β-Elemene Restrains PTEN mRNA Degradation to Restrain the Growth of Lung Cancer Cells via METTL3-Mediated N6 Methyladenosine Modification

Lung cancer is one of the most fatal malignancies and the leading cause of cancer death worldwide. β-Elemene, a well-known anticancer drug, has drawn a great deal of attention from researchers attributed to its limited side impacts. N6-Methyladenosine (m6A) modification is the most common RNA modification and plays a vital role in the pathogenesis of multiple tumors. However, the functional link between β-elemene and the m6A modification in lung cancer development remains unexplored. In this study, we investigated whether m6A modification was responsible for the impacts of β-elemene on lung cancer. Firstly, outcomes suggested that β-elemene restrained the malignant behaviors of A549 together with H1299 cells. Thereafter, we observed that β-elemene markedly regulated METTL3, YTHDF1, and YTHDC1 among various m6A modulators. METTL3 was selected for further study because of its oncogenic function in lung cancer. RT-qRCR and western blot assays exhibited that the mRNA and protein expression levels of METTL3 were lessened by the administration of β-elemene. Mechanistically, β-elemene exerted the restrictive impacts on the cell growth of lung cancer in vivo and in vitro through targeting METTL3. More importantly, β-elemene contributed to the augmented PTEN expression via suppressing its m6A modification. To sum up, we provided strong clues that β-elemene promoted PTEN expression to retard lung cancer progression by the regulation of METTL3-mediated m6A modification.

Focal neural perturbations reshape low-dimensional trajectories of brain activity supporting cognitive performance

The emergence of distributed patterns of neural activity supporting brain functions and behavior can be understood by study of the brain’s low-dimensional topology. Functional neuroimaging demonstrates that brain activity linked to adaptive behavior is constrained to low-dimensional manifolds. In human participants, we tested whether these low-dimensional constraints preserve working memory performance following local neuronal perturbations. We combined multi-session functional magnetic resonance imaging, non-invasive transcranial magnetic stimulation (TMS), and methods translated from the fields of complex systems and computational biology to assess the functional link between changes in local neural activity and the reshaping of task-related low dimensional trajectories of brain activity. We show that specific reconfigurations of low-dimensional trajectories of brain activity sustain effective working memory performance following TMS manipulation of local activity on, but not off, the space traversed by these trajectories. We highlight an association between the multi-scale changes in brain activity underpinning cognitive function.

Peptide-centric analyses of human plasma enable increased resolution of biological insights into non-small cell lung cancer relative to protein-centric analysis

Comprehensive assessment of the human proteome remains challenging due to multiple forms of a protein, or proteoforms, arising from alternative splicing, allelic variation, and protein modifications. As proteoforms can serve distinct functions and act as functional link between genotype and phenotype, proteoform-level knowledge is critical in understanding the molecular mechanisms underlying health and disease. However, identification of proteoforms requires unbiased protein coverage at amino acid resolution. Scalable, deep, and unbiased proteomics studies have been impractical due to cumbersome and lengthy workflows required for complex samples, like blood plasma. Here, we demonstrate the power of the Proteograph™ Product Suite in enabling unbiased, deep, and rapid proteomics at scale in a proof-of-concept proteoform analysis to dissect differences between protein isoforms in plasma samples from 80 healthy controls and 61 patients with early-stage non-small-cell lung cancer (NSCLC). Processing the 141 plasma samples with Proteograph yielded 22,993 peptides corresponding to 2,569 protein groups at a confidence of 1% false discovery rate. We extracted four proteins with peptides with significant abundance differences (p < 0.05; Benjamini-Hochberg corrected) in healthy control and cancer plasma samples. For one, the abundance variation can be explained by underlying annotated protein isoforms. For a second, we find evidence for differentially transcribed isoforms in the broader sequence data, but not in the known annotated protein isoforms. The others may be explained by novel isoforms or post-translational modifications. In addition, we sought to identify protein variants arising from allelic variation. To this end, we first performed whole exome sequencing on buffy coat samples from 29 individuals in the NSCLC study. Then, we created personalized mass spectrometry search databases for each individual subject from the exome sequences. From these libraries, we identified 422 protein variants, one of which has previously been shown to relate to lung cancer. In conclusion, our results demonstrate that Proteograph can generate unbiased and deep plasma proteome profiles that enable identification of proteoforms present in plasma at a scale sufficient to enable population-scale proteomic studies powered to reveal novel mechanistic and biomedical insights.

A Robust Feature Extraction and Deep Learning Approach for Cancer Gene Prognosis

Mutated genes are one of the prominent factors in origination and spread of cancer disease. Here we have used Genomic signal processing methods to identify the patterns that differentiate cancer and non-cancerous genes. Furthermore, Deep learning algorithms were used to model a system that automatically predicts the cancer gene. Unlike the existing methods, two feature extraction modules are deployed to extract six attributes. Power Spectral Density based module was used to extract statistical parameters like Mean, Median, Standard deviation, Mean Deviation and Median Deviation. Adaptive Functional Link Network (AFLN) based filter module was used to extract Normalized Mean Square Error (NMSE). The uniqueness of this paper is identification of six input features that differentiates cancer genes. In this work artificial neural network is developed to predict cancer genes. Comparison is done on three sets of datasets with 6 attributes, 5 attributes and one attribute. We performed all the training and testing on the Tensorflow using the Keras library in Python using Google Colab. The developed approach proved its efficiency with 6 attributes attaining an accuracy of 98% for 150 epochs. The ANN model was also compared with existing work and attained a 10 fold cross validation accuracy of 96.26% with an increase of 1.2%.

Performance Evaluation of Machine Learning-Based Channel Equalization Techniques: New Trends and Challenges

Wireless communication systems have evolved and offered more smart and advanced systems like ad hoc and sensor-based infrastructure fewer networks. These networks are evaluated with two fundamental parameters including data rate and spectral efficiency. To achieve a high data rate and robust wireless communication, the most significant task is channel equalization at the receiver side. The transmitted data symbols when passing through the wireless channel suffer from various types of impairments, such as fading, Doppler shifts, and Intersymbol Interference (ISI), and degraded the overall network performance. To mitigate channel-related impairments, many channel equalization algorithms have been proposed for communication systems. The channel equalization problem can also be solved as a classification problem by using Machine Learning (ML) methods. In this paper, channel equalization is performed by using ML techniques in terms of Bit Error Rate (BER) analysis and comparison. Radial Basis Functions (RBFs), Multilayer Perceptron (MLP), Support Vector Machines (SVM), Functional Link Artificial Neural Network (FLANN), Long-Short Term Memory (LSTM), and Polynomial-based Neural Networks (NNs) are adopted for channel equalization.

Mitochondrial Membrane Remodeling

Mitochondria are key regulators of many important cellular processes and their dysfunction has been implicated in a large number of human disorders. Importantly, mitochondrial function is tightly linked to their ultrastructure, which possesses an intricate membrane architecture defining specific submitochondrial compartments. In particular, the mitochondrial inner membrane is highly folded into membrane invaginations that are essential for oxidative phosphorylation. Furthermore, mitochondrial membranes are highly dynamic and undergo constant membrane remodeling during mitochondrial fusion and fission. It has remained enigmatic how these membrane curvatures are generated and maintained, and specific factors involved in these processes are largely unknown. This review focuses on the current understanding of the molecular mechanism of mitochondrial membrane architectural organization and factors critical for mitochondrial morphogenesis, as well as their functional link to human diseases.

Analysis of the genetic diversity of Phalaenopsis orchids with single nucleotide polymorphisms and snap markers derived from the Pto gene

The Pto gene is a plant gene that has been reported to be involved in resistance to bacterial pathogens. A partial genomic sequence corresponding to Pto (~449 bp) was isolated from 16 species and four hybrids of Phalaenopsis during 2017 at the Department of Agronomy and Horticulture, IPB University, Bogor, Indonesia. Multiple sequence analysis was performed to find putative single nucleotide polymorphisms (SNPs) and design the corresponding single nucleotide-amplified polymorphism (SNAP) markers, which were in turn used to estimate the genetic diversity of 25 Phalaenopsis species. In total, 20 SNPs, of which 14 were nonsynonymous, were identified from the partial Pto sequences. Eighteen SNAP primers were then developed based on these 14 nonsynonymous and four synonymous SNPs. Validation results showed that 15 SNAP primers showed a polymorphism information content exceeding 0.3, suggesting the existence of more than two alleles for this locus. Upon their use, the SNAP markers described 86% of all interspecies variability. The Pto 52, Pto 349, Pto 229, and Pto 380 SNAP markers were very informative in the determination of genetic diversity. Notably, the existence of these nonsynonymous SNPs implied the possibility of functional changes within the amino acid sequence of the putative PTO protein. Thus, the resulting differences in the activity of the PTO protein may be used to breed tolerance to pathogen infection. Further work may be required to establish a functional link between tolerance to pathogens and the presence of Pto-SNAP markers in Phalaenopsis properly.

Mediator is broadly recruited to gene promoters via a Tail-independent mechanism

Mediator (MED) is a conserved factor with important roles in both basal and activated transcription. It is believed that MED plays a direct role in transcriptional regulation at most genes by functionally bridging enhancers and promoters. Here, we investigate the genome-wide roles of yeast MED by rapid depletion of its activator-binding domain (Tail) and monitoring changes in nascent transcription. We find that MED Tail and activator-mediated MED recruitment regulate only a small subset of genes. At most genes, MED bypasses the UAS and is directly recruited to promoters to facilitate transcription initiation. Our results define three classes of genes that differ in PIC assembly pathways and the requirements for MED Tail, SAGA, TFIID and BET factors Bdf1/2. We also find that the depletion of the MED middle module subunit Med7 mimics inactivation of Tail, suggesting a functional link. Our combined results have broad implications for the roles of MED, other coactivators, and mechanisms of transcriptional regulation at different gene classes.