Paramecium Genetics, Genomics, and Evolution.

The ciliate genus Paramecium served as one of the first model systems in microbial eukaryotic genetics, contributing much to the early understanding of phenomena as diverse as genome rearrangement, cryptic speciation, cytoplasmic inheritance, and endosymbiosis, as well as more recently to the evolution of mating types, introns, and roles of small RNAs in DNA processing. Substantial progress has recently been made in the area of comparative and population genomics. Paramecium species combine some of the lowest known mutation rates with some of the largest known effective populations, along with likely very high recombination rates, thereby harboring a population-genetic environment that promotes an exceptionally efficient capacity for selection. As a consequence, the genomes are extraordinarily streamlined, with very small intergenic regions combined with small numbers of tiny introns. The subject of the bulk of Paramecium research, the ancient Paramecium aurelia species complex, is descended from two whole-genome duplication events that retain high degrees of synteny, thereby providing an exceptional platform for studying the fates of duplicate genes. Despite having a common ancestor dating to several hundred million years ago, the known descendant species are morphologically indistinguishable, raising significant questions about the common view that gene duplications lead to the origins of evolutionary novelties.

Biases in ARG-based inference of historical population size in populations experiencing selection.

Inferring the demographic history of populations provides fundamental insights into species dynamics and is essential for developing a null model to accurately study selective processes. However, background selection and selective sweeps can produce genomic signatures at linked sites that mimic or mask signals associated with historical population size change. While the theoretical biases introduced by the linked effects of selection have been well established, it is unclear whether ARG-based approaches to demographic inference in typical empirical analyses are susceptible to mis-inference due to these effects. To address this, we developed highly realistic forward simulations of human and Drosophila melanogaster populations, including empirically estimated variability of gene density, mutation rates, recombination rates, purifying and positive selection, across different historical demographic scenarios, to broadly assess the impact of selection on demographic inference using a genealogy-based approach. Our results indicate that the linked effects of selection minimally impact demographic inference for human populations, though it could cause mis-inference in populations with similar genome architecture and population parameters experiencing more frequent recurrent sweeps. We found that accurate demographic inference of D. melanogaster populations by ARG-based methods is compromised by the presence of pervasive background selection alone, leading to spurious inferences of recent population expansion which may be further worsened by recurrent sweeps, depending on the proportion and strength of beneficial mutations. Caution and additional testing with species-specific simulations are needed when inferring population history with non-human populations using ARG-based approaches to avoid mis-inference due to the linked effects of selection.

Developing an appropriate evolutionary baseline model for the study of SARS-CoV-2 patient samples.

Over the past 3 years, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has spread through human populations in several waves, resulting in a global health crisis. In response, genomic surveillance efforts have proliferated in the hopes of tracking and anticipating the evolution of this virus, resulting in millions of patient isolates now being available in public databases. Yet, while there is a tremendous focus on identifying newly emerging adaptive viral variants, this quantification is far from trivial. Specifically, multiple co-occurring and interacting evolutionary processes are constantly in operation and must be jointly considered and modeled in order to perform accurate inference. We here outline critical individual components of such an evolutionary baseline model-mutation rates, recombination rates, the distribution of fitness effects, infection dynamics, and compartmentalization-and describe the current state of knowledge pertaining to the related parameters of each in SARS-CoV-2. We close with a series of recommendations for future clinical sampling, model construction, and statistical analysis.

Recommendations for improving statistical inference in population genomics.

The field of population genomics has grown rapidly in response to the recent advent of affordable, large-scale sequencing technologies. As opposed to the situation during the majority of the 20th century, in which the development of theoretical and statistical population genetic insights outpaced the generation of data to which they could be applied, genomic data are now being produced at a far greater rate than they can be meaningfully analyzed and interpreted. With this wealth of data has come a tendency to focus on fitting specific (and often rather idiosyncratic) models to data, at the expense of a careful exploration of the range of possible underlying evolutionary processes. For example, the approach of directly investigating models of adaptive evolution in each newly sequenced population or species often neglects the fact that a thorough characterization of ubiquitous nonadaptive processes is a prerequisite for accurate inference. We here describe the perils of these tendencies, present our consensus views on current best practices in population genomic data analysis, and highlight areas of statistical inference and theory that are in need of further attention. Thereby, we argue for the importance of defining a biologically relevant baseline model tuned to the details of each new analysis, of skepticism and scrutiny in interpreting model fitting results, and of carefully defining addressable hypotheses and underlying uncertainties.

Soft selective sweeps: Addressing new definitions, evaluating competing models, and interpreting empirical outliers.

The ability to accurately identify and quantify genetic signatures associated with soft selective sweeps based on patterns of nucleotide variation has remained controversial. We here provide counter viewpoints to recent publications in PLOS Genetics that have argued not only for the statistical identifiability of soft selective sweeps, but also for their pervasive evolutionary role in both Drosophila and HIV populations. We present evidence that these claims owe to a lack of consideration of competing evolutionary models, unjustified interpretations of empirical outliers, as well as to new definitions of the processes themselves. Our results highlight the dangers of fitting evolutionary models based on hypothesized and episodic processes without properly first considering common processes and, more generally, of the tendency in certain research areas to view pervasive positive selection as a foregone conclusion.

Developing an Evolutionary Baseline Model for Humans: Jointly Inferring Purifying Selection with Population History.

Building evolutionarily appropriate baseline models for natural populations is not only important for answering fundamental questions in population genetics-including quantifying the relative contributions of adaptive versus nonadaptive processes-but also essential for identifying candidate loci experiencing relatively rare and episodic forms of selection (e.g., positive or balancing selection). Here, a baseline model was developed for a human population of West African ancestry, the Yoruba, comprising processes constantly operating on the genome (i.e., purifying and background selection, population size changes, recombination rate heterogeneity, and gene conversion). Specifically, to perform joint inference of selective effects with demography, an approximate Bayesian approach was employed that utilizes the decay of background selection effects around functional elements, taking into account genomic architecture. This approach inferred a recent 6-fold population growth together with a distribution of fitness effects that is skewed towards effectively neutral mutations. Importantly, these results further suggest that, although strong and/or frequent recurrent positive selection is inconsistent with observed data, weak to moderate positive selection is consistent but unidentifiable if rare.

Dynamics of Gene Loss following Ancient Whole-Genome Duplication in the Cryptic Paramecium Complex.

Whole-genome duplications (WGDs) have shaped the gene repertoire of many eukaryotic lineages. The redundancy created by WGDs typically results in a phase of massive gene loss. However, some WGD-derived paralogs are maintained over long evolutionary periods, and the relative contributions of different selective pressures to their maintenance are still debated. Previous studies have revealed a history of three successive WGDs in the lineage of the ciliate Paramecium tetraurelia and two of its sister species from the Paramecium aurelia complex. Here, we report the genome sequence and analysis of 10 additional P. aurelia species and 1 additional out group, revealing aspects of post-WGD evolution in 13 species sharing a common ancestral WGD. Contrary to the morphological radiation of vertebrates that putatively followed two WGD events, members of the cryptic P. aurelia complex have remained morphologically indistinguishable after hundreds of millions of years. Biases in gene retention compatible with dosage constraints appear to play a major role opposing post-WGD gene loss across all 13 species. In addition, post-WGD gene loss has been slower in Paramecium than in other species having experienced genome duplication, suggesting that the selective pressures against post-WGD gene loss are especially strong in Paramecium. A near complete lack of recent single-gene duplications in Paramecium provides additional evidence for strong selective pressures against gene dosage changes. This exceptional data set of 13 species sharing an ancestral WGD and 2 closely related out group species will be a useful resource for future studies on Paramecium as a major model organism in the evolutionary cell biology.

A Population-Genetic Lens into the Process of Gene Loss Following Whole-Genome Duplication.

Whole-genome duplications (WGDs) have occurred in many eukaryotic lineages. However, the underlying evolutionary forces and molecular mechanisms responsible for the long-term retention of gene duplicates created by WGDs are not well understood. We employ a population-genomic approach to understand the selective forces acting on paralogs and investigate ongoing duplicate-gene loss in multiple species of Paramecium that share an ancient WGD. We show that mutations that abolish protein function are more likely to be segregating in retained WGD paralogs than in single-copy genes, most likely because of ongoing nonfunctionalization post-WGD. This relaxation of purifying selection occurs in only one WGD paralog, accompanied by the gradual fixation of nonsynonymous mutations and reduction in levels of expression, and occurs over a long period of evolutionary time, "marking" one locus for future loss. Concordantly, the fitness effects of new nonsynonymous mutations and frameshift-causing indels are significantly more deleterious in the highly expressed copy compared with their paralogs with lower expression. Our results provide a novel mechanistic model of gene duplicate loss following WGDs, wherein selection acts on the sum of functional activity of both duplicate genes, allowing the two to wander in expression and functional space, until one duplicate locus eventually degenerates enough in functional efficiency or expression that its contribution to total activity is too insignificant to be retained by purifying selection. Retention of duplicates by such mechanisms predicts long times to duplicate-gene loss, which should not be falsely attributed to retention due to gain/change in function.

The Impact of Purifying and Background Selection on the Inference of Population History: Problems and Prospects.

Current procedures for inferring population history generally assume complete neutrality-that is, they neglect both direct selection and the effects of selection on linked sites. We here examine how the presence of direct purifying selection and background selection may bias demographic inference by evaluating two commonly-used methods (MSMC and fastsimcoal2), specifically studying how the underlying shape of the distribution of fitness effects and the fraction of directly selected sites interact with demographic parameter estimation. The results show that, even after masking functional genomic regions, background selection may cause the mis-inference of population growth under models of both constant population size and decline. This effect is amplified as the strength of purifying selection and the density of directly selected sites increases, as indicated by the distortion of the site frequency spectrum and levels of nucleotide diversity at linked neutral sites. We also show how simulated changes in background selection effects caused by population size changes can be predicted analytically. We propose a potential method for correcting for the mis-inference of population growth caused by selection. By treating the distribution of fitness effect as a nuisance parameter and averaging across all potential realizations, we demonstrate that even directly selected sites can be used to infer demographic histories with reasonable accuracy.

Inferring the distribution of fitness effects in patient-sampled and experimental virus populations: two case studies.

We here propose an analysis pipeline for inferring the distribution of fitness effects (DFE) from either patient-sampled or experimentally-evolved viral populations, that explicitly accounts for non-Wright-Fisher and non-equilibrium population dynamics inherent to pathogens. We examine the performance of this approach via extensive power and performance analyses, and highlight two illustrative applications - one from an experimentally-passaged RNA virus, and the other from a clinically-sampled DNA virus. Finally, we discuss how such DFE inference may shed light on major research questions in virus evolution, ranging from a quantification of the population genetic processes governing genome size, to the role of Hill-Robertson interference in dictating adaptive outcomes, to the potential design of novel therapeutic approaches to eradicate within-patient viral populations via induced mutational meltdown.

Population Genomics of Paramecium Species.

Population-genomic analyses are essential to understanding factors shaping genomic variation and lineage-specific sequence constraints. The dearth of such analyses for unicellular eukaryotes prompted us to assess genomic variation in Paramecium, one of the most well-studied ciliate genera. The Paramecium aurelia complex consists of ∼15 morphologically indistinguishable species that diverged subsequent to two rounds of whole-genome duplications (WGDs, as long as 320 MYA) and possess extremely streamlined genomes. We examine patterns of both nuclear and mitochondrial polymorphism, by sequencing whole genomes of 10-13 worldwide isolates of each of three species belonging to the P. aurelia complex: P. tetraurelia, P. biaurelia, P. sexaurelia, as well as two outgroup species that do not share the WGDs: P. caudatum and P. multimicronucleatum. An apparent absence of global geographic population structure suggests continuous or recent dispersal of Paramecium over long distances. Intergenic regions are highly constrained relative to coding sequences, especially in P. caudatum and P. multimicronucleatum that have shorter intergenic distances. Sequence diversity and divergence are reduced up to ∼100-150 bp both upstream and downstream of genes, suggesting strong constraints imposed by the presence of densely packed regulatory modules. In addition, comparison of sequence variation at non-synonymous and synonymous sites suggests similar recent selective pressures on paralogs within and orthologs across the deeply diverging species. This study presents the first genome-wide population-genomic analysis in ciliates and provides a valuable resource for future studies in evolutionary and functional genetics in Paramecium.

Differential retention and divergent resolution of duplicate genes following whole-genome duplication.

The Paramecium aurelia complex is a group of 15 species that share at least three past whole-genome duplications (WGDs). The macronuclear genome sequences of P. biaurelia and P. sexaurelia are presented and compared to the published sequence of P. tetraurelia. Levels of duplicate-gene retention from the recent WGD differ by > 10% across species, with P. sexaurelia losing significantly more genes than P. biaurelia or P. tetraurelia. In addition, historically high rates of gene conversion have homogenized WGD paralogs, probably extending the paralogs' lifetimes. The probability of duplicate retention is positively correlated with GC content and expression level; ribosomal proteins, transcription factors, and intracellular signaling proteins are overrepresented among maintained duplicates. Finally, multiple sources of evidence indicate that P. sexaurelia diverged from the two other lineages immediately following, or perhaps concurrent with, the recent WGD, with approximately half of gene losses between P. tetraurelia and P. sexaurelia representing divergent gene resolutions (i.e., silencing of alternative paralogs), as expected for random duplicate loss between these species. Additionally, though P. biaurelia and P. tetraurelia diverged from each other much later, there are still more than 100 cases of divergent resolution between these two species. Taken together, these results indicate that divergent resolution of duplicate genes between lineages acts to reinforce reproductive isolation between species in the Paramecium aurelia complex.

Hill-Robertson interference may bias the inference of fitness effects of new mutations in highly selfing species.

The accurate estimation of the distribution of fitness effects (DFE) of new mutations is critical for population genetic inference but remains a challenging task. While various methods have been developed for DFE inference using the site frequency spectrum of putatively neutral and selected sites, their applicability in species with diverse life history traits and complex demographic scenarios is not well understood. Selfing is common among eukaryotic species and can lead to decreased effective recombination rates, increasing the effects of selection at linked sites, including interference between selected alleles. We employ forward simulations to investigate the limitations of current DFE estimation approaches in the presence of selfing and other model violations, such as linkage, departures from semidominance, population structure, and uneven sampling. We find that distortions of the site frequency spectrum due to Hill-Robertson interference in highly selfing populations lead to mis-inference of the deleterious DFE of new mutations. Specifically, while accounting for the decrease in the effective population size due to linked effects of selection allows an accurate estimation of selection coefficients in moderately selfing populations, this correction is unable to accurately estimate selection coefficients in highly selfing populations when interference between selected alleles is pervasive. In addition, the presence of cryptic population structure with low rates of migration and uneven sampling across subpopulations leads to the false inference of a deleterious DFE skewed towards effectively neutral/mildly deleterious mutations. Finally, the proportion of adaptive substitutions estimated at high rates of selfing is substantially overestimated. Our observations apply broadly to species and genomic regions with little/no recombination and where interference might be pervasive.

Pharmaceutically active micropollutants: origin, hazards and removal.

Pharmaceuticals, recognized for their life-saving potential, have emerged as a concerning class of micropollutants in the environment. Even at minute concentrations, chronic exposure poses a significant threat to ecosystems. Various pharmaceutically active micropollutants (PhAMP), including antibiotics, analgesics, and hormones, have been detected in underground waters, surface waters, seawater, sewage treatment plants, soils, and activated sludges due to the absence of standardized regulations on pharmaceutical discharge. Prolonged exposureof hospital waste and sewage treatment facilities is linked to the presence of antibiotic-resistant bacteria. Conventional water treatment methods prove ineffective, prompting the use of alternative techniques like photolysis, reverse osmosis, UV-degradation, bio-degradation, and nano-filtration. However, commercial implementation faces challenges such as incomplete removal, toxic sludge generation, high costs, and the need for skilled personnel. Research gaps include the need to comprehensively identify and understand various types of pharmaceutically active micropollutants, investigate their long-term ecological impact, develop more sensitive monitoring techniques, and explore integrated treatment approaches. Additionally, there is a gap in understanding the socio-economic implications of pharmaceutical pollution and the efficacy of public awareness campaigns. Future research should delve into alternative strategies like phagotherapy, vaccines, and natural substance substitutes to address the escalating threat of pharmaceutical pollution.

Evaluating power to detect recurrent selective sweeps under increasingly realistic evolutionary null models.

The detection of selective sweeps from population genomic data often relies on the premise that the beneficial mutations in question have fixed very near the sampling time. As it has been previously shown that the power to detect a selective sweep is strongly dependent on the time since fixation as well as the strength of selection, it is naturally the case that strong, recent sweeps leave the strongest signatures. However, the biological reality is that beneficial mutations enter populations at a rate, one that partially determines the mean wait time between sweep events and hence their age distribution. An important question thus remains about the power to detect recurrent selective sweeps when they are modeled by a realistic mutation rate and as part of a realistic distribution of fitness effects, as opposed to a single, recent, isolated event on a purely neutral background as is more commonly modeled. Here we use forward-in-time simulations to study the performance of commonly used sweep statistics, within the context of more realistic evolutionary baseline models incorporating purifying and background selection, population size change, and mutation and recombination rate heterogeneity. Results demonstrate the important interplay of these processes, necessitating caution when interpreting selection scans; specifically, false-positive rates are in excess of true-positive across much of the evaluated parameter space, and selective sweeps are often undetectable unless the strength of selection is exceptionally strong.

Evaluating power to detect recurrent selective sweeps under increasingly realistic evolutionary null models.

The detection of selective sweeps from population genomic data often relies on the premise that the beneficial mutations in question have fixed very near the sampling time. As it has been previously shown that the power to detect a selective sweep is strongly dependent on the time since fixation as well as the strength of selection, it is naturally the case that strong, recent sweeps leave the strongest signatures. However, the biological reality is that beneficial mutations enter populations at a rate, one that partially determines the mean wait time between sweep events and hence their age distribution. An important question thus remains about the power to detect recurrent selective sweeps when they are modelled by a realistic mutation rate and as part of a realistic distribution of fitness effects (DFE), as opposed to a single, recent, isolated event on a purely neutral background as is more commonly modelled. Here we use forward-in-time simulations to study the performance of commonly used sweep statistics, within the context of more realistic evolutionary baseline models incorporating purifying and background selection, population size change, and mutation and recombination rate heterogeneity. Results demonstrate the important interplay of these processes, necessitating caution when interpreting selection scans; specifically, false positive rates are in excess of true positive across much of the evaluated parameter space, and selective sweeps are often undetectable unless the strength of selection is exceptionally strong. Teaser Text: Outlier-based genomic scans have proven a popular approach for identifying loci that have potentially experienced recent positive selection. However, it has previously been shown that an evolutionarily appropriate baseline model that incorporates non-equilibrium population histories, purifying and background selection, and variation in mutation and recombination rates is necessary to reduce often extreme false positive rates when performing genomic scans. Here we evaluate the power to detect recurrent selective sweeps using common SFS-based and haplotype-based methods under these increasingly realistic models. We find that while these appropriate evolutionary baselines are essential to reduce false positive rates, the power to accurately detect recurrent selective sweeps is generally low across much of the biologically relevant parameter space.

Developing an evolutionary baseline model for humans: jointly inferring purifying selection with population history.

Building evolutionarily appropriate baseline models for natural populations is not only important for answering fundamental questions in population genetics - including quantifying the relative contributions of adaptive vs. non-adaptive processes - but it is also essential for identifying candidate loci experiencing relatively rare and episodic forms of selection ( e.g., positive or balancing selection). Here, a baseline model was developed for a human population of West African ancestry, the Yoruba, comprising processes constantly operating on the genome ( i.e. , purifying and background selection, population size changes, recombination rate heterogeneity, and gene conversion). Specifically, to perform joint inference of selective effects with demography, an approximate Bayesian approach was employed that utilizes the decay of background selection effects around functional elements, taking into account genomic architecture. This approach inferred a recent 6-fold population growth together with a distribution of fitness effects that is skewed towards effectively neutral mutations. Importantly, these results further suggest that, while strong and/or frequent recurrent positive selection is inconsistent with observed data, weak to moderate positive selection is consistent but unidentifiable if rare.

An Integrated Approach to Identify Intrinsically Disordered Regions in Osteopontin with its Interacting Network in Rheumatoid Arthritis.

BACKGROUND: Credentials of molecular diagnostic approaches are an important goal. Since protein-protein interaction (PPI) network analysis is an apposite method for molecular valuation, a PPI grid related to Intrinsically Disordered Proteins (IDPs) of RA was targeted in the present research. AIM: The aim of the study is to analyse the role of highly disordered proteins and their functional parameters in causing Rheumatoid Arthritis (RA). METHODS: Cytoscape software helped in identifying molecular interaction networks. Intrinsically disordered proteins lack higher order structure and have functional advantages, but their dysregulation can cause several diseases. All the significant proteins responsible for RA were identified. On the basis of the data obtained, highly disordered proteins were selected. Further, MSA was done to find the similarity among the highly disordered proteins and their functional partners. To determine the most relevant functional partner( s)/interacting protein(s) out of large network, three filters were introduced in the methodology. RESULTS: The two filtered proteins, IBSP and FGF2, have common functions and also play a vital role in the pathways of RA. Thus, gives an in-depth knowledge of molecular mechanisms involved in Rheumatoid Arthritis and targeted therapeutics. CONCLUSION: The network analysis of these proteins has been explored using Cytoscape, and the proteins with favourable values of graph centrality parameters such as IBSP and FGF2 are identified. Interesting functional cross talk such as bio mineralization, boneremodelling, angiogenesis, cell differentiation, etc., of SPP1 with IBSP and FGF2 is found, which throws light into the fact that these two proteins play a vital role in the pathways of RA.

Isolation and characterization of novel lytic bacteriophages that infect multi drug resistant clinical strains of Escherichia coli.

The pathogenic strains of Escherichia coli (E. coli) are frequent cause of urinary tract infections including catheter-associated, soft tissue infections and sepsis. The growing antibiotic resistance in E. coli is a major health concern. Bacteriophages are specific for their bacterial host, thus providing a novel and effective alternatives. This study focuses on isolation of bacteriophages from urban sewage treatment plants. Initially 50 different bacteriophages have been isolated against non-resistant reference E. coli strain and fifty multidrug resistant clinical isolates of extraintestinal infections. Out of which only thirty-one lytic phages which gave clear plaques were further analysed for different physico-chemical aspects such as thermal inactivation, pH, effect of organic solvents and detergents. Two bacteriophages, ASEC2201 and ASEC2202, were selected for their ability to withstand temperature fluctuation from -20 to 62 °C and a pH range from 4 to 10. They also showed good survival (40-94%) in the presence of organic solvents like ethanol, acetone, DMSO and chloroform or ability to form plaques even after the treatment with detergents like SDS, CTAB and sarkosyl. Both efficiently killed reference strain and 40-44% of multidrug resistant clinical isolates of E. coli. Later ASEC2201 and ASEC2202 were subjected to morphological characterisation through transmission electron microscopy, which revealed them to be tailed phages. The genomic analysis confirmed them to be Escherichia phages which belonged to family Drexlerviridae of Caudovirales.

Developing an Appropriate Evolutionary Baseline Model for the Study of Human Cytomegalovirus.

Human cytomegalovirus (HCMV) represents a major threat to human health, contributing to both birth defects in neonates as well as organ transplant failure and opportunistic infections in immunocompromised individuals. HCMV exhibits considerable interhost and intrahost diversity, which likely influences the pathogenicity of the virus. Therefore, understanding the relative contributions of various evolutionary forces in shaping patterns of variation is of critical importance both mechanistically and clinically. Herein, we present the individual components of an evolutionary baseline model for HCMV, with a particular focus on congenital infections for the sake of illustration-including mutation and recombination rates, the distribution of fitness effects, infection dynamics, and compartmentalization-and describe the current state of knowledge of each. By building this baseline model, researchers will be able to better describe the range of possible evolutionary scenarios contributing to observed variation as well as improve power and reduce false-positive rates when scanning for adaptive mutations in the HCMV genome.