness of breath, headache, immunosuppressive medications, and chronic respiratory disease. Predictors of mAb infusion (Fig 2) included 3+ symptoms at time of referral, diarrhea, myalgias, male gender, being employed, and history of chronic kidney or respiratory disease. Time of onset of symptoms-to-referral was < 5 days and test-to-referral was 2-3 days (Fig 3). 60 subjects were selected but not infused. 19 (32%) were not infused due to becoming ineligible after referral. Reasons for ineligibility included clinical decompensation or passing eligibility window. 39 (65%) were not infused due to refusal after selection. Most common reasons for refusal included feeling better (12, 20%), concerns about mAb (6, 10%), and lack of caregiver for a family member (4, 7%). Figure 1.Predictors of Selection for mAb InfusionFigure 2.Predictors of mAb Infusion After SelectionFigure 3.Days of Symptoms to Testing and Referral Conclusion Our study identified factors that impact selection and infusion of mAb in a real-world setting. This can help address potential barriers in operationalizing the administration of COVID-19 mAb in the future. Disclosures All Authors: No reported disclosures.

Selective sweeps linked to the colonization of novel habitats and climatic changes in a wild tomato species.

Positive selection is the driving force underpinning local adaptation and leaves footprints of selective sweeps on the underlying major genes. Quantifying the timing of selection and revealing the genetic bases of adaptation in plant species occurring in steep and varying environmental gradients are crucial to predict a species' ability to colonize new niches. We use whole-genome sequence data from six populations across three different habitats of the wild tomato species Solanum chilense to infer the past demographic history and search for genes under strong positive selection. We then correlate current and past climatic projections with the demographic history, allele frequencies, the age of selection events and distribution shifts. Several selective sweeps occur at regulatory networks involved in root-hair development in low altitude and response to photoperiod and vernalization in high-altitude populations. These sweeps appear to occur in a concerted fashion in a given regulatory gene network at particular periods of substantial climatic change. Using a unique combination of genome scans and modelling of past climatic data, we quantify the timing of selection at genes likely underpinning local adaptation to semiarid habitats.

Population genetic analyses unveiled genetic stratification and differential natural selection signatures across the G-gene of viral hemorrhagic septicemia virus.

Introduction: Viral hemorrhagic septicemia virus (VHSV) is the most lethal pathogen in aquaculture, infecting more than 140 fish species in marine, estuarine, and freshwater environments. Viral hemorrhagic septicemia virus is an enveloped RNA virus that belongs to the family Rhabdoviridae and the genus Novirhabdovirus. The current study is designed to infer the worldwide Viral hemorrhagic septicemia virus isolates' genetic diversity and evolutionary dynamics based on G-gene sequences. Methods: The complete G-gene sequences of viral hemorrhagic septicemia virus were retrieved from the public repositories with known timing and geography details. Pairwise statistical analysis was performed using Arlequin. The Bayesian model-based approach implemented in STRUCTURE software was used to investigate the population genetic structure, and the phylogenetic tree was constructed using MEGA X and IQ-TREE. The natural selection analysis was assessed using different statistical approaches, including IFEL, MEME, and SLAC. Results and Discussion: The global Viral hemorrhagic septicemia virus samples are stratified into five genetically distinct subpopulations. The STRUCTURE analysis unveiled spatial clustering of genotype Ia into two distinct clusters at K = 3. However, at K = 5, the genotype Ia samples, deposited from Denmark, showed temporal distribution into two groups. The analyses unveiled that the genotype Ia samples stratified into subpopulations possibly based on spatiotemporal distribution. Several viral hemorrhagic septicemia virus samples are characterized as genetically admixed or recombinant. In addition, differential or subpopulation cluster-specific natural selection signatures were identified across the G-gene codon sites among the viral hemorrhagic septicemia virus isolates. Evidence of low recombination events elucidates that genetic mutations and positive selection events have possibly driven the observed genetic stratification of viral hemorrhagic septicemia virus samples.

Analysis of ATG8 Family Members Using LC3-Interacting Regions (LIR)-Based Molecular Traps.

The ATG8 family of proteins regulates the autophagy process from the autophagosome maturation and cargo recruitment up to degradation. Autophagy dysfunction is involved in the development of multiple diseases. The LC3 interacting region (LIR)-based molecular traps have been designed to isolate endogenous ATG8 proteins and their interactors in order to facilitate the study of selective autophagy events. Here, we summarize protocols describing LC3 traps and sample preparation as well as adaptations for the analysis of ATG8 proteins in different biological models. This protocol was optimized to prepare affinity columns, reduce background, and improve the protein recovery to be analyzed by immunodetection with antibodies recognizing proteins of interest.

Assessment and Distribution of Runs of Homozygosity in Horse Breeds Representing Different Utility Types

The present study reports runs of homozygosity (ROH) distribution in the genomes of six horse breeds (571 horses in total) representing three horse types (primitive, light, and draft horses) based on the 65k Equine BeadChip assay. Of major interest was the length, quantity, and frequency of ROH characteristics, as well as differences between horse breeds and types. Noticeable differences in the number, length and distribution of ROH between breeds were observed, as well as in genomic inbreeding coefficients. We also identified regions of the genome characterized by high ROH coverage, known as ROH islands, which may be signals of recent selection events. Eight to fourteen ROH islands were identified per breed, which spanned multiple genes. Many were involved in important horse breed characteristics, including WFIKNN2, CACNA1G, STXBP4, NOG, FAM184B, QDPR, LCORL, and the zinc finger protein family. Regions of the genome with zero ROH occurrences were also of major interest in specific populations. Depending on the breed, we detected between 2 to 57 no-ROH regions and identified 27 genes in these regions that were common for five breeds. These genes were involved in, e.g., muscle contractility (CACNA1A) and muscle development (miR-23, miR-24, miR-27). To sum up, the obtained results can be furthered analyzed in the topic of identification of markers unique for specific horse breed characteristics.

M&A performance in emerging markets: do they behave in unison or otherwise?

PurposeThe study aims to evaluate and compare the mergers and acquisitions (M&As) performance utilising a sample of deals originating from Brazil, Russia, India, China and South Africa (BRICS). In addition to nation-wise performance analysis, a further sub-sample analysis is conducted based on the target location (domestic and cross-border), development status (developed and emerging) and the acquired ownership stakes (majority and minority).Design/methodology/approachThe final sample of the study includes 7,105 deals announced between 2000 and 2019. M&A performance is proxied by the abnormal returns earned over the select event windows. Multiple parametric and non-parametric tests are employed for testing the robustness.FindingsThe results indicate significant performance differences across BRICS markets, with the highest and lowest abnormal returns reported for Chinese and Russian acquirers, respectively. The disaggregated analysis also affirms the performance differences for the select sub-samples.Research limitations/implicationsThe study highlights the need for acknowledging and expounding the differences in M&As across emerging markets. Further, the results of the study provide a possible explanation of the disagreement over the M&A performance results reported in the previous literature.Practical implicationsAcknowledging and understanding the potential performance differences based on location, ownership strategies and development status can aid executives in sharpening decision-making and also help general investors.Originality/valueThe study contributes by examining a comprehensive sample of deals across five major emerging economies, as against the majority of previous studies which have their results based on either single nation samples or have utilised only a sub-sample of domestic or foreign acquisitions.

Studying T Cell Development in Neonatal and Adult Thymic Slices.

T cell development occurs in the thymus and is coordinated temporally and spatially within the highly complex thymic microenvironment. Therefore, T cell selection and maturation events cannot be fully recapitulated using traditional two-dimensional tissue culture in vitro. The thymic slice system provides a highly versatile system for studying T cell development ex vivo while preserving three-dimensional thymic architecture. Using the thymic slice system, T cell selection and maturation events can be visualized by live imaging and quantified by flow cytometry. Here we describe the process for generating slices from neonatal and adult mice.

A Bayesian Statistical Model Is Able to Predict Target-by-Target Selection Behaviour in a Human Foraging Task.

Foraging refers to search involving multiple targets or multiple types of targets, and as a model task has a long history in animal behaviour and human cognition research. Foraging behaviour is usually operationalized using summary statistics, such as average distance covered during target collection (the path length) and the frequency of switching between target types. We recently introduced an alternative approach, which is to model each instance of target selection as random selection without replacement. Our model produces estimates of a set of foraging biases, such as a bias to select closer targets or targets of a particular category. Here we apply this model to predict individual target selection events. We add a new start position bias to the model, and generate foraging paths using the parameters estimated from individual participants' pre-existing data. The model predicts which target the participant will select next with a range of accuracy from 43% to 69% across participants (chance is 11%). The model therefore explains a substantial proportion of foraging behaviour in this paradigm. The situations where the model makes errors reveal useful information to guide future research on those aspects of foraging that we have not yet explained.

Inferring parameters of cancer evolution in chronic lymphocytic leukemia.

As a cancer develops, its cells accrue new mutations, resulting in a heterogeneous, complex genomic profile. We make use of this heterogeneity to derive simple, analytic estimates of parameters driving carcinogenesis and reconstruct the timeline of selective events following initiation of an individual cancer, where two longitudinal samples are available for sequencing. Using stochastic computer simulations of cancer growth, we show that we can accurately estimate mutation rate, time before and after a driver event occurred, and growth rates of both initiated cancer cells and subsequently appearing subclones. We demonstrate that in order to obtain accurate estimates of mutation rate and timing of events, observed mutation counts should be corrected to account for clonal mutations that occurred after the founding of the tumor, as well as sequencing coverage. Chronic lymphocytic leukemia (CLL), which often does not require treatment for years after diagnosis, presents an optimal system to study the untreated, natural evolution of cancer cell populations. When we apply our methodology to reconstruct the individual evolutionary histories of CLL patients, we find that the parental leukemic clone typically appears within the first fifteen years of life.