Search

Our understanding of how genomic DNA is tightly packed inside the nucleus, yet is still accessible for vital cellular processes, has grown dramatically over recent years with advances in microscopy and genomics technologies. Computational methods have played a pivotal role in the structural interpretation of experimental data, which helped unravel some organizational principles of genome folding. Here, we give an overview of current computational efforts in mechanistic and data-driven 3D chromatin structure modeling. We discuss strengths and limitations of different methods and evaluate the added value and benefits of computational approaches to infer the 3D structural and dynamic properties of the genome and its underlying mechanisms at different scales and resolution, ranging from the dynamic formation of chromatin loops and topological associated domains to nuclear compartmentalization of chromatin and nuclear bodies.

It has long been proposed that nuclear RNAs might play an important role in organizing the structure of the nucleus. Initial experiments performed more than 30 years ago found that global disruption of RNA led to visible rearrangements of nuclear organization. Yet, this idea remained controversial for many years, in large part because it was unclear what specific RNAs might be involved, and which specific nuclear structures might be dependent on RNA. Over the past few years, the contributions of RNA to organizing nuclear structures have become clearer with the discovery that many nuclear bodies are enriched for specific noncoding RNAs (ncRNAs); in specific cases, ncRNAs have been shown to be essential for establishment and maintenance of these nuclear structures. More recently, many different ncRNAs have been shown to play critical roles in initiating the three-dimensional (3D) spatial organization of DNA, RNA, and protein molecules in the nucleus. These examples, combined with global imaging and genomic experiments, have begun to paint a picture of a broader role for RNA in nuclear organization and to uncover a unifying mechanism that may explain why RNA is a uniquely suited molecule for this role. In this review, we provide an overview of the history of RNA and nuclear structure and discuss key examples of RNA-mediated bodies, the global roles of ncRNAs in shaping nuclear structure, and emerging insights into mechanisms of RNA-mediated nuclear organization.

Insights into the genetic basis of human disease are helping to address some of the key challenges in new drug development including the very high rates of failure. Here we review the recent history of an emerging, genomics-assisted approach to pharmaceutical research and development, and its relationship to Mendelian randomization (MR), a well-established analytical approach to causal inference. We demonstrate how human genomic data linked to pharmaceutically relevant phenotypes can be used for (1) drug target identification (mapping relevant drug targets to diseases), (2) drug target validation (inferring the likely effects of drug target perturbation), (3) evaluation of the effectiveness and specificity of compound-target engagement (inferring the extent to which the effects of a compound are exclusive to the target and distinguishing between on-target and off-target compound effects), and (4) the selection of end points in clinical trials (the diseases or conditions to be evaluated as trial outcomes). We show how genomics can help identify indication expansion opportunities for licensed drugs and repurposing of compounds developed to clinical phase that proved safe but ineffective for the original intended indication. We outline statistical and biological considerations in using MR for drug target validation (drug target MR) and discuss the obstacles and challenges for scaled applications of these genomics-based approaches.

The analysis of curated genomic, metagenomic and proteomic data is of paramount importance in the fields of biology, medicine, education, and bioinformatics. Although this type of data is usually hosted in raw format on free international repositories, the full access requires lots of computing power and large storage disk space for the domestic user. The purpose of the study is to offer a comprehensive set of microbial genomic and proteomic reference databases in an accessible and easy-to-use form to the scientific community and demonstrate its advantages and usefulness. Also, we present a case study on the applicability of the sketched data, for the determination of overall genomic coherence between two members of the Brucellacea family, which suggests they belong to the same genomospecies that remain as discrete ecotypes. A representative set of genomes, proteomes (from type material), and metagenomes were directly collected from the NCBI Assembly database and Genome Taxonomy Database (GTDB), associated with the major groups of Bacteria, Archaea, Virus, and Fungi. Sketched databases were subsequently created and stored on handy reduced representations by using the MinHash algorithm implemented in Mash software. The obtained dataset contains more than 133 GB of space disk reduced to 883.25 MB and represents 125,110 genomics/proteomic records from eight informative contexts, which have been prefiltered to make them accessible, usable, and user-friendly with limited computational resources. Potential uses of these sketched databases are discussed, including but not limited to microbial species delimitation, estimation of genomic distances and genomic novelties, paired comparisons between proteomes, genomes, and metagenomes; phylogenetic neighbor’s exploration and selection, among others. Resumo A análise de dados genômicos, metagenômicos e proteômicos com curadoria é de suma importância nos campos da biologia, medicina, educação e bioinformática. Embora esse tipo de dados geralmente seja hospedado em formato bruto em repositórios internacionais gratuitos, o acesso total requer muita capacidade de computação e grande espaço em disco de armazenamento para o usuário doméstico. Os objetivos do estudo são oferecer um conjunto abrangente de bancos de dados de referência genômica e proteômica microbiana de forma acessível e fácil de usar para a comunidade científica e demonstrar suas vantagens e utilidade. Além disso, apresentamos um estudo de caso sobre a aplicabilidade dos dados esboçados para a determinação da coerência genômica geral entre dois membros da família Brucellacea, o que sugere que eles pertencem às mesmas genomoespécies que permanecem como ecótipos discretos. Um conjunto representativo de genomas, proteomas (de material tipo) e metagenomas foi coletado diretamente do banco de dados NCBI Assembly e do banco de dados de taxonomia do genoma (GTDB), associada aos principais grupos de bactérias, Archaea, vírus e fungos. Bancos de dados esboçados foram subsequentemente criados e armazenados em representações reduzidas práticas usando o algoritmo MinHash implementado no software Mash. O conjunto de dados obtido contém mais de 133 GB de espaço em disco reduzido para 883,25 MB e representa 125,110 registros genômicos/proteômicos de oito contextos informativos, que foram pré-filtrados para torná-los acessíveis, utilizáveis e amigáveis com recursos computacionais limitados. Os usos potenciais desses bancos de dados esboçados são discutidos, incluindo, mas não se limitando, a delimitação de espécies microbianas, estimativa de distâncias genômicas e novidades genômicas, comparações emparelhadas entre proteomas, genomas e metagenomas, exploração e seleção filogenética de vizinhos, entre outros.

microRNAs (miRNAs) are recognized as diabetes mellitus type 2 (T2DM) biomarkers useful for disease metabolism comprehension and have great potential as therapeutics targets. BDNF and IGF1 increased expression are highly involved in the benefits of insulin and glucose paths, however, they are down-regulated in insulin resistance conditions, while their expression increase is correlated to the improvement of glucose and insulin metabolism. Studies suggest the microRNA regulation of these genes in several different contexts, providing a novel investigation approach for comprehending T2DM metabolism and revealing potential therapeutic targets. In the present study, we investigate in different animal models (human, rat, and mouse) miRNAs that target BDNF and IGF1 in skeletal muscle tissue with T2DM physiological conditions. Bioinformatics tools and databases were used to miRNA prediction, molecular homology, experimental validation of interactions, expression in the studied physiological condition, and network interaction. The findings showed three miRNAs candidates for IGF1(miR-29a, miR-29b, and miR-29c) and one for BDNF (miR-206). The experimental evaluations and the search for the expression in skeletal muscle from T2DM subjects confirmed the predicted interaction between miRNA-mRNA for miR-29b and miR-206 through human, rat, and mouse models. This interaction was reaffirmed in multiple network analyses. In conclusion, our results show the regulation relationship between miR-29b and miR-206 with the investigated genes, in several tissues, suggesting an inhibition pattern. Nevertheless, these data show a large number of possible interaction physiological processes, for future biotechnological prospects. Resumo Os microRNAs (miRNAs) são reconhecidos como biomarcadores do diabetes mellitus tipo 2 (DM2), úteis para a compreensão do metabolismo da doença, e possuem grande potencial como alvos terapêuticos. O aumento da expressão de BDNF e IGF1 está altamente envolvido nos benefícios as vias de insulina e glicose, porém, são regulados negativamente em condições de resistência à insulina, enquanto seu aumento de expressão está correlacionado com a melhora do metabolismo da glicose e da insulina. Estudos sugerem a regulação desses genes por microRNA em vários contextos diferentes, proporcionando uma nova abordagem de investigação para compreender o metabolismo do DM2 e revelar potenciais alvos terapêuticos. No presente estudo, investigamos em diferentes modelos animais (humanos, ratos e camundongos) miRNAs que têm como alvo BDNF e IGF1 em tecido muscular esquelético com condições fisiológicas de DM2. As análises foram realizadas utilizando ferramentas de bioinformática e bancos de dados para predição de miRNA, homologia molecular, validação experimental de interações, expressão na condição fisiológica estudada e interação em rede. Os resultados mostraram três candidatos a miRNAs para IGF1 (miR-29a, miR-29b e miR-29c) e um para BDNF (miR-206). As avaliações experimentais e a busca pela expressão no músculo esquelético de indivíduos com DM2 confirmaram a interação prevista entre miRNA-mRNA para miR-29b e miR-206 através de modelos humanos, ratos e camundongos. Essa interação foi reafirmada em múltiplas análises de rede. Em conclusão, nossos resultados mostram a relação de regulação entre miR-29b e miR-206 com os genes investigados, em diversos tecidos, sugerindo um padrão de inibição. Contudo, esses dados mostram um grande número de possíveis processos fisiológicos de interação para perspectivas biotecnológicas.

Phylogenetic methods are emerging as a useful tool to understand cancer evolutionary dynamics, including tumor structure, heterogeneity, and progression. Most currently used approaches utilize either bulk whole genome sequencing (WGS) or single-cell DNA sequencing (scDNA-seq) and are based on calling copy number alterations and single nucleotide variants (SNVs). Here we explore the potential of single-cell RNA sequencing (scRNA-seq) to reconstruct cancer evolutionary dynamics. scRNA-seq is commonly applied to explore differential gene expression of cancer cells throughout tumor progression. The method exacerbates the single-cell sequencing problem of low yield per cell with uneven expression levels. This accounts for low and uneven sequencing coverage and makes SNV detection and phylogenetic analysis challenging. In this paper, we demonstrate for the first time that scRNA-seq data contains sufficient evolutionary signal and can be utilized in phylogenetic analyses. We explore and compare results of such analyses based on both expression levels and SNVs called from our scRNA-seq data. Both techniques are shown to be useful for reconstructing phylogenetic relationships between cells, reflecting the clonal composition of a tumor. Without an explicit error model, standardized expression values appear to be more powerful and informative than the SNV values at a lower computational cost, due to being a by-product of standard expression analysis. Our results suggest that scRNA-seq can be a competitive alternative or useful addition to conventional scDNA-seq phylogenetic reconstruction. Our results open up a new direction of somatic phylogenetics based on scRNA-seq data. Further research is required to refine and improve these approaches to capture the full picture of somatic evolutionary dynamics in cancer.

Mammalian genomes have distinct levels of spatial organization and structure that have been hypothesized to play important roles in transcription regulation. Although much has been learned about these architectural features with ensemble techniques, single-cell studies are showing a new universal trend: Genomes are stochastic and dynamic at every level of organization. Stochastic gene expression, on the other hand, has been studied for years. In this review, we probe whether there is a causative link between the two phenomena. We specifically discuss the functionality of chromatin state, topologically associating domains (TADs), and enhancer biology in light of their stochastic nature and their specific roles in stochastic gene expression. We highlight persistent fundamental questions in this area of research.

The microbiome is hypothesized to have a significant impact on cancer development. In gastric cancer (GC), Helicobacter pylori is an established class I carcinogen. However, additional organisms in the intratumoral microbiome play an important role in GC pathogenesis and progression. In this study, we characterize the full spectrum of the microbes present within GC and identify distinctions among molecular subtypes.A microbiome bioinformatics pipeline that is generalizable across multiple next-generation sequencing platforms was developed. Microbial profiles for alpha diversity and enrichment were generated for 2 large, demographically distinct cohorts: (1) internal Memorial Sloan Kettering Cancer Center (MSKCC) and (2) The Cancer Genome Atlas (TCGA) cohorts. A total of 520 GC samples were compared with select tumor-adjacent nonmalignant samples. Microbiome differences among the GC molecular subtypes were identified.Compared with nonmalignant samples, GC had significantly decreased microbial diversity in both MSKCC and TCGA cohorts ( P0.05). Helicobacter , Lactobacillus , Streptococcus , Prevotella , and Bacteroides were significantly more enriched in GC samples when compared with nonmalignant tissue ( P0.05). Microsatellite instability-high GC had distinct microbial enrichment compared with other GC molecular subtypes.Distinct patterns of microbial diversity and species enrichment were identified in patients with GC. Given the varied spectrum of disease progression and treatment response of GC, understanding unique microbial signatures will provide the landscape to explore key microbial targets for therapy.

The maternal-to-zygotic transition (MZT) is a conserved embryonic process in animals where developmental control shifts from the maternal to zygotic genome. A key step in this transition is zygotic transcription, and deciphering the MZT requires classifying newly transcribed genes. However, due to current technological limitations, this starting point remains a challenge for studying many species. Here we present an alternative approach that characterizes transcriptome changes based solely on RNA-seq data. By combining intron-mapping reads and transcript-level quantification, we characterized transcriptome dynamics during the Drosophila melanogaster MZT. Our approach provides an accessible platform to investigate transcriptome dynamics that can be applied to the MZT in non-model organisms. In addition to classifying zygotically transcribed genes, our analysis revealed that over 300 genes express different maternal and zygotic transcript isoforms due to alternative splicing, polyadenylation, and promoter usage. The vast majority of these zygotic isoforms have the potential to be subject to different regulatory control, and over two-thirds encode different proteins. Thus, our analysis reveals an additional layer of regulation during the MZT, where new zygotic transcripts can generate additional proteome diversity.

Background: Bladder cancer (BC) is a heterogeneous disease with high recurrence rate. The depth of response to platinum-based chemotherapeutic agents in urothelial neoplasm tissues varies greatly. Biomarkers that have practical value in prognosis stratification and early determination of therapeutic effect are increasingly needed. Our study aimed to select a set of BC-related genes involved in both platinum resistance and survival, then use these genes to establish the prognostic model.Methods: Platinum resistance-related DEGs and tumorigenesis-related DEGs were identified. Among the intersection of them, 10 most predictive genes were acquired through Cox, lasso, and stepwise regressions followed by building a risk score model with their regression coefficients. Survival analysis and ROC plot were used to evaluate the predictive accuracy of our model. Combined with age and TMN stages, a nomogram was generated to create a graphical representation of survival rates at 1-, 3-, 5-, and 8-year in BC patients. The prognostic performance of risk score was also validated in three independent BC datasets including cisplatin-treated patients and a gastric cancer cohort with platinum-based chemotherapy. The potential mechanism of the gene-based model was explored by enrichment analysis.Results: PPP2R2B, TSPAN7, ATAD3C, SYT15, SAPCD1, AKR1B1, TCHH, AKAP12, AGLN3, and IGF2 were selected for our prognostic model. Patients in high- and low-risk groups exhibited a significant survival difference (HR = 2.7, p < 0.0001). The prognostic nomogram of predicting 3-year OS for BC patients could yield an AUC of 0.819. In the external validation dataset, the risk score also has a robust predictive ability. Conclusion: A prognostic model derived from platinum resistance-related genes was constructed, we confirmed its value in predicting platinum-based chemotherapy benefits and overall survival for BC patients. The model might assist in therapeutic decisions for bladder malignancy.Subjects: Urology, Oncology, Bioinformatics, Molecular Biology.

Quantitative trait locus (QTL) mapping is a useful method for revealing the mechanism of complex genetic traits and identifying new genomic information to accelerate crop improvement. In the present study, 154 F2:3 strains and their parents were used for restriction site-associated DNA sequencing, single-nucleotide polymorphism (SNP) identification, and genetic map construction. After filtering based on stringent filtering standards, 297.5 Gb of clean data were obtained. Further, 5,191 polymorphic SNP markers were identified from each sample, of which 1,997 polymorphic SNP markers were successfully mapped onto 18 different linkage groups. Six QTLs (QPH, QFBW, QDBW, QFW, QFT, and QFC) were identified based on the genetic map using the multiple QTL mapping (MQM) method, which were then assigned to three linkage groups, LG16, LG8, and LG3. QPH, QFBW, QDBW, and QFW were related to fiber yield, while QFT and QFC were related to fiber quality. This is the first study of its kind to map QTL of fiber yield and fiber quality, which will facilitate further understanding of the molecular genetic basis of these traits. However, there are limitations regarding the utilization of this map because several large gaps remain in some linkage groups. Therefore, additional markers need to be developed to further narrow these regions.

Background Detailed understanding of pre-, early and late neoplastic states in gastric cancer helps develop better models of risk of progression to gastric cancers (GCs) and medical treatment to intercept such progression. Methods We built a Boolean implication network of gastric cancer and deployed machine learning algorithms to develop predictive models of known pre-neoplastic states, e.g., atrophic gastritis, intestinal metaplasia (IM) and low- to high-grade intestinal neoplasia (L/HGIN), and GC. Our approach exploits the presence of asymmetric Boolean implication relationships that are likely to be invariant across almost all gastric cancer datasets. Invariant asymmetric Boolean implication relationships can decipher fundamental time-series underlying the biological data. Pursuing this method, we developed a healthy mucosa → GC continuum model based on this approach. Results Our model performed better against publicly available models for distinguishing healthy versus GC samples. Although not trained on IM and L/HGIN datasets, the model could identify the risk of progression to GC via the metaplasia → dysplasia → neoplasia cascade in patient samples. The model could rank all publicly available mouse models for their ability to best recapitulate the gene expression patterns during human GC initiation and progression. Conclusions A Boolean implication network enabled the identification of hitherto undefined continuum states during GC initiation. The developed model could now serve as a starting point for rationalizing candidate therapeutic targets to intercept GC progression.

Pangenomics is emerging as a powerful computational paradigm in bioinformatics. This field uses population-level genome reference structures, typically consisting of a sequence graph, to mitigate reference bias and facilitate analyses that were challenging with previous reference-based methods. In this work, we extend these methods into transcriptomics to analyze sequencing data using the pantranscriptome: a population-level transcriptomic reference. Our novel toolchain can construct spliced pangenome graphs, map RNA-seq data to these graphs, and perform haplotype-aware expression quantification of transcripts in a pantranscriptome. This workflow improves accuracy over state-of-the-art RNA-seq mapping methods, and it can efficiently quantify haplotype-specific transcript expression without needing to characterize a sample’s haplotypes beforehand.

Unsupervised graph-level representation learning has recently shown great potential in a variety of domains, ranging from bioinformatics to social networks. Plenty of graph contrastive learning methods have been proposed to generate discriminative graph-level representations recently. They typically design multiple types of graph augmentations and enforce a graph to have consistent representations under different views. However, these techniques mostly neglect the intrinsic hierarchical structure of the graph, resulting in a limited exploration of semantic information for graph representation. Moreover, they often rely on a large number of negative samples to prevent collapsing into trivial solutions, while a great need for negative samples may lead to memory issues during optimization in graph domains. To address the two issues, this paper develops an unsupervised graph-level representation learning framework named Hierarchical Graph Contrastive Learning (HGCL), which investigates the hierarchical structural semantics of a graph at both node and graph levels. Specifically, our HGCL consists of three parts, i.e., node-level contrastive learning, graph-level contrastive learning, and mutual contrastive learning to capture graph semantics hierarchically. Furthermore, the Siamese network and momentum update are further involved to release the demand for excessive negative samples. Finally, the experimental results on both benchmark datasets for graph classification and large-scale OGB datasets for transfer learning demonstrate that our proposed HGCL significantly outperforms a broad range of state-of-the-art baselines.

Influenza polymerase (FluPol) plays a key role in the viral infection cycle by transcribing and replicating the viral genome. FluPol is a multifunctional, heterotrimeric enzyme with cap-binding, endonuclease, RNA-dependent RNA polymerase and polyadenylation activities. It performs its functions in the context of the viral ribonucleoprotein particle (RNP), wherein the template viral RNA is coated by multiple copies of viral nucleoprotein. Moreover, it interacts with a number of host proteins that are essential cofactors and, consequently, adaptive mutations in the polymerase are required for crossing the avian-human species barrier. In this review, we show how mechanistic understanding of how FluPol performs its multiple functions has greatly advanced over the last decade through determination of high-resolution structures by X-ray crystallography and cryo-electron microscopy. These have revealed not only the detailed architecture of FluPol but highlighted the remarkably conformational flexibility that is inherent to its functioning as a dynamic RNA synthesis machine. Structural studies are also underpinning current attempts to develop next-generation anti-influenza drugs that directly target FluPol.

Artificial intelligence (AI) methods have been and are now being increasingly integrated in prediction software implemented in bioinformatics and its glycoscience branch known as glycoinformatics. AI techniques have evolved in the past decades, and their applications in glycoscience are not yet widespread. This limited use is partly explained by the peculiarities of glyco-data that are notoriously hard to produce and analyze. Nonetheless, as time goes, the accumulation of glycomics, glycoproteomics, and glycan-binding data has reached a point where even the most recent deep learning methods can provide predictors with good performance. We discuss the historical development of the application of various AI methods in the broader field of glycoinformatics. A particular focus is placed on shining a light on challenges in glyco-data handling, contextualized by lessons learnt from related disciplines. Ending on the discussion of state-of-the-art deep learning approaches in glycoinformatics, we also envision the future of glycoinformatics, including development that need to occur in order to truly unleash the capabilities of glycoscience in the systems biology era.

Most currently used conventional influenza vaccines are based on 1940s technology. Advances in vaccine immunogen design and delivery emerging over the last decade promise new options for improving influenza vaccines. In addition, new technologies for immune profiling provide better-defined immune correlates of protection and precise surrogate biomarkers for vaccine evaluations. Major technological advances include single-cell analysis, high-throughput antibody discovery, next-generation sequencing of antibody gene transcripts, antibody ontogeny, structure-guided immunogen design, nanoparticle display, delivery and formulation options, and better adjuvants. In this review, we provide our prospective outlook for improved influenza vaccines in the foreseeable future.

Competition in nature is traditionally conceptualised as a battle between individual organisms in the constant struggle for survival. The triumph of one organism results in the demise of the other. Cooperation on the other hand involves individuals acting in a mutually beneficial manner and is therefore often regarded as the opposite of competition. From the perspective of individual cells, multicellular life is perhaps the most extreme form of cooperation. However, even within multicellular organisms, cells compete for space and survival. Genetically damaged “loser” cells that grow at a slower rate than their neighbouring “winner” cells are eliminated from the body by apoptosis. Importantly, cell competition is context-dependent; loser cells are perfectly viable if the whole organism is composed of them. The demise of loser cells is triggered specifically by the presence of cells that are perceived to be more fit. Multiple triggers and pathways involved with cell competition have been discovered, but the mechanism by which cells measure and communicate their relative fitness remains elusive. Current models of cell competition assert a priori winner or loser status to competing cell types. By their nature, such models cannot explain how the winner or loser identity is attained. In this thesis, we develop a modelling framework to study the emergence of winners and losers in cell competition. Specifically, we construct models of heterotypic populations where cell types can only vary in their model parameters. Using this approach, we show that i) variations in mechanical parameters are not sufficient for cell competition in a vertex-based model, and ii) winner or loser status in a competition system based on the exchange of death signals is determined by the emission rate of death signals and the tolerance to death signals. Finally, we make concrete suggestions for the experimental validation of our predictions.

Bi- and multispecific antibodies are designated as the next generation of antibody-based therapy, not least because of their wide therapeutical applications, in particular in the field of immuno-oncology. Here, we present the Db-Ig platform enabling the generation of a variety of IgG-like molecules with varying valency and specificity with great potentials to generate therapeutic antibodies., Projekt DEAL

TheISCB Student Counciland its Regional Student Groups organizedthe 19th Asian Student Council Symposium (#SCS2023),directly preceding the31st Annual Intelligent Systems For Molecular Biology and the 22nd Annual European Conference on Computational Biology (#ISMBECCB2023). The Student Council Symposium is a forum forstudents and young researchers in Computational Biology and Bioinformatics. Participants hadthe opportunity to present their work to an international audience, build a network within the computational biology community and develop important soft skills in an environment that fosters the exchange of ideas and knowledge.

Antibodies are highly useful therapeutics but their development can be costly and resource intensive. As increasing quantities of high quality B cell receptor (BCR) data become available, the challenge of how to best utilise the data to improve the antibody development process remains. In the results chapters of this thesis, we begin by describing our novel approach to numbering antibody sequences, ANARCII. Our model, trained using deep learning-based sequence embeddings derived from BCR data, can correctly number antibody sequences that existing tools cannot. Next, we describe our development of existing antibody comparison tools Ab-Ligity and paratyping, to create computational pipelines for identification of antibodies likely to bind to a target antigen, with or without a known binding antibody. We compare antibodies based on sequence, paratope and structural similarity to predict similarlybinding antibodies from an antibody model library. We expand on the approach of identifying structurally similar, functionally similar, antibodies in our final results chapter where we describe our novel computational epitope binning method, SPACE. Our method of clustering antibody models based on predicted structural similarity can group sequence-dissimilar antibodies into epitope bins with high accuracy, as demonstrated on coronavirus-binding and malaria-binding antibodies. We conclude with potential future research directions, including how latest developments in antibody structure prediction and protein language models might be incorporated into our work.

Effectively presenting epitopes on immunogens, in order to raise conformationally selective antibodies through active immunization, is a central problem in treating protein misfolding diseases, particularly neurodegenerative diseases such as Alzheimer’s disease or Parkinson’s disease. We seek to selectively target conformations enriched in toxic, oligomeric propagating species while sparing the healthy forms of the protein that are often more abundant. To this end, we computationally modelled scaffolded epitopes in cyclic peptides by inserting/deleting a variable number of flanking glycines (“glycindels”), to best mimic a misfolding-specific conformation of an epitope of α-synuclein enriched in the oligomer ensemble, as characterized by a region most readily disordered and solvent-exposed in a stressed, partially denatured protofibril. We screen and rank the cyclic peptide scaffolds of α-synuclein in silico based on their ensemble overlap properties with the fibril, oligomer-model, and isolated monomer ensembles. We present experimental data of seeded aggregation that supports nucleation rates consistent with computationally predicted cyclic peptide conformational similarity. We also introduce a method for screening against structured off-pathway targets in the human proteome, by selecting scaffolds with minimal conformational similarity between their epitope and the same solvent-exposed primary sequence in structured human proteins. Different cyclic peptide scaffolds with variable numbers of glycines are predicted computationally to have markedly different conformational ensembles. Ensemble comparison and overlap was quantified by the Jensen-Shannon Divergence, and a new measure introduced here—the embedding depth, which determines the extent to which a given ensemble is subsumed by another ensemble, and which may be a more useful measure in developing immunogens that confer conformational-selectivity to an antibody.Graphical TOC Entry

Estimating the accuracy of protein tertiary structural models is a critical task in protein bioinformatics. The need for robust methods in estimation of protein model accuracy (EMA) is prevalent in the field of protein structure prediction, where computationally-predicted structures need to be screened rapidly for the reliability of the positions predicted for each of their amino acid residues and their overall quality. Current methods proposed for EMA are either coupled tightly to existing protein structure prediction methods or, albeit as a standalone method, evaluate protein structures without sufficiently leveraging the rich, geometric information available in such structures to guide accuracy estimation. In this work, we propose a geometric message passing neural network referred to as the Geometry-Complete Perceptron Network (GCPNet) for protein structure EMA, where we demonstrate through rigorous computational benchmarks that GCPNet's accuracy estimations are more than 8% more correlated with ground-truth measures of residue structural accuracy compared to baseline state-of-the-art methods for EMA including AlphaFold 2. The source code, data, and instructions to train GCPNet, reproduce our results, or quickly estimate the accuracy of predicted protein structures are freely available on GitHub., The data contained within this repository includes the standardized (i.e., sequence/structure-nonredundant) training, validation, and test decoy (and ground-truth) PDB structures for the task of estimating a protein structural model's accuracy (EMA), following EnQA by Chen et al. 2023 (https://academic.oup.com/bioinformatics/article/39/1/btad030/6986970).

Chromatin accessibility assays enable base pair resolution measurements of the chromatin landscape and facilitate inferences about the nature of regulatory state. These assays determine which genomic regions are actively regulated by quantifying enzymatic digestion of DNA in a given region. However, the enzymes which conduct this digestion are biased with respect to their propensity to cleave specific sequences. Enzymatic sequence bias can introduce artifacts into data, leading to misinterpretation of downstream analysis. Previous work to address this bias relied on calculating enzymatic bias for specific k-mers and correcting these values to their genomic frequency, known as k-mer scaling. K-mer scaling was successful in correcting nuclease bias, but not the bias of Tn5 transposase, the enzyme used in ATAC-seq. This dissertation illustrates that the breadth and complexity of Tn5 bias hinders the use of k-mer scaling for sequence bias correction. Comparison of Tn5 bias with nucleases highlights why k-mer scaling is ineffective: Tn5 sequence bias is based on a region greater than 20 bp. K-mer scaling can only be applied to k-mers which have many instances in the data set and genome, an impossibility with k-mer sizes greater than 9, due to the number of reads contained in most ATAC-seq experimental data sets. To model this large bias window, we used a machine learning approach, rule ensemble, which integrates information from many input k-mers into a computational bias correction. We created a workflow using this approach, seqOutATACBias, in order to promote bias correction in other studies. We applied seqOutATACBias to naked DNA and found that it effectively diminishes both local sequence bias in addition to correcting a previously unreported Tn5 regional bias for high GC content. Correction of enzymatic sequence bias is of utmost importance for determining ground truth of chromatin accessibility assays.

(1) Background: Next-generation sequencing (NGS) of patients with advanced tumors is becoming an established method in Molecular Tumor Boards. However, somatic variant detection, interpretation, and report generation, require in-depth knowledge of both bioinformatics and oncology. (2) Methods: MIRACUM-Pipe combines many individual tools into a seamless workflow for comprehensive analyses and annotation of NGS data including quality control, alignment, variant calling, copy number variation estimation, evaluation of complex biomarkers, and RNA fusion detection. (3) Results: MIRACUM-Pipe offers an easy-to-use, one-prompt standardized solution to analyze NGS data, including quality control, variant calling, copy number estimation, annotation, visualization, and report generation. (4) Conclusions: MIRACUM-Pipe, a versatile pipeline for NGS, can be customized according to bioinformatics and clinical needs and to support clinical decision-making with visual processing and interactive reporting.

Immediately following the discovery of the structure of DNA and the semi-conservative replication of the parental DNA sequence into two new DNA strands, it became apparent that DNA replication is organized in a temporal and spatial fashion during the S phase of the cell cycle, correlated with the large-scale organization of chromatin in the nucleus. After many decades of limited progress, technological advances in genomics, genome engineering, and imaging have finally positioned the field to tackle mechanisms underpinning the temporal and spatial regulation of DNA replication and the causal relationships between DNA replication and other features of large-scale chromosome structure and function. In this review, we discuss these major recent discoveries as well as expectations for the coming decade.

Due to the development of next-generation RNA sequencing (NGS) technologies, there has been tremendous progress in research involving determining the role of genomics, transcriptomics and epigenomics in complex biological systems. However, scientists have realized that information obtained using earlier technology, frequently called ‘bulk RNA-seq’ data, provides information averaged across all the cells present in a tissue. Relatively newly developed single cell (scRNA-seq) technology allows us to provide transcriptomic information at a single-cell resolution. Nevertheless, these high-resolution data have their own complex natures and demand novel statistical data analysis methods to provide effective and highly accurate results on complex biological systems. In this review, we cover many such recently developed statistical methods for researchers wanting to pursue scRNA-seq statistical and computational research as well as scientific research about these existing methods and free software tools available for their generated data. This review is certainly not exhaustive due to page limitations. We have tried to cover the popular methods starting from quality control to the downstream analysis of finding differentially expressed genes and concluding with a brief description of network analysis.

Germline mutation rates are essential for genetic and evolutionary analyses. Yet, estimating accurate fine-scale mutation rates across the genome is a great challenge, due to relatively few observed mutations and intricate relationships between predictors and mutation rates. Here we present MuRaL (Mutation Rate Learner), a deep learning framework to predict mutation rates at the nucleotide level using only genomic sequences as input. Harnessing human germline variants for comprehensive assessment, we show that MuRaL achieves better predictive performance than current state-of-the-art methods. Moreover, MuRaL can build models with relatively few training mutations and a moderate number of sequenced individuals, and can leverage transfer learning to further reduce data and time demands. We apply MuRaL to produce genome-wide mutation rate maps for four representative species - Homo sapiens, Macaca mulatta, Arabidopsis thaliana and Drosophila melanogaster, demonstrating its high applicability. As an example, we use improved mutation rate estimates to stratify human genes into distinct groups which are enriched for different functions, and highlight that many developmental genes are subject to high mutational burden. The open-source software and generated mutation rate maps can greatly facilitate related research.

Detection of insertions and deletions (InDels) by short-read next-generation sequencing (NGS) technology can be challenging because of frequent misaligned reads. A systematic analysis of short InDels (1 to 30 bases) and fms-related receptor tyrosine kinase 3 (FLT3) internal tandem duplications (ITDs; 6 to 183 bases) from 46 clinical cases of solid or hematologic malignancy processed with a clinical NGS assay identified misaligned reads in every case, ranging from 3% to 100% of reads with the InDel showing mismapped bases. Mismaps also increased with InDel size. As a consequence, the clinical NGS bioinformatics pipeline undercalled the variant allele frequency by 1% to 84%, incorrectly called simultaneous single-base substitutions along with InDels, or did not report an FLT3 ITD that had been detected by capillary electrophoresis. To improve the ability of the pipeline to better detect and quantify InDels, we utilized a software program called Assembly-Based ReAligner (ABRA2) to more accurately remap reads. ABRA2 was able to correct 41% to 100% of the reads with mismapped bases and led to absolute increases in the variant allele frequency from 1% to 61% along with correction of all of the single-base substitutions except for two cases. ABRA2 could also detect multiple FLT3 ITD clones except for one 183-base ITD. Our analysis has found that ABRA2 performs well on short InDels as well as FLT3 ITDs that are100 bases.

Identifying drug-target interactions has been a key step in drug discovery. Many computational methods have been proposed to directly determine whether drugs and targets can interact or not. Drug-target binding affinity is another type of data which could show the strength of the binding interaction between a drug and a target. However, it is more challenging to predict drug-target binding affinity, and thus a very few studies follow this line. In our work, we propose a novel co-regularized variational autoencoders (Co-VAE) to predict drug-target binding affinity based on drug structures and target sequences. The Co-VAE model consists of two VAEs for generating drug SMILES strings and target sequences, respectively, and a co-regularization part for generating the binding affinities. We theoretically prove that the Co-VAE model is to maximize the lower bound of the joint likelihood of drug, protein and their affinity. The Co-VAE could predict drug-target affinity and generate new drugs which share similar targets with the input drugs. The experimental results on two datasets show that the Co-VAE could predict drug-target affinity better than existing affinity prediction methods such as DeepDTA and DeepAffinity, and could generate more new valid drugs than existing methods such as GAN and VAE.

The increasing availability of high-throughput datasets allows amalgamating research information across a large body of genome regulation studies. Given the recent success of meta-analyses on transcriptional regulators, epigenetic marks, and enhancer:gene associations, we expect that such surveys will continue to provide novel and reproducible insights. However, meta-analyses are severely hampered by the diversity of available data, concurring protocols, an eclectic amount of bioinformatics tools, and myriads of conceivable parameter combinations. Such factors can easily bar life scientists from synthesizing omics data and substantially curb their interpretability. Despite statistical challenges of the method, we would like to emphasize the advantages of joining data from different sources through vote-counting and showcase examples that achieve a simple but highly intuitive data integration.

The adaptor proteins play a crucially important role in regulating lymphocyte activation. Rapid and efficient identification of adaptor proteins is essential for understanding their functions. However, biochemical methods require not only expensive experimental costs, but also long experiment cycles and more personnel. Therefore, a computational method that could accurately identify adaptor proteins is urgently needed. To solve this issue, we developed a classifier that combined the support vector machine (SVM) with the composition of k-Spaced Amino Acid Pairs (CKSAAP) and the amino acid composition (AAC) to identify adaptor proteins. Analysis of variance (ANOVA) was used to select the optimized features which could generate the maximum prediction performance. By examining the proposed model on independent data, we found that the 447 optimized features could achieve an accuracy of 92.39% with an AUC of 0.9766, demonstrating the powerful capabilities of our model. We hope that the proposed model could provide more clues for studying adaptor proteins.

SUMMARYCohesin-mediated loop extrusion folds interphase chromosomes at the ten to hundreds kilobases scale. This process produces structural features such as loops and topologically associating domains. We identify three types of cis-elements that define the chromatin folding landscape generated by loop extrusion. First, CTCF sites form boundaries by stalling extruding cohesin, as shown before. Second, transcription termination sites form boundaries by acting as cohesin unloading sites. RNA polymerase II contributes to boundary formation at transcription termination sites. Third, transcription start sites form boundaries that are mostly independent of cohesin, but are sites where cohesin can pause. Together with cohesin loading at enhancers, and possibly other cis-elements, these loci create a dynamic pattern of cohesin traffic along the genome that guides enhancer-promoter interactions. Disturbing this traffic pattern, by removing CTCF barriers, renders cells sensitive to knock-out of genes involved in transcription initiation, such as the SAGA and TFIID complexes, and RNA processing such DEAD-Box RNA helicases. In the absence of CTCF, several of these factors fail to be efficiently recruited to active promoters. We propose that the complex pattern of cohesin movement along chromatin contributes to appropriate promoter-enhancer interactions and localization of transcription and RNA processing factors to active genes.HIGHLIGHTSAt least three types of chromatin boundaries regulate a cohesin traffic pattern.The cohesin traffic pattern guides enhancer-promoter interactions.Removing CTCF renders cells sensitive to deletion of RNA processing and gene regulation genes.Depleting CTCF affects localization of RNA processing and gene regulatory proteins.

Background: Formalin-fixed, paraffin-embedded (FFPE) tissues are a valuable resource for clinical and basic science research. Paraffin blocks and the resulting unstained sections (USS) are often st...

The hydrophobic force is one of the most dominant factors in protein folding. A protein becomes functional only when it achieves its three-dimensional structure and stability upon folding. For a better understanding of the hydrophobic effects and their function in protein folding, quantitative measurement of the hydrophobicity of amino acid side chains is crucial. Spike protein is the primary structural protein in SARS-CoV-2 and SARS-CoV. This study explores how protein sequences in SARS-CoV-2 and SARS-CoV spike proteins encode hydrophobic interactions. Computational tools/techniques have been utilized to investigate the protein sequences of the spike proteins of SARS-CoV-2 and SARS-CoV. Investigations provided an estimate of hydrophobic distribution and its relative strength, indicating a hydrophobic pattern. Analysis of the spike protein's hydrophobic profile may help identify and treat the virus-caused disease; additionally, it can give an insight into the transmissibility and pathogenicity of the virus.

The severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), also referred to as COVID-19, has spread to several countries and caused a serious threat to human health worldwide. Patients with confirmed COVID-19 infection spread the disease rapidly throughout the region. Multiple myeloma (MM) and diffuse large B-cell lymphoma (DLBCL) are risk factors for COVID-19, although the molecular mechanisms underlying the relationship among MM, DLBCL, and COVID-19 have not been elucidated so far. In this context, transcriptome analysis was performed in the present study to identify the shared pathways and molecular indicators of MM, DLBCL, and COVID-19, which benefited the overall understanding of the effect of COVID-19 in patients with MM and DLBCL. Three datasets (GSE16558, GSE56315, and GSE152418) were downloaded from the Gene Expression Omnibus (GEO) and searched for the shared differentially expressed genes (DEGs) in patients with MM and DLBCL who were infected with SARS-CoV-2. The objective was to detect similar pathways and prospective medicines. A total of 29 DEGs that were common across these three datasets were selected. A protein-protein interaction (PPI) network was constructed using data from the STRING database followed by the identification of hub genes. In addition, the association of MM and DLBCL with COVID-19 infection was analyzed through functional analysis using ontologies terms and pathway analysis. Three relationships were observed in the evaluated datasets: transcription factor-gene interactions, protein-drug interactions, and an integrated regulatory network of DEGs and miRNAs with mutual DEGs. The findings of the present study revealed potential pharmaceuticals that could be beneficial in the treatment of COVID-19.

Background. The prognostic value of tumor-infiltrating immune cells has been widely studied in nasopharyngeal carcinoma (NPC). However, the role of tumor-infiltrating immune cells in the diagnosis of NPC has not been fully elucidated. Thus, tumor-infiltrating immune cell-related biomarkers in the diagnosis of NPC patients were explored in the current study. Methods. Gene expression profiles of NPC patients were downloaded from the Gene Expression Omnibus (GEO) database. Differentially infiltrating immune cells (DDICs) between NPC and control samples were analyzed by the CIBERSORT algorithm. Weighted gene coexpression network analysis (WGCNA) was performed to screen hub genes significantly correlated with DDIC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of hub genes were performed with R package clusterProfiler. The diagnostic value of hub genes was evaluated by receiver operating characteristic (ROC) curves. RT-qPCR was conducted to validate the expression patterns of diagnostic markers in NPC and adjacent control tissues. The correlations between diagnostic markers and immunomodulators were analyzed using the TISIDB. The protein-protein interaction (PPI) network based on immunomodulators significantly associated with diagnostic biomarkers was constructed and visualized by STRING. The functional enrichment analysis of genes in the PPI network was analyzed by the WebGestalt online tool. Results. The abundances of memory B cells, plasma cells, follicular helper T cells, activated NK cells, M0 macrophages, M1 macrophages, M2 macrophages, resting mast cells, and activated mast cells were significantly different between NPC and control samples. Dark orange was identified as the hub module, with a total of 371 genes associated with memory B cells, plasma cells, and M0 and M1 macrophages defined as hub genes, which were enriched into immune-related biological processes and pathways. FCER2, KHDRBS2, and IGSF9 were considered diagnostic biomarkers with areas under ROC curves as 0.985, 0.978, and 0.975, respectively. Moreover, real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) suggested that the expression patterns of FCER2, KHDRBS2, and IGSF9 were consistent with the results in GEO datasets. TISIDB analysis revealed that FCER2, KHDRBS2, and IGSF9 had a strong association with 8 immunoinhibitors (BTLA, CD160, CD96, LAG3, PDCD1, TIGIT, CD244, and TGFB1) and 11 immunostimulators (CD27, CD28, CD40LG, CD48, ICOS, KLRC1, KLRK1, TMIGD2, TNFRSF13C, CXCR4, and C10 or f54). The PPI network implied that these 19 immunomodulators had interactions with other 50 genes. WebGestalt analysis demonstrated that 69 genes in the PPI network were enriched into cytokine-cytokine receptor interaction, NF-kappa B signaling pathway, and pathways in cancer. Conclusion. Our study identified novel diagnostic biomarkers and revealed potential immune-related mechanisms in NPC. These findings enlighten the investigation of the molecular mechanisms of tumor-infiltrating immune cells regulating NPC.

Following the 2011 Arab Spring, autocrats have sought to limit citizens’ ability to publicize offline protests over social media. In this article, we explore how users adjust to these restrictions. To do so, we analyse 33 million tweets sent from Egypt during the “Day of Anger” protests in September 2020. We find evidence of online tactical evasion in a highly repressive context. Compared to neutral users, regime opponents are more likely to issue calls for offline protests using new or dedicated accounts that contain no personal information. Users are also more likely to delete tweets calling for mobilization ex-post in a bid to conceal their activism. We find weaker evidence suggesting that regime opponents try to evade laws targeting critical accounts with over 5000 followers. The findings illustrate how activists in autocracies use social media to mobilize street-level contention while attempting to mitigate the risk of state repression.

Object. Osteosarcoma is an intractable malignant disease, and few therapeutic methods can thoroughly eradicate its focuses. This study attempted to investigate the related mechanism of osteosarcoma by bioinformatics methods. Methods. GSE70367 and GSE69470 were obtained from the GEO database. The differentially expressed genes (DEGs) and miRNAs were analyzed using the GEO2R tool and then visualized with R software. Moreover, the targets of the miRNAs in the DEGs were screened and then used for enrichment analysis. Besides, the STRING database and Cytoscape were applied to illustrate the protein-protein interaction network. RT-qPCR was performed to measure the expression of key genes and miRNAs. Western blot was applied to detect the signaling pathway. Results. 9 upregulated genes and 39 downregulated genes in GSE69470 were identified as the DEGs, and 31 upregulated genes and 56 downregulated genes in GSE70367 were identified as the DEGs. Moreover, 21 common genes were found in the DEGs of GSE70367 and GSE69470. The enrichment analysis showed that the common DEGs of GSE70367 and GSE69470 were related with cell development, covalent chromatin modification, and histone modification and involve in the regulation of MAPK, mTOR, and AMPK pathways. Besides, the miRNAs including miR-543, miR-495-3p, miR-433-3p, miR-381-3p, miR-301a-3p, miR-199b-5p, and miR-125b-5p were identified as the biomarkers of osteosarcoma. In addition, the target genes including HSPA5, PPARG, MAPK14, RAB11A, RAB5A, MAPK8, LEF1, HIF1A, CAV1, GS3KB, FOXO3, IGF1, and NFKBIA were identified as hub nodes. It was found that miR-301a-3p expression was decreased and mRNA expression of RAB5A and NFKBIA was increased in the pathological tissues. The AKT-PI3K-mTOR signaling pathway was activated in pathological tissues. Conclusion. In this study, 7 miRNAs and 13 hub genes were identified, which might be candidate markers. miR-301a-3p, RAB5A, and NFKBIA were abnormally expressed in osteosarcoma tissues.

Objective. This study is aimed at screening out effective active compounds of Qizhen capsule (QZC) and exploring the underlying mechanisms against gastric cancer (GACA) by combining both bioinformatic analysis and experimental approaches. Weighted gene coexpression network analysis (WGCNA), network pharmacology, molecular docking simulation, survival analysis, and data-based differential gene and protein expression analysis were employed to predict QZC’s potential targets and explore the underlying mechanisms. Subsequently, multiple experiments, including cell viability, apoptosis, and protein expression analyses, were conducted to validate the bioinformatics-predicted therapeutic targets. The results indicated that luteolin, rutin, quercetin, and kaempferol were vital active compounds, and TP53, MAPK1, and AKT1 were key targets. Molecular docking simulation showed that the four abovementioned active compounds had high binding affinities to the three main targets. Enrichment analysis showed that vital active compounds exerted therapeutic effects on GACA through regulating the TP53 pathway, MAPK pathway, and PI3K/AKT pathway. Furthermore, data-based gene expression analysis revealed that TP53 and JUN genes were not only differentially expressed between normal and GACA tissues but also correlated with clinical stages. In parallel, in vitro experimental results suggested that QZC exerted therapeutic effects on GACA by decreasing IC50 values, downregulating AKT expression, upregulating TP53 and MAPK expression, and increasing apoptosis of SGC-7901 cells. This study highlights the potential candidate biomarkers, therapeutic targets, and basic mechanisms of QZC in treating GACA, providing a foundation for new drug development, target mining, and related animal studies in GACA.

Background. Severe asthma (SA), a heterogeneous inflammatory disease characterized by immune cell infiltration, is particularly difficult to treat and manage. The airway epithelium is an important tissue in regulating innate and adaptive immunity, and targeting airway epithelial cell may contribute to improving the efficacy of asthma therapy. Methods. Bioinformatics methods were utilized to identify the hub genes and signaling pathways involved in SA. Experiments were performed to determine whether these hub genes and signaling pathways were affected by the differences in immune cell infiltration. Results. The weighted gene coexpression network analysis identified 14 coexpression modules, among which the blue and salmon modules exhibited the strongest associations with SA. The blue module was mainly enriched in actomyosin structure organization and was associated with regulating stem cell pluripotency signaling pathways. The salmon module was mainly involved in cornification, skin development, and glycosphingolipid biosynthesis-lacto and neolacto series. The protein-protein interaction network and module analysis identified 11 hub genes in the key modules. The CIBERSORTx algorithm revealed statistically significant differences in CD8+ T cells ( P = 0.013 ), T follicular helper cells ( P = 0.002 ), resting mast cells ( P = 0.004 ), and neutrophils ( P = 0.002 ) between patients with SA and mild-moderate asthma patients. Pearson’s correlation analysis identified 11 genes that were significantly associated with a variety of immune cells. We further predicted the utility of some potential drugs and validated our results in external datasets. Conclusion. Our results may help provide a better understanding of the relationship between the airway epithelial transcriptome and clinical data of SA. And this study will help to guide the development of SA-targeted molecular therapy.

The role of human microbiota in cancer initiation and progression is recognized in recent years. In order to investigate the interactions between cancer cells and microbes, a systematic analysis using various emerging techniques is required. Owing to the label-free, non-invasive and molecular fingerprinting characteristics, vibrational spectroscopy is uniquely suited to decode and understand the relationship and interactions between cancer and the microbiota at the molecular level. In this review, we first provide a quick overview of the fundamentals of vibrational spectroscopic techniques, namely Raman and infrared spectroscopy. Next, we discuss the emerging evidence underscoring utilities of these spectroscopic techniques to study cancer or microbes separately, and share our perspective on how vibrational spectroscopy can be employed at the intersection of the two fields. Finally, we envision the potential opportunities in exploiting vibrational spectroscopy not only in basic cancer-microbiome research but also in its clinical translation, and discuss the challenges in the bench to bedside translation.

Background: MicroRNAs (miRNAs) are about 22-nucleotide, small, noncoding RNAs that control gene expression post-transcriptionally. Helminth parasites usually express a unique repertoire of genes, including miRNAs, across different developmental stages with subtle regulatory mechanisms. Objective: There is a necessity to investigate the involvement of miRNAs in the development of parasites, host-parasite interaction, immune evasion and their abilities to govern infection in hosts. MiRNAs present in helminth parasites have been summarized in the current systematic review (SR). Methods: Electronic databases, including PubMed, Scopus, ProQuest, Embase, and Google Scholar search engine, were searched to identify helminth miRNA studies published from February 1993 till December 2019. Only the published articles in English were included in the study. Results: A total of 1769 articles were preliminarily recorded. Following the strict inclusion and exclusion criteria, 105 studies were included in this SR. Most of these studies focused on the identification of miRNAs in helminth parasites and/or probing of differentially expressed host miRNA profiles in specific relevant tissues, while 12 studies aimed to detect parasite-derived miRNAs in host circulating system and 15 studies characterized extracellular vesicles (EV)-derived miRNAs secreted by parasites. Conclusion: In the current SR, information regarding all miRNAs expressed in helminth parasites has been comprehensively provided and the utility of helminth parasitesderived miRNAs in diagnosis and control of parasitic infections has been discussed. Furthermore, functional studies on helminth-derived miRNAs have also been presented.

The development of microbial products for cancer treatment has been in the spotlight in recent years. In order to accelerate the lengthy and expensive drug development process, in silico screening tools are systematically employed, especially during the initial discovery phase. Moreover, considering the steadily increasing number of molecules approved by authorities for commercial use, there is a demand for faster methods to repurpose such drugs. Here we present a review on virtual screening web tools, such as publicly available databases of molecular targets and libraries of ligands, with the aim to facilitate the discovery of potential anticancer drugs based on microbial products. We provide an entry-level step-by-step description of the workflow for virtual screening of microbial metabolites with known protein targets, as well as two practical examples using freely available web tools. The first case presents a virtual screening study of drugs developed from microbial products using Caver Web, a web tool that performs docking along a tunnel. The second case comprises a comparative analysis between a wild type isocitrate dehydrogenase 1 and a mutant that results in cancer, using the recently developed web tool PredictSNPOnco. In summary, this review provides the basic and essential background information necessary for virtual screening experiments, which may accelerate the discovery of novel anticancer drugs.

Drug discovery is a costly and time-consuming process, and most drugs exert therapeutic efficacy by targeting specific proteins. However, there are a large number of proteins that are not targeted by any drug. Recently, miRNA-based therapeutics are becoming increasingly important, since miRNA can regulate the expressions of specific genes and affect a variety of human diseases. Therefore, it is of great significance to study the associations between miRNAs and drugs to enable drug discovery and disease treatment. In this work, we propose a novel method named DMR-PEG, which facilitates drug-miRNA resistance association (DMRA) prediction by leveraging positional encoding graph neural network with layer attention (LAPEG) and multi-channel neural network (MNN). LAPEG considers both the potential information in the miRNA-drug resistance heterogeneous network and the specific characteristics of entities (i.e., drugs and miRNAs) to learn favorable representations of drugs and miRNAs. And MNN models various sophisticated relations and synthesizes the predictions from different perspectives effectively. In the comprehensive experiments, DMR-PEG achieves the area under the precision-recall curve (AUPR) score of 0.2793 and the area under the receiver-operating characteristic curve (AUC) score of 0.9475, which outperforms the most state-of-the-art methods. Further experimental results show that our proposed method has good robustness and stability. The ablation study demonstrates each component in DMR-PEG is essential for drug-miRNA drug resistance association prediction. And real-world case study presents that DMR-PEG is promising for DMRA inference.

Altered expression and localization of the glutamate transporter EAAT2 is found in schizophrenia and other neuropsychiatric (major depression, MDD) and neurological disorders (amyotrophic lateral sclerosis, ALS). However, the EAAT2 interactome, the network of proteins that physically or functionally interact with EAAT2 to support its activity, has yet to be characterized in severe mental illness. We compiled a list of "core" EAAT2 interacting proteins. Using Kaleidoscope, an R-shiny application, we data mined publically available postmortem transcriptome datasets to determine whether components of the EAAT2 interactome are differentially expressed in schizophrenia and, using Reactome, identify which interactome-associated biological pathways are altered. Overall, these "look up" studies highlight region-specific, primarily frontal cortex (dorsolateral prefrontal cortex and anterior cingulate cortex), changes in the EAAT2 interactome and implicate altered metabolism pathways in schizophrenia. Pathway analyses also suggest that perturbation of components of the EAAT2 interactome in animal models of antipsychotic administration impact metabolism. Similar changes in metabolism pathways are seen in ALS, in addition to altered expression of many components of the EAAT2 interactome. However, although EAAT2 expression is altered in a postmortem MDD dataset, few other components of the EAAT2 interactome are changed. Thus, "look up" studies suggest region- and disease-relevant biological pathways related to the EAAT2 interactome that implicate glutamate reuptake perturbations in schizophrenia, while providing a useful tool to exploit "omics" datasets.

Recent advances have led to the emergence of highly comprehensive and analytical approaches, such as omics analysis and high-resolution, time-resolved bioimaging analysis. These technologies have made it possible to obtain vast data from a single measurement. Subsequently, large datasets have pioneered the data-driven approach, an alternative to the traditional hypothesis-testing system, for researchers. However, processing, interpreting, and elucidating enormous datasets is no longer possible without computation. Bioinformatics is a field that has developed over long periods, intending to understand biological phenomena using methods collected from information science and statistics, thus solving this proposed research challenge. This review presents the latest methodologies and applications in sequencing, imaging, and mass spectrometry that were developed using bioinformatics. We presented the features of individual techniques and outlines in each part, avoiding the use of complex algorithms and formulas to allow beginning researchers to understand an overview. In the section on sequencing, we focused on comparative genomic, transcriptomic, and bacterial microbiome analyses, which are frequently used as applications of next-generation sequencing. Bioinformatic methods for handling sequence data and case studies were described. In the section on imaging, we introduced the analytical methods and microscopy imaging informatics techniques used in animal cell biology and plant physiology. We introduce informatics technologies for maximizing the value of measured data, including predicting the structure of unknown molecules and untargeted analysis in the section on mass spectrometry. Finally, we discuss the future outlook of this field. We anticipate that this review will assist biologists in using bioinformatics more effectively.

By extracting molecular interactions identified by experiments, gene regulatory networks or gene circuits have documented in a large number of knowledge-based repositories. They provide systematic information and guidance of the functional connections between regulators, e.g., transcription factor proteins and miRNAs, and target genes. Network activity is defined as the degree of consistency between a regulatory network architecture and a specific cellular context of gene expression and can also be measured as a score of statistical significance. The gene network activities are closely related to the dynamics of cell states. To evaluate the activity of regulatory events in the form of network, we propose a network activity evaluation (NAE) framework by measuring the consistency between network architecture and gene expression data across specific states based on mathematical programming. NAE firstly employs the dynamic Bayesian network model to formulate the network structure with time series profiling data. For the constraints of prior knowledge about gene regulatory network, NAE introduces an interpretable general loss function with regularization penalties to calculate the degree of consistency between gene network and gene expression data. Moreover, we design a fast and convergent alternating direction method of multipliers algorithm to optimize the regularized constraint programming. The efficiency and advantage of the NAE framework is deduced through numerous experiments and comparison studies. It reflects the possibility and potential of the match between network and data, thereby helping to reveal the network activity and to explain the dynamic responds underlying the network structure caused by changes in molecular environment of living cells. The code of NAE is freely available for academic use (https://github.com/zpliulab/NAE).