Biology subjects

Pennings, P. S.

Publications and source records attributed to Pennings, P. S..

13 recordsLinked to original sources

bioRxiv2023

Using a decision tree to predict COVID case numbers: a tutorial for beginners

This manuscript describes the development of a module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on machine learning and decision tree concepts in an interactive format that uses appropriate cloud resources for data access and analyses. Machine learning (ML) is an important tool in biomedical research and can lead to improvements in diagnosis, treatment, and prevention of diseases. During the COVID pandemic ML was used for predictions at the patient and community levels. Given its ubiquity, it is important that future doctors, researchers and teachers get acquainted with ML and its contributions to research. Our goal is to make it easier for everyone to learn about machine learning. The learning module we present here is based on a small COVID dataset, videos, annotated code and the use of Google Colab or the Google Cloud Platform (GCP). The benefit of these platforms is that students do not have to set up a programming environment on their computer which saves time and is also an important democratization factor. The module focuses on learning the basics of decision trees by applying them to COVID data. It introduces basic terminology used in supervised machine learning and its relevance to research. Our experience with biology students at San Francisco State University suggests that the material increases interest in ML.

scientific communication and education↗

bioRxiv2022

SCIP: A self-paced, community-based summer coding program creates community and increases coding confidence

In 2020, many students lost summer opportunities due to the COVID-19 pandemic. We wanted to offer students an opportunity to learn computational skills and be part of a community while they were stuck at home. Because the pandemic was very isolating, it was important to support students to learn and build community online. We used lessons learned from literature and our own experience to design, run and test an online program for students called the Science Coding Immersion Program (SCIP). In our program, students worked in small teams for 8 hours a week spread over the week, with one participant as the team leader and Zoom host. Teams worked on an online R or Python class at their own pace with support on Slack from the organizing team. For motivation and career advice, we hosted a weekly webinar with guest speakers. We used pre- and post-program surveys to determine how different aspects of the program impacted students. We were able to recruit a large and diverse group of participants who were happy with the program, found community in their team, and improved their coding confidence. We hope that our work will inspire others to start their own version of SCIP.

scientific communication and education↗

bioRxiv2022

A phylogenetic approach to studying the roles of within-host evolution and between-host transmission of resistance for clinical Escherichia coli infections

Bacterial antibiotic resistance represents a public health concern that will remain relevant for the foreseeable future. Antibiotic resistant bacterial infections can occur in two ways: (1) a host is infected by a resistant bacterial strain (due to between-host transmission of resistance), or (2) a host is infected infection by a susceptible strain, followed by the de novo evolution or acquisition of resistance (due to within-host evolution of resistance). While both are critical to understanding how the evolution of resistance happens in natural settings, the relative rate at which they occur is unclear. Here, we employ phylogenetic comparative methods to examine the evolutionary dynamics of resistance in Escherichia coli for multiple common antibiotics. We report evolutionary patterns consistent with common de novo evolution of resistance for some antibiotics and sustained transmission of resistant strains for others. For example, we observe 79 putative de novo resistance evolution events for resistance to Cefuroxime but only 31 for resistance to Ciprofloxacin, despite similar numbers of observed infections (239 and 267 respectively). We find that clusters of resistance are generally larger for Ciprofloxacin, Ceftazidima and AmoxiClav, which suggests that for these drugs, resistance is often transmitted from patient to patient. In contrast, we find that cluster sizes for resistance are generally smaller for PipTaz, Cefuroxime and Gentamicin, suggesting that resistance to these drugs is less often transmitted from patient to patient and instead evolves de novo. In addition to differences between drugs, we also find that cluster sizes were generally larger in phylogroup B2 compared to the other phylogroups, suggesting that transmission of resistant strains is more common in this phylogroup compared to the others. Our study proposes new approaches for determining the importance of de novo evolution or acquisition (within-host evolution) from resistance from infection with an already resistant strain (between-host transmission). Significantly, this work also bridges an important gap between evolutionary genomics and epidemiology, opening up a range of opportunities for studying the evolutionary dynamics of bacterial antibiotic resistance.

evolutionary biology↗

bioRxiv2022

Sequence type diversity amongst antibiotic-resistant bacterial strains is lower than amongst antibiotic-susceptible strains

The increasing number of antibiotic resistant bacterial infections is a global threat to human health. Antibiotic resistant bacterial strains generally evolve from susceptible strains by either horizontal gene transfer or chromosomal mutations. After evolving within a host, such resistant strains can be transmitted to other hosts and increase in frequency in the population at large. Population genetic theory postulates that the increase in frequency of an adaptive trait can lead to signatures of selective sweeps. One would thus expect to observe reduced genetic diversity amongst that part of the population that carries the adaptive trait. Specifically, if the evolution of new resistant strains is rare, it is expected that resistant strains represent only a subset of the diversity of susceptible strains. It is currently unknown if diversity of resistant strains is indeed lower than diversity of susceptible strains when considering antibiotic resistance. Here we show that in several bacterial species in several different datasets, sequence-type diversity amongst antibiotic-resistant bacterial strains is indeed lower than amongst antibiotic-susceptible strains in most cases. We re-analysed eight existing clinical datasets with Escherichia coli, Staphylococcus aureus and Enterococcus faecium samples. These datasets consisted of 53 - 1094 patient samples, with multi-locus sequence types and antibiotic resistance phenotypes for 3 - 19 different antibiotics. Out of 59 comparisons, we found that resistant strains were significantly less diverse than susceptible strains in 51 cases (86%). In addition, we show that sequence-type diversity of antibiotic-resistant strains is lower if resistance is rare, compared to when resistance is common, which is consistent with rare resistance being due to fewer evolutionary origins. Our results show that for several different bacterial species, we observe reduced diversity of resistant strains, which is consistent with the evolution of resistance driven by selective sweeps stemming from a limited number of evolutionary origins. In future studies, more detailed analysis of such sweep signatures is warranted.

evolutionary biology↗

bioRxiv2020

Reversion is most likely under high mutation supply, when compensatory mutations don't fully restore fitness costs

Adaptive mutations are often associated with a fitness cost. These costs can be compensated for through the acquisition of additional mutations, or the adaptations can be lost through reversion in settings where they are no longer favored. While the dynamics of adaptation, reversion and compensation have been central features in several studies of microbial evolution, few studies have attempted to resolve the population genetics underlying how and when either compensation or reversion occur. Specifically, questions remain regarding how certain actors--the evolution of mutators and whether compensatory mutations alleviate costs fully or partially-- may influence evolutionary dynamics of compensation and reversion. In this study, we attempt to explain findings from an experimental evolution study by utilizing computational and theoretical approaches towards a more refined understanding of how mutation rate and the fitness effects of compensatory mutations influence evolutionary dynamics. We find that high mutation rates increase the probability of reversion towards the wild type when compensation is only partial. However, the existence of even a single fully compensatory mutation is associated with a dramatically decreased probability of reversion to the wild type. These findings help to explain specific findings from experimental evolution, where compensation was observed in non-mutator strains, but reversion (sometimes with compensation) was observed in mutator strains, indicating that real-world compensatory mutations are often unable to fully alleviate the costs associated with resistance. Our findings emphasize the potential role of the supply and quality of mutations in crafting the evolution of antibiotic resistance, and more generally highlight the importance of population genetic context for explaining findings from experimental evolution.

evolutionary biology↗

bioRxiv2020

Assessing in vivo mutation frequencies and creating a high-resolution genome-wide map of fitness costs of Hepatitis C virus

Like many viruses, Hepatitis C Virus (HCV) has a high nutation rate, which helps the virus adapt quickly, but mutations come with fitness costs. Fitness costs can be studied by different approaches, such as experimental or frequency-based approaches. The frequency-based approach is particularly useful to estimate in vivo fitness costs, but this approach works best with deep sequencing data from many hosts, are. In this study, we applied the frequency-based approach to a large dataset of 195 patients and estimated the fitness costs of mutations at 7957 sites along the HCV genome. We used beta regression and random forest models to better understand how different factors influenced fitness costs. Our results revealed that costs of nonsynonymous mutations were three times higher than those of synonymous mutations, and mutations at nucleotides A or T had higher costs than those at C or G. Genome location had a modest effect, with lower costs for mutations in HVR1 and higher costs for mutations in Core and NS5B. Resistance mutations were, on average, costlier than other mutations. Our results show that in vivo fitness costs of mutations can be virus specific, reinforcing the utility of constructing in vivo fitness cost maps of viral genomes. Author SummaryUnderstanding how viruses evolve within patients is important for combatting viral diseases, yet studying viruses within patients is difficult. Laboratory experiments are often used to understand the evolution of viruses, in place of assessing the evolution in natural populations (patients), but the dynamics will be different. In this study, we aimed to understand the within-patient evolution of Hepatitis C virus, which is an RNA virus that replicates and mutates extremely quickly, by taking advantage of high-throughput next generation sequencing. Here, we describe the evolutionary patterns of Hepatitis C virus from 195 patients: We analyzed mutation frequencies and estimated how costly each mutation was. We also assessed what factors made a mutation more costly, including the costs associated with drug resistance mutations. We were able to create a genome-wide fitness map of within-patient mutations in Hepatitis C virus which proves that, with technological advances, we can deepen our understanding of within-patient viral evolution, which can contribute to develop better treatments and vaccines.

evolutionary biology↗

bioRxiv2020

Genetic Adaptation in New York City Rats

Brown rats (Rattus norvegicus) thrive in urban environments by navigating the anthropocentric environment and taking advantage of human resources and by-products. From the human perspective, rats are a chronic problem that causes billions of dollars in damage to agriculture, health and infrastructure. Did genetic adaptation play a role in the spread of rats in cities? To approach this question, we collected whole-genome sequences from 29 brown rats from New York City (NYC) and scanned for genetic signatures of adaptation. We tested for (i) high-frequency, extended haplotypes that could indicate selective sweeps and (ii) loci of extreme genetic differentiation between the NYC sample and a sample from the presumed ancestral range of brown rats in northeast China. We found candidate selective sweeps near or inside genes associated with metabolism, diet, the nervous system and locomotory behavior. Patterns of differentiation between NYC and Chinese rats at putative sweep loci suggests that many sweeps began after the split from the ancestral population. Together, our results suggest several hypotheses on adaptation in rats living in close proximity to humans.

evolutionary biology↗

bioRxiv2019

Challenging conventional wisdom on the evolution of resistance to multi-drug HIV treatment: Lessons from data and modeling.

1Triple-drug therapies have transformed HIV from a fatal condition to a chronic one. These therapies should prevent HIV drug resistance evolution, because one or more drugs suppress any partially-resistant viruses. In practice, such therapies drastically reduced, but did not eliminate, resistance evolution. In this article, we reanalyze published data from an evolutionary perspective and demonstrate several intriguing patterns about HIV resistance evolution - resistance evolves (1) even after years on successful therapy, (2) sequentially, often via one mutation at a time and (3) in a partially predictable order. We describe how these observations might emerge under two models of HIV drugs varying in space or time. Despite decades of work in this area, much opportunity remains to create models with realistic parameters for three drugs, and to match model outcomes to resistance rates and genetic patterns from patients on triple-drug therapy. Further, lessons from HIV may inform other systems.

evolutionary biology↗

bioRxiv2019

Evolutionary dynamics in structured populations under strong population genetic forces

High rates of migration between subpopulations result in little population differentiation in the long-term neutral equilibrium. However, in the short-term, even very abundant migration may not be enough for subpopulations to equilibrate immediately. In this study, we investigate dynamical patterns of short-term population differentiation in adapting populations via stochastic and analytical modeling through time. We characterize a regime in which selection and migration interact to create non-monotonic patterns of the population differentiation statistic FST when migration is weaker than selection, but stronger than drift. We demonstrate how these patterns can be leveraged to estimate high migration rates that would lead to panmixia in the long term equilibrium using an approximate Bayesian computation approach. We apply this approach to estimate fast migration in a rapidly adapting intra-host Simian-HIV population sampled from different anatomical locations. Notably, we find differences in estimated migration rates between different compartments, all above Nem = 1. This work demonstrates how studying demographic processes on the timescale of selective sweeps illuminates processes too fast to leave signatures on neutral timescales.

evolutionary biology↗

bioRxiv2019

Drug resistance evolution in HIV in the late 1990s: hard sweeps, soft sweeps, clonal interference and the accumulation of drug resistance mutations

1The goal of this paper is to provide examples of evolutionary dynamics of HIV within patients who are treated with antiretrovirals. We hope that the figures in this paper will be used in evolution and population genetics classes. We show a wide variety of patterns, specifically: soft sweeps, hard sweeps, softening sweeps and hardening sweeps, simultaneous sweeps, accumulation of mutations and clonal interference.

evolutionary biology↗

bioRxiv2018

The clarifying role of time series data in the population genetics of HIV

HIV can evolve remarkably quickly in response to anti-retroviral therapies and the immune system. This evolution stymies treatment effectiveness and prevents the development of an HIV vaccine. Consequently, there has been great interest in using population genetics to disentangle the forces that govern the HIV adaptive landscape (selection, drift, mutation, recombination). Traditional population genetics approaches look at the current state of genetic variation and infer the processes that can generate them [1, 2, 3, 4]. However, because HIV evolves rapidly, we can also sample populations repeatedly over time and watch evolution in action [5, 6, 7]. In this paper, we demonstrate how time series data can bound evolutionary parameters in a way that complements and informs traditional population genetic approaches.\n\nSpecifically, we focus on our recent paper [2], in which we show that, as improved HIV drugs have led to fewer patients failing therapy due to resistance evolution, less genetic diversity has been maintained following the fixation of drug resistance mutations. We interpret this as evidence that resistance to early HIV drugs that failed quickly and predictably was driven by soft sweeps while evolution of resistance to better drugs is both less frequent and when it takes place it is associated with harder sweeps due to an effectively lower HIV population mutation rate ({theta}). Recently, Harris et al. have proposed an alternative interpretation [8]: the signal could be due to an increase in the selective benefit of mutations conferring resistance to better drugs. Therefore, better drugs lead to faster sweeps with less opportunity for recombination to rescue diversity. In this paper, we use time series data to show that drug resistance evolution during ineffective treatment is very fast, providing new evidence that soft sweeps drove early HIV treatment failure.

evolutionary biology↗

bioRxiv2018

Inferring Population Genetics Parameters of Evolving Viruses Using Time-series Data

1With the advent of deep sequencing techniques, it is now possible to track the evolution of viruses with ever-increasing detail. Here we present FITS (Flexible Inference from Time-Series) - a computational framework that allows inference of either the fitness of a mutation, the mutation rate or the population size from genomic time-series sequencing data. FITS was designed first and foremost for analysis of either short-term Evolve & Resequence (E&R) experiments, or for rapidly recombining populations of viruses. We thoroughly explore the performance of FITS on noisy simulated data, and highlight its ability to infer meaningful information even in those circumstances. In particular FITS is able to categorize a mutation as Advantageous, Neutral or Deleterious. We next apply FITS to empirical data from an E&R experiment on poliovirus where parameters were determined experimentally and demonstrate extremely high accuracy in inference. We highlight the ease of use of FITS for step-wise or iterative inference of mutation rates, population size, and fitness values for each mutation sequenced, when deep sequencing data is available at multiple time-points.\n\nAvailabilityFITS is written in C++ and is available both with a highly user friendly graphical user interface but also as a command line program that allows parallel high throughput analyses. Source code, binaries (Windows and Mac) and complementary scripts, are available from GitHub at https://github.com/SternLabTAU/FITS.\n\nContactsternadi@tau.ac.il

evolutionary biology↗

bioRxiv2017

Soft sweeps and beyond: Understanding the patterns and probabilities of selection footprints under rapid adaptation

O_LIThe tempo and mode of adaptive evolution determine how natural selection shapes patterns of genetic diversity in DNA polymorphism data. While slow mutation-limited adaptation leads to classical footprints of \"hard\" selective sweeps, these patterns are different when adaptation responds quickly to a novel selection pressure, acting either on standing genetic variation or on recurrent new mutation. In the past decade, corresponding footprints of \"soft\" selective sweeps have been described both in theoretical models and in empirical data.\nC_LIO_LIHere, we summarize the key theoretical concepts and contrast model predictions with observed patterns in Drosophila, humans, and microbes.\nC_LIO_LIEvidence in all cases shows that \"soft\" patterns of rapid adaptation are frequent. However, theory and data also point to a role of complex adaptive histories in rapid evolution.\nC_LIO_LIWhile existing theory allows for important implications on the tempo and mode of the adaptive process, complex footprints observed in data are, as yet, insufficiently covered by models. They call for in-depth empirical study and further model development.\nC_LI

evolutionary biology↗