Identifying essential genes in a given organism is important for research on their fundamental roles in organism survival. Furthermore, if possible, uncovering the links between core functions or ...pathways with these essential genes will further help us obtain deep insight into the key roles of these genes. In this study, we investigated the essential and non-essential genes reported in a previous study and extracted gene ontology (GO) terms and biological pathways that are important for the determination of essential genes. Through the enrichment theory of GO and KEGG pathways, we encoded each essential/non-essential gene into a vector in which each component represented the relationship between the gene and one GO term or KEGG pathway. To analyze these relationships, the maximum relevance minimum redundancy (mRMR) was adopted. Then, the incremental feature selection (IFS) and support vector machine (SVM) were employed to extract important GO terms and KEGG pathways. A prediction model was built simultaneously using the extracted GO terms and KEGG pathways, which yielded nearly perfect performance, with a Matthews correlation coefficient of 0.951, for distinguishing essential and non-essential genes. To fully investigate the key factors influencing the fundamental roles of essential genes, the 21 most important GO terms and three KEGG pathways were analyzed in detail. In addition, several genes was provided in this study, which were predicted to be essential genes by our prediction model. We suggest that this study provides more functional and pathway information on the essential genes and provides a new way to investigate related problems.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Nucleotide-binding, leucine-rich repeat receptors (NLRs) are major immune receptors in plants and animals. Upon activation, the Arabidopsis NLR protein ZAR1 forms a pentameric resistosome in vitro ...and triggers immune responses and cell death in plants. In this study, we employed single-molecule imaging to show that the activated ZAR1 protein can form pentameric complexes in the plasma membrane. The ZAR1 resistosome displayed ion channel activity in Xenopus oocytes in a manner dependent on a conserved acidic residue Glu11 situated in the channel pore. Pre-assembled ZAR1 resistosome was readily incorporated into planar lipid-bilayers and displayed calcium-permeable cation-selective channel activity. Furthermore, we show that activation of ZAR1 in the plant cell led to Glu11-dependent Ca2+ influx, perturbation of subcellular structures, production of reactive oxygen species, and cell death. The results thus support that the ZAR1 resistosome acts as a calcium-permeable cation channel to trigger immunity and cell death.
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•Resistosome formed by the immune receptor ZAR1 is a cation-selective channel•The ZAR1 channel is permeable to calcium•The activated ZAR1 forms a pentamer in the plasma membrane•The channel activity triggers immune signaling and cell death in plants
ZAR1 resistosome is a calcium-permeable channel that triggers immune signaling and cell death in plants.
Colorectal cancer is the third most common cancer in males and second in females. This disease can be caused by genetic and acquired/environmental factors. Microsatellite instability (MSI) is one of ...the major mechanisms in colorectal cancer. This mechanism is a specific condition of genetic hyper mutability that results from incompetent DNA mismatch repair. MSI has been applied to classify different colorectal cancer subtypes. However, the effects of MSI status on gene expression are largely unknown. In our study, we integrated the gene expression profile and MSI status of all CRC samples from the TCGA database, and then categorized the CRC samples into three subgroups, namely, MSI‐stable, MSI‐low, and MSI‐high, according to the MSI status. We applied a novel computational method based on machine learning and screened the genes specifically expressed for the different colorectal cancer subtypes. The results showed the distinct mechanisms of the different colorectal cancer subtypes with MSI status and provided the genes that may be the optimal standards to further classify the various molecular subtypes of colorectal cancer with distinct MSI status.
What's new?
Microsatellite instability (MSI), a key genetic mechanism implicated in colorectal cancer (CRC), is linked to drug reactivity and sensitivity in CRC patients and is useful for CRC subtype classification. Yet, little is known about the identity of MSI‐associated genes or their role in CRC. Here, combined analysis of datasets on gene‐expression profile and MSI status enabled the investigation of a number of differentially expressed genes from CRC samples. Genes optimal for the classification of CRC subtypes with different MSI statuses were identified. The gene panel could facilitate the discovery of biomarkers specific for CRCs with known MSI status.
Targeted saturation mutagenesis of crop genes could be applied to produce genetic variants with improved agronomic performance. However, tools for directed evolution of plant genes, such as ...error-prone PCR or DNA shuffling, are limited
. We engineered five saturated targeted endogenous mutagenesis editors (STEMEs) that can generate de novo mutations and facilitate directed evolution of plant genes. In rice protoplasts, STEME-1 edited cytosine and adenine at the same target site with C > T efficiency up to 61.61% and simultaneous C > T and A > G efficiency up to 15.10%. STEME-NG, which incorporates the nickase Cas9-NG protospacer-adjacent motif variant, was used with 20 individual single guide RNAs in rice protoplasts to produce near-saturated mutagenesis (73.21%) for a 56-amino-acid portion of the rice acetyl-coenzyme A carboxylase (OsACC). We also applied STEME-1 and STEME-NG for directed evolution of the OsACC gene in rice and obtained herbicide resistance mutations. This set of two STEMEs will accelerate trait development and should work in any plants amenable to CRISPR-based editing.
Achieving efficient blue electroluminescence (EL) remains the fundamental challenge that impedes perovskite light‐emitting diodes (PeLEDs) towards commercial applications. The bottleneck accounting ...for the inefficient blue PeLEDs is broadly attributed to the poor‐emissive blue perovskite emitters based on either mixed halide engineering or reduced‐dimensional strategy. Herein, we report the high‐performing sky‐blue PeLEDs (490 nm) with the maximum EQE exceeding 15 % by incorporating a molecular modifier, namely 4,4′‐Difluorophenone, for significantly suppressing the non‐radiative recombination and tuning of the low‐dimensional phase distribution of quasi‐2D blue perovskites, which represents a remarkable paradigm for developing the new generation of blue lighting sources.
The high‐performing sky‐blue perovskite light‐emitting diodes (PeLEDs) with peak external quantum efficiency exceeding 15 % are attained by the use of a synergetic modulation strategy, regarding defect passivation and phase distribution regulation, via the incorporation of a bifunctional molecular additive.
The main reason for large energy loss in all‐inorganic perovskites is ascribed to the slow nucleation and fast crystallization of all‐inorganic perovskite films. Herein, a manipulating strategy is ...demonstrated to simultaneously realize rapid nucleation and slow crystal growth of CsPbI3 perovskite films by employing solvent molecular sieves in the antisolvent. First, the antisolvent treatment of mixed chlorobenzene and ethyl alcohol can induce the instantaneous supersaturation of perovskites to achieve rapid nucleation. Subsequently, the molecular layer of phthalimide (2‐N) molecules on the perovskite surface can be used as solvent molecular sieves to precisely control the evaporation of the solvent through molecule–solvent interactions. In addition, the molecules remaining on the surface can also effectively passivate the surface defects and improve the device performance. By this strategy, a synchronous regulation of rapid nucleation and slow crystal growth of perovskite films is realized for the first time. As a result, the CsPbI3 film with 2‐N treatment presents high‐quality crystallinity with large grains and less defects. The champion device exhibits an outdoor power conversion efficiency (PCE) up to 20.14% under AM1.5G illumination, and an indoor PCE up to 40.07% (Pout:133.9 µW cm–2) under a commonly used light‐emitting diode light source (2956 K, 1062 lux).
It is demonstrated that phthalimide (2‐N) molecules as antisolvent additives can achieve crystal growth regulation, including rapid nucleation and slow crystal growth process. The perovskite films treated with 2‐N exhibit the best crystallinity, largest grain size, and lowest trap density, while the corresponding device efficiency reaching 20.14% under AM 1.5G illumination and the highest indoor efficiency of 40.14% under light‐emitting diode conditions (1062 lux, @2956 K).
Cancer stem cells (CSCs) are cancer‐initiating cells that are not only a source of tumorigenesis but also the cause of tumour progression, metastasis and therapy resistance. EBV‐associated gastric ...cancer (EBVaGC) is a distinct subtype of gastric cancer with unique clinicopathological and molecular features. However, whether CSCs exist in EBVaGC, and the tumorigenic mechanism of EBV, remains unclear. Here, NOD/SCID mice were injected subcutaneously with the EBVaGC cell line SNU719 and treated with 5‐fluorouracil weekly. Successive generations of xenografts yielded a highly malignant EBVaGC cell line, SNU‐4th, which displays properties of CSCs and mainly consists of CD44+CD24− cells. In SNU‐4th cells, an EBV‐encoded circRNA, ebv‐circLMP2A, expression increased and plays crucial roles in inducing and maintaining stemness phenotypes through targeting miR‐3908/TRIM59/p53 axis. Additionally, high expression of ebv‐circLMP2A is significantly associated with metastasis and poor prognosis in patients with EBVaGC. These findings not only provide evidence for the existence of CSCs in EBVaGC and elucidate the pathogenic mechanism of ebv‐circLMP2A in EBVaGC, but also provide a promising therapeutic target for EBVaGC.
Synopsis
The circRNA LMP2A produced by the Epstein‐Barr virus induces stemness of EBV‐associated gastric cancer cells by attenuating the tumor suppressive effect of the miR‐3908/TRIM59/p53 axis, thereby promoting metastasis and tumor progression.
Cells with properties of cancer stem cells were isolated form EBV‐associated gastric cancer (EBVaGC).
The levels of an EBV‐encoded circRNA (ebv‐circLMP2A) are significantly increased in EBVaGC.
ebv‐circLMP2A has crucial roles in inducing and maintaining cancer stemness in EBVaGC.
High expression of ebv‐circLMP2A is significantly associated with metastasis and poor prognosis in EBVaGC patients.
The circRNA LMP2A produced by the Epstein‐Barr virus induces stemness of EBV‐associated gastric cancer cells by attenuating the tumor suppressive effect of the miR‐3908/TRIM59/p53 axis, thereby promoting metastasis and tumor progression.
Metal halide perovskites are ideal candidates for indoor photovoltaics (IPVs) because of their easy‐to‐adjust bandgaps, which can be designed to cover the spectrum of any artificial light source. ...However, the serious non‐radiative carrier recombination under low light illumination restrains the application of perovskite‐based IPVs (PIPVs). Herein, polar molecules of amino naphthalene sulfonates are employed to functionalize the TiO2 substrate, anchoring the CsPbI3 perovskite crystal grains with a strong ion–dipole interaction between the molecule‐level polar interlayer and the ionic perovskite film. The resulting high‐quality CsPbI3 films with the merit of defect‐immunity and large shunt resistance under low light conditions enable the corresponding PIPVs with an indoor power conversion efficiency of up to 41.2% (Pin: 334.11 µW cm−2, Pout: 137.66 µW cm−2) under illumination from a commonly used indoor light‐emitting diode light source (2956 K, 1062 lux). Furthermore, the device also achieves efficiencies of 29.45% (Pout: 9.80 µW cm−2) and 32.54% (Pout: 54.34 µW cm−2) at 106 (Pin: 33.84 µW cm−2) and 522 lux (Pin: 168.21 µW cm−2), respectively.
The amino naphthalene sulfonates (ANS) molecules are incorporated as a dipolar interlayer at the buried interface to fabricate CsPbI3 perovskite indoor photovoltaics (PIPVs). The strong ion–dipole interaction between polar ANS molecules and ionic perovskites enables the target PIPVs to deliver a record indoor PCE of up to 41.2% (Pout:137.66 µW cm−2) under a standard LED light source (2956 K, 1062 lux).
Synthetic lethality is the synthesis of mutations leading to cell death. Tumor‐specific synthetic lethality has been targeted in research to improve cancer therapy. With the advances of techniques in ...molecular biology, such as RNAi and CRISPR/Cas9 gene editing, efforts have been made to systematically identify synthetic lethal interactions, especially for frequently mutated genes in cancers. However, elucidating the mechanism of synthetic lethality remains a challenge because of the complexity of its influencing conditions. In this study, we proposed a new computational method to identify critical functional features that can accurately predict synthetic lethal interactions. This method incorporates several machine learning algorithms and encodes protein‐coding genes by an enrichment system derived from gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways to represent their functional features. We built a random forest‐based prediction engine by using 2120 selected features and obtained a Matthews correlation coefficient of 0.532. We examined the top 15 features and found that most of them have potential roles in synthetic lethality according to previous studies. These results demonstrate the ability of our proposed method to predict synthetic lethal interactions and provide a basis for further characterization of these particular genetic combinations.
A computational analysis of synthetic lethality was performed in this study. Synthetic lethality gene pairs were encoded via enrichment theory of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Advanced computational methods were adopted to build an optimal prediction model and extract important features.
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