Intra- vs. Interhost Evolution of SARS-CoV-2 Driven by Uncorrelated Selection-The Evolution Thwarted

In viral evolution, a new mutation has to proliferate within the host (Stage I) in order to be transmitted and then compete in the host population (Stage II). We now analyze the intrahost single nucleotide variants (iSNVs) in a set of 79 SARS-CoV-2 infected patients with most transmissions tracked. Here, every mutation has two measures: 1) iSNV frequency within each individual host in Stage I; 2) occurrence among individuals ranging from 1 (private), 2-78 (public), to 79 (global) occurrences in Stage II. In Stage I, a small fraction of nonsynonymous iSNVs are sufficiently advantageous to rise to a high frequency, often 100%. However, such iSNVs usually fail to become public mutations. Thus, the selective forces in the two stages of evolution are uncorrelated and, possibly, antagonistic. For that reason, successful mutants, including many variants of concern, have to avoid being eliminated in Stage I when they first emerge. As a result, they may not have the transmission advantage to outcompete the dominant strains and, hence, are rare in the host population. Few of them could manage to slowly accumulate advantageous mutations to compete in Stage II. When they do, they would appear suddenly as in each of the six successive waves of SARS-CoV-2 strains. In conclusion, Stage I evolution, the gate-keeper, may contravene the long-term viral evolution and should be heeded in viral studies.

Using an Unsupervised Clustering Model to Detect the Early Spread of SARS-CoV-2 Worldwide

Deciphering the population structure of SARS-CoV-2 is critical to inform public health management and reduce the risk of future dissemination. With the continuous accruing of SARS-CoV-2 genomes worldwide, discovering an effective way to group these genomes is critical for organizing the landscape of the population structure of the virus. Taking advantage of recently published state-of-the-art machine learning algorithms, we used an unsupervised deep learning clustering algorithm to group a total of 16,873 SARS-CoV-2 genomes. Using single nucleotide polymorphisms as input features, we identified six major subtypes of SARS-CoV-2. The proportions of the clusters across the continents revealed distinct geographical distributions. Comprehensive analysis indicated that both genetic factors and human migration factors shaped the specific geographical distribution of the population structure. This study provides a different approach using clustering methods to study the population structure of a never-seen-before and fast-growing species such as SARS-CoV-2. Moreover, clustering techniques can be used for further studies of local population structures of the proliferating virus.

Trends in the molecular epidemiology and population genetics of emerging Sporothrix species

Sporothrix (Ophiostomatales) comprises species that are pathogenic to humans and other mammals as well as environmental fungi. Developments in molecular phylogeny have changed our perceptions about the epidemiology, host-association, and virulence of Sporothrix. The classical agent of sporotrichosis, Sporothrix schenckii, now comprises several species nested in a clinical clade with S. brasiliensis, S. globosa, and S. luriei. To gain a more precise view of outbreaks dynamics, structure, and origin of genetic variation within and among populations of Sporothrix, we applied three sets of discriminatory AFLP markers (#3 EcoRI-GA/MseI-TT, #5 EcoRI-GA/MseI-AG, and #6 EcoRI-TA/MseI-AA) and mating-type analysis to a large collection of human, animal and environmental isolates spanning the major endemic areas. A total of 451 polymorphic loci were amplified in vitro from 188 samples, and revealed high polymorphism information content (PIC = 0.1765-0.2253), marker index (MI = 0.0001-0.0002), effective multiplex ratio (E = 15.1720-23.5591), resolving power (Rp = 26.1075-40.2795), discriminating power (D = 0.9766-0.9879), expected heterozygosity (H = 0.1957-0.2588), and mean heterozygosity (Havp  = 0.000007-0.000009), demonstrating the effectiveness of AFLP markers to speciate Sporothrix. Analysis using the program structure indicated three genetic clusters matching S. brasiliensis (population 1), S. schenckii (population 2), and S. globosa (population 3), with the presence of patterns of admixture amongst all populations. AMOVA revealed highly structured clusters (PhiPT = 0.458-0.484, P < 0.0001), with roughly equivalent genetic variability within (46-48 %) and between (52-54 %) populations. Heterothallism was the exclusive mating strategy, and the distributions of MAT1-1 or MAT1-2 idiomorphs were not significantly skewed (1:1 ratio) for S. schenckii (χ2 = 2.522; P = 0.1122), supporting random mating. In contrast, skewed distributions were found for S. globosa (χ2 = 9.529; P = 0.0020) with a predominance of MAT1-1 isolates, and regional differences were highlighted for S. brasiliensis with the overwhelming occurrence of MAT1-2 in Rio de Janeiro (χ2 = 14.222; P = 0.0002) and Pernambuco (χ2 = 7.364; P = 0.0067), in comparison to a higher prevalence of MAT1-1 in the Rio Grande do Sul (χ2 = 7.364; P = 0.0067). Epidemiological trends reveal the geographic expansion of cat-transmitted sporotrichosis due to S. brasiliensis via founder effect. These data support Rio de Janeiro as the centre of origin that has led to the spread of this disease to other regions in Brazil. Our ability to reconstruct the source, spread, and evolution of the ongoing outbreaks from molecular data provides high-quality information for decision-making aimed at mitigating the progression of the disease. Other uses include surveillance, rapid diagnosis, case connectivity, and guiding access to appropriate antifungal treatment.

Microbial driven genetic variation in holobionts

Genetic variation in holobionts, (host and microbiome), occurring by changes in both host and microbiome genomes, can be observed from two perspectives: observable variations and the processes that bring about the variation. The observable includes the enormous genetic diversity of prokaryotes, which gave rise to eukaryotic organisms. Holobionts then evolved a rich microbiome with a stable core containing essential genes, less so common taxa, and a more diverse non-core enabling considerable genetic variation. The result being that, the human gut microbiome, for example, contains 1,000 times more unique genes than are present in the human genome. Microbial driven genetic variation processes in holobionts include: (1) Acquisition of novel microbes from the environment, which bring in multiple genes in one step, (2) amplification/reduction of certain microbes in the microbiome, that contribute to holobiont` s adaptation to changing conditions, (3) horizontal gene transfer between microbes and between microbes and host, (4) mutation, which plays an important role in optimizing interactions between different microbiota and between microbiota and host. We suggest that invertebrates and plants, where microbes can live intracellularly, have a greater chance of genetic exchange between microbiota and host, thus a greater chance of vertical transmission and a greater effect of microbiome on evolution of host than vertebrates. However, even in vertebrates the microbiome can aid in environmental fluctuations by amplification/reduction and by acquisition of novel microorganisms.

On the founder effect in COVID-19 outbreaks: how many infected travelers may have started them all?

How many incoming travelers (I0 at time 0, equivalent to the 'founders' in evolutionary genetics) infected with SARS-CoV-2 who visit or return to a region could have started the epidemic of that region? I0 would be informative about the initiation and progression of epidemics. To obtain I0 , we analyze the genetic divergence among viral populations of different regions. By applying the 'individual-output' model of genetic drift to the SARS-CoV-2 diversities, we obtain I0 < 10, which could have been achieved by one infected traveler in a long-distance flight. The conclusion is robust regardless of the source population, the continuation of inputs (It for t > 0) or the fitness of the variants. With such a tiny trickle of human movement igniting many outbreaks, the crucial stage of repressing an epidemic in any region should, therefore, be the very first sign of local contagion when positive cases first become identifiable. The implications of the highly 'portable' epidemics, including their early evolution prior to any outbreak, are explored in the companion study (Ruan et al., personal communication).

Mutations Beget More Mutations-Rapid Evolution of Mutation Rate in Response to the Risk of Runaway Accumulation

The rapidity with which the mutation rate evolves could greatly impact evolutionary patterns. Nevertheless, most studies simply assume a constant rate in the time scale of interest (Kimura 1983; Drake 1991; Kumar 2005; Li 2007; Lynch 2010). In contrast, recent studies of somatic mutations suggest that the mutation rate may vary by several orders of magnitude within a lifetime (Kandoth et al. 2013; Lawrence et al. 2013). To resolve the discrepancy, we now propose a runaway model, applicable to both the germline and soma, whereby mutator mutations form a positive-feedback loop. In this loop, any mutator mutation would increase the rate of acquiring the next mutator, thus triggering a runaway escalation in mutation rate. The process can be initiated more readily if there are many weak mutators than a few strong ones. Interestingly, even a small increase in the mutation rate at birth could trigger the runaway process, resulting in unfit progeny. In slowly reproducing species, the need to minimize the risk of this uncontrolled accumulation would thus favor setting the mutation rate low. In comparison, species that starts and ends reproduction sooner do not face the risk and may set the baseline mutation rate higher. The mutation rate would evolve in response to the risk of runaway mutation, in particular, when the generation time changes. A rapidly evolving mutation rate may shed new lights on many evolutionary phenomena (Elango et al. 2006; Thomas et al. 2010, 2018; Langergraber et al. 2012; Besenbacher et al. 2019).

Tumorigenesis as the Paradigm of Quasi-neutral Molecular Evolution

In the absence of both positive and negative selections, coding sequences evolve at a neutral rate (R = 1). Such a high genomic rate is generally not achievable due to the prevalence of negative selection against codon substitutions. Remarkably, somatic evolution exhibits the seemingly neutral rate R ∼ 1 across normal and cancerous tissues. Nevertheless, R ∼ 1 may also mean that positive and negative selections are both strong, but equal in intensity. We refer to this regime as quasi-neutral. Indeed, individual genes in cancer cells often evolve at a much higher, or lower, rate than R ∼ 1. Here, we show that 1) quasi-neutrality is much more likely when populations are small (N < 50); 2) stem-cell populations in single normal tissue niches, from which tumors likely emerge, have a small N (usually <50) but selection at this stage is measurable and strong; 3) when N dips below 50, selection efficacy decreases precipitously; and 4) notably, N is smaller in the stem-cell niche of the small intestine than in the colon. Hence, the ∼70-fold higher rate of phenotypic evolution (observed as cancer risk) in the latter can be explained by the greater efficacy of selection, which then leads to the fixation of more advantageous and fewer deleterious mutations in colon cancers. In conclusion, quasi-neutral evolution sheds a new light on a general evolutionary principle that helps to explain aspects of cancer evolution.

The Ultimate Guide to Bacterial Swarming: An Experimental Model to Study the Evolution of Cooperative Behavior

Cooperation has fascinated biologists since Darwin. How did cooperative behaviors evolve despite the fitness cost to the cooperator? Bacteria have cooperative behaviors that make excellent models to take on this age-old problem from both proximate (molecular) and ultimate (evolutionary) angles. We delve into Pseudomonas aeruginosa swarming, a phenomenon where billions of bacteria move cooperatively across distances of centimeters in a matter of a few hours. Experiments with swarming have unveiled a strategy called metabolic prudence that stabilizes cooperation, have showed the importance of spatial structure, and have revealed a regulatory network that integrates environmental stimuli and direct cooperative behavior, similar to a machine learning algorithm. The study of swarming elucidates more than proximate mechanisms: It exposes ultimate mechanisms valid to all scales, from cells in cancerous tumors to animals in large communities.

Measuring Clonal Evolution in Cancer with Genomics

Cancers originate from somatic cells in the human body that have accumulated genetic alterations. These mutations modify the phenotype of the cells, allowing them to escape the homeostatic regulation that maintains normal cell number. Viewed through the lens of evolutionary biology, the transformation of normal cells into malignant cells is evolution in action. Evolution continues throughout cancer growth, progression, treatment resistance, and disease relapse, driven by adaptation to changes in the cancer's environment, and intratumor heterogeneity is an inevitable consequence of this evolutionary process. Genomics provides a powerful means to characterize tumor evolution, enabling quantitative measurement of evolving clones across space and time. In this review, we discuss concepts and approaches to quantify and measure this evolutionary process in cancer using genomics.