Neutral genetic drift can aid functional protein evolution

BACKGROUND: Many of the mutations accumulated by naturally evolving proteins are neutral in the sense that they do not significantly alter a protein's ability to perform its primary biological function. However, new protein functions evolve when selection begins to favor other, "promiscuous" functions that are incidental to a protein's biological role. If mutations that are neutral with respect to a protein's primary biological function cause substantial changes in promiscuous functions, these mutations could enable future functional evolution. RESULTS: Here we investigate this possibility experimentally by examining how cytochrome P450 enzymes that have evolved neutrally with respect to activity on a single substrate have changed in their abilities to catalyze reactions on five other substrates. We find that the enzymes have sometimes changed as much as four-fold in the promiscuous activities. The changes in promiscuous activities tend to increase with the number of mutations, and can be largely rationalized in terms of the chemical structures of the substrates. The activities on chemically similar substrates tend to change in a coordinated fashion, potentially providing a route for systematically predicting the change in one function based on the measurement of several others. CONCLUSIONS: Our work suggests that initially neutral genetic drift can lead to substantial changes in protein functions that are not currently under selection, in effect poising the proteins to more readily undergo functional evolution should selection "ask new questions" in the future

Combining Past, Present and Future: A Self-Supervised Approach for Class Incremental Learning

Class Incremental Learning (CIL) aims to handle the scenario where data of novel classes occur continuously and sequentially. The model should recognize the sequential novel classes while alleviating the catastrophic forgetting. In the self-supervised manner, it becomes more challenging to avoid the conflict between the feature embedding spaces of novel classes and old ones without any class labels. To address the problem, we propose a self-supervised CIL framework CPPF, meaning Combining Past, Present and Future. In detail, CPPF consists of a prototype clustering module (PC), an embedding space reserving module (ESR) and a multi-teacher distillation module (MTD). 1) The PC and the ESR modules reserve embedding space for subsequent phases at the prototype level and the feature level respectively to prepare for knowledge learned in the future. 2) The MTD module maintains the representations of the current phase without the interference of past knowledge. One of the teacher networks retains the representations of the past phases, and the other teacher network distills relation information of the current phase to the student network. Extensive experiments on CIFAR100 and ImageNet100 datasets demonstrate that our proposed method boosts the performance of self-supervised class incremental learning. We will release code in the near future

Placental expression of AChE, α7nAChR and NF-κB in patients with preeclampsia

Objectives: This study aimed to investigate placental expression of AChE, α7nAChR and NF-κB in patients with preeclampsia and discuss about its clinical significance. Material and methods: mRNA expression levels of acetylcholine (AChE), alpha-7 nicotinic acetylcholine receptor (α7nAChR) and nuclear factor-kB (NF-κB) in placenta were detected by qRT-PCR, and protein levels were determined by immunohis­tological analysis and Western Blot in 35 women with preeclampsia (including 20 cases of mild preeclampsia and 15 cases of severe preeclampsia) and 30 cases in control group, respectively. Results: The expression of AChE mRNA and protein in placenta increased significantly in patients with preeclampsia compared with the control group (p < 0.01). It was lower in patients with severe preeclampsia than in patients with mild preeclampsia (p < 0.05). The expression of α7nAChR mRNA and protein in placenta decreased significantly in patients with preeclampsia compared with the control group (p < 0.01). However, the expression of α7nAChR mRNA and protein in patients with severe preeclampsia was higher than that in patients with mild preeclampsia, without significant difference(p > 0.05). The expression of NF-κB protein in placenta decreased significantly in patients with preeclampsia compared with the control group(p < 0.01). It was higher in patients with severe preeclampsia than in patients with mild preeclampsia (p < 0.05), but there was no significant difference between preeclampsia group and control group in the expression of NF-κB mRNA in placenta (p > 0.05). The results of Western blotting assay were consistent with those of immunohistochemistry. Conclusions: Abnormal expression of AChE, α7nAChR and NF-κB in placenta may be associated with preeclampsia. Cho­linergic anti-inflammatory pathway may play an important role in the pathogenesis of preeclampsia

Disentangling land model uncertainty via Matrix-based Ensemble Model Inter-comparison Platform (MEMIP)

Background Large uncertainty in modeling land carbon (C) uptake heavily impedes the accurate prediction of the global C budget. Identifying the uncertainty sources among models is crucial for model improvement yet has been difficult due to multiple feedbacks within Earth System Models (ESMs). Here we present a Matrix-based Ensemble Model Inter-comparison Platform (MEMIP) under a unified model traceability framework to evaluate multiple soil organic carbon (SOC) models. Using the MEMIP, we analyzed how the vertically resolved soil biogeochemistry structure influences SOC prediction in two soil organic matter (SOM) models. By comparing the model outputs from the C-only and CN modes, the SOC differences contributed by individual processes and N feedback between vegetation and soil were explicitly disentangled. Results Results showed that the multi-layer models with a vertically resolved structure predicted significantly higher SOC than the single layer models over the historical simulation (1900–2000). The SOC difference between the multi-layer models was remarkably higher than between the single-layer models. Traceability analysis indicated that over 80% of the SOC increase in the multi-layer models was contributed by the incorporation of depth-related processes, while SOC differences were similarly contributed by the processes and N feedback between models with the same soil depth representation. Conclusions The output suggested that feedback is a non-negligible contributor to the inter-model difference of SOC prediction, especially between models with similar process representation. Further analysis with TRENDY v7 and more extensive MEMIP outputs illustrated the potential important role of multi-layer structure to enlarge the current ensemble spread and the necessity of more detail model decomposition to fully disentangle inter-model differences. We stressed the importance of analyzing ensemble outputs from the fundamental model structures, and holding a holistic view in understanding the ensemble uncertainty

Evolution favors protein mutational robustness in sufficiently large populations

BACKGROUND: An important question is whether evolution favors properties such as mutational robustness or evolvability that do not directly benefit any individual, but can influence the course of future evolution. Functionally similar proteins can differ substantially in their robustness to mutations and capacity to evolve new functions, but it has remained unclear whether any of these differences might be due to evolutionary selection for these properties. RESULTS: Here we use laboratory experiments to demonstrate that evolution favors protein mutational robustness if the evolving population is sufficiently large. We neutrally evolve cytochrome P450 proteins under identical selection pressures and mutation rates in populations of different sizes, and show that proteins from the larger and thus more polymorphic population tend towards higher mutational robustness. Proteins from the larger population also evolve greater stability, a biophysical property that is known to enhance both mutational robustness and evolvability. The excess mutational robustness and stability is well described by existing mathematical theories, and can be quantitatively related to the way that the proteins occupy their neutral network. CONCLUSIONS: Our work is the first experimental demonstration of the general tendency of evolution to favor mutational robustness and protein stability in highly polymorphic populations. We suggest that this phenomenon may contribute to the mutational robustness and evolvability of viruses and bacteria that exist in large populations

Circulating Long Noncoding RNAs as Biomarkers for Predicting Head and Neck Squamous Cell Carcinoma

Background/Aims: The anatomical complexity of the head and neck region and the lack of sufficiently specific and sensitive biomarkers often lead to the diagnosis of head and neck squamous cell carcinoma (HNSCC) at advanced stages. To identify novel biomarkers for early diagnosis of primary HNSCC through a minimally invasive method, we investigated circulating long noncoding RNA (lncRNA) levels in plasma of HNSCC patients. Methods: The global lncRNA expression profiles of HNSCC patients were measured using microarray and next-generation RNA-sequencing (RNA-seq) data from both circulating and tissue samples. The diagnosis prediction model based on the lncRNA signatures and clinical features was evaluated by multi-stage validation and risk score analysis. Results: The data showed that 432 lncRNA transcripts were differentially expressed by fold changes of > 4 in circulating samples and 333 in tissues samples, respectively. Only 12 lncRNAs consistently emerged in these two kinds of samples. After the risk score analysis including a multistage validation, we identified three lncRNAs, namely, HOXA11-AS, LINC00964 and MALAT1, which were up-regulated in the plasma of HNSCC patients compared with those in healthy controls with merged areas under the curve (AUCs) in training and validation sets of 0.925 and 0.839, respectively. Conclusion: HOXA11-AS, LINC00964 and MALAT1 might be potential circulating biomarkers for the early detection of HNSCC in the future

A Robust Anti-Thermal-Quenching Phosphor Based on Zero-Dimensional Metal Halide Rb3InCl6:xSb3.

High-power phosphor-converted white light-emitting diodes (hp-WLEDs) have been widely involved in modern society as outdoor lighting sources. In these devices, due to the Joule effect, the high applied currents cause high operation temperatures (>500 K). Under these conditions, most phosphors lose their emission, an effect known as thermal quenching (TQ). Here, we introduce a zero-dimensional (0D) metal halide, Rb3InCl6:xSb3+, as a suitable anti-TQ phosphor offering robust anti-TQ behavior up to 500 K. We ascribe this behavior of the metal halide to two factors: (1) a compensation process via thermally activated energy transfer from structural defects to emissive centers and (2) an intrinsic structural rigidity of the isolated octahedra in the 0D structure. The anti-TQ phosphor-based WLEDs can stably work at a current of 2000 mA. The low synthesis cost and nontoxic composition reported here can herald a new generation of anti-TQ phosphors for hp-WLED