The microbiome compositional and functional differences between rectal mucosa and feces.

In recent years, most studies on the gut microbiome have primarily focused on feces samples, leaving the microbial communities in the intestinal mucosa relatively unexplored. To address this gap, our study employed shotgun metagenomics to analyze the microbial compositions in normal rectal mucosa and matched feces from 20 patients with colonic polyps. Our findings revealed a pronounced distinction of the microbial communities between these two sample sets. Compared with feces, the mucosal microbiome contains fewer genera, with Burkholderia being the most discriminating genus between feces and mucosa, highlighting its significant influence on the mucosa. Furthermore, based on the microbial classification and KEGG Orthology (KO) annotation results, we explored the association between rectal mucosal microbiota and factors such as age, gender, BMI, and polyp risk level. Notably, we identified novel biomarkers for these phenotypes, such as Clostridium ramosum and Enterobacter cloacae in age. The mucosal microbiota showed an enrichment of KO pathways related to sugar transport and short chain fatty acid metabolism. Our comprehensive approach not only bridges the knowledge gap regarding the microbial community in the rectal mucosa but also underscores the complexity and specificity of microbial interactions within the human gut, particularly in the Chinese population. IMPORTANCE: This study presents a system-level map of the differences between feces and rectal mucosal microbial communities in samples with colorectal cancer risk. It reveals the unique microecological characteristics of rectal mucosa and its potential influence on health. Additionally, it provides novel insights into the role of the gut microbiome in the pathogenesis of colorectal cancer and paves the way for the development of new prevention and treatment strategies.

Deep Learning Driven Drug Discovery: Tackling Severe Acute Respiratory Syndrome Coronavirus 2.

Deep learning significantly accelerates the drug discovery process, and contributes to global efforts to stop the spread of infectious diseases. Besides enhancing the efficiency of screening of antimicrobial compounds against a broad spectrum of pathogens, deep learning has also the potential to efficiently and reliably identify drug candidates against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Consequently, deep learning has been successfully used for the identification of a number of potential drugs against SARS-CoV-2, including Atazanavir, Remdesivir, Kaletra, Enalaprilat, Venetoclax, Posaconazole, Daclatasvir, Ombitasvir, Toremifene, Niclosamide, Dexamethasone, Indomethacin, Pralatrexate, Azithromycin, Palmatine, and Sauchinone. This mini-review discusses recent advances and future perspectives of deep learning-based SARS-CoV-2 drug discovery.

Proteomic and Transcriptome Profiling of G-Quadruplex Aptamers Developed for Cell Internalization.

Nucleic acid medicine is expected to be among the most promising next-generation therapies. Applications of nucleic acid in vivo are still challenging as a result of the difficulties in direct cell penetration without external assistance. To facilitate the cellular delivery of therapeutic nucleic acid, we developed cell-penetrating aptamers using cell-internalization Systematic Evolution of Ligands by EXponential enrichment (SELEX). Moreover, C20-4 min, a G-quadruplex-forming DNA aptamer, was discovered, showing a higher cell-penetrating capacity compared with other candidates, including AS1411. To verify the formation and understand the G-quadruplex folding topologies of enriched aptamer motifs, characteristic circular dichroism (CD) spectral features are analyzed. The CD spectra of C20-4 min strongly support the formation of parallel G-quadruplexes. Systematic analyses of the G-quadruplex regulation pathway have been performed by combining aptamer pull-down with mass spectrometry. We profiled G-quadruplex aptamers interacting with cellular proteins during internalization and identified helicases and GTPase proteins as cellular interacting partners. In addition, whole transcriptome analysis was performed to study the effects of G-quadruplex aptamers, revealing differentially expressed genes involved in the regulation of GTPase functions. Integrative analyses of transcriptome and proteomic have aided in understanding the functional hierarchy of molecular players in G-quadruplex nucleic acid mechanisms of internalization, which might facilitate developing a novel delivery system.

Deep Learning for Imaging and Detection of Microorganisms.

Despite tremendous recent interest, the application of deep learning in microbiology has still not reached its full potential. To tackle the challenges faced by human-operated microscopy, deep-learning-based methods have been proposed for microscopic image analysis of a wide range of microorganisms, including viruses, bacteria, fungi, and parasites. We believe that deep-learning technology-based systems will be on the front line of monitoring and investigation of microorganisms.

Genomic pan-cancer classification using image-based deep learning.

Accurate cancer type classification based on genetic mutation can significantly facilitate cancer-related diagnosis. However, existing methods usually use feature selection combined with simple classifiers to quantify key mutated genes, resulting in poor classification performance. To circumvent this problem, a novel image-based deep learning strategy is employed to distinguish different types of cancer. Unlike conventional methods, we first convert gene mutation data containing single nucleotide polymorphisms, insertions and deletions into a genetic mutation map, and then apply the deep learning networks to classify different cancer types based on the mutation map. We outline these methods and present results obtained in training VGG-16, Inception-v3, ResNet-50 and Inception-ResNet-v2 neural networks to classify 36 types of cancer from 9047 patient samples. Our approach achieves overall higher accuracy (over 95%) compared with other widely adopted classification methods. Furthermore, we demonstrate the application of a Guided Grad-CAM visualization to generate heatmaps and identify the top-ranked tumor-type-specific genes and pathways. Experimental results on prostate and breast cancer demonstrate our method can be applied to various types of cancer. Powered by the deep learning, this approach can potentially provide a new solution for pan-cancer classification and cancer driver gene discovery. The source code and datasets supporting the study is available at https://github.com/yetaoyu/Genomic-pan-cancer-classification.