Lineage-specific genes are clustered with HET-domain genes and respond to environmental and genetic manipulations regulating reproduction in Neurospora.

Lineage-specific genes (LSGs) have long been postulated to play roles in the establishment of genetic barriers to intercrossing and speciation. In the genome of Neurospora crassa, most of the 670 Neurospora LSGs that are aggregated adjacent to the telomeres are clustered with 61% of the HET-domain genes, some of which regulate self-recognition and define vegetative incompatibility groups. In contrast, the LSG-encoding proteins possess few to no domains that would help to identify potential functional roles. Possible functional roles of LSGs were further assessed by performing transcriptomic profiling in genetic mutants and in response to environmental alterations, as well as examining gene knockouts for phenotypes. Among the 342 LSGs that are dynamically expressed during both asexual and sexual phases, 64% were detectable on unusual carbon sources such as furfural, a wildfire-produced chemical that is a strong inducer of sexual development, and the structurally-related furan 5-hydroxymethyl furfural (HMF). Expression of a significant portion of the LSGs was sensitive to light and temperature, factors that also regulate the switch from asexual to sexual reproduction. Furthermore, expression of the LSGs was significantly affected in the knockouts of adv-1 and pp-1 that regulate hyphal communication, and expression of more than one quarter of the LSGs was affected by perturbation of the mating locus. These observations encouraged further investigation of the roles of clustered lineage-specific and HET-domain genes in ecology and reproduction regulation in Neurospora, especially the regulation of the switch from the asexual growth to sexual reproduction, in response to dramatic environmental conditions changes.

The durability of natural infection and vaccine-induced immunity against future infection by SARS-CoV-2.

The durability of vaccine-mediated immunity to SARS-CoV-2, the durations to breakthrough infection, and the optimal timings of booster vaccination are crucial knowledge for pandemic response. Here, we applied comparative evolutionary analyses to estimate the durability of immunity and the likelihood of breakthrough infections over time following vaccination by BNT162b2 (Pfizer-BioNTech), mRNA-1273 (Moderna), ChAdOx1 (Oxford-AstraZeneca), and Ad26.COV2.S (Johnson & Johnson/Janssen). We evaluated anti-Spike (S) immunoglobulin G (IgG) antibody levels elicited by each vaccine relative to natural infection. We estimated typical trajectories of waning and corresponding infection probabilities, providing the distribution of times to breakthrough infection for each vaccine under endemic conditions. Peak antibody levels elicited by messenger RNA (mRNA) vaccines mRNA-1273 and BNT1262b2 exceeded that of natural infection and are expected to typically yield more durable protection against breakthrough infections (median 29.6 mo; 5 to 95% quantiles 10.9 mo to 7.9 y) than natural infection (median 21.5 mo; 5 to 95% quantiles 3.5 mo to 7.1 y). Relative to mRNA-1273 and BNT1262b2, viral vector vaccines ChAdOx1 and Ad26.COV2.S exhibit similar peak anti-S IgG antibody responses to that from natural infection and are projected to yield lower, shorter-term protection against breakthrough infection (median 22.4 mo and 5 to 95% quantiles 4.3 mo to 7.2 y; and median 20.5 mo and 5 to 95% quantiles 2.6 mo to 7.0 y; respectively). These results leverage the tools from evolutionary biology to provide a quantitative basis for otherwise unknown parameters that are fundamental to public health policy decision-making.

Rare Drivers at Low Prevalence with High Cancer Effects in T-Cell and B-Cell Pediatric Acute Lymphoblastic Leukemia.

The genomic analyses of pediatric acute lymphoblastic leukemia (ALL) subtypes, particularly T-cell and B-cell lineages, have been pivotal in identifying potential therapeutic targets. Typical genomic analyses have directed attention toward the most commonly mutated genes. However, assessing the contribution of mutations to cancer phenotypes is crucial. Therefore, we estimated the cancer effects (scaled selection coefficients) for somatic substitutions in T-cell and B-cell cohorts, revealing key insights into mutation contributions. Cancer effects for well-known, frequently mutated genes like NRAS and KRAS in B-ALL were high, which underscores their importance as therapeutic targets. However, less frequently mutated genes IL7R, XBP1, and TOX also demonstrated high cancer effects, suggesting pivotal roles in the development of leukemia when present. In T-ALL, KRAS and NRAS are less frequently mutated than in B-ALL. However, their cancer effects when present are high in both subtypes. Mutations in PIK3R1 and RPL10 were not at high prevalence, yet exhibited some of the highest cancer effects in individual T-cell ALL patients. Even CDKN2A, with a low prevalence and relatively modest cancer effect, is potentially highly relevant for the epistatic effects that its mutated form exerts on other mutations. Prioritizing investigation into these moderately frequent but potentially high-impact targets not only presents novel personalized therapeutic opportunities but also enhances the understanding of disease mechanisms and advances precision therapeutics for pediatric ALL.

Attribution of Cancer Origins to Endogenous, Exogenous, and Preventable Mutational Processes.

Mutational processes in tumors create distinctive patterns of mutations, composed of neutral "passenger" mutations and oncogenic drivers that have quantifiable effects on the proliferation and survival of cancer cell lineages. Increases in proliferation and survival are mediated by natural selection, which can be quantified by comparing the frequency at which we detect substitutions to the frequency at which we expect to detect substitutions assuming neutrality. Most of the variants detectable with whole-exome sequencing in tumors are neutral or nearly neutral in effect, and thus the processes generating the majority of mutations may not be the primary sources of the tumorigenic mutations. Across 24 cancer types, we identify the contributions of mutational processes to each oncogenic variant and quantify the degree to which each process contributes to tumorigenesis. We demonstrate that the origination of variants driving melanomas and lung cancers is predominantly attributable to the preventable, exogenous mutational processes associated with ultraviolet light and tobacco exposure, respectively, whereas the origination of selected variants in gliomas and prostate adenocarcinomas is largely attributable to endogenous processes associated with aging. Preventable mutations associated with pathogen exposure and apolipoprotein B mRNA-editing enzyme activity account for a large proportion of the cancer effect within head-and-neck, bladder, cervical, and breast cancers. These attributions complement epidemiological approaches-revealing the burden of cancer driven by single-nucleotide variants caused by either endogenous or exogenous, nonpreventable, or preventable processes, and crucially inform public health strategies.

Infection by SARS-CoV-2 with alternate frequencies of mRNA vaccine boosting.

One of the most consequential unknowns of the COVID-19 pandemic is the frequency at which vaccine boosting provides sufficient protection from infection. We quantified the statistical likelihood of breakthrough infections over time following different boosting schedules with messenger RNA (mRNA)-1273 (Moderna) and BNT162b2 (Pfizer-BioNTech). We integrated anti-Spike IgG antibody optical densities with profiles of the waning of antibodies and corresponding probabilities of infection associated with coronavirus endemic transmission. Projecting antibody levels over time given boosting every 6 months, 1, 1.5, 2, or 3 years yielded respective probabilities of fending off infection over a 6-year span of >93%, 75%, 55%, 40%, and 24% (mRNA-1273) and >89%, 69%, 49%, 36%, and 23% (BNT162b2). Delaying the administration of updated boosters has bleak repercussions. It increases the probability of individual infection by SARS-CoV-2, and correspondingly, ongoing disease spread, prevalence, morbidity, hospitalization, and mortality. Instituting regular, population-wide booster vaccination updated to predominant variants has the potential to substantially forestall-and with global, widespread uptake, eliminate-COVID-19.

Origins of lineage-specific elements via gene duplication, relocation, and regional rearrangement in Neurospora crassa.

The origin of new genes has long been a central interest of evolutionary biologists. However, their novelty means that they evade reconstruction by the classical tools of evolutionary modelling. This evasion of deep ancestral investigation necessitates intensive study of model species within well-sampled, recently diversified, clades. One such clade is the model genus Neurospora, members of which lack recent gene duplications. Several Neurospora species are comprehensively characterized organisms apt for studying the evolution of lineage-specific genes (LSGs). Using gene synteny, we documented that 78% of Neurospora LSG clusters are located adjacent to the telomeres featuring extensive tracts of non-coding DNA and duplicated genes. Here, we report several instances of LSGs that are likely from regional rearrangements and potentially from gene rebirth. To broadly investigate the functions of LSGs, we assembled transcriptomics data from 68 experimental data points and identified co-regulatory modules using Weighted Gene Correlation Network Analysis, revealing that LSGs are widely but peripherally involved in known regulatory machinery for diverse functions. The ancestral status of the LSG mas-1, a gene with roles in cell-wall integrity and cellular sensitivity to antifungal toxins, was investigated in detail alongside its genomic neighbours, indicating that it arose from an ancient lysophospholipase precursor that is ubiquitous in lineages of the Sordariomycetes. Our discoveries illuminate a "rummage region" in the N. crassa genome that enables the formation of new genes and functions to arise via gene duplication and relocation, followed by fast mutation and recombination facilitated by sequence repeats and unconstrained non-coding sequences.

Placing human gene families into their evolutionary context.

Following the draft sequence of the first human genome over 20 years ago, we have achieved unprecedented insights into the rules governing its evolution, often with direct translational relevance to specific diseases. However, staggering sequence complexity has also challenged the development of a more comprehensive understanding of human genome biology. In this context, interspecific genomic studies between humans and other animals have played a critical role in our efforts to decode human gene families. In this review, we focus on how the rapid surge of genome sequencing of both model and non-model organisms now provides a broader comparative framework poised to empower novel discoveries. We begin with a general overview of how comparative approaches are essential for understanding gene family evolution in the human genome, followed by a discussion of analyses of gene expression. We show how homology can provide insights into the genes and gene families associated with immune response, cancer biology, vision, chemosensation, and metabolism, by revealing similarity in processes among distant species. We then explain methodological tools that provide critical advances and show the limitations of common approaches. We conclude with a discussion of how these investigations position us to gain fundamental insights into the evolution of gene families among living organisms in general. We hope that our review catalyzes additional excitement and research on the emerging field of comparative genomics, while aiding the placement of the human genome into its existentially evolutionary context.

Identifying modules of cooperating cancer drivers.

Identifying cooperating modules of driver alterations can provide insights into cancer etiology and advance the development of effective personalized treatments. We present Cancer Rule Set Optimization (CRSO) for inferring the combinations of alterations that cooperate to drive tumor formation in individual patients. Application to 19 TCGA cancer types revealed a mean of 11 core driver combinations per cancer, comprising 2-6 alterations per combination and accounting for a mean of 70% of samples per cancer type. CRSO is distinct from methods based on statistical co-occurrence, which we demonstrate is a suboptimal criterion for investigating driver cooperation. CRSO identified well-studied driver combinations that were not detected by other approaches and nominated novel combinations that correlate with clinical outcomes in multiple cancer types. Novel synergies were identified in NRAS-mutant melanomas that may be therapeutically relevant. Core driver combinations involving NFE2L2 mutations were identified in four cancer types, supporting the therapeutic potential of NRF2 pathway inhibition. CRSO is available at https://github.com/mikekleinsgit/CRSO/.

Mechanistic basis of post-treatment control of SIV after anti-α4ß7 antibody therapy.

Treating macaques with an anti-α4ß7 antibody under the umbrella of combination antiretroviral therapy (cART) during early SIV infection can lead to viral remission, with viral loads maintained at < 50 SIV RNA copies/ml after removal of all treatment in a subset of animals. Depletion of CD8+ lymphocytes in controllers resulted in transient recrudescence of viremia, suggesting that the combination of cART and anti-α4ß7 antibody treatment led to a state where ongoing immune responses kept the virus undetectable in the absence of treatment. A previous mathematical model of HIV infection and cART incorporates immune effector cell responses and exhibits the property of two different viral load set-points. While the lower set-point could correspond to the attainment of long-term viral remission, attaining the higher set-point may be the result of viral rebound. Here we expand that model to include possible mechanisms of action of an anti-α4ß7 antibody operating in these treated animals. We show that the model can fit the longitudinal viral load data from both IgG control and anti-α4ß7 antibody treated macaques, suggesting explanations for the viral control associated with cART and an anti-α4ß7 antibody treatment. This effective perturbation to the virus-host interaction can also explain observations in other nonhuman primate experiments in which cART and immunotherapy have led to post-treatment control or resetting of the viral load set-point. Interestingly, because the viral kinetics in the various treated animals differed-some animals exhibited large fluctuations in viral load after cART cessation-the model suggests that anti-α4ß7 treatment could act by different primary mechanisms in different animals and still lead to post-treatment viral control. This outcome is nonetheless in accordance with a model with two stable viral load set-points, in which therapy can perturb the system from one set-point to a lower one through different biological mechanisms.

Jot: guiding journal selection with suitability metrics.

Researchers grapple with a challenging and consequential decision each time they choose a journal for manuscript submission. There are several online tools that attempt to identify appropriate journals for a manuscript, but each of these tools has shortcomings in terms of the journal data they provide and the exploration functionality they offer-and not one of these tools is open source. Jot is a free and open-source web application that matches manuscripts in the fields of biomedicine and life sciences with suitable journals, based on a manuscript's title, abstract, and (optionally) citations. Jot gathers a wealth of data on journal quality, impact, fit, and open access options that can be explored through a dashboard of linked, interactive visualizations.

Unmatched Level of Molecular Convergence among Deeply Divergent Complex Multicellular Fungi.

Convergent evolution is pervasive in nature, but it is poorly understood how various constraints and natural selection limit the diversity of evolvable phenotypes. Here, we analyze the transcriptome across fruiting body development to understand the independent evolution of complex multicellularity in the two largest clades of fungi-the Agarico- and Pezizomycotina. Despite >650 My of divergence between these clades, we find that very similar sets of genes have convergently been co-opted for complex multicellularity, followed by expansions of their gene families by duplications. Over 82% of shared multicellularity-related gene families were expanding in both clades, indicating a high prevalence of convergence also at the gene family level. This convergence is coupled with a rich inferred repertoire of multicellularity-related genes in the most recent common ancestor of the Agarico- and Pezizomycotina, consistent with the hypothesis that the coding capacity of ancestral fungal genomes might have promoted the repeated evolution of complex multicellularity. We interpret this repertoire as an indication of evolutionary predisposition of fungal ancestors for evolving complex multicellular fruiting bodies. Our work suggests that evolutionary convergence may happen not only when organisms are closely related or are under similar selection pressures, but also when ancestral genomic repertoires render certain evolutionary trajectories more likely than others, even across large phylogenetic distances.

Molecular Biology and Evolution of Cancer: From Discovery to Action.

Cancer progression is an evolutionary process. During this process, evolving cancer cell populations encounter restrictive ecological niches within the body, such as the primary tumor, circulatory system, and diverse metastatic sites. Efforts to prevent or delay cancer evolution-and progression-require a deep understanding of the underlying molecular evolutionary processes. Herein we discuss a suite of concepts and tools from evolutionary and ecological theory that can inform cancer biology in new and meaningful ways. We also highlight current challenges to applying these concepts, and propose ways in which incorporating these concepts could identify new therapeutic modes and vulnerabilities in cancer.

A comprehensive phylogeny of birds (Aves) using targeted next-generation DNA sequencing.

Although reconstruction of the phylogeny of living birds has progressed tremendously in the last decade, the evolutionary history of Neoaves--a clade that encompasses nearly all living bird species--remains the greatest unresolved challenge in dinosaur systematics. Here we investigate avian phylogeny with an unprecedented scale of data: >390,000 bases of genomic sequence data from each of 198 species of living birds, representing all major avian lineages, and two crocodilian outgroups. Sequence data were collected using anchored hybrid enrichment, yielding 259 nuclear loci with an average length of 1,523 bases for a total data set of over 7.8 × 10(7) bases. Bayesian and maximum likelihood analyses yielded highly supported and nearly identical phylogenetic trees for all major avian lineages. Five major clades form successive sister groups to the rest of Neoaves: (1) a clade including nightjars, other caprimulgiforms, swifts, and hummingbirds; (2) a clade uniting cuckoos, bustards, and turacos with pigeons, mesites, and sandgrouse; (3) cranes and their relatives; (4) a comprehensive waterbird clade, including all diving, wading, and shorebirds; and (5) a comprehensive landbird clade with the enigmatic hoatzin (Opisthocomus hoazin) as the sister group to the rest. Neither of the two main, recently proposed Neoavian clades--Columbea and Passerea--were supported as monophyletic. The results of our divergence time analyses are congruent with the palaeontological record, supporting a major radiation of crown birds in the wake of the Cretaceous-Palaeogene (K-Pg) mass extinction.

Implementation of Syringe Services Programs to Prevent Rapid Human Immunodeficiency Virus Transmission in Rural Counties in the United States: A Modeling Study.

BACKGROUND: Syringe services programs (SSPs) are effective venues for delivering harm-reduction services to people who inject drugs (PWID). However, SSPs often face significant barriers to implementation, particularly in the absence of known human immunodeficiency virus (HIV) outbreaks. METHODS: Using an agent-based model, we simulated HIV transmission in Scott County, Indiana, a rural county with a 1.7% prevalence of injection drug use. We compared outcomes arising in the absence of an SSP, in the presence of a pre-existing SSP, and with implementation of an SSP after the detection of an HIV outbreak among PWID over 5 years following the introduction of a single infection into the network. RESULTS: In the absence of an SSP, the model predicted an average of 176 infections among PWID over 5 years or an incidence rate of 12.1/100 person-years. Proactive implementation averted 154 infections and decreased incidence by 90.3%. With reactive implementation beginning operations 10 months after the first infection, an SSP would prevent 107 infections and decrease incidence by 60.8%. Reductions in incidence were also observed among people who did not inject drugs. CONCLUSIONS: Based on model predictions, proactive implementation of an SSP in Scott County had the potential to avert more HIV infections than reactive implementation after the detection of an outbreak. The predicted impact of reactive SSP implementation was highly dependent on timely implementation after detecting the earliest infections. Consequently, there is a need for expanded proactive SSP implementation in the context of enhanced monitoring of outbreak vulnerability in Scott County and similar rural contexts.

Optimal Rates for Phylogenetic Inference and Experimental Design in the Era of Genome-Scale Data Sets.

With the rise of genome-scale data sets, there has been a call for increased data scrutiny and careful selection of loci that are appropriate to use in an attempt to resolve a phylogenetic problem. Such loci should maximize phylogenetic information content while minimizing the risk of homoplasy. Theory posits the existence of characters that evolve at an optimum rate, and efforts to determine optimal rates of inference have been a cornerstone of phylogenetic experimental design for over two decades. However, both theoretical and empirical investigations of optimal rates have varied dramatically in their conclusions: spanning no relationship to a tight relationship between the rate of change and phylogenetic utility. Herein, we synthesize these apparently contradictory views, demonstrating both empirical and theoretical conditions under which each is correct. We find that optimal rates of characters-not genes-are generally robust to most experimental design decisions. Moreover, consideration of site rate heterogeneity within a given locus is critical to accurate predictions of utility. Factors such as taxon sampling or the targeted number of characters providing support for a topology are additionally critical to the predictions of phylogenetic utility based on the rate of character change. Further, optimality of rates and predictions of phylogenetic utility are not equivalent, demonstrating the need for further development of comprehensive theory of phylogenetic experimental design. [Divergence time; GC bias; homoplasy; incongruence; information content; internode length; optimal rates; phylogenetic informativeness; phylogenetic theory; phylogenetic utility; phylogenomics; signal and noise; subtending branch length; state space; taxon and character sampling.].

PhyloOncology: Understanding cancer through phylogenetic analysis.

Despite decades of research and an enormity of resultant data, cancer remains a significant public health problem. New tools and fresh perspectives are needed to obtain fundamental insights, to develop better prognostic and predictive tools, and to identify improved therapeutic interventions. With increasingly common genome-scale data, one suite of algorithms and concepts with potential to shed light on cancer biology is phylogenetics, a scientific discipline used in diverse fields. From grouping subsets of cancer samples to tracing subclonal evolution during cancer progression and metastasis, the use of phylogenetics is a powerful systems biology approach. Well-developed phylogenetic applications provide fast, robust approaches to analyze high-dimensional, heterogeneous cancer data sets. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

The ancestral levels of transcription and the evolution of sexual phenotypes in filamentous fungi.

Changes in gene expression have been hypothesized to play an important role in the evolution of divergent morphologies. To test this hypothesis in a model system, we examined differences in fruiting body morphology of five filamentous fungi in the Sordariomycetes, culturing them in a common garden environment and profiling genome-wide gene expression at five developmental stages. We reconstructed ancestral gene expression phenotypes, identifying genes with the largest evolved increases in gene expression across development. Conducting knockouts and performing phenotypic analysis in two divergent species typically demonstrated altered fruiting body development in the species that had evolved increased expression. Our evolutionary approach to finding relevant genes proved far more efficient than other gene deletion studies targeting whole genomes or gene families. Combining gene expression measurements with knockout phenotypes facilitated the refinement of Bayesian networks of the genes underlying fruiting body development, regulation of which is one of the least understood processes of multicellular development.

Neutral Theory and the Somatic Evolution of Cancer.

Kimura's neutral theory argued that positive selection was not responsible for an appreciable fraction of molecular substitutions. Correspondingly, quantitative analysis reveals that the vast majority of substitutions in cancer genomes are not detectably under selection. Insights from the somatic evolution of cancer reveal that beneficial substitutions in cancer constitute a small but important fraction of the molecular variants. The molecular evolution of cancer community will benefit by incorporating the neutral theory of molecular evolution into their understanding and analysis of cancer evolution-and accepting the use of tractable, predictive models, even when there is some evidence that they are not perfect.

Climatic and evolutionary drivers of phase shifts in the plague epidemics of colonial India.

Immune heterogeneity in wild host populations indicates that disease-mediated selection is common in nature. However, the underlying dynamic feedbacks involving the ecology of disease transmission, evolutionary processes, and their interaction with environmental drivers have proven challenging to characterize. Plague presents an optimal system for interrogating such couplings: Yersinia pestis transmission exerts intense selective pressure driving the local persistence of disease resistance among its wildlife hosts in endemic areas. Investigations undertaken in colonial India after the introduction of plague in 1896 suggest that, only a decade after plague arrived, a heritable, plague-resistant phenotype had become prevalent among commensal rats of cities undergoing severe plague epidemics. To understand the possible evolutionary basis of these observations, we developed a mathematical model coupling environmentally forced plague dynamics with evolutionary selection of rats, capitalizing on extensive archival data from Indian Plague Commission investigations. Incorporating increased plague resistance among rats as a consequence of intense natural selection permits the model to reproduce observed changes in seasonal epidemic patterns in several cities and capture experimentally observed associations between climate and flea population dynamics in India. Our model results substantiate Victorian era claims of host evolution based on experimental observations of plague resistance and reveal the buffering effect of such evolution against environmental drivers of transmission. Our analysis shows that historical datasets can yield powerful insights into the transmission dynamics of reemerging disease agents with which we have limited contemporary experience to guide quantitative modeling and inference.

Vaccination strategies against respiratory syncytial virus.

Respiratory syncytial virus (RSV) is the most common cause of US infant hospitalization. Additionally, RSV is responsible for 10,000 deaths annually among the elderly across the United States, and accounts for nearly as many hospitalizations as influenza. Currently, several RSV vaccine candidates are under development to target different age groups. To evaluate the potential effectiveness of age-specific vaccination strategies in averting RSV incidence, we developed a transmission model that integrates data on daily infectious viral load and changes of behavior associated with RSV symptoms. Calibrating to RSV weekly incidence rates in Texas, California, Colorado, and Pennsylvania, we show that in all states considered, an infected child under 5 y of age is more than twice as likely as a person over 50 y of age to transmit the virus. Geographic variability in the effectiveness of a vaccination program across states arises from interplay between seasonality patterns, population demography, vaccination uptake, and vaccine mechanism of action. Regardless of these variabilities, our analysis showed that allocating vaccine to children under 5 y of age would be the most efficient strategy per dose to avert RSV in both children and adults. Furthermore, due to substantial indirect protection, the targeting of children is even predicted to reduce RSV in the elderly more than directly vaccinating the elderly themselves. Our results can help inform ongoing clinical trials and future recommendations on RSV vaccination.