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.
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.
Forecasting alterations in protein stability caused by variations holds immense importance. Improving the thermal stability of proteins is important for biomedical and industrial applications. This ...review discusses the latest methods for predicting the effects of mutations on protein stability, databases containing protein mutations and thermodynamic parameters, and experimental techniques for efficiently assessing protein stability in high‐throughput settings. Various publicly available databases for protein stability prediction are introduced. Furthermore, state‐of‐the‐art computational approaches for anticipating protein stability changes due to variants are reviewed. Each method's types of features, base algorithm, and prediction results are also detailed. Additionally, some experimental approaches for verifying the prediction results of computational methods are introduced. Finally, the review summarizes the progress and challenges of protein stability prediction and discusses potential models for future research directions.
Bacteria are now generally believed to adopt two main lifestyles: planktonic individuals, or surface-attached biofilms. However, in recent years medical microbiologists started to stress that ...suspended bacterial aggregates are a major form of bacterial communities in chronic infection sites. Despite sharing many similarities with surface-attached biofilms and are thus generally defined as biofilm-like aggregates, these non-attached clumps of cells
show much smaller sizes and different formation mechanisms. Furthermore,
clinical isolates were frequently reported to be less attached to abiotic surfaces when compared to standard type strains. While this third lifestyle is starting to draw heavy attention in clinical studies, it has a long history in natural and environmental sciences. For example, marine gel particles formed by bacteria attachment to phytoplankton exopolymers have been well documented in oceans; large river and lake snows loaded with bacterial aggregates are frequently found in freshwater systems; multispecies bacterial "flocs" have long been used in wastewater treatment. This review focuses on non-attached aggregates found in a variety of natural and clinical settings, as well as some recent technical developments facilitating aggregate research. The aim is to summarise the characteristics of different types of bacterial aggregates, bridging the knowledge gap, provoking new perspectives for researchers from different fields, and highlighting the importance of more research input in this third lifestyle of bacteria closely relevant to our daily life.
Background
The standard 5 years of endocrine therapy has demonstrated additional benefits compared with short‐term (2‐3 years) treatment in patients with estrogen receptor (ER)‐positive breast ...cancer; however, data specific to ER‐low positive breast cancer (1%‐10% by immunohistochemistry) are limited, and it is unclear whether long‐term treatment is still necessary for this subgroup.
Methods
The authors used the prospectively maintained Breast Surgery Database of Fudan University Shanghai Cancer Center for this propensity‐matched analysis. The primary end point was disease‐free survival. Multivariate Cox regression analysis and propensity score‐matching methods were used to minimize bias. Hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated. All statistics were 2‐sided.
Results
From 2012 to 2017, 22,768 consecutive women had pathologically confirmed, early stage breast cancer, and 1013 (4.45%) were identified with ER‐low positive disease. Among these, 634 patients met the inclusion criteria and were divided into 3 groups: those who received no endocrine therapy (n = 89), those who received 2 to 3 years of endocrine therapy (n = 185), and those who received approximately 5 years of endocrine therapy (n = 360). At a median follow‐up of 65 months, there was no significant difference in disease‐free survival between patients who received 2 to 3 years and 5 years of endocrine therapy (HR, 0.82; 95% CI, 0.51‐1.33; P = .43). The findings were consistent after multivariate Cox analysis of the propensity score‐matched samples (5 vs 2‐3 years of treatment: HR, 0.74; 95% CI, 0.41‐1.31; P = .30).
Conclusions
Short‐term endocrine therapy for 2 to 3 years might be an alternative for patients who have ER‐low positive breast cancer instead of the standard 5 years of treatment.
Short‐term endocrine therapy for 2 to 3 years might be an alternative for patients who have estrogen receptor‐low positive breast cancer instead of the standard 5 years. More extensive studies and translational research on identifying endocrine‐sensitive cases within this population are still needed.
Melatonin is a pleiotropic signaling molecule that provides physiological protection against diverse environmental stresses in plants. Nonetheless, the mechanisms for melatonin‐mediated ...thermotolerance remain largely unknown. Here, we report that endogenous melatonin levels increased with a rise in ambient temperature and that peaked at 40°C. Foliar pretreatment with an optimal dose of melatonin (10 μmol/L) or the overexpression of N‐acetylserotonin methyltransferase (ASMT) gene effectively ameliorated heat‐induced photoinhibition and electrolyte leakage in tomato plants. Both exogenous melatonin treatment and endogenous melatonin manipulation by overexpression of ASMT decreased the levels of insoluble and ubiquitinated proteins, but enhanced the expression of heat‐shock proteins (HSPs) to refold denatured and unfolded proteins under heat stress. Meanwhile, melatonin also induced expression of several ATG genes and formation of autophagosomes to degrade aggregated proteins under the same stress. Proteomic profile analyses revealed that protein aggregates for a large number of biological processes accumulated in wild‐type plants. However, exogenous melatonin treatment or overexpression of ASMT reduced the accumulation of aggregated proteins. Aggregation responsive proteins such as HSP70 and Rubisco activase were preferentially accumulated and ubiquitinated in wild‐type plants under heat stress, while melatonin mitigated heat stress‐induced accumulation and ubiquitination of aggregated proteins. These results suggest that melatonin promotes cellular protein protection through induction of HSPs and autophagy to refold or degrade denatured proteins under heat stress in tomato plants.
Colloidal noble metal nanoparticles (NPs) are composed of metal cores and organic or inorganic ligand shells. These NPs support size‐ and shape‐dependent plasmonic resonances. They can be assembled ...from dispersions into artificial metamolecules which have collective plasmonic resonances originating from coupled bright and dark optical electric and magnetic modes that form depending on the size and shape of the constituent NPs and their number, arrangement, and interparticle distance. NPs can also be assembled into extended 2D and 3D metamaterials that are glassy thin films or ordered thin films or crystals, also known as superlattices and supercrystals. The metamaterials have tunable optical properties that depend on the size, shape, and composition of the NPs, and on the number of NP layers and their interparticle distance. Interestingly, strong light‐matter interactions in superlattices form plasmon polaritons. Tunable interparticle distances allow designer materials with dielectric functions tailorable from that characteristic of an insulator to that of a metal, and serve as strong optical absorbers or scatterers, respectively. In combination with lithography techniques, these extended assemblies can be patterned to create subwavelength NP superstructures and form large‐area 2D and 3D metamaterials that manipulate the amplitude, phase, and polarization of transmitted or reflected light.
Noble metal nanoparticles (NPs) serve as building blocks in the assembly of artificial metamolecules and large‐area metamaterials. These metastructures have exotic optical properties that depend on the NP number, arrangement, and interparticle spacing, and on the geometry of their patterned 2D and 3D superstructures. Their strong light‐matter interactions are harnessed to manipulate the amplitude, phase, and polarization of light.
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.
Melatonin regulates broad aspects of plant responses to various biotic and abiotic stresses, but the upstream regulation of melatonin biosynthesis by these stresses remains largely unknown. Herein, ...we demonstrate that transcription factor heat‐shock factor A1a (HsfA1a) conferred cadmium (Cd) tolerance to tomato plants, in part through its positive role in inducing melatonin biosynthesis under Cd stress. Analysis of leaf phenotype, chlorophyll content, and photosynthetic efficiency revealed that silencing of the HsfA1a gene decreased Cd tolerance, whereas its overexpression enhanced plant tolerance to Cd. HsfA1a‐silenced plants exhibited reduced melatonin levels, and HsfA1a overexpression stimulated melatonin accumulation and the expression of the melatonin biosynthetic gene caffeic acid O‐methyltransferase 1 (COMT1) under Cd stress. Both an in vitro electrophoretic mobility shift assay and in vivo chromatin immunoprecipitation coupled with qPCR analysis revealed that HsfA1a binds to the COMT1 gene promoter. Meanwhile, Cd stress induced the expression of heat‐shock proteins (HSPs), which was compromised in HsfA1a‐silenced plants and more robustly induced in HsfA1a‐overexpressing plants under Cd stress. COMT1 silencing reduced HsfA1a‐induced Cd tolerance and melatonin accumulation in HsfA1a‐overexpressing plants. Additionally, the HsfA1a‐induced expression of HSPs was partially compromised in COMT1‐silenced wild‐type or HsfA1a‐overexpressing plants under Cd stress. These results demonstrate that HsfA1a confers Cd tolerance by activating transcription of the COMT1 gene and inducing accumulation of melatonin that partially upregulates expression of HSPs.
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