Statistical summaries of unlabelled evolutionary trees.

Rooted and ranked phylogenetic trees are mathematical objects that are useful in modelling hierarchical data and evolutionary relationships with applications to many fields such as evolutionary biology and genetic epidemiology. Bayesian phylogenetic inference usually explores the posterior distribution of trees via Markov chain Monte Carlo methods. However, assessing uncertainty and summarizing distributions remains challenging for these types of structures. While labelled phylogenetic trees have been extensively studied, relatively less literature exists for unlabelled trees that are increasingly useful, for example when one seeks to summarize samples of trees obtained with different methods, or from different samples and environments, and wishes to assess the stability and generalizability of these summaries. In our paper, we exploit recently proposed distance metrics of unlabelled ranked binary trees and unlabelled ranked genealogies, or trees equipped with branch lengths, to define the Fréchet mean, variance and interquartile sets as summaries of these tree distributions. We provide an efficient combinatorial optimization algorithm for computing the Fréchet mean of a sample or of distributions on unlabelled ranked tree shapes and unlabelled ranked genealogies. We show the applicability of our summary statistics for studying popular tree distributions and for comparing the SARS-CoV-2 evolutionary trees across different locations during the COVID-19 epidemic in 2020. Our current implementations are publicly available at https://github.com/RSamyak/fmatrix.

adaPop: Bayesian inference of dependent population dynamics in coalescent models.

The coalescent is a powerful statistical framework that allows us to infer past population dynamics leveraging the ancestral relationships reconstructed from sampled molecular sequence data. In many biomedical applications, such as in the study of infectious diseases, cell development, and tumorgenesis, several distinct populations share evolutionary history and therefore become dependent. The inference of such dependence is a highly important, yet a challenging problem. With advances in sequencing technologies, we are well positioned to exploit the wealth of high-resolution biological data for tackling this problem. Here, we present adaPop, a probabilistic model to estimate past population dynamics of dependent populations and to quantify their degree of dependence. An essential feature of our approach is the ability to track the time-varying association between the populations while making minimal assumptions on their functional shapes via Markov random field priors. We provide nonparametric estimators, extensions of our base model that integrate multiple data sources, and fast scalable inference algorithms. We test our method using simulated data under various dependent population histories and demonstrate the utility of our model in shedding light on evolutionary histories of different variants of SARS-CoV-2.

Statistical Challenges in Tracking the Evolution of SARS-CoV-2.

Genomic surveillance of SARS-CoV-2 has been instrumental in tracking the spread and evolution of the virus during the pandemic. The availability of SARS-CoV-2 molecular sequences isolated from infected individuals, coupled with phylodynamic methods, have provided insights into the origin of the virus, its evolutionary rate, the timing of introductions, the patterns of transmission, and the rise of novel variants that have spread through populations. Despite enormous global efforts of governments, laboratories, and researchers to collect and sequence molecular data, many challenges remain in analyzing and interpreting the data collected. Here, we describe the models and methods currently used to monitor the spread of SARS-CoV-2, discuss long-standing and new statistical challenges, and propose a method for tracking the rise of novel variants during the epidemic.

Adaptive Preferential Sampling in Phylodynamics With an Application to SARS-CoV-2.

Longitudinal molecular data of rapidly evolving viruses and pathogens provide information about disease spread and complement traditional surveillance approaches based on case count data. The coalescent is used to model the genealogy that represents the sample ancestral relationships. The basic assumption is that coalescent events occur at a rate inversely proportional to the effective population size N e (t), a time-varying measure of genetic diversity. When the sampling process (collection of samples over time) depends on N e (t), the coalescent and the sampling processes can be jointly modeled to improve estimation of N e (t). Failing to do so can lead to bias due to model misspecification. However, the way that the sampling process depends on the effective population size may vary over time. We introduce an approach where the sampling process is modeled as an inhomogeneous Poisson process with rate equal to the product of N e (t) and a time-varying coefficient, making minimal assumptions on their functional shapes via Markov random field priors. We provide efficient algorithms for inference, show the model performance vis-a-vis alternative methods in a simulation study, and apply our model to SARS-CoV-2 sequences from Los Angeles and Santa Clara counties. The methodology is implemented and available in the R package adapref. Supplementary files for this article are available online.

Enumeration of binary trees compatible with a perfect phylogeny.

Evolutionary models used for describing molecular sequence variation suppose that at a non-recombining genomic segment, sequences share ancestry that can be represented as a genealogy-a rooted, binary, timed tree, with tips corresponding to individual sequences. Under the infinitely-many-sites mutation model, mutations are randomly superimposed along the branches of the genealogy, so that every mutation occurs at a chromosomal site that has not previously mutated; if a mutation occurs at an interior branch, then all individuals descending from that branch carry the mutation. The implication is that observed patterns of molecular variation from this model impose combinatorial constraints on the hidden state space of genealogies. In particular, observed molecular variation can be represented in the form of a perfect phylogeny, a tree structure that fully encodes the mutational differences among sequences. For a sample of n sequences, a perfect phylogeny might not possess n distinct leaves, and hence might be compatible with many possible binary tree structures that could describe the evolutionary relationships among the n sequences. Here, we investigate enumerative properties of the set of binary ranked and unranked tree shapes that are compatible with a perfect phylogeny, and hence, the binary ranked and unranked tree shapes conditioned on an observed pattern of mutations under the infinitely-many-sites mutation model. We provide a recursive enumeration of these shapes. We consider both perfect phylogenies that can be represented as binary and those that are multifurcating. The results have implications for computational aspects of the statistical inference of evolutionary parameters that underlie sets of molecular sequences.

The impact of the COVID-19 preventive measures on influenza transmission: molecular and epidemiological evidence.

OBJECTIVE: We quantify the impact of COVID-19-related control measures on the spread of human influenza virus H1N1 and H3N2. METHODS: We analyzed case numbers to estimate the end of the 2019-2020 influenza season and compared it with the median of the previous 9 seasons. In addition, we used influenza molecular data to compare within-region and between-region genetic diversity and effective population size from 2019 to 2020. Finally, we analyzed personal behavior and policy stringency data for each region. RESULTS: The 2019-2020 influenza season ended earlier than the median of the previous 9 seasons in all regions. For H1N1 and H3N2, there was an increase in between-region genetic diversity in most pairs of regions between 2019 and 2020. There was a decrease in within-region genetic diversity for 12 of 14 regions for H1N1 and 9 of 12 regions for H3N2. There was a decrease in effective population size for 10 of 13 regions for H1N1 and 3 of 7 regions for H3N2. CONCLUSIONS: We found consistent evidence of a decrease in influenza incidence after the introduction of preventive measures due to COVID-19 emergence.

Agitation, confusion, and aggression in critically ill traumatic brain injury-a pilot cohort study (ACACIA-PILOT).

BACKGROUND: Agitated behaviors are problematic in intensive care unit (ICU) patients recovering from traumatic brain injury (TBI) as they create substantial risks and challenges for healthcare providers. To date, there have been no studies evaluating their epidemiology and impact in the ICU. Prior to planning a multicenter study, assessment of recruitment, feasibility, and pilot study procedures is needed. In this pilot study, we aimed to evaluate the feasibility of conducting a large multicenter prospective cohort study. METHODS: This feasibility study recruited adult patients admitted to the ICU with TBI and an abnormal cerebral CT scan. In all patients, we documented Richmond Agitation Sedation Score (RASS) and agitated behaviors every 8-h nursing shift using a dedicated tool documenting 14 behaviors. Our feasibility objectives were to obtain consent from at least 2 patients per month; completion of screening logs for agitated behaviors by bedside nurses for more than 90% of 8-h shifts; completion of data collection in an average of 6 h or less; and obtain 6-month follow-up for surviving patients. The main clinical outcome was the incidence of agitation and individual agitated behaviors. RESULTS: In total, 47 eligible patients were approached for inclusion and 30 (64% consent rate) were recruited over a 10-month period (3 patients/month). In total, 794 out of 827 (96%) possible 8-h periods of agitated behavior logs were completed by bedside nurses, with a median of 24 observations (IQR 28.0) per patient. During the ICU stay, 17 of 30 patients developed agitation (56.7%; 95% CI 0.37-0.75) defined as RASS ≥ 2 during at least one observation period and for a median of 4 days (IQR 5.5). At 6 months post-TBI, among the 24 available patients, an unfavorable score (GOS-E < 5 including death) was reported in 12 patients (50%). In the 14 patients who were alive and available at 6 months, the median QOLIBRI score was 74.5 (IQR 18.5). CONCLUSIONS: This study demonstrates the feasibility of conducting a larger cohort study to evaluate the epidemiology and impact of agitated behaviors in critically ill TBI patients. This study also shows that agitated behaviors are frequent and are associated with adverse events.

Distance metrics for ranked evolutionary trees.

Genealogical tree modeling is essential for estimating evolutionary parameters in population genetics and phylogenetics. Recent mathematical results concerning ranked genealogies without leaf labels unlock opportunities in the analysis of evolutionary trees. In particular, comparisons between ranked genealogies facilitate the study of evolutionary processes of different organisms sampled at multiple time periods. We propose metrics on ranked tree shapes and ranked genealogies for lineages isochronously and heterochronously sampled. Our proposed tree metrics make it possible to conduct statistical analyses of ranked tree shapes and timed ranked tree shapes or ranked genealogies. Such analyses allow us to assess differences in tree distributions, quantify estimation uncertainty, and summarize tree distributions. We show the utility of our metrics via simulations and an application in infectious diseases.

Adaptive preferential sampling in phylodynamics.

Longitudinal molecular data of rapidly evolving viruses and pathogens provide information about disease spread and complement traditional surveillance approaches based on case count data. The coalescent is used to model the genealogy that represents the sample ancestral relationships. The basic assumption is that coalescent events occur at a rate inversely proportional to the effective population size $N_{e}(t)$, a time-varying measure of genetic diversity. When the sampling process (collection of samples over time) depends on $N_{e}(t)$, the coalescent and the sampling processes can be jointly modeled to improve estimation of $N_{e}(t)$. Failing to do so can lead to bias due to model misspecification. However, the way that the sampling process depends on the effective population size may vary over time. We introduce an approach where the sampling process is modeled as an inhomogeneous Poisson process with rate equal to the product of $N_{e}(t)$ and a time-varying coefficient, making minimal assumptions on their functional shapes via Markov random field priors. We provide scalable algorithms for inference, show the model performance vis-a-vis alternative methods in a simulation study, and apply our model to SARS-CoV-2 sequences from Los Angeles and Santa Clara counties. The methodology is implemented and available in the R package adapref.

High-throughput SARS-CoV-2 and host genome sequencing from single nasopharyngeal swabs.

During COVID19 and other viral pandemics, rapid generation of host and pathogen genomic data is critical to tracking infection and informing therapies. There is an urgent need for efficient approaches to this data generation at scale. We have developed a scalable, high throughput approach to generate high fidelity low pass whole genome and HLA sequencing, viral genomes, and representation of human transcriptome from single nasopharyngeal swabs of COVID19 patients.

SEQUENTIAL IMPORTANCE SAMPLING FOR MULTIRESOLUTION KINGMAN-TAJIMA COALESCENT COUNTING.

Statistical inference of evolutionary parameters from molecular sequence data relies on coalescent models to account for the shared genealogical ancestry of the samples. However, inferential algorithms do not scale to available data sets. A strategy to improve computational efficiency is to rely on simpler coalescent and mutation models, resulting in smaller hidden state spaces. An estimate of the cardinality of the state-space of genealogical trees at different resolutions is essential to decide the best modeling strategy for a given dataset. To our knowledge, there is neither an exact nor approximate method to determine these cardinalities. We propose a sequential importance sampling algorithm to estimate the cardinality of the sample space of genealogical trees under different coalescent resolutions. Our sampling scheme proceeds sequentially across the set of combinatorial constraints imposed by the data, which in this work are completely linked sequences of DNA at a non recombining segment. We analyze the cardinality of different genealogical tree spaces on simulations to study the settings that favor coarser resolutions. We apply our method to estimate the cardinality of genealogical tree spaces from mtDNA data from the 1000 genomes and a sample from a Melanesian population at the ß-globin locus.

Bayesian Estimation of Population Size Changes by Sampling Tajima's Trees.

The large state space of gene genealogies is a major hurdle for inference methods based on Kingman's coalescent. Here, we present a new Bayesian approach for inferring past population sizes, which relies on a lower-resolution coalescent process that we refer to as "Tajima's coalescent." Tajima's coalescent has a drastically smaller state space, and hence it is a computationally more efficient model, than the standard Kingman coalescent. We provide a new algorithm for efficient and exact likelihood calculations for data without recombination, which exploits a directed acyclic graph and a correspondingly tailored Markov Chain Monte Carlo method. We compare the performance of our Bayesian Estimation of population size changes by Sampling Tajima's Trees (BESTT) with a popular implementation of coalescent-based inference in BEAST using simulated and human data. We empirically demonstrate that BESTT can accurately infer effective population sizes, and it further provides an efficient alternative to the Kingman's coalescent. The algorithms described here are implemented in the R package phylodyn, which is available for download at https://github.com/JuliaPalacios/phylodyn.

Exact limits of inference in coalescent models.

Recovery of population size history from molecular sequence data is an important problem in population genetics. Inference commonly relies on a coalescent model linking the population size history to genealogies. The high computational cost of estimating parameters from these models usually compels researchers to select a subset of the available data or to rely on insufficient summary statistics for statistical inference. We consider the problem of recovering the true population size history from two possible alternatives on the basis of coalescent time data previously considered by Kim et al. (2015). We improve upon previous results by giving exact expressions for the probability of correctly distinguishing between the two hypotheses as a function of the separation between the alternative size histories, the number of individuals, loci, and the sampling times. In more complicated settings we estimate the exact probability of correct recovery by Monte Carlo simulation. Our results give considerably more pessimistic inferential limits than those previously reported. We also extended our analyses to pairwise SMC and SMC' models of recombination. This work is relevant for optimal design when the inference goal is to test scientific hypotheses about population size trajectories in coalescent models with and without recombination.

No Evidence for Recent Selection at FOXP2 among Diverse Human Populations.

FOXP2, initially identified for its role in human speech, contains two nonsynonymous substitutions derived in the human lineage. Evidence for a recent selective sweep in Homo sapiens, however, is at odds with the presence of these substitutions in archaic hominins. Here, we comprehensively reanalyze FOXP2 in hundreds of globally distributed genomes to test for recent selection. We do not find evidence of recent positive or balancing selection at FOXP2. Instead, the original signal appears to have been due to sample composition. Our tests do identify an intronic region that is enriched for highly conserved sites that are polymorphic among humans, compatible with a loss of function in humans. This region is lowly expressed in relevant tissue types that were tested via RNA-seq in human prefrontal cortex and RT-PCR in immortalized human brain cells. Our results represent a substantial revision to the adaptive history of FOXP2, a gene regarded as vital to human evolution.

Uric Acid Crystals Induce Placental Inflammation and Alter Trophoblast Function via an IL-1-Dependent Pathway: Implications for Fetal Growth Restriction.

Excessive placental inflammation is associated with several pathological conditions, including stillbirth and fetal growth restriction. Although infection is a known cause of inflammation, a significant proportion of pregnancies have evidence of inflammation without any detectable infection. Inflammation can also be triggered by endogenous mediators, called damage associated molecular patterns or alarmins. One of these damage-associated molecular patterns, uric acid, is increased in the maternal circulation in pathological pregnancies and is a known agonist of the Nlrp3 inflammasome and inducer of inflammation. However, its effects within the placenta and on pregnancy outcomes remain largely unknown. We found that uric acid (monosodium urate [MSU]) crystals induce a proinflammatory profile in isolated human term cytotrophoblast cells, with a predominant secretion of IL-1ß and IL-6, a result confirmed in human term placental explants. The proinflammatory effects of MSU crystals were shown to be IL-1-dependent using a caspase-1 inhibitor (inhibits IL-1 maturation) and IL-1Ra (inhibits IL-1 signaling). The proinflammatory effect of MSU crystals was accompanied by trophoblast apoptosis and decreased syncytialization. Correspondingly, administration of MSU crystals to rats during late gestation induced placental inflammation and was associated with fetal growth restriction. These results make a strong case for an active proinflammatory role of MSU crystals at the maternal-fetal interface in pathological pregnancies, and highlight a key mediating role of IL-1. Furthermore, our study describes a novel in vivo animal model of noninfectious inflammation during pregnancy, which is triggered by MSU crystals and leads to reduced fetal growth.

phylodyn: an R package for phylodynamic simulation and inference.

We introduce phylodyn, an r package for phylodynamic analysis based on gene genealogies. The package's main functionality is Bayesian nonparametric estimation of effective population size fluctuations over time. Our implementation includes several Markov chain Monte Carlo-based methods and an integrated nested Laplace approximation-based approach for phylodynamic inference that have been developed in recent years. Genealogical data describe the timed ancestral relationships of individuals sampled from a population of interest. Here, individuals are assumed to be sampled at the same point in time (isochronous sampling) or at different points in time (heterochronous sampling); in addition, sampling events can be modelled with preferential sampling, which means that the intensity of sampling events is allowed to depend on the effective population size trajectory. We assume the coalescent and the sequentially Markov coalescent processes as generative models of genealogies. We include several coalescent simulation functions that are useful for testing our phylodynamics methods via simulation studies. We compare the performance and outputs of various methods implemented in phylodyn and outline their strengths and weaknesses. r package phylodyn is available at https://github.com/mdkarcher/phylodyn.

Sterile inflammation and pregnancy complications: a review.

Inflammation is essential for successful embryo implantation, pregnancy maintenance and delivery. In the last decade, important advances have been made in regard to endogenous, and therefore non-infectious, initiators of inflammation, which can act through the same receptors as pathogens. These molecules are referred to as damage-associated molecular patterns (DAMPs), and their involvement in reproduction has only recently been unraveled. Even though inflammation is necessary for successful reproduction, untimely activation of inflammatory processes can have devastating effect on pregnancy outcomes. Many DAMPs, such as uric acid, high-mobility group box 1 (HMGB1), interleukin (IL)-1 and cell-free fetal DNA, have been associated with pregnancy complications, such as miscarriages, preeclampsia and preterm birth in preclinical models and in humans. However, the specific contribution of alarmins to these conditions is still under debate, as currently there is lack of information on their mechanism of action. In this review, we discuss the role of sterile inflammation in reproduction, including early implantation and pregnancy complications. Particularly, we focus on major alarmins vastly implicated in numerous sterile inflammatory processes, such as uric acid, HMGB1, IL-1α and cell-free DNA (especially that of fetal origin) while giving an overview of the potential role of other candidate alarmins.

Quantifying and Mitigating the Effect of Preferential Sampling on Phylodynamic Inference.

Phylodynamics seeks to estimate effective population size fluctuations from molecular sequences of individuals sampled from a population of interest. One way to accomplish this task formulates an observed sequence data likelihood exploiting a coalescent model for the sampled individuals' genealogy and then integrating over all possible genealogies via Monte Carlo or, less efficiently, by conditioning on one genealogy estimated from the sequence data. However, when analyzing sequences sampled serially through time, current methods implicitly assume either that sampling times are fixed deterministically by the data collection protocol or that their distribution does not depend on the size of the population. Through simulation, we first show that, when sampling times do probabilistically depend on effective population size, estimation methods may be systematically biased. To correct for this deficiency, we propose a new model that explicitly accounts for preferential sampling by modeling the sampling times as an inhomogeneous Poisson process dependent on effective population size. We demonstrate that in the presence of preferential sampling our new model not only reduces bias, but also improves estimation precision. Finally, we compare the performance of the currently used phylodynamic methods with our proposed model through clinically-relevant, seasonal human influenza examples.

Novel Noncompetitive IL-1 Receptor-Biased Ligand Prevents Infection- and Inflammation-Induced Preterm Birth.

Preterm birth (PTB) is firmly linked to inflammation regardless of the presence of infection. Proinflammatory cytokines, including IL-1ß, are produced in gestational tissues and can locally upregulate uterine activation proteins. Premature activation of the uterus by inflammation may lead to PTB, and IL-1 has been identified as a key inducer of this condition. However, all currently available IL-1 inhibitors are large molecules that exhibit competitive antagonism properties by inhibiting all IL-1R signaling, including transcription factor NF-κB, which conveys important physiological roles. We hereby demonstrate the efficacy of a small noncompetitive (all-d peptide) IL-1R-biased ligand, termed rytvela (labeled 101.10) in delaying IL-1ß-, TLR2-, and TLR4-induced PTB in mice. The 101.10 acts without significant inhibition of NF-κB, and instead selectively inhibits IL-1R downstream stress-associated protein kinases/transcription factor c-jun and Rho GTPase/Rho-associated coiled-coil-containing protein kinase signaling pathways. The 101.10 is effective at decreasing proinflammatory and/or prolabor genes in myometrium tissue and circulating leukocytes in all PTB models independently of NF-κB, undermining NF-κB role in preterm labor. In this work, biased signaling modulation of IL-1R by 101.10 uncovers a novel strategy to prevent PTB without inhibiting NF-κB.