Mutational signature dynamics indicate SARS-CoV-2's evolutionary capacity is driven by host antiviral molecules.

The COVID-19 pandemic has been characterised by sequential variant-specific waves shaped by viral, individual human and population factors. SARS-CoV-2 variants are defined by their unique combinations of mutations and there has been a clear adaptation to more efficient human infection since the emergence of this new human coronavirus in late 2019. Here, we use machine learning models to identify shared signatures, i.e., common underlying mutational processes and link these to the subset of mutations that define the variants of concern (VOCs). First, we examined the global SARS-CoV-2 genomes and associated metadata to determine how viral properties and public health measures have influenced the magnitude of waves, as measured by the number of infection cases, in different geographic locations using regression models. This analysis showed that, as expected, both public health measures and virus properties were associated with the waves of regional SARS-CoV-2 reported infection numbers and this impact varies geographically. We attribute this to intrinsic differences such as vaccine coverage, testing and sequencing capacity and the effectiveness of government stringency. To assess underlying evolutionary change, we used non-negative matrix factorisation and observed three distinct mutational signatures, unique in their substitution patterns and exposures from the SARS-CoV-2 genomes. Signatures 1, 2 and 3 were biased to C→T, T→C/A→G and G→T point mutations. We hypothesise assignments of these mutational signatures to the host antiviral molecules APOBEC, ADAR and ROS respectively. We observe a shift amidst the pandemic in relative mutational signature activity from predominantly Signature 1 changes to an increasingly high proportion of changes consistent with Signature 2. This could represent changes in how the virus and the host immune response interact and indicates how SARS-CoV-2 may continue to generate variation in the future. Linkage of the detected mutational signatures to the VOC-defining amino acids substitutions indicates the majority of SARS-CoV-2's evolutionary capacity is likely to be associated with the action of host antiviral molecules rather than virus replication errors.

HIHISIV: a database of gene expression in HIV and SIV host immune response.

In the battle of the host against lentiviral pathogenesis, the immune response is crucial. However, several questions remain unanswered about the interaction with different viruses and their influence on disease progression. The simian immunodeficiency virus (SIV) infecting nonhuman primates (NHP) is widely used as a model for the study of the human immunodeficiency virus (HIV) both because they are evolutionarily linked and because they share physiological and anatomical similarities that are largely explored to understand the disease progression. The HIHISIV database was developed to support researchers to integrate and evaluate the large number of transcriptional data associated with the presence/absence of the pathogen (SIV or HIV) and the host response (NHP and human). The datasets are composed of microarray and RNA-Seq gene expression data that were selected, curated, analyzed, enriched, and stored in a relational database. Six query templates comprise the main data analysis functions and the resulting information can be downloaded. The HIHISIV database, available at  https://hihisiv.github.io , provides accurate resources for browsing and visualizing results and for more robust analyses of pre-existing data in transcriptome repositories.

Lysogeny destabilizes computationally simulated microbiomes.

Microbiomes are ecosystems, and their stability can impact the health of their hosts. Theory predicts that predators influence ecosystem stability. Phages are key predators of bacteria in microbiomes, but phages are unusual predators because many have lysogenic life cycles. It has been hypothesized that lysogeny can destabilize microbiomes, but lysogeny has no direct analog in classical ecological theory, and no formal theory exists. We studied the stability of computationally simulated microbiomes with different numbers of temperate (lysogenic) and virulent (obligate lytic) phage species. Bacterial populations were more likely to fluctuate over time when there were more temperate phages species. After disturbances, bacterial populations returned to their pre-disturbance densities more slowly when there were more temperate phage species, but cycles engendered by disturbances dampened more slowly when there were more virulent phage species. Our work offers the first formal theory linking lysogeny to microbiome stability.

How could a pooled testing policy have performed in managing the early stages of the COVID-19 pandemic? Results from a simulation study.

A coordinated testing policy is an essential tool for responding to emerging epidemics, as was seen with COVID-19. However, it is very difficult to agree on the best policy when there are multiple conflicting objectives. A key objective is minimizing cost, which is why pooled testing (a method that involves pooling samples taken from multiple individuals and analyzing this with a single diagnostic test) has been suggested. In this article, we present results from an extensive and realistic simulation study comparing testing policies based on individually testing subjects with symptoms (a policy resembling the UK strategy at the start of the COVID-19 pandemic), individually testing subjects at random or pools of subjects randomly combined and tested. To compare these testing methods, a dynamic model compromised of a relationship network and an extended SEIR model is used. In contrast to most existing literature, testing capacity is considered as fixed and limited rather than unbounded. This article then explores the impact of the proportion of symptomatic infections on the expected performance of testing policies. Symptomatic testing performs better than pooled testing unless a low proportion of infections are symptomatic. Additionally, we include the novel feature for testing of non-compliance and perform a sensitivity analysis for different compliance assumptions. Our results suggest for the pooled testing scheme to be superior to testing symptomatic people individually, only a small proportion of the population ( > 10 % $$ >10\% $$ ) needs to not comply with the testing procedure.

Online multiple hypothesis testing.

Modern data analysis frequently involves large-scale hypothesis testing, which naturally gives rise to the problem of maintaining control of a suitable type I error rate, such as the false discovery rate (FDR). In many biomedical and technological applications, an additional complexity is that hypotheses are tested in an online manner, one-by-one over time. However, traditional procedures that control the FDR, such as the Benjamini-Hochberg procedure, assume that all p-values are available to be tested at a single time point. To address these challenges, a new field of methodology has developed over the past 15 years showing how to control error rates for online multiple hypothesis testing. In this framework, hypotheses arrive in a stream, and at each time point the analyst decides whether to reject the current hypothesis based both on the evidence against it, and on the previous rejection decisions. In this paper, we present a comprehensive exposition of the literature on online error rate control, with a review of key theory as well as a focus on applied examples. We also provide simulation results comparing different online testing algorithms and an up-to-date overview of the many methodological extensions that have been proposed.

Evaluating pooled testing for asymptomatic screening of healthcare workers in hospitals.

BACKGROUND: There is evidence that during the COVID pandemic, a number of patient and HCW infections were nosocomial. Various measures were put in place to try to reduce these infections including developing asymptomatic PCR (polymerase chain reaction) testing schemes for healthcare workers. Regularly testing all healthcare workers requires many tests while reducing this number by only testing some healthcare workers can result in undetected cases. An efficient way to test as many individuals as possible with a limited testing capacity is to consider pooling multiple samples to be analysed with a single test (known as pooled testing). METHODS: Two different pooled testing schemes for the asymptomatic testing are evaluated using an individual-based model representing the transmission of SARS-CoV-2 in a 'typical' English hospital. We adapt the modelling to reflect two scenarios: a) a retrospective look at earlier SARS-CoV-2 variants under lockdown or social restrictions, and b) transitioning back to 'normal life' without lockdown and with the omicron variant. The two pooled testing schemes analysed differ in the population that is eligible for testing. In the 'ward' testing scheme only healthcare workers who work on a single ward are eligible and in the 'full' testing scheme all healthcare workers are eligible including those that move across wards. Both pooled schemes are compared against the baseline scheme which tests only symptomatic healthcare workers. RESULTS: Including a pooled asymptomatic testing scheme is found to have a modest (albeit statistically significant) effect, reducing the total number of nosocomial healthcare worker infections by about 2[Formula: see text] in both the lockdown and non-lockdown setting. However, this reduction must be balanced with the increase in cost and healthcare worker isolations. Both ward and full testing reduce HCW infections similarly but the cost for ward testing is much less. We also consider the use of lateral flow devices (LFDs) for follow-up testing. Considering LFDs reduces cost and time but LFDs have a different error profile to PCR tests. CONCLUSIONS: Whether a PCR-only or PCR and LFD ward testing scheme is chosen depends on the metrics of most interest to policy makers, the virus prevalence and whether there is a lockdown.

Herd immunity to endemic diseases: Historical concepts and implications for public health policy.

BACKGROUND: "Herd immunity" became a contested term during the COVID-19 pandemic. Although the term "herd immunity" is often used to refer to thresholds at which some diseases can be eliminated (e.g., due to mass vaccination), the term has multiple referents. Different concepts of herd immunity have been relevant throughout the history of immunology and infectious disease epidemiology. For some diseases, herd immunity plays a role in the development of an endemic equilibrium, rather than elimination via threshold effects. METHODS: We reviewed academic literature from 1920 to 2022, using historical and philosophical analysis to identify and develop relevant concepts of herd immunity. RESULTS: This paper analyses the ambiguity surrounding the concept of herd immunity during the pandemic. We argue for the need to recapture a long-standing interpretation of this concept as one of the factors that leads to a dynamic endemic equilibrium between a host population and a mutating respiratory pathogen. CONCLUSIONS: Informed by the history of infectious disease epidemiology, we argue that understanding the concept in this way will help us manage both SARS-CoV-2 and hundreds of other seasonal respiratory pathogens with which we live but which have been disrupted due to sustained public health measures/non-pharmaceutical interventions targeting SARS-CoV-2.

Familiarity Is Key: Exploring the Effect of Familiarity on the Face-Voice Correlation.

Recent research has examined the extent to which face and voice processing are associated by virtue of the fact that both tap into a common person perception system. However, existing findings do not yet fully clarify the role of familiarity in this association. Given this, two experiments are presented that examine face-voice correlations for unfamiliar stimuli (Experiment 1) and for familiar stimuli (Experiment 2). With care being taken to use tasks that avoid floor and ceiling effects and that use realistic speech-based voice clips, the results suggested a significant positive but small-sized correlation between face and voice processing when recognizing unfamiliar individuals. In contrast, the correlation when matching familiar individuals was significant and positive, but much larger. The results supported the existing literature suggesting that face and voice processing are aligned as constituents of an overarching person perception system. However, the difference in magnitude of their association here reinforced the view that familiar and unfamiliar stimuli are processed in different ways. This likely reflects the importance of a pre-existing mental representation and cross-talk within the neural architectures when processing familiar faces and voices, and yet the reliance on more superficial stimulus-based and modality-specific analysis when processing unfamiliar faces and voices.

Therapy-induced normal tissue damage promotes breast cancer metastasis.

Disseminated tumor cells frequently exhibit a period of dormancy, rendering them chemotherapy insensitive; conversely, the systemic delivery of chemotherapies can result in normal tissue damage. Using multiple mouse and human breast cancer models, we demonstrate that prior chemotherapy administration enhances metastatic colonization and outgrowth. In vitro, chemotherapy-treated fibroblasts display a pro-tumorigenic senescence-associated secretory phenotype (SASP) and are effectively eliminated by targeting the anti-apoptotic protein BCL-xL. In vivo, chemotherapy treatment induces SASP expression in normal tissues; however, the accumulation of senescent cells is limited, and BCL-xL inhibitors are unable to reduce chemotherapy-enhanced metastasis. This likely reflects that chemotherapy-exposed stromal cells do not enter a BCL-xL-dependent phenotype or switch their dependency to other anti-apoptotic BCL-2 family members. This study highlights the role of the metastatic microenvironment in controlling outgrowth of disseminated tumor cells and the need to identify additional approaches to limit the pro-tumorigenic effects of therapy-induced normal tissue damage.

Biodistribution and therapeutic efficacy of a gold nanoparticle-based targeted drug delivery system against pancreatic cancer.

Pancreatic cancer is characterized by desmoplasia; crosstalk between pancreatic cancer cells (PCCs) and pancreatic stellate cells (PSCs) leads to the deposition of extracellular matrix proteins in the tumor environment resulting in poor vascularity. Targeting either PCCs or PSCs individually has produced mixed results, and there is currently no effective strategy to target both cell types simultaneously. Previously, we demonstrated, through in vitro cell culture experiments, that a specific gold nanoparticle-based nanoformulation containing the anti-EGFR antibody cetuximab (C225) as a targeting agent and gemcitabine as a chemotherapeutic agent effectively targets both PCCs and PSCs simultaneously. Herein, we extend our studies to test the ability of these in vitro tested nano formulations to inhibit tumor growth in an orthotopic co-implantation model of pancreatic cancer in vivo. Orthotopic tumors were established by co-implantation of equal numbers of PCCs and PSCs in the mouse pancreas. Among the various formulations tested, 5 nm gold nanoparticles coated with gemcitabine, cetuximab and poly-ethylene glycol (PEG) of molecular weight 1000 Da, which we named ACGP441000, demonstrated optimal efficacy in inhibiting tumor growth. The current study reveals an opportunity to target PCCs and PSCs simultaneously, by exploiting their overexpression of EGFR as a target, in order to inhibit pancreatic cancer growth.