A short plus long-amplicon based sequencing approach improves genomic coverage and variant detection in the SARS-CoV-2 genome

High viral transmission in the COVID-19 pandemic has enabled SARS‐CoV‐2 to acquire new mutations that may impact genome sequencing methods. The ARTIC.v3 primer pool that amplifies short amplicons in a multiplex-PCR reaction is one of the most widely used methods for sequencing the SARS-CoV-2 genome. We observed that some genomic intervals are poorly captured with ARTIC primers. To improve the genomic coverage and variant detection across these intervals, we designed long amplicon primers and evaluated the performance of a short (ARTIC) plus long amplicon (MRL) sequencing approach. Sequencing assays were optimized on VR-1986D-ATCC RNA followed by sequencing of nasopharyngeal swab specimens from fifteen COVID-19 positive patients. ARTIC data covered 94.47% of the virus genome fraction in the positive control and patient samples. Variant analysis in the ARTIC data detected 217 mutations, including 209 single nucleotide variants (SNVs) and eight insertions & deletions. On the other hand, long-amplicon data detected 156 mutations, of which 80% were concordant with ARTIC data. Combined analysis of ARTIC + MRL data improved the genomic coverage to 97.03% and identified 214 high confidence mutations. The combined final set of 214 mutations included 203 SNVs, 8 deletions and 3 insertions. Analysis showed 26 SARS-CoV-2 lineage defining mutations including 4 known variants of concern K417N, E484K, N501Y, P618H in spike gene. Hybrid analysis identified 7 nonsynonymous and 5 synonymous mutations across the genome that were either ambiguous or not called in ARTIC data. For example, G172V mutation in the ORF3a protein and A2A mutation in Membrane protein were missed by the ARTIC assay. Thus, we show that while the short amplicon (ARTIC) assay provides good genomic coverage with high throughput, complementation of poorly captured intervals with long amplicon data can significantly improve SARS-CoV-2 genomic coverage and variant detection.

Reexamining Waddington: Canalization and new mutations are not required for the evolution of genetic assimilation

Over 65 years ago, Waddington demonstrated ancestrally phenotypically plastic traits can evolve to become constitutive, a process he termed genetic assimilation. Genetic assimilation evolves rapidly, assumed to be in large part due to segregating genetic variation only expressed in rare/novel environments, but otherwise phenotypically cryptic. Despite previous work suggesting a substantial role of cryptic genetic variation contributing to the evolution of genetic assimilation, some have argued for a prominent role for new mutations of large effect concurrent with selection. Interestingly, Waddington was less concerned by the relative contribution of CGV or new variants, but aimed to test the role of canalization, an evolved form of robustness. While canalization has been extensively studied, its role in the evolution of genetic assimilation is disputed, in part because explicit tests of evolved robustness are lacking. To address these questions, we recreated Waddington's selection experiments on an environmentally sensitive change in Drosophila wing morphology (crossvein development), using many independently evolved replicate lineages. Using these, we show that 1) a polygenic CGV, but not new variants of large effect are largely responsible for the evolved response demonstrated using both genomic and genetic approaches. 2) Using both environmental manipulations and mutagenesis of the evolved lineages that there is no evidence for evolved changes in canalization contributing to genetic assimilation. Finally, we demonstrate that 3) CGV has potentially pleiotropic and fitness consequences in natural populations and may not be entirely cryptic.

Haplotype-based inference of the distribution of fitness effects

Recent genome sequencing studies with large sample sizes in humans have discovered a vast quantity of low-frequency variants, providing an important source of information to analyze how selection is acting on human genetic variation. In order to estimate the strength of natural selection acting on low-frequency variants, we have developed a likelihood-based method that uses the lengths of pairwise identity-by-state between haplotypes carrying low-frequency variants. We show that in some non-equilibrium populations (such as those that have had recent population expansions) it is possible to distinguish between positive or negative selection acting on a set of variants. With our new framework, one can infer a fixed selection intensity acting on a set of variants at a particular frequency, or a distribution of selection coefficients for standing variants and new mutations. We show an application of our method to the UK10K phased haplotype dataset of individuals.

Spread of new mutations through space

The terms population size and population density are often used interchangeably, when in fact they are quite different. When viewed in a spatial landscape, density is defined as the number of individuals within a square unit of distance, while population size is simply the total count of a population. In discrete population genetics models, the effective population size is known to influence the interaction between selection and random drift with selection playing a larger role in large populations while random drift has more influence in smaller populations. Using a spatially explicit simulation software we investigate how population density affects the flow of new mutations through a geographical space. Using population density, selectional advantage, and dispersal distributions, a model is developed to predict the speed at which the new allele will travel, obtaining more accurate results than current diffusion approximations provide. We note that the rate at which a neutral mutation spreads begins to decay over time while the rate of spread of an advantageous allele remains constant. We also show that new advantageous mutations spread faster in dense populations.

The runaway evolution of SARS-CoV-2 leading to the highly evolved Delta strain

In new epidemics after the host shift, the pathogens may experience accelerated evolution driven by novel selective pressures. When the accelerated evolution enters a positive feedback loop with the expanding epidemics, the pathogen's runaway evolution may be triggered. To test this possibility in COVID-19, we analyze the extensive databases and identify 5 major waves of strains, one replacing the previous one in 2020-2021. The mutations differ entirely between waves and the number of mutations continues to increase, from 3-4 to 21-31. The latest wave is the Delta strain which accrues 31 new mutations to become highly prevalent. Interestingly, these new mutations in Delta strain emerge in multiple stages with each stage driven by 6-12 coding mutations that form a fitness group. In short, the evolution of SARS-CoV-2 from the oldest to the youngest wave, and from the earlier to the later stages of the Delta wave, is a process of acceleration with more and more mutations. The global increase in the viral population size (M(t), at time t) and the mutation accumulation (R(t)) may have indeed triggered the runaway evolution in late 2020, leading to the highly evolved Alpha and then Delta strain. To suppress the pandemic, it is crucial to break the positive feedback loop between M(t) and R(t), neither of which has yet to be effectively dampened by late 2021. New waves beyond Delta, hence, should not be surprising.

Unveiling the omicron B.1.1. 529: The variant of concern that is rattling the globe

Most viruses–including SARS-CoV-2, seem to have evolved over time. The lack of stringent proofreading mechanisms makes viral DNA/RNA replication error-prone. When a virus replicates, it sometimes changes a little bit, which is called mutations. Any virus with one or more new mutations can be referred to as a “variant” of the original virus. The last 2 years have witnessed the emergence of a large number of variants. Since the pandemic’s beginning, the SARS-CoV-2 coronavirus has mutated extensively, resulting in the emergence of different variants of the virus. One of these is the delta variant (arising from Pango lineage B.1.617.2) that took the word in a storm this year (February-July). The current a variant of concern is the B.1.1.529 (Omicron) variant reported first from South Africa on November 24, 2021. In recent weeks, infections have been widely reported, along with the increased detection of the B.1.1.529 variant. We reviewed the emergence of the new variant (B1.1.529) and its possible outcomes.

Updated picture of SARS-CoV-2 variants and mutations

The worldwide burden of coronavirus disease 2019 (COVID-19) is still unremittingly prosecuting, with nearly 300 million infections and over 5.3 million deaths recorded so far since the origin of the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic at the end of the year 2019. The fight against this new highly virulent beta coronavirus appears one of the most strenuous and long challenges that humanity has ever faced, since a definitive treatment has not been identified so far. The adoption of potentially useful physical preventive measures such as lockdowns, social distancing and face masking seems only partially effective for mitigating viral spread, though efficacy and continuation of such measures on the long term is questionable, due to many social and economic reasons. Many COVID-19 vaccines have been developed and are now widely used, though their effectiveness is challenged by several aspects such as low uptake and limited efficacy in some specific populations, as well as by continuous emergence of new mutations in the SARS-CoV-2 genome, accompanying the origin and spread of new variants, which in turn may contribute to further decrease the effectiveness of current vaccines and treatments. This article is hence aimed to provide an updated picture of SARS-CoV-2 variants and mutations that have emerged from November 2019 to present time (i.e., early December 2021).

The evolutionary impacts of synonymous mutations

During the 50 years since the genetic code was cracked, our understanding of the evolutionary consequences of synonymous mutations has undergone a dramatic shift. Synonymous codon changes were initially considered selectively neutral, and as such, exemplars of evolution via genetic drift. However, the pervasive and non-negligible fitness impacts of synonymous mutations are now clear across organisms. Despite the accumulated evidence, it remains challenging to incorporate the effects of synonymous changes in studies of selection, because the existing analytical framework was built with a focus on the fitness effects of nonsynonymous mutations. In this chapter, I trace the development of this topic and discuss the evidence that gradually transformed our thinking about the role of synonymous mutations in evolution. I suggest that our evolutionary framework should encompass the impacts of all mutations on various forms of information transmission. Folding synonymous mutations into a common distribution – rather than setting them apart as a distinct category – will allow a more complete and cohesive picture of the evolutionary consequences of new mutations.

Pregnancy-Associated Plasma Protein (PAPP)-A2 in Physiology and Disease

The growth hormone (GH)/insulin-like growth factor (IGF) axis plays fundamental roles during development, maturation, and aging. Members of this axis, composed of various ligands, receptors, and binding proteins, are regulated in a tissue- and time-specific manner that requires precise control that is not completely understood. Some of the most recent advances in understanding the implications of this axis in human growth are derived from the identifications of new mutations in the gene encoding the pregnancy-associated plasma protein PAPP-A2 protease that liberates IGFs from their carrier proteins in a selective manner to allow binding to the IGF receptor 1. The identification of three nonrelated families with mutations in the PAPP-A2 gene has shed light on how this protease affects human physiology. This review summarizes our understanding of the implications of PAPP-A2 in growth physiology, obtained from studies in genetically modified animal models and the PAPP-A2 deficient patients known to date.