Phosphoregulation of Phase Separation by the SARS-CoV-2 N Protein Suggests a Biophysical Basis for its Dual Functions.

The nucleocapsid (N) protein of coronaviruses serves two major functions: compaction of the RNA genome in the virion and regulation of viral gene transcription. It is not clear how the N protein mediates such distinct functions. The N protein contains two RNA-binding domains surrounded by regions of intrinsic disorder. Phosphorylation of the central disordered region promotes the protein's transcriptional function, but the underlying mechanism is not known. Here, we show that the N protein of SARS-CoV-2, together with viral RNA, forms biomolecular condensates. Unmodified N protein forms partially ordered gel-like condensates and discrete 15-nm particles based on multivalent RNA-protein and protein-protein interactions. Phosphorylation reduces these interactions, generating a more liquid-like droplet. We propose that distinct oligomeric states support the two functions of the N protein: unmodified protein forms a structured oligomer that is suited for nucleocapsid assembly, and phosphorylated protein forms a liquid-like compartment for viral genome processing.

Manipulation of Aphid Behavior by a Persistent Plant Virus.

Plants are frequently infected with cytoplasmic RNA viruses that persist for many generations through nearly 100% vertical transmission without producing any symptoms. Movement between plant cells and horizontal transmission have not been observed with these viruses; instead, they are distributed to all host cells through host cell division. Jalapeño peppers (Capsicum annuum) are all infected with Pepper cryptic virus 1 (PCV-1; family Partitiviridae). We compared the effect of odor cues from PCV-1-infected (J+) and virus-free (J-) jalapeño peppers on the aphid Myzus persicae, a common vector of acute plant viruses. Pairwise preference experiments showed a stark contrast to insect-plant interactions in acute virus infections-that is, the virus-infected plants deterred aphids. The acute plant virus Cucumber mosaic virus (CMV) manipulates its host's volatile emissions to attract aphid vectors and facilitate its transmission. We inoculated J+ and J- plants with CMV. Volatiles of J+ and J- CMV-infected plants were more attractive to aphids than those of J+ and J- mock-inoculated plants. However, in pairwise preference experiments with J+ CMV- and J- CMV-infected plants, aphids preferred the J- CMV volatile blend. Aphid reproduction on J+ and J- plants was measured as an indicator of the effect of PCV-1 on host quality for aphids. Aphid reproduction on J+ plants was more than 2-fold lower than that on J- plants.IMPORTANCE This study demonstrates that a persistent plant virus can manipulate aphid behavior. This manipulation is in stark contrast to previously described effects of acute viruses on their hosts that facilitate their transmission. This study demonstrates a positive relationship between Pepper cryptic virus 1 and jalapeño pepper (Capsicum annuum) plants wherein the virus protects the plants from the vector of acute viruses and reduces aphid herbivory. This work reveals an important implication of persistent plant viruses for pest and pathogen management in agriculture.

Coevolution of a Persistent Plant Virus and Its Pepper Hosts.

There are many nonpathogenic viruses that are maintained in a persistent lifestyle in plants. Plant persistent viruses are widespread, replicating in their hosts for many generations. So far, Endornaviridae is the only family of plant persistent viruses with a single-stranded RNA genome, containing one large open reading frame. Bell pepper endornavirus (BPEV), Hot pepper endornavirus, Capsicum frutescens endornavirus 1 (CFEV 1) have been identified from peppers. Peppers are native to Central and South America and, as domesticated plants, human selection accelerated their evolution. We investigated the evolution of these endornaviruses in different peppers including Capsicum annuum, C. chacoense, C. chinense, C. frutescens, C. baccutum, and C. pubescens using two fragments from the viral helicase (Hel) and RNA dependent RNA polymerase (RdRp) domains. In addition, using single nucleotide polymorphisms, we analyzed the pepper host populations and phylogenies. The endornaviruses phylogeny was correlated with its Capsicum species host. In this study, BPEV was limited to C. annuum species, and the RdRp and Hel phylogenies identified two clades that correlated with the host pungency. No C. annuum infected with CFEV 1 was found in this study, but the CFEV 1 RdRp fragment was recovered from C. chinense, C. frutescens, C. baccutum, and C. pubescens. Hence, during pepper speciation, the ancestor of CFEV 1 may have evolved as a new endornavirus, BPEV, in C. annuum peppers.

Predicting mortality and recurrence in colorectal cancer: Comparative assessment of predictive models.

Introduction: Colorectal cancer (CRC), also known as colorectal cancer, is a significant disease marked by high fatality rates, ranking as the third leading cause of global mortality. The main objective of this study was to assess the accuracy of predictive models in predicting both mortality events and the probability of disease recurrence. Method: A retrospective analysis was conducted on a cohort of 284 individuals diagnosed with colorectal cancer between 2001 and 2017. Demographic and clinical data, including gender, disease stage, age at diagnosis, recurrence status, and treatment details, were meticulously recorded. We rigorously evaluated various predictive models, including Decision Trees, Random Forests, Random Survival Forests (RSF), Gradient Boosting, mboost, Deep Learning Neural Network (DLNN), and Cox regression. Performance metrics, such as sensitivity, positive predictive value (PPV), specificity, area under the receiver operating characteristic curve (ROC area), and overall accuracy, were calculated for each model to predict mortality and disease recurrence. The analysis was performed using R version 4.1.3 software and the Python programming language. Results: For mortality prediction, the mboost model demonstrated the highest sensitivity at 96.9% (95% CI: 0.83-0.99) and an ROC area of 0.88. It also exhibited high specificity at 80% (95% CI: 0.59-0.93), a positive predictive value of 86.1% (95% CI: 0.70-0.95), and an overall accuracy of 89% (95% CI: 0.78-0.96). Random Forests showed perfect sensitivity of 100% (95% CI: 0.85-1) but had low specificity at 0% (95% CI: 0-0.52) and poor overall accuracy (50%). On the other hand, DLNN had the lowest performance metrics for mortality prediction, with a sensitivity of 24% (95% CI: 0.222-0.268), specificity of 75% (95% CI: 0.73-0.77), and a lower positive predictive value of 42% (95% CI: 0.38-0.45). The Gradient Boosting model showed the best performance in predicting recurrence, achieving perfect sensitivity of 100% (95% CI: 0.87-1) and high specificity at 92.9% (95% CI: 0.76-0.99). It also had a high positive predictive value of 93.3% (95% CI: 0.77-0.99). Gradient Boosting, with an ROC area of 96.4%, and mboost, with an ROC area of 75%, demonstrated remarkable performance. DLNN had the lowest performance metrics for recurrence prediction, with sensitivity at 1.75% (95% CI: 0.01-0.02), specificity at 98% (95% CI: 0.97-0.98), and a lower positive predictive value at 52.6% (95% CI: 0.39-0.65). Conclusion: In summary, the mboost model demonstrated outstanding performance in predicting mortality, achieving exceptional results across various evaluation metrics. Random Forests exhibited perfect sensitivity but showed poor specificity and overall accuracy. The DLNN model displayed the lowest performance metrics for mortality prediction. In terms of recurrence prediction, the Gradient Boosting model outperformed other models with perfect sensitivity, high specificity, and positive predictive value. The DLNN model had the lowest performance metrics for recurrence prediction. Overall, the results emphasize the effectiveness of the mboost and Gradient Boosting models in predicting mortality and recurrence in colorectal cancer patients.

Survival prediction and prognostic factors in colorectal cancer after curative surgery: insights from cox regression and neural networks.

Medical research frequently relies on Cox regression to analyze the survival distribution of cancer patients. Nonetheless, in specific scenarios, neural networks hold the potential to serve as a robust alternative. In this study, we aim to scrutinize the effectiveness of Cox regression and neural network models in assessing the survival outcomes of patients who have undergone treatment for colorectal cancer. We conducted a retrospective study on 284 colorectal cancer patients who underwent surgery at Imam Khomeini clinic in Hamadan between 2001 and 2017. The data was used to train both Cox regression and neural network models, and their predictive accuracy was compared using diagnostic measures such as sensitivity, specificity, positive predictive value, accuracy, negative predictive value, and area under the receiver operating characteristic curve. The analyses were performed using STATA 17 and R4.0.4 software. The study revealed that the best neural network model had a sensitivity of 74.5% (95% CI 61.0-85.0), specificity of 83.3% (65.3-94.4), positive predictive value of 89.1% (76.4-96.4), negative predictive value of 64.1% (47.2-78.8), AUC of 0.79 (0.70-0.88), and accuracy of 0.776 for death prediction. For recurrence, the best neural network model had a sensitivity of 88.1% (74.4-96.0%), specificity of 83.7% (69.3-93.2%), positive predictive value of 84.1% (69.9-93.4%), negative predictive value of 87.8% (73.8-95.9%), AUC of 0.86 (0.78-0.93), and accuracy of 0.859. The Cox model had comparable results, with a sensitivity of 73.6% (64.8-81.2) and 85.5% (78.3-91.0), specificity of 89.6% (83.8-93.8) and 98.0% (94.4-99.6), positive predictive value of 84.0% (75.6-90.4) and 97.4% (92.6-99.5), negative predictive value of 82.0% (75.6-90.4) and 88.8% (0.83-93.1), AUC of 0.82 (0.77-0.86) and 0.92 (0.89-0.95), and accuracy of 0.88 and 0.92 for death and recurrence prediction, respectively. In conclusion, the study found that both Cox regression and neural network models are effective in predicting early recurrence and death in patients with colorectal cancer after curative surgery. The neural network model showed slightly better sensitivity and negative predictive value for death, while the Cox model had better specificity and positive predictive value for recurrence. Overall, both models demonstrated high accuracy and AUC, indicating their usefulness in predicting these outcomes.

Functional and structural segregation of overlapping helices in HIV-1.

Overlapping coding regions balance selective forces between multiple genes. One possible division of nucleotide sequence is that the predominant selective force on a particular nucleotide can be attributed to just one gene. While this arrangement has been observed in regions in which one gene is structured and the other is disordered, we sought to explore how overlapping genes balance constraints when both protein products are structured over the same sequence. We use a combination of sequence analysis, functional assays, and selection experiments to examine an overlapped region in HIV-1 that encodes helical regions in both Env and Rev. We find that functional segregation occurs even in this overlap, with each protein spacing its functional residues in a manner that allows a mutable non-binding face of one helix to encode important functional residues on a charged face in the other helix. Additionally, our experiments reveal novel and critical functional residues in Env and have implications for the therapeutic targeting of HIV-1.

Pollen Protein: Lipid Macronutrient Ratios May Guide Broad Patterns of Bee Species Floral Preferences.

Pollinator nutritional ecology provides insights into plant-pollinator interactions, coevolution, and the restoration of declining pollinator populations. Bees obtain their protein and lipid nutrient intake from pollen, which is essential for larval growth and development as well as adult health and reproduction. Our previous research revealed that pollen protein to lipid ratios (P:L) shape bumble bee foraging preferences among pollen host-plant species, and these preferred ratios link to bumble bee colony health and fitness. Yet, we are still in the early stages of integrating data on P:L ratios across plant and bee species. Here, using a standard laboratory protocol, we present over 80 plant species' protein and lipid concentrations and P:L values, and we evaluate the P:L ratios of pollen collected by three bee species. We discuss the general phylogenetic, phenotypic, behavioral, and ecological trends observed in these P:L ratios that may drive plant-pollinator interactions; we also present future research questions to further strengthen the field of pollination nutritional ecology. This dataset provides a foundation for researchers studying the nutritional drivers of plant-pollinator interactions as well as for stakeholders developing planting schemes to best support pollinators.

How does the genome structure and lifestyle of a virus affect its population variation?

Viruses use diverse strategies for their replication, related in part to the genome structure (double-stranded or single-stranded; positive sense or negative sense; RNA or DNA). During each round of replication, mutations are introduced in the viral genome and the mode of replication (stamping machine and geometric replication) may affect the population dynamics of the progeny virus. Our understanding of the relationships among genome strandedness, mode of replication and the population variation is still limited. Here we will review what is known about virus replication by stamping machine or geometric modes, and how that relates to the biology of single stranded versus double stranded RNA genomes. We will present how this may affect the mutation frequency and population dynamics. Finally the potential importance of the population dynamics in acute viruses and persistent viruses will be discussed.

Prediction of time to recurrence and influencing factors for gastric cancer in Iran.

BACKGROUND: The patterns of gastric cancer recurrence vary across societies. We designed the current study in an attempt to evaluate and reveal the outbreak of the recurrence patterns of gastric cancer and also prediction of time to recurrence and its effected factors in Iran. MATERIALS AND METHODS: This research was performed from March 2003 to February 2007. Demographic characteristics, clinical and pathological diagnosis and classification including pathologic stage, tumor grade, tumor site and tumor size in of patients with GC recurrent were collected from patients' data files. To evaluate of factors affected on the relapse of the GC patients, gender, age at diagnosis, treatment type and Hgb were included in the research. Data were analyzed using Kaplan-Meier and logistic regression models. RESULTS: After treatment, 82 patients suffered recurrence, 42, 33 and 17 by the ends of first, second and third years. The mean ( SD) and median ( IQR) time to recurrence in patients with GC were 25.5 (20.6-30.1) and 21.5 (15.6-27.1) months, respectively. The results of multivariate analysis logistic regression showed that only pathologic stage, tumor grade and tumor site significantly affected the recurrence. CONCLUSIONS: We found that pathologic stage, tumor grade and tumor site significantly affect on the recurrence of GC which has a high positive prognostic value and might be functional for better follow-up and selecting the patients at risk. We also showed time to recurrence to be an important factor for follow-up of patients.