OBJECTIVE: To explore the pathogenic mechanisms of Candida albicans (C. albicans), focusing on its impact on human health, particularly through invasive infections in the gastrointestinal and respiratory tracts.
METHODS: In this study, we evaluated the demographic and clinical profiles of 7 pneumonia patients. Meanwhile, we used Gene Set Enrichment Analysis (GSEA) and Evolutionary Dynamics method to analyze the role of candidalysin in C. albicans pathogenicity.
RESULTS: By analyzing genomic data and conducting biomedical text mining, we identified novel mutation sites in the candidalysin coding gene ECE1-III, shedding light into the genetic diversity within C. albicans strains and their potential implications for antifungal resistance. Our results revealed significant associations between C. albicans and respiratory as well as gastrointestinal diseases, emphasizing the fungus\'s role in the pathogenesis of these diseases. Additionally, we identified a new mutation site in the C. albicans strain YF2-5, isolated from patients with pneumonia. This mutation may be associated with its heightened pathogenicity.
CONCLUSIONS: Our research advances the understanding of C. albicans pathogenicity and opens new avenues for developing targeted antifungal therapies. By focusing on the molecular basis of fungal virulence, we aim to contribute to the development of more effective treatment strategies, addressing the challenge of multidrug resistance in invasive fungal infections.

Cytoplasmic incompatibility (CI), a non-Mendelian genetic phenomenon, involves manipulation of host reproduction by Wolbachia, a maternally transmitted alphaproteobacterium. The underlying mechanism is centered around the CIF system governed by two genes, cifA and cifB, where cifB induces embryonic lethality, and cifA counteracts it. Recent investigations have unveiled intriguing facets of this system, including diverse cifB variants, prophage association in specific strains, copy-number variation, and rapid component divergence, hinting at a complex evolutionary history. We utilized comparative genomics to systematically classify CIF systems, analyze their locus structure and domain architectures, and reconstruct their diversification and evolutionary trajectories. Our new classification identifies ten distinct CIF types, featuring not just versions present in Wolbachia, but also other intracellular bacteria, and eukaryotic hosts. Significantly, our analysis of CIF loci reveals remarkable variability in gene composition and organization, encompassing an array of diverse endonucleases, variable toxin domains, deubiquitinating peptidases (DUBs), prophages, and transposons. We present compelling evidence that the components within the loci have been diversifying their sequences and domain architectures through extensive, independent lateral transfers and inter-locus recombination involving gene conversion. The association with diverse transposons and prophages, coupled with selective pressures from host immunity, likely underpins the emergence of CIF loci as recombination hotspots. Our investigation also posits the origin of CifB-REase domains from mobile elements akin to CR-effectors and Tribolium Medea1 factor, which is linked to another non-Mendelian genetic phenomenon. This comprehensive genomic analysis offers novel insights into the molecular evolution and genomic foundations of Wolbachia-mediated host reproductive control.

The plant R genes encode the NLR proteins comprising nucleotide-binding sites (NBS) and variable-length C-terminal leucine-rich repeat domains. The proteins act as intracellular immune receptors and recognize effector proteins of phytopathogens, which convene virulence. Among stresses, diseases contribute majorly to yield loss in crop plants, and R genes confer disease resistance against phytopathogens. We investigated the NLRome of Chenopodium quinoa for intraspecific diversity, characterization, and contribution to immune response regulation against phytopathogens. One eighty-three NBS proteins were identified and grouped into four distinct classes. Exon-intron organization displayed discrimination in gene structure patterns among NLR proteins. Thirty-eight NBS proteins revealed ontology with defense response, ADP binding, and inter alia cellular components. These proteins had shown functional homology with disease-resistance proteins involved in the plant-pathogen interaction pathway. Likewise, expression analysis demonstrated that NLRs encoding genes showed differential expression patterns. However, most genes displayed high expression levels in plant defense response with varying magnitude compared to ADP binding and cellular components. Twenty-four NBS genes were selected based on Heatmap analysis for quantitative polymerase chain reaction under Cercospora disease stress, and their progressive expression pattern provides insights into their functional role under stress conditions. The protein-protein interaction analysis revealed functional enrichment of NLR proteins in regulating hypersensitive, immune, and stress responses. This study, the first to identify and characterize NBS genes in C. quinoa, reveals their contribution to disease response and divulges their dynamic involvement in inducing plant immunity against phytopathogens.
UNASSIGNED: The online version contains supplementary material available at 10.1007/s12298-024-01475-0.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a positive-sense, single-stranded RNA genome-containing virus which has infected millions of people all over the world. The virus has been mutating rapidly enough, resulting in the emergence of new variants and sub-variants which have reportedly been spread from Wuhan city in China, the epicenter of the virus, to the rest of China and all over the world. The occurrence of mutations in the viral genome, especially in the viral spike protein region, has resulted in the evolution of multiple variants and sub-variants which gives the virus the benefit of host immune evasion and thus renders modern-day vaccines and therapeutics ineffective. Therefore, there is a continuous need to study the genetic characteristics and evolutionary dynamics of the SARS-CoV-2 variants. Hence, in this study, a total of 832 complete genomes of SARS-CoV-2 variants from the cities of Taiyuan and Wuhan in China was genetically characterized and their phylogenetic and evolutionary dynamics studied using phylogenetics, genetic similarity, and phylogenetic network analyses. This study shows that the four most prevalent lineages in Taiyuan and Wuhan are as follows: the Omicron lineages EG.5.1.1, followed by HK.3, FY.3, and XBB.1.16 (Pangolin classification), and clades 23F (EG.5.1), followed by 23H (HK.3), 22F (XBB), and 23D (XBB.1.9) (Nextclade classification), and lineage B followed by the Omicron FY.3, lineage A, and Omicron FL.2.3 (Pangolin classification), and the clades 19A, followed by 22F (XBB), 23F (EG.5.1), and 23H (HK.3) (Nextclade classification), respectively. Furthermore, our genetic similarity analysis show that the SARS-CoV-2 clade 19A-B.4 from Wuhan (name starting with 412981) has the least genetic similarity of about 95.5% in the spike region of the genome as compared to the query sequence of Omicron XBB.2.3.2 from Taiyuan (name starting with 18495234), followed by the Omicron FR.1.4 from Taiyuan (name starting with 18495199) with ~97.2% similarity and Omicron DY.3 (name starting with 17485740) with ~97.9% similarity. The rest of the variants showed ≥98% similarity with the query sequence of Omicron XBB.2.3.2 from Taiyuan (name starting with 18495234). In addition, our recombination analysis results show that the SARS-CoV-2 variants have three statistically significant recombinant events which could have possibly resulted in the emergence of Omicron XBB.1.16 (recombination event 3), FY.3 (recombination event 5), and FL.2.4 (recombination event 7), suggesting some very important information regarding viral evolution. Also, our phylogenetic tree and network analyses show that there are a total of 14 clusters and more than 10,000 mutations which may have probably resulted in the emergence of cluster-I, followed by 47 mutations resulting in the emergence of cluster-II and so on. The clustering of the viral variants of both cities reveals significant information regarding the phylodynamics of the virus among them. The results of our temporal phylogenetic analysis suggest that the variants of Taiyuan have likely emerged as independent variants separate from the variants of Wuhan. This study, to the best of our knowledge, is the first ever genetic comparative study between Taiyuan and Wuhan cities in China. This study will help us better understand the virus and cope with the emergence and spread of new variants at a local as well as an international level, and keep the public health authorities informed for them to make better decisions in designing new viral vaccines and therapeutics. It will also help the outbreak investigators to better examine any future outbreak.

Variation in mutation rates at sites in proteins can largely be understood by the constraint that proteins must fold into stable structures. Models that calculate site-specific rates based on protein structure and a thermodynamic stability model have shown a significant but modest ability to predict empirical site-specific rates calculated from sequence. Models that use detailed atomistic models of protein energetics do not outperform simpler approaches using packing density. We demonstrate that a fundamental reason for this is that empirical site-specific rates are the result of the average effect of many different microenvironments in a phylogeny. By analyzing the results of evolutionary dynamics simulations, we show how averaging site-specific rates across many extant protein structures can lead to correct recovery of site-rate prediction. This result is also demonstrated in natural protein sequences and experimental structures. Using predicted structures, we demonstrate that atomistic models can improve upon contact density metrics in predicting site-specific rates from a structure. The results give fundamental insights into the factors governing the distribution of site-specific rates in protein families.

Direct reciprocity is a mechanism for the evolution of cooperation in repeated social interactions. According to the literature, individuals naturally learn to adopt conditionally cooperative strategies if they have multiple encounters with their partner. Corresponding models have greatly facilitated our understanding of cooperation, yet they often make strong assumptions on how individuals remember and process payoff information. For example, when strategies are updated through social learning, it is commonly assumed that individuals compare their average payoffs. This would require them to compute (or remember) their payoffs against everyone else in the population. To understand how more realistic constraints influence direct reciprocity, we consider the evolution of conditional behaviours when individuals learn based on more recent experiences. Even in the most extreme case that they only take into account their very last interaction, we find that cooperation can still evolve. However, such individuals adopt less generous strategies, and they cooperate less often than in the classical setup with average payoffs. Interestingly, once individuals remember the payoffs of two or three recent interactions, cooperation rates quickly approach the classical limit. These findings contribute to a literature that explores which kind of cognitive capabilities are required for reciprocal cooperation. While our results suggest that some rudimentary form of payoff memory is necessary, it suffices to remember a few interactions.

Experimental studies on DNA transposable elements (TEs) have been limited in scale, leading to a lack of understanding of the factors influencing transposition activity, evolutionary dynamics, and application potential as genome engineering tools. We predicted 130 active DNA TEs from 102 metazoan genomes and evaluated their activity in human cells. We identified 40 active (integration-competent) TEs, surpassing the cumulative number (20) of TEs found previously. With this unified comparative data, we found that the Tc1/mariner superfamily exhibits elevated activity, potentially explaining their pervasive horizontal transfers. Further functional characterization of TEs revealed additional divergence in features such as insertion bias. Remarkably, in CAR-T therapy for hematological and solid tumors, Mariner2_AG (MAG), the most active DNA TE identified, largely outperformed two widely used vectors, the lentiviral vector and the TE-based vector SB100X. Overall, this study highlights the varied transposition features and evolutionary dynamics of DNA TEs and increases the TE toolbox diversity.

This study aimed to investigate the evolutionary profile (including diversity, activity, and abundance) of retrotransposons (RTNs) with long terminal repeats (LTRs) in ten species of Tetraodontiformes. These species, Arothron firmamentum, Lagocephalus sceleratus, Pao palembangensis, Takifugu bimaculatus, Takifugu flavidus, Takifugu ocellatus, Takifugu rubripes, Tetraodon nigroviridis, Mola mola, and Thamnaconus septentrionalis, are known for having the smallest genomes among vertebrates. Data mining revealed a high diversity and wide distribution of LTR retrotransposons (LTR-RTNs) in these compact vertebrate genomes, with varying abundances among species. A total of 819 full-length LTR-RTN sequences were identified across these genomes, categorized into nine families belonging to four different superfamilies: ERV (Orthoretrovirinae and Epsilon retrovirus), Copia, BEL-PAO, and Gypsy (Gmr, Mag, V-clade, CsRN1, and Barthez). The Gypsy superfamily exhibited the highest diversity. LTR family distribution varied among species, with Takifugu bimaculatus, Takifugu flavidus, Takifugu ocellatus, and Takifugu rubripes having the highest richness of LTR families and sequences. Additionally, evidence of recent invasions was observed in specific tetraodontiform genomes, suggesting potential transposition activity. This study provides insights into the evolution of LTR retrotransposons in Tetraodontiformes, enhancing our understanding of their impact on the structure and evolution of host genomes.

Virus-encoded replicases often generate aberrant RNA genomes, known as defective viral genomes (DVGs). When co-infected with a helper virus providing necessary proteins, DVGs can multiply and spread. While DVGs depend on the helper virus for propagation, they can in some cases disrupt infectious virus replication, impact immune responses, and affect viral persistence or evolution. Understanding the dynamics of DVGs alongside standard viral genomes during infection remains unclear. To address this, we conducted a long-term experimental evolution of two betacoronaviruses, the human coronavirus OC43 (HCoV-OC43) and the murine hepatitis virus (MHV), in cell culture at both high and low multiplicities of infection (MOI). We then performed RNA-seq at regular time intervals, reconstructed DVGs, and analyzed their accumulation dynamics. Our findings indicate that DVGs evolved to exhibit greater diversity and abundance, with deletions and insertions being the most common types. Notably, some high MOI deletions showed very limited temporary existence, while others became prevalent over time. We observed differences in DVG abundance between high and low MOI conditions in HCoV-OC43 samples. The size distribution of HCoV-OC43 genomes with deletions differed between high and low MOI passages. In low MOI lineages, short and long DVGs were the most common, with an additional cluster in high MOI lineages which became more prevalent along evolutionary time. MHV also showed variations in DVG size distribution at different MOI conditions, though they were less pronounced compared to HCoV-OC43, suggesting a more random distribution of DVG sizes. We identified hotspot regions for deletions that evolved at a high MOI, primarily within cistrons encoding structural and accessory proteins. In conclusion, our study illustrates the widespread formation of DVGs during betacoronavirus evolution, influenced by MOI and cell- and virus-specific factors.

Organisms from microbes to humans engage in a variety of social behaviors, which affect fitness in complex, often nonlinear ways. The question of how these behaviors evolve has consequences ranging from antibiotic resistance to human origins. However, evolution with nonlinear social interactions is challenging to model mathematically, especially in combination with spatial, group, and/or kin assortment. We derive a mathematical condition for natural selection with synergistic interactions among any number of individuals. This result applies to populations with arbitrary (but fixed) spatial or network structure, group subdivision, and/or mating patterns. In this condition, nonlinear fitness effects are ascribed to collectives, and weighted by a new measure of collective relatedness. For weak selection, this condition can be systematically evaluated by computing branch lengths of ancestral trees. We apply this condition to pairwise games between diploid relatives, and to dilemmas of collective help or harm among siblings and on spatial networks. Our work provides a rigorous basis for extending the notion of \"actor\", in the study of social evolution, from individuals to collectives.