Showing papers in "Computational and structural biotechnology journal in 2021"

Survival analysis across the entire transcriptome identifies biomarkers with the highest prognostic power in breast cancer

Abstract: Introduction Extensive research is directed to uncover new biomarkers capable to stratify breast cancer patients into clinically relevant cohorts. However, the overall performance ranking of such marker candidates compared to other genes is virtually absent. Here, we present the ranking of all survival related genes in chemotherapy treated basal and estrogen positive/HER2 negative breast cancer. Methods We searched the GEO repository to uncover transcriptomic datasets with available follow-up and clinical data. After quality control and normalization, samples entered an integrated database. Molecular subtypes were designated using gene expression data. Relapse-free survival analysis was performed using Cox proportional hazards regression. False discovery rate was computed to combat multiple hypothesis testing. Kaplan-Meier plots were drawn to visualize the best performing genes. Results The entire database includes 7,830 unique samples from 55 independent datasets. Of those with available relapse-free survival time, 3,382 samples were estrogen receptor-positive and 696 were basal. In chemotherapy treated ER positive/ERBB2 negative patients the significant prognostic biomarker genes achieved hazard rates between 1.76 and 3.33 with a p value below 5.8E−04. The significant prognostic genes in adjuvant chemotherapy treated basal breast cancer samples reached hazard rates between 1.88 and 3.61 with a p value below 7.2E−04. Our integrated platform was extended enabling the validation of future biomarker candidates. Conclusions A reference ranking for all genes in two chemotherapy treated breast cancer cohorts is presented. The results help to neglect those with unlikely clinical significance and to focus future research on the most promising candidates.

The language of proteins: NLP, machine learning & protein sequences.

Abstract: Natural language processing (NLP) is a field of computer science concerned with automated text and language analysis In recent years, following a series of breakthroughs in deep and machine learning, NLP methods have shown overwhelming progress Here, we review the success, promise and pitfalls of applying NLP algorithms to the study of proteins Proteins, which can be represented as strings of amino-acid letters, are a natural fit to many NLP methods We explore the conceptual similarities and differences between proteins and language, and review a range of protein-related tasks amenable to machine learning We present methods for encoding the information of proteins as text and analyzing it with NLP methods, reviewing classic concepts such as bag-of-words, k-mers/n-grams and text search, as well as modern techniques such as word embedding, contextualized embedding, deep learning and neural language models In particular, we focus on recent innovations such as masked language modeling, self-supervised learning and attention-based models Finally, we discuss trends and challenges in the intersection of NLP and protein research

The DisGeNET cytoscape app: Exploring and visualizing disease genomics data.

Abstract: Thanks to the unbiased exploration of genomic variants at large scale, hundreds of thousands of disease-associated loci have been uncovered. In parallel, network-based approaches have proven to be essential to understand the molecular mechanisms underlying human diseases. The use of these approaches has been boosted by the abundance of information about disease associated genes and variants, high quality human interactomics data, and the emergence of new types of omics data. The DisGeNET Cytoscape App combines the capabilities of Cytoscape with those of DisGeNET, a knowledge platform based on a comprehensive catalogue of disease-associated genes and variants. The DisGeNET Cytoscape App contains functions to query, analyze, and visualize different network representations of the gene-disease and variant-disease associations available in DisGeNET. It supports a wide variety of applications through its query and filter functionalities, including the annotation of foreign networks generated by other apps or uploaded by the user. The new release of the DisGeNET Cytoscape App has been designed to support Cytoscape 3.x and incorporates novel distinctive features such as visualization and analysis of variant-disease networks, disease enrichment analysis for genes and variants, and analytic support through Cytoscape Automation. Moreover, the DisGeNET Cytoscape App features an API to access its core functionalities via the REST protocol fostering the development of reproducible and scalable analysis workflows based on DisGeNET data.

Chemokines and chemokine receptors during COVID-19 infection

Abstract: Chemokines are crucial inflammatory mediators needed during an immune response to clear pathogens. However, their excessive release is the main cause of hyperinflammation. In the recent COVID-19 outbreak, chemokines may be the direct cause of acute respiratory disease syndrome, a major complication leading to death in about 40% of severe cases. Several clinical investigations revealed that chemokines are directly involved in the different stages of SARS-CoV-2 infection. Here, we review the role of chemokines and their receptors in COVID-19 pathogenesis to better understand the disease immunopathology which may aid in developing possible therapeutic targets for the infection.

Review on circular RNAs and new insights into their roles in cancer.

Abstract: Circular RNAs (circRNAs) are a very interesting class of conserved single-stranded RNA molecules derived from exonic or intronic sequences by precursor mRNA back-splicing. Unlike canonical linear RNAs, circRNAs form covalently closed, continuous stable loops without a 5'end cap and 3'end poly(A) tail, and therefore are resistant to exonuclease digestion. The majority of circRNAs are highly abundant, and conserved across different species with a tissue or developmental-stage-specific expression. circRNAs have been shown to play important roles as microRNA sponges, regulators of gene splicing and transcription, RNA-binding protein sponges and protein/peptide translators. Emerging evidence reveals that circRNAs function in various human diseases, particularly cancers, and may function as better predictive biomarkers and therapeutic targets for cancer treatment. In consideration of their potential clinical relevance, circRNAs have become a new research hotspot in the field of tumor pathology. In the present study, the current understanding of the biogenesis, characteristics, databases, research methods, biological functions subcellular distribution, epigenetic regulation, extracellular transport and degradation of circRNAs was discussed. In particular, the multiple databases and methods involved in circRNA research were first summarized, and the recent advances in determining the potential roles of circRNAs in tumor growth, migration and invasion, which render circRNAs better predictive biomarkers, were described. Furthermore, future perspectives for the clinical application of circRNAs in the management of patients with cancer were proposed, which could provide new insights into circRNAs in the future.

The oralome and its dysbiosis: New insights into oral microbiome-host interactions

Abstract: The oralome is the summary of the dynamic interactions orchestrated between the ecological community of oral microorganisms (comprised of up to approximately 1000 species of bacteria, fungi, viruses, archaea and protozoa - the oral microbiome) that live in the oral cavity and the host. These microorganisms form a complex ecosystem that thrive in the dynamic oral environment in a symbiotic relationship with the human host. However, the microbial composition is significantly affected by interspecies and host-microbial interactions, which in turn, can impact the health and disease status of the host. In this review, we discuss the composition of the oralome and inter-species and host-microbial interactions that take place in the oral cavity and examine how these interactions change from healthy (eubiotic) to disease (dysbiotic) states. We further discuss the dysbiotic signatures associated with periodontitis and caries and their sequalae, (e.g., tooth/bone loss and pulpitis), and the systemic diseases associated with these oral diseases, such as infective endocarditis, atherosclerosis, diabetes, Alzheimer's disease and head and neck/oral cancer. We then discuss current computational techniques to assess dysbiotic oral microbiome changes. Lastly, we discuss current and novel techniques for modulation of the dysbiotic oral microbiome that may help in disease prevention and treatment, including standard hygiene methods, prebiotics, probiotics, use of nano-sized drug delivery systems (nano-DDS), extracellular polymeric matrix (EPM) disruption, and host response modulators.

From pyroptosis, apoptosis and necroptosis to PANoptosis: A mechanistic compendium of programmed cell death pathways

Abstract: Pyroptosis, apoptosis and necroptosis are the most genetically well-defined programmed cell death (PCD) pathways, and they are intricately involved in both homeostasis and disease. Although the identification of key initiators, effectors and executioners in each of these three PCD pathways has historically delineated them as distinct, growing evidence has highlighted extensive crosstalk among them. These observations have led to the establishment of the concept of PANoptosis, defined as an inflammatory PCD pathway regulated by the PANoptosome complex with key features of pyroptosis, apoptosis and/or necroptosis that cannot be accounted for by any of these PCD pathways alone. In this review, we provide a brief overview of the research history of pyroptosis, apoptosis and necroptosis. We then examine the intricate crosstalk among these PCD pathways to discuss the current evidence for PANoptosis. We also detail the molecular evidence for the assembly of the PANoptosome complex, a molecular scaffold for contemporaneous engagement of key molecules from pyroptosis, apoptosis, and/or necroptosis. PANoptosis is now known to be critically involved in many diseases, including infection, sterile inflammation and cancer, and future discovery of novel PANoptotic components will continue to broaden our understanding of the fundamental processes of cell death and inform the development of new therapeutics.

Integration strategies of multi-omics data for machine learning analysis.

Abstract: Increased availability of high-throughput technologies has generated an ever-growing number of omics data that seek to portray many different but complementary biological layers including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. New insight from these data have been obtained by machine learning algorithms that have produced diagnostic and classification biomarkers. Most biomarkers obtained to date however only include one omic measurement at a time and thus do not take full advantage of recent multi-omics experiments that now capture the entire complexity of biological systems. Multi-omics data integration strategies are needed to combine the complementary knowledge brought by each omics layer. We have summarized the most recent data integration methods/ frameworks into five different integration strategies: early, mixed, intermediate, late and hierarchical. In this mini-review, we focus on challenges and existing multi-omics integration strategies by paying special attention to machine learning applications.

UCell: Robust and scalable single-cell gene signature scoring.

Abstract: UCell is an R package for evaluating gene signatures in single-cell datasets. UCell signature scores, based on the Mann-Whitney U statistic, are robust to dataset size and heterogeneity, and their calculation demands less computing time and memory than other available methods, enabling the processing of large datasets in a few minutes even on machines with limited computing power. UCell can be applied to any single-cell data matrix, and includes functions to directly interact with Seurat objects. The UCell package and documentation are available on GitHub at https://github.com/carmonalab/UCell.

A review on machine learning approaches and trends in drug discovery.

Abstract: Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.

Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis.

Abstract: While cost-effective high-throughput technologies provide an increasing amount of data, the analyses of single layers of data seldom provide causal relations. Multi-omics data integration strategies across different cellular function levels, including genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes offer unparalleled opportunities to understand the underlying biology of complex diseases, such as cancer. We review some of the most frequently used data integration methods and outline research areas where multi-omics significantly benefit our understanding of the process and outcome of the malignant transformation. We discuss algorithmic frameworks developed to reveal cancer subtypes, disease mechanisms, and methods for identifying driver genomic alterations and consider the significance of multi-omics in tumor classifications, diagnostics, and prognostications. We provide a comprehensive summary of each omics strategy's most recent advances within the clinical context and discuss the main challenges facing their clinical implementations. Despite its unparalleled advantages, multi-omics data integration is slow to enter everyday clinics. One major obstacle is the uneven maturity of different omics approaches and the growing gap between generating large volumes of data compared to data processing capacity. Progressive initiatives to enforce the standardization of sample processing and analytical pipelines, multidisciplinary training of experts for data analysis and interpretation are vital to facilitate the translatability of theoretical findings.

Automated methods for cell type annotation on scRNA-seq data.

Abstract: The advent of single-cell sequencing started a new era of transcriptomic and genomic research, advancing our knowledge of the cellular heterogeneity and dynamics. Cell type annotation is a crucial step in analyzing single-cell RNA sequencing data, yet manual annotation is time-consuming and partially subjective. As an alternative, tools have been developed for automatic cell type identification. Different strategies have emerged to ultimately associate gene expression profiles of single cells with a cell type either by using curated marker gene databases, correlating reference expression data, or transferring labels by supervised classification. In this review, we present an overview of the available tools and the underlying approaches to perform automated cell type annotations on scRNA-seq data.

An overview of current COVID-19 vaccine platforms.

Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the coronavirus disease 2019 (COVID-19) pandemic that emerged in December 2019 in Wuhan city, China. An effective vaccine is urgently needed to protect humans and to mitigate the economic and societal impacts of the pandemic. Despite standard vaccine development usually requiring an extensive process and taking several years to complete all clinical phases, there are currently 184 vaccine candidates in pre-clinical testing and another 88 vaccine candidates in clinical phases based on different vaccine platforms as of April 13, 2021. Moreover, three vaccine candidates have recently been granted an Emergency Use Authorization by the United States Food and Drug Administration (for Pfizer/BioNtech, Moderna mRNA vaccines, and Johnson and Johnson viral vector vaccine) and by the UK government (for University of Oxford/AstraZeneca viral vector vaccine). Here we aim to briefly address the current advances in reverse genetics system of SARS-CoV-2 and the use of this in development of SARS-CoV-2 vaccines. Additionally, we cover the essential points concerning the different platforms of current SARS-CoV-2 vaccine candidates and the advantages and drawbacks of these platforms. We also assess recommendations for controlling the COVID-19 pandemic and future pandemics using the benefits of genetic engineering technology to design effective vaccines against emerging and re-emerging viral diseases with zoonotic and/or pandemic potential.

Network analysis methods for studying microbial communities: A mini review.

Abstract: Microorganisms including bacteria, fungi, viruses, protists and archaea live as communities in complex and contiguous environments. They engage in numerous inter- and intra- kingdom interactions which can be inferred from microbiome profiling data. In particular, network-based approaches have proven helpful in deciphering complex microbial interaction patterns. Here we give an overview of state-of-the-art methods to infer intra-kingdom interactions ranging from simple correlation- to complex conditional dependence-based methods. We highlight common biases encountered in microbial profiles and discuss mitigation strategies employed by different tools and their trade-off with increased computational complexity. Finally, we discuss current limitations that motivate further method development to infer inter-kingdom interactions and to robustly and comprehensively characterize microbial environments in the future.

Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring.

Abstract: Advances in nucleic acid sequencing technology have enabled expansion of our ability to profile microbial diversity. These large datasets of taxonomic and functional diversity are key to better understanding microbial ecology. Machine learning has proven to be a useful approach for analyzing microbial community data and making predictions about outcomes including human and environmental health. Machine learning applied to microbial community profiles has been used to predict disease states in human health, environmental quality and presence of contamination in the environment, and as trace evidence in forensics. Machine learning has appeal as a powerful tool that can provide deep insights into microbial communities and identify patterns in microbial community data. However, often machine learning models can be used as black boxes to predict a specific outcome, with little understanding of how the models arrived at predictions. Complex machine learning algorithms often may value higher accuracy and performance at the sacrifice of interpretability. In order to leverage machine learning into more translational research related to the microbiome and strengthen our ability to extract meaningful biological information, it is important for models to be interpretable. Here we review current trends in machine learning applications in microbial ecology as well as some of the important challenges and opportunities for more broad application of machine learning to understanding microbial communities.

Nanopore sequencing and its application to the study of microbial communities.

Abstract: Since its introduction, nanopore sequencing has enhanced our ability to study complex microbial samples through the possibility to sequence long reads in real time using inexpensive and portable technologies. The use of long reads has allowed to address several previously unsolved issues in the field, such as the resolution of complex genomic structures, and facilitated the access to metagenome assembled genomes (MAGs). Furthermore, the low cost and portability of platforms together with the development of rapid protocols and analysis pipelines have featured nanopore technology as an attractive and ever-growing tool for real-time in-field sequencing for environmental microbial analysis. This review provides an up-to-date summary of the experimental protocols and bioinformatic tools for the study of microbial communities using nanopore sequencing, highlighting the most important and recent research in the field with a major focus on infectious diseases. An overview of the main approaches including targeted and shotgun approaches, metatranscriptomics, epigenomics, and epitranscriptomics is provided, together with an outlook to the major challenges and perspectives over the use of this technology for microbial studies.

The molecular feature of macrophages in tumor immune microenvironment of glioma patients.

Abstract: Background Gliomas are one of the most common types of primary tumors in central nervous system. Previous studies have found that macrophages actively participate in tumor growth. Methods Weighted gene co-expression network analysis was used to identify meaningful macrophage-related gene genes for clustering. Pamr, SVM, and neural network were applied for validating clustering results. Somatic mutation and methylation were used for defining the features of identified clusters. Differentially expressed genes (DEGs) between the stratified groups after performing elastic regression and principal component analyses were used for the construction of MScores. The expression of macrophage-specific genes were evaluated in tumor microenvironment based on single cell sequencing analysis. A total of 2365 samples from 15 glioma datasets and 5842 pan-cancer samples were used for external validation of MScore. Results Macrophages were identified to be negatively associated with the survival of glioma patients. Twenty-six macrophage-specific DEGs obtained by elastic regression and PCA were highly expressed in macrophages at single-cell level. The prognostic value of MScores in glioma was validated by the active proinflammatory and metabolic profile of infiltrating microenvironment and response to immunotherapies of samples with this signature. MScores managed to stratify patient survival probabilities in 15 external glioma datasets and pan-cancer datasets, which predicted worse survival outcome. Sequencing data and immunohistochemistry of Xiangya glioma cohort confirmed the prognostic value of MScores. A prognostic model based on MScores demonstrated high accuracy rate. Conclusion Our findings strongly support a modulatory role of macrophages, especially M2 macrophages in glioma progression and warrants further experimental studies.

Recent advances in exploring the heavy metal(loid) resistant microbiome.

Abstract: Heavy metal(loid)s exert selective pressure on microbial communities and evolution of metal resistance determinants. Despite increasing knowledge concerning the impact of metal pollution on microbial community and ecological function, it is still a challenge to identify a consistent pattern of microbial community composition along gradients of elevated metal(loid)s in natural environments. Further, our current knowledge of the microbial metal resistome at the community level has been lagging behind compared to the state-of-the-art genetic profiling of bacterial metal resistance mechanisms in a pure culture system. This review provides an overview of the core metal resistant microbiome, development of metal resistance strategies, and potential factors driving the diversity and distribution of metal resistance determinants in natural environments. The impacts of biotic factors regulating the bacterial metal resistome are highlighted. We finally discuss the advances in multiple technologies, research challenges, and future directions to better understand the interface of the environmental microbiome with the metal resistome. This review aims to highlight the diversity and wide distribution of heavy metal(loid)s and their corresponding resistance determinants, helping to better understand the resistance strategy at the community level.

Dependence of SARS-CoV-2 infection on cholesterol-rich lipid raft and endosomal acidification.

Abstract: Coronavirus disease 2019 is a kind of viral pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) However, the mechanism whereby SARS-CoV-2 invades host cells remains poorly understood Here we used SARS-CoV-2 pseudoviruses to infect human angiotensin-converting enzyme 2 (ACE2) expressing HEK293T cells and evaluated virus infection We confirmed that SARS-CoV-2 entry was dependent on ACE2 and sensitive to pH of endosome/lysosome in HEK293T cells The infection of SARS-CoV-2 pseudoviruses is independent of dynamin, clathrin, caveolin and endophilin A2, as well as macropinocytosis Instead, we found that the infection of SARS-CoV-2 pseudoviruses was cholesterol-rich lipid raft dependent Cholesterol depletion of cell membranes with methyl-β-cyclodextrin resulted in reduction of pseudovirus infection The infection of SARS-CoV-2 pseudoviruses resumed with cholesterol supplementation Together, cholesterol-rich lipid rafts, and endosomal acidification, are key steps of SARS-CoV-2 required for infection of host cells Therefore, our finding expands the understanding of SARS-CoV-2 entry mechanism and provides a new anti-SARS-CoV-2 strategy

Host transcriptomic profiling of COVID-19 patients with mild, moderate, and severe clinical outcomes

Abstract: Characterizing key molecular and cellular pathways involved in COVID-19 is essential for disease prognosis and management. We perform shotgun transcriptome sequencing of human RNA obtained from nasopharyngeal swabs of patients with COVID-19, and identify a molecular signature associated with disease severity. Specifically, we identify globally dysregulated immune related pathways, such as cytokine-cytokine receptor signaling, complement and coagulation cascades, JAK-STAT, and TGF- β signaling pathways in all, though to a higher extent in patients with severe symptoms. The excessive release of cytokines and chemokines such as CCL2, CCL22, CXCL9 and CXCL12 and certain interferons and interleukins related genes like IFIH1, IFI44, IFIT1 and IL10 were significantly higher in patients with severe clinical presentation compared to mild and moderate presentations. Differential gene expression analysis identified a small set of regulatory genes that might act as strong predictors of patient outcome. Our data suggest that rapid transcriptome analysis of nasopharyngeal swabs can be a powerful approach to quantify host molecular response and may provide valuable insights into COVID-19 pathophysiology.

Farnesoid X receptor (FXR): Structures and ligands.

Abstract: Farnesoid X receptor (FXR) is a bile acid activated nuclear receptor (BAR) and is mainly expressed in the liver and intestine. Upon ligand binding, FXR regulates key genes involved in the metabolic process of bile acid synthesis, transport and reabsorption and is also involved in the metabolism of carbohydrates and lipids. Because of its important functions, FXR is considered as a promising drug target for the therapy of bile acid-related liver diseases. With the approval of obeticholic acid (OCA) as the first small molecule to target FXR, many other small molecules are being evaluated in clinical trials. This review summarizes the structures of FXR, especially its ligand binding domain, and the development of small molecules (including agonists and antagonists) targeting FXR.

A review of computational tools for generating metagenome-assembled genomes from metagenomic sequencing data

Abstract: Metagenomic sequencing provides a culture-independent avenue to investigate the complex microbial communities by constructing metagenome-assembled genomes (MAGs). A MAG represents a microbial genome by a group of sequences from genome assembly with similar characteristics. It enables us to identify novel species and understand their potential functions in a dynamic ecosystem. Many computational tools have been developed to construct and annotate MAGs from metagenomic sequencing, however, there is a prominent gap to comprehensively introduce their background and practical performance. In this paper, we have thoroughly investigated the computational tools designed for both upstream and downstream analyses, including metagenome assembly, metagenome binning, gene prediction, functional annotation, taxonomic classification, and profiling. We have categorized the commonly used tools into unique groups based on their functional background and introduced the underlying core algorithms and associated information to demonstrate a comparative outlook. Furthermore, we have emphasized the computational requisition and offered guidance to the users to select the most efficient tools. Finally, we have indicated current limitations, potential solutions, and future perspectives for further improving the tools of MAG construction and annotation. We believe that our work provides a consolidated resource for the current stage of MAG studies and shed light on the future development of more effective MAG analysis tools on metagenomic sequencing.

Association of risk factors with type 2 diabetes: A systematic review

Abstract: Diabetes is the leading cause of severe health complications and one of the top 10 causes of death worldwide. To date, diabetes has no cure, and therefore, it is necessary to take precautionary measures to avoid its occurrence. The main aim of this systematic review is to identify the majority of the risk factors for the incidence/prevalence of type 2 diabetes mellitus on one hand, and to give a critical analysis of the cohort/cross-sectional studies which examine the impact of the association of risk factors on diabetes. Consequently, we provide insights on risk factors whose interactions are major players in developing diabetes. We conclude with recommendations to allied health professionals, individuals and government institutions to support better diagnosis and prognosis of the disease.

Mechanistic insights into the effect of phosphorylation on Ras conformational dynamics and its interactions with cell signaling proteins

Abstract: Ras undergoes interconversion between the active GTP-bound state and the inactive GDP-bound state This GTPase cycle, which controls the activities of Ras, is accelerated by Ras GTPase-activating proteins (GAPs) and guanine nucleotide exchange factors (SOS) Oncogenic Ras mutations could affect the GTPase cycle and impair Ras functions Additionally, Src-induced K-Ras Y32/64 dual phosphorylation has been reported to disrupt GTPase cycle and hinder Ras downstream signaling However, the underlying mechanisms remain unclear To address this, we performed molecular dynamics simulations (~30 μs in total) on unphosphorylated and phosphorylated K-Ras4B in GTP- and GDP-bound states, and on their complexes with GTPase cycle regulators (GAP and SOS) and the effector protein Raf We found that K-Ras4B dual phosphorylation mainly alters the conformation at the nucleotide binding site and creates disorder at the catalytic site, resulting in the enlargement of GDP binding pocket and the retard of Ras-GTP intrinsic hydrolysis We observed phosphorylation-induced shift in the distribution of Ras-GTP inactive-active sub-states and recognized potential druggable pockets in the phosphorylated Ras-GTP Moreover, decreased catalytic competence or signal delivery abilities due to reduced binding affinities and/or distorted catalytic conformations of GAP, SOS and Raf were observed In addition, the allosteric pathway from Ras/Raf interface to the distal Raf L4 loop was compromised by Ras phosphorylation These results reveal the mechanisms by which phosphorylation influences the intrinsic or GAP/SOS catalyzed transformations between GTP- and GDP-bound states of Ras and its signal transduction to Raf Our findings project Ras phosphorylation as a target for cancer drug discovery

Antioxidants and pentoxifylline as coadjuvant measures to standard therapy to improve prognosis of patients with pneumonia by COVID-19.

Abstract: The type 2 coronavirus causes severe acute respiratory syndrome (SARS-CoV-2) and produces pneumonia with pulmonary alveolar collapse. In some cases it also causes sepsis and septic shock. There is no specific treatment for coronavirus disease 2019 (COVID-19). Vitamin C (Vit C), Vitamin E (Vit E), N-acetylcysteine (NAC) and Melatonin (MT) increase the intracellular content of GSH, kidnap free radicals and protect DNA, proteins in the cytosol and lipids in cell membranes. Pentoxifylline (Px) has anti-inflammatory activities. Here we evaluate the effect of Vit C, Vit E, NAC, and MT plus Px in COVID-19 patients with moderate and severe pneumonia. 110 patients of either sex were included. They were divided into five groups with 22 patients each. Group 1 received Vit C+Px, group 2 Vit E+Px, group 3 NAC+Px, group 4 MT+Px, and group 5 only Px. Oxidative stress (OS) markers such as lipid peroxidation (LPO) levels, total antioxidant capacity (TAC) and nitrites (NO2-) were evaluated in plasma. The antioxidant therapy improved the survival scores including the Sequential Organ Failure Assessment (SOFA), the Acute Physiology and chronic Health Evaluation II (Apache II), the Simplified Acute Physiology Score II (SAPS II), the Critical Illness Risk Score, Launched during COVID-19 crisis (COVIDGRAM) and the Glasgow Coma Scale (GCS). We found that LPO was elevated (p≤0.04) and TAC (p≤0.03), NO2- (p≤0.04) and inflammation markers such as interleukin-6 (IL-6, p≤0.01) C reactive protein (CRP, p≤0.01) and procalcitonin (PCT, p≤0.05) were diminished in COVID-19 patients upon admission to the hospital. The different antioxidants reversed this alteration at the end of the treatment. The treatment with antioxidant supplements such as Vit C, E, NAC, and MT plus Px could decelerate the aggressive and lethal development of COVID-19. Antioxidant therapy can be effective in this pandemia since it improves the survival scores including SOFA, Apache II, SAPS II, COVIDGRAM, GCS by lowering the LPO, IL-6, CRP, PCT and increasing systemic TAC and NO2-.

Skin microbiome transplantation and manipulation: Current state of the art.

Abstract: Many skin conditions are associated with an imbalance in the skin microbiome. In recent years, the skin microbiome has become a hot topic, for both therapeutic and cosmetic purposes. The possibility of manipulating the human skin microbiome to address skin conditions has opened exciting new paths for therapy. Here we review the skin microbiome manipulation strategies, ranging from skin microbiome transplantation, over skin bacteriotherapy to the use of prebiotics, probiotics and postbiotics. We summarize all efforts undertaken to exchange, manipulate, transplant or selectively apply the skin microbiome to date. Multiple microbial groups have been targeted, since they have been proven to be beneficial for skin health. We focus on the most common skin disorders and their associated skin microbiome dysbiosis and we review the existing scientific data and clinical trials undertaken to combat these skin conditions. The skin microbiome represents a novel platform for therapy. Transplantation of a complete microbiome or application of single strains has demonstrated beneficial therapeutic application.

Machine learning and deep learning methods that use omics data for metastasis prediction

Abstract: Knowing metastasis is the primary cause of cancer-related deaths, incentivized research directed towards unraveling the complex cellular processes that drive the metastasis. Advancement in technology and specifically the advent of high-throughput sequencing provides knowledge of such processes. This knowledge led to the development of therapeutic and clinical applications, and is now being used to predict the onset of metastasis to improve diagnostics and disease therapies. In this regard, predicting metastasis onset has also been explored using artificial intelligence approaches that are machine learning, and more recently, deep learning-based. This review summarizes the different machine learning and deep learning-based metastasis prediction methods developed to date. We also detail the different types of molecular data used to build the models and the critical signatures derived from the different methods. We further highlight the challenges associated with using machine learning and deep learning methods, and provide suggestions to improve the predictive performance of such methods.

Statistical and machine learning methods for spatially resolved transcriptomics with histology.

Abstract: Recent developments in spatially resolved transcriptomics (SRT) technologies have enabled scientists to get an integrated understanding of cells in their morphological context. Applications of these technologies in diverse tissues and diseases have transformed our views of transcriptional complexity. Most published studies utilized tools developed for single-cell RNA sequencing (scRNA-seq) for data analysis. However, SRT data exhibit different properties from scRNA-seq. To take full advantage of the added dimension on spatial location information in such data, new methods that are tailored for SRT are needed. Additionally, SRT data often have companion high-resolution histology information available. Incorporating histological features in gene expression analysis is an underexplored area. In this review, we will focus on the statistical and machine learning aspects for SRT data analysis and discuss how spatial location and histology information can be integrated with gene expression to advance our understanding of the transcriptional complexity. We also point out open problems and future research directions in this field.

Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review

Abstract: The worldwide health crisis caused by the SARS-Cov-2 virus has resulted in>3 million deaths so far. Improving early screening, diagnosis and prognosis of the disease are critical steps in assisting healthcare professionals to save lives during this pandemic. Since WHO declared the COVID-19 outbreak as a pandemic, several studies have been conducted using Artificial Intelligence techniques to optimize these steps on clinical settings in terms of quality, accuracy and most importantly time. The objective of this study is to conduct a systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019. We included 101 studies, published from January 1st, 2020 to December 30th, 2020, that developed AI prediction models which can be applied in the clinical setting. We identified in total 14 models for screening, 38 diagnostic models for detecting COVID-19 and 50 prognostic models for predicting ICU need, ventilator need, mortality risk, severity assessment or hospital length stay. Moreover, 43 studies were based on medical imaging and 58 studies on the use of clinical parameters, laboratory results or demographic features. Several heterogeneous predictors derived from multimodal data were identified. Analysis of these multimodal data, captured from various sources, in terms of prominence for each category of the included studies, was performed. Finally, Risk of Bias (RoB) analysis was also conducted to examine the applicability of the included studies in the clinical setting and assist healthcare providers, guideline developers, and policymakers.

Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis.

Abstract: Artificial Intelligence (AI) has recently altered the landscape of cancer research and medical oncology using traditional Machine Learning (ML) algorithms and cutting-edge Deep Learning (DL) architectures. In this review article we focus on the ML aspect of AI applications in cancer research and present the most indicative studies with respect to the ML algorithms and data used. The PubMed and dblp databases were considered to obtain the most relevant research works of the last five years. Based on a comparison of the proposed studies and their research clinical outcomes concerning the medical ML application in cancer research, three main clinical scenarios were identified. We give an overview of the well-known DL and Reinforcement Learning (RL) methodologies, as well as their application in clinical practice, and we briefly discuss Systems Biology in cancer research. We also provide a thorough examination of the clinical scenarios with respect to disease diagnosis, patient classification and cancer prognosis and survival. The most relevant studies identified in the preceding year are presented along with their primary findings. Furthermore, we examine the effective implementation and the main points that need to be addressed in the direction of robustness, explainability and transparency of predictive models. Finally, we summarize the most recent advances in the field of AI/ML applications in cancer research and medical oncology, as well as some of the challenges and open issues that need to be addressed before data-driven models can be implemented in healthcare systems to assist physicians in their daily practice.