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Background: Cholangiocarcinoma (CCA) is a malignant epithelial tumor characterized by a dismal prognosis. Given the lack of therapeutic strategies and durable treatment options currently available, identifying innovative treatments for CCA is an urgent unmet clinical need. Cucurbitacin C (CuC) is a distinct variant of the cucurbitacin family, displaying promising anti-cancer activity against various tumor types. The primary objective of our research is to elucidate the promising effects of CuC on CCA. Methods: The impact of CuC on CCA cell lines was assessed by cell count kit-8 assay, EdU staining assay, colony formation assay, wound-healing assay, and Transwell assay. Flow cytometric analysis was conducted to explore the function of CuC treatments on cell-cycle distribution and apoptosis in CCA cells. Computational biology and network pharmacology approaches were utilized to predict potential targets of CuC. Furthermore, a tumor xenograft mouse model was established using CCA cells to explore the anti-cancer effects of CuC in vivo. Results: Our research findings revealed that CuC exerted a suppressive effect on CCA cell progression. Cell viability assays, EdU staining assays, and colony formation assays demonstrated that CuC effectively suppressed viability and proliferation of CCA cells. Wound-healing assays and Transwell assays indicated that CuC effectively inhibits the migratory and invasive capabilities of CCA cells. Flow cytometry analysis elucidated that CuC played its anti-proliferative role in CCA cells by arresting G0/G1 phase and increasing apoptosis. Through bioinformatics and network pharmacology analysis, in conjunction with western blot analysis, we demonstrated CuC mediated the inhibition of CCA cell progression through modulation of JAK2/STAT3 pathway. Additionally, the CCA xenograft tumor model was established, and the results supported the inhibition of CuC treatment against CCA progression in vivo. Conclusion: Our study demonstrates that CuC possesses notable capabilities to suppress cell proliferation, migration, and invasion in CCA. Importantly, the inhibitory effects of CuC on CCA progression are attributed to its modulation of the JAK2/STAT3 signaling pathway. Altogether, our study demonstrated that CuC holds promise as a prospective therapeutic agent for treating CCA. [ABSTRACT FROM AUTHOR]

RNA-ligand docking is a part of computational biology, which is currently lowly recognized compared to the protein-ligand docking procedure commonly applied for drug discovery. This in silico study aims to create a simplified protocol for RNA-ligand docking, which is applicable to RNA-targeting small molecular drug screening. Four alkaloids (berberine, colchicine, nicotine, and tomatine) were subjected to this study and contended against the SARS-CoV-2 genomic RNA -1 PRF component targeting control drug, merafloxacin, including two known intercalator berberine and colchicine, a small alkaloid nicotine and a large alkaloid tomatine. The alkaloids were screened for drug-likeness properties (Lipinski’s Rules of 5 or LRo5), bioavailability indexes, and synthetic accessibility values using SwissADME before docking. The docking used PyRx – Autodock Vina and re-scored for RNA-ligand scoring using AnnapuRNA. The docking results have the interactions mapped using fingeRNAt and visualized using Discovery Studio. Molecular dynamics using CHARMM36 and AMBER forcefields were simulated in NAMD. The molecular dynamics 1 ns simulation results showed that the ligand interaction over time did not cause much interference with the RNA, indicated by the low number of RMSD changes between RNA itself and the RNA-ligand complex. Additionally, CHARMM36 forcefield provided more stable fluctuation compared to AMBER. The results indicated that tomatine disobeyed LRo5 and had a low bioavailability index and bad synthetic accessibility value, while the rest alkaloids passed. In the end, berberine has an even higher docking score than the control drug. The study also shows that this protocol can be useful for future RNA-ligand computational studies. [ABSTRACT FROM AUTHOR]

This article explores the progressive integration of -omics methods, including genomics, metabolomics, and proteomics, into sports research, highlighting the development of the concept of "sportomics." We discuss how sportomics can be used to comprehend the multilevel metabolism during exercise in real-life conditions faced by athletes, enabling potential personalized interventions to improve performance and recovery and reduce injuries, all with a minimally invasive approach and reduced time. Sportomics may also support highly personalized investigations, including the implementation of n-of-1 clinical trials and the curation of extensive datasets through long-term follow-up of athletes, enabling tailored interventions for athletes based on their unique physiological responses to different conditions. Beyond its immediate sport-related applications, we delve into the potential of utilizing the sportomics approach to translate Big Data regarding top-level athletes into studying different human diseases, especially with nontargeted analysis. Furthermore, we present how the amalgamation of bioinformatics, artificial intelligence, and integrative computational analysis aids in investigating biochemical pathways, and facilitates the search for various biomarkers. We also highlight how sportomics can offer relevant information about doping control analysis. Overall, sportomics offers a comprehensive approach providing novel insights into human metabolism during metabolic stress, leveraging cutting-edge systems science techniques and technologies. [ABSTRACT FROM AUTHOR]

Motivation Many approaches in systems biology have been applied in drug repositioning due to the increased availability of the omics data and computational biology tools. Using a multi-omics integrated network, which contains information of various biological interactions, could offer a more comprehensive inspective and interpretation for the drug mechanism of action (MoA). Results We developed a computational pipeline for dissecting the hidden MoAs of drugs (Open MoA). Our pipeline computes confidence scores to edges that represent connections between genes/proteins in the integrated network. The interactions showing the highest confidence score could indicate potential drug targets and infer the underlying molecular MoAs. Open MoA was also validated by testing some well-established targets. Additionally, we applied Open MoA to reveal the MoA of a repositioned drug (JNK-IN-5A) that modulates the PKLR expression in HepG2 cells and found STAT1 is the key transcription factor. Overall, Open MoA represents a first-generation tool that could be utilized for predicting the potential MoA of repurposed drugs and dissecting de novo targets for developing effective treatments. Availability and implementation Source code is available at https://github.com/XinmengLiao/Open_MoA. [ABSTRACT FROM AUTHOR]

The Free Energy Principle (FEP) and Integrated Information Theory (IIT) are two ambitious theoretical approaches. The first aims to make a formal framework for describing self-organizing and life-like systems in general, and the second attempts a mathematical theory of conscious experience based on the intrinsic properties of a system. They are each concerned with complementary aspects of the properties of systems, one with life and behavior, the other with meaning and experience, so combining them has potential for scientific value. In this paper, we take a first step towards such a synthesis by expanding on the results of an earlier published evolutionary simulation study, which show a relationship between IIT-measures and fitness in differing complexities of tasks. We relate a basic information theoretic measure from the FEP, surprisal, to this result, finding that the surprisal of simulated agents' observations is inversely related to the general increase in fitness and integration over evolutionary time. Moreover, surprisal fluctuates together with IIT-based consciousness measures in within-trial time. This suggests that the consciousness measures used in IIT indirectly depend on the relation between the agent and the external world, and that it should therefore be possible to relate them to the theoretical concepts used in the FEP. Lastly, we suggest a future approach for investigating this relationship empirically. Author summary: Two influential theoretical frameworks in cognitive science, neuroscience and computational biology, are the Free Energy Principle and Integrated Information Theory. The first is a formal approach to self-organization and adaptive behavior ‐ in short, life ‐ based on first principles from statistical physics. The second is an attempt at formally describing the intrinsic experience of a given system, that is, how it feels to be that system. In this way, these two theories provide tools for understanding two complementary aspects of a given organism; namely, how it acts in a goal-directed manner based on statistical beliefs about the world, and how it feels to be that system in that process. In this paper, we provide an initial numerical investigation of the potential relation of these theoretical frameworks. We simulate agents that undergo evolution, and show that as their level of integration (Φ, a measure from Integrated Information Theory) increases, information theoretic surprisal (a quantity used in the Free Energy Principle) decreases. We also see that Φ and surprisal fluctuate together, and that these fluctuations depend on sensory input. Finally we provide considerations for future simulation work, and how to bring these two theoretical frameworks closer together. [ABSTRACT FROM AUTHOR]

Deep generative models, such as variational autoencoders (VAE), have gained increasing attention in computational biology due to their ability to capture complex data manifolds which subsequently can be used to achieve better performance in downstream tasks, such as cancer type prediction or subtyping of cancer. However, these models are difficult to train due to the large number of hyperparameters that need to be tuned. To get a better understanding of the importance of the different hyperparameters, we examined six different VAE models when trained on TCGA transcriptomics data and evaluated on the downstream tasks of cluster agreement with cancer subtypes and survival analysis. We studied the effect of the latent space dimensionality, learning rate, optimizer, initialization and activation function on the quality of subsequent downstream tasks on the TCGA samples. We found β-TCVAE and DIP-VAE to have a good performance, on average, despite being more sensitive to hyperparameters selection. Based on these experiments, we derived recommendations for selecting the different hyperparameters settings. To ensure generalization, we tested all hyperparameter configurations on the GTEx dataset. We found a significant correlation (ρ = 0.7) between the hyperparameter effects on clustering performance in the TCGA and GTEx datasets. This highlights the robustness and generalizability of our recommendations. In addition, we examined whether the learned latent spaces capture biologically relevant information. Hereto, we measured the correlation and mutual information of the different representations with various data characteristics such as gender, age, days to metastasis, immune infiltration, and mutation signatures. We found that for all models the latent factors, in general, do not uniquely correlate with one of the data characteristics nor capture separable information in the latent factors even for models specifically designed for disentanglement. [ABSTRACT FROM AUTHOR]

Pancreatic ductal adenocarcinoma (PDAC) is a highly malignant cancer with limited treatment options. Mannose, a common monosaccharide taken up by cells through the same transporters as glucose, has been shown to induce growth retardation and enhance cell death in response to chemotherapy in several cancers, including PDAC. However, the molecular targets and mechanisms underlying mannose's action against PDAC are not well understood. In this study, we used an integrative approach of network pharmacology, bioinformatics analysis, and experimental verification to investigate the pharmacological targets and mechanisms of mannose against PDAC. Our results showed that the protein Src is a key target of mannose in PDAC. Additionally, computational analysis revealed that mannose is a highly soluble compound that meets Lipinski's rule of five and that the expression of its target molecules is correlated with survival rates and prognosis in PDAC patients. Finally, we validated our findings through in vitro and in vivo experiments. In conclusion, our study provides evidence that mannose plays a critical role in inhibiting PDAC growth by targeting Src, suggesting that it may be a promising therapeutic candidate for PDAC. [ABSTRACT FROM AUTHOR]

This review investigates the intricate role of human endogenous retroviruses (HERVs) in cancer development and progression, explicitly focusing on HERV-K (HML-2). This paper sheds light on the latest research advancements and potential treatment strategies by examining the historical context of HERVs and their involvement in critical biological processes such as embryonic development, immune response, and disease progression. This review covers computational modeling for drug-target binding assessment, systems biology modeling for simulating HERV-K viral cargo dynamics, and using antiviral drugs to combat HERV-induced diseases. The findings presented in this review contribute to our understanding of HERV-mediated disease mechanisms and provide insights into future therapeutic approaches. They emphasize why HERV-K holds significant promise as a biomarker and a target. [ABSTRACT FROM AUTHOR]

The research deals with an evolutionary-based mutation with functional annotation to identify protein complexes within PPI networks. An important field of research in computational biology is the difficult and fundamental challenge of revealing complexes in protein interaction networks. The complex detection models that have been developed to tackle challenges are mostly dependent on topological properties and rarely use the biological properties of PPI networks. This research aims to push the evolutionary algorithm to its maximum by employing gene ontology (GO) to communicate across proteins based on biological information similarity for direct genes. The outcomes show that the suggested method can be utilized to improve the predictability of the complexes identified. The GO functional annotation of proteins as a heuristic guide is injected into the framework of single-objective evolutionary algorithms (EAs), while the complex detection community score (CS) model works as a fitness function in EAs. In the experiments, we analyzed the performance of our proposed algorithm when applied to the publicly accessible yeast protein networks. The results show a considerable improvement in the detection ability of complexes in the PPI network. [ABSTRACT FROM AUTHOR]

The use of high-throughput sequencing technologies and bioinformatic tools has greatly transformed microbial genome research. With the help of sophisticated computational tools, it has become easier to perform whole genome assembly, identify and compare different species based on their genomes, and predict the presence of genes responsible for proteins, antimicrobial resistance, and toxins. These bioinformatics resources are likely to continuously improve in quality, become more user-friendly to analyze the multiple genomic data, efficient in generating information and translating it into meaningful knowledge, and enhance our understanding of the genetic mechanism of AMR. In this manuscript, we provide an essential guide for selecting the popular resources for microbial research, such as genome assembly and annotation, antibiotic resistance gene profiling, identification of virulence factors, and drug interaction studies. In addition, we discuss the best practices in computer-oriented microbial genome research, emerging trends in microbial genomic data analysis, integration of multi-omics data, the appropriate use of machine-learning algorithms, and open-source bioinformatics resources for genome data analytics. [ABSTRACT FROM AUTHOR]

In thinking back on Richard Lerner's impact on our lab's trajectory at Scripps Research, we see his fingerprints on all the efforts we have undertaken over the last 40 years. Immunology and virology have been a continuing driving focus in our software development and application within the Molecular Graphics Lab (now the Center for Computational Structural Biology – CCSB), spurred on by the marriage of chemistry with biology that Richard enabled at Scripps. Many of our earliest and continuing computational docking targets have been focused on HIV with collaborations with Chi‐Huey Wong and Barry Sharpless. Along with subsequent computational work with Jeff Kelly, Reza Ghadiri, Udi Keinan and Ben Cravatt we have been able to sustain continuous efforts in developing and applying docking and other computational technologies to structural chemistry problems. In this remembrance, we give our reflections of Richard Lerner's character, vision, and his inspiration in our scientific lives and in the environment that he created at Scripps Research. [ABSTRACT FROM AUTHOR]

Background and Objectives: Computational biology analyses the theoretical tertiary structure of proteins and identifies the 'topological' differences between RhD and RhCE. Our aim was to identify the theoretical structural differences between the four isoforms of RhCE and RhD using computational biological tools. Materials and Methods: Physicochemical profile was determined by hydrophobicity and electrostatic potential analysis. Secondary and tertiary structures were generated using computational biology tools. The structures were evaluated and validated using Ramachandran algorithm, which calculates the single score, p‐value and root mean square deviation (RMSD). Structures were overlaid on local refinement of 'RhAG‐RhCE‐ANK' (PBDID 7uzq) and RhAG to compare their spatial distribution within the membrane. Results: All proteins differed in surface area and electrostatic distance due to variations in hydrophobicity and electrostatic potential. The RMSD between RhD and RhCE was 0.46 ± 0.04 Å, and the comparison within RhCE was 0.57 ± 0.08 Å. The percentage of amino acids in the hydrophobic thickness was 50.24% for RhD while for RhCE it ranged between 73.08% and 76.68%. The RHAG hydrophobic thickness was 34.2 Å, and RhCE's hydrophobic thickness was 33.83 Å. We suggest that the C/c antigens differ exofacially at loops L1 and L2. For the E/e antigens, the difference lies in L6. By contrast, L4 is the same for all proteins except Rhce. Conclusion: The physicochemical properties of Rh proteins made them different, although their genes are homologous. Using computational biology, we model structures with sufficient precision, similar to those obtained experimentally. An amino acid variation alters the folding of the tertiary structure and the interactions with other proteins, modifying the electrostatic environment, the spatial conformations and therefore the antigenic recognition. [ABSTRACT FROM AUTHOR]

The article titled "Molecular World Today and Tomorrow: Recent Trends in Biological Sciences 2.0" explores the impact of molecular techniques on scientific advancements in biological sciences. It emphasizes the role of molecular techniques in focused medication creation and disease diagnostics. The article highlights the integration of molecular phylogenetics and omics approaches to enhance our understanding of molecular mechanisms and stress responses in biological systems. The special issue features 20 papers that demonstrate the interdisciplinary nature of molecular sciences and its potential for personalized medicine and treatments. [Extracted from the article]

The article discusses the concept of evolvability and its significance in evolutionary biology. The authors argue that evolvability, which refers to the factors that influence an evolutionary system's ability to evolve, is a crucial addition to evolutionary theory. They highlight the diverse conceptions of evolvability within different fields of biology and emphasize the need for a comprehensive synthesis of these ideas. The article also explores the challenges in defining evolvability and the potential for it to bridge gaps in our understanding of evolution. While the authors acknowledge the historical roots of evolvability research, they suggest that a more focused and restricted definition is necessary for further development. They also note the missed opportunities in exploring the relationship between evolvability and sex, as well as the role of environmental change in evolvability. Despite these limitations, the article concludes that the book reviewed is a significant step towards understanding and integrating evolvability into modern evolutionary theory. [Extracted from the article]

The article offers information on the 89th Annual Meeting of the Indian Academy of Sciences (IASc), which was held in Bengaluru.

Understanding the mechanism by which cells coordinate their differentiation and migration is critical to our understanding of many fundamental processes such as wound healing, disease progression, and developmental biology. Mathematical models have been an essential tool for testing and developing our understanding, such as models of cells as soft spherical particles, reaction-diffusion systems that couple cell movement to environmental factors, and multi-scale multi-physics simulations that combine bottom-up rule-based models with continuum laws. However, mathematical models can often be loosely related to data or have so many parameters that model behaviour is weakly constrained. Recent methods in machine learning introduce new means by which models can be derived and deployed. In this review, we discuss examples of mathematical models of aspects of developmental biology, such as cell migration, and how these models can be combined with these recent machine learning methods. [ABSTRACT FROM AUTHOR]

Background: Recently we developed a gene orthology inference tool based on genome rearrangements (Journal of Bioinformatics and Computational Biology 19:6, 2021). Given a set of genomes our method first computes all pairwise gene similarities. Then it runs pairwise ILP comparisons to compute optimal gene matchings, which minimize, by taking the similarities into account, the weighted rearrangement distance between the analyzed genomes (a problem that is NP-hard). The gene matchings are then integrated into gene families in the final step. The mentioned ILP includes an optimal capping that connects each end of a linear segment of one genome to an end of a linear segment in the other genome, producing an exponential increase of the search space. Results: In this work, we design and implement a heuristic capping algorithm that replaces the optimal capping by clustering (based on their gene content intersections) the linear segments into m ≥ 1 subsets, whose ends are capped independently. Furthermore, in each subset, instead of allowing all possible connections, we let only the ends of content-related segments be connected. Although there is no guarantee that m is much bigger than one, and with the possible side effect of resulting in sub-optimal instead of optimal gene matchings, the heuristic works very well in practice, from both the speed performance and the quality of computed solutions. Our experiments on primate and fruit fly genomes show two positive results. First, for complete assemblies of five primates the version with heuristic capping reports orthologies that are very similar to the orthologies computed by the version of our tool with optimal capping. Second, we were able to efficiently analyze fruit fly genomes with incomplete assemblies distributed in hundreds or even thousands of contigs, obtaining gene families that are very similar to F L Y B A S E families. Indeed, our tool inferred a higher number of complete cliques, with a higher intersection with F L Y B A S E , when compared to gene families computed by other inference tools. We added a post-processing for refining, with the aid of the M C L algorithm, our ambiguous families (those with more than one gene per genome), improving even more the accuracy of our results. Our approach is implemented into a pipeline incorporating the pre-computation of gene similarities and the post-processing refinement of ambiguous families with M C L . Both the original version with optimal capping and the new modified version with heuristic capping can be downloaded, together with their detailed documentations, at https://gitlab.ub.uni-bielefeld.de/gi/FFGC or as a Conda package at https://anaconda.org/bioconda/ffgc. [ABSTRACT FROM AUTHOR]

Largely unknown just a few decades ago, computational systems biology is now a central methodology for biological and medical research. This amazing ascent raises the question of what the community should do next. The article outlines our personal vision for the future of computational systems biology, suggesting the need to address both mindsets and methodologies. We present this vision by focusing on current and anticipated research goals, the development of strong computational tools, likely prominent applications, education of the nextgeneration of scientists, and outreach to the public. In our opinion, two classes of broad research goals have emerged in recent years and will guide future efforts. The first goal targets computational models of increasing size and complexity, aimed at solving emerging health-related challenges, such as realistic whole-cell and organ models, disease simulators and digital twins, in silico clinical trials, and clinically translational applications in the context of therapeutic drug development. Such large models will also lead us toward solutions to pressing issues in agriculture and environmental sustainability, including sufficient food availability and life in changing habitats. The second goal is a deep understanding of the essence of system designs and strategies with which nature solves problems. This understanding will help us explain observed biological structures and guide forays into synthetic biological systems. Regarding effective methodologies, we suggest efforts toward automated data pipelines from raw biomedical data all the way to spatiotemporal mechanistic model. These will be supported by dynamic methods of statistics, machine learning, artificial intelligence and streamlined strategies of dynamic model design, striking a fine balance between modeling realistic complexity and abstracted simplicity. Finally, we suggest the need for a concerted, community-wide emphasis on effective education in systems biology, implemented as a combination of formal instruction and hands-on mentoring. The educational efforts should furthermore be extended toward the public through books, blogs, social media, and interactive networking opportunities, with the ultimate goal of training in state-of-the-art technology while recapturing the lost art of synthesis. [ABSTRACT FROM AUTHOR]

Sorcin (Sri), a member of penta EF-hand protein family plays a diverse role in maintaining calcium homeostasis, cell cycle and vesicular trafficking. Sri is highly conserved amongst mammals and consists of N-terminal glycine rich domain and C-terminal calcium binding domain that mediates its dimerization and interacts with different compounds. In the present study, with the help of combination of computational and molecular biology techniques, we have identified a novel isoform (Sri-N) in mouse which differs only in the C-terminal domain with that of Sri reported earlier. The novel isoform contains a new last exon that is different from the one present in the reported transcript (Sri). The presence of the novel isoform was further validated in different tissues by RT-PCR and DNA sequencing. The transcript was conceptually translated and subjected to in-silico analysis using different bioinformatics tools. The novel transcript variant encodes for a longer protein isoform without any change in the sub-cellular localization as predicted by PSORT-II online tool. Molecular modelling was performed to compare the structural changes in Sri-N and Sri isoforms. The structural characterization of the novel isoform using MD simulation depicted its overall stability under the physiological conditions. The molecular docking of proteins with various chemotherapeutic drugs revealed that their binding affinity is more for Sri-N as compared to that for the previously reported transcript Sri. [ABSTRACT FROM AUTHOR]

Solute carrier (SLC) transporters are a highly understudied class of proteins that transport ions, nutrients, and drugs, across biological membranes, and are often mutated in disease. Unlocking this large and complex group of proteins is essential to identify unknown SLC targets and develop next-generation SLC drugs. Recent advances in chemical, structural, and computational biology have allowed development of innovative strategies to modulate their function as well as unique tool compounds and future drugs. Solute carrier (SLCs) transporters mediate the transport of a broad range of solutes across biological membranes. Dysregulation of SLCs has been associated with various pathologies, including metabolic and neurological disorders, as well as cancer and rare diseases. SLCs are therefore emerging as key targets for therapeutic intervention with several recently approved drugs targeting these proteins. Unlocking this large and complex group of proteins is essential to identifying unknown SLC targets and developing next-generation SLC therapeutics. Recent progress in experimental and computational techniques has significantly advanced SLC research, including drug discovery. Here, we review emerging topics in therapeutic discovery of SLCs, focusing on state-of-the-art approaches in structural, chemical, and computational biology, and discuss current challenges in transporter drug discovery. [ABSTRACT FROM AUTHOR]

Understanding binding related changes in antibody conformations is important for epitope prediction and antibody refinement. The increase of available data in the PDB allowed a more detailed investigation of the conformational landscape for free and bound antibodies. A dataset containing a total of 835 unique PDB entries of antibodies that were crystallized in complex with their antigen and in a free state was constructed. It was examined for binding related conformation changes. We present further evidence supporting the theory of a pre-existing-equilibrium in experimental data. Multiple sequence alignments did not show binding induced tendencies in the solvent accessibility of residues in any specific position. Evaluating the changes in solvent accessibility per residue revealed a certain binding induced increase for several amino acids. Antibody-antigen interaction statistics were established and quantify a significant directional asymmetry between many interacting antibody and antigen residue pairs, especially a richness in tyrosine in the antibody epitope compared to its paratope. This asymmetry could potentially facilitate an increase in the success rate of computationally guided antibody refinement. [ABSTRACT FROM AUTHOR]

The advancement of high‐throughput genomic assays has led to enormous growth in the availability of large‐scale biological datasets. Over the last two decades, these increasingly complex data have required statistical approaches that are more sophisticated than traditional linear models. Machine learning methodologies such as neural networks have yielded state‐of‐the‐art performance for prediction‐based tasks in many biomedical applications. However, a notable downside of these machine learning models is that they typically do not reveal how or why accurate predictions are made. In many areas of biomedicine, this "black box" property can be less than desirable—particularly when there is a need to perform in silico hypothesis testing about a biological system, in addition to justifying model findings for downstream decision‐making, such as determining the best next experiment or treatment strategy. Explainable and interpretable machine learning approaches have emerged to overcome this issue. While explainable methods attempt to derive post hoc understanding of what a model has learned, interpretable models are designed to inherently provide an intelligible definition of their parameters and architecture. Here, we review the model transparency spectrum moving from black box and explainable, to interpretable machine learning methodology. Motivated by applications in genomics, we provide background on the advances across this spectrum, detailing specific approaches in both supervised and unsupervised learning. Importantly, we focus on the promise of incorporating existing biological knowledge when constructing interpretable machine learning methods for biomedical applications. We then close with considerations and opportunities for new development in this space. This article is categorized under:Statistical Models > Nonlinear ModelsApplications of Computational Statistics > Genomics/Proteomics/GeneticsApplications of Computational Statistics > Computational and Molecular Biology [ABSTRACT FROM AUTHOR]

In the recent decades, chimeric antigen receptor (CAR) therapy signaled a new revolutionary approach to cancer treatment. This method seeks to engineer immune cells expressing an artificially designed receptor, which would endue those cells with the ability to recognize and eliminate tumor cells. While some CAR therapies received FDA approval and others are subject to clinical trials, many aspects of their workings remain elusive. Techniques of systems and computational biology have been frequently employed to explain the operating principles of CAR therapy and suggest further design improvements. In this review, we sought to provide a comprehensive account of those efforts. Specifically, we discuss various computational models of CAR therapy ranging in scale from organismal to molecular. Then, we describe the molecular and functional properties of costimulatory domains frequently incorporated in CAR structure. Finally, we describe the signaling cascades by which those costimulatory domains elicit cellular response against the target. We hope that this comprehensive summary of computational and experimental studies will further motivate the use of systems approaches in advancing CAR therapy. [ABSTRACT FROM AUTHOR]

Among the different drug targets of SARS-CoV-2, a multi-domain protein known as NSP3 is a critical element of the translational and replication machinery. The macrodomain-I, in particular, has been reported to have an essential role in the viral attack on the innate immune response. In this study, we explore natural medicinal compounds and identify potential inhibitors to target the SARS-CoV-2–NSP3 macrodomain-I. Computational modeling and simulation tools were utilized to investigate the structural-dynamic properties using triplicates of 100 ns MD simulations. In addition, the MM/GBSA method was used to calculate the total binding free energy of each inhibitor bound to macrodomain-I. Two significant hits were identified: 3,5,7,4′-tetrahydroxyflavanone 3′-(4-hydroxybenzoic acid) and 2-hydroxy-3-O-beta-glucopyranosyl-benzoic acid. The structural-dynamic investigation of both compounds with macrodomain-I revealed stable dynamics and compact behavior. In addition, the total binding free energy for each complex demonstrated a robust binding affinity, of ΔG −61.98 ± 0.9 kcal/mol for Compound A, while for Compound B, the ΔG was −45.125 ± 2.8 kcal/mol, indicating the inhibitory potential of these compounds. In silico bioactivity and dissociation constant (KD) determination for both complexes further validated the inhibitory potency of each compound. In conclusion, the aforementioned natural products have the potential to inhibit NSP3, to directly rescue the host immune response. The current study provides the basis for novel drug development against SARS-CoV-2 and its variants. [ABSTRACT FROM AUTHOR]

New imaging technologies are dramatically transforming all of biology. These methods will also allow users to accurately survey and record sensitive plant communities through time. These photographs might serve as a virtual voucher of a rare species (when destructive sampling would be detrimental to the population) or as a source of plant traits for ecological or evolutionary research, but field photographs of plants are rarely standardized. In this technique, which allows the spatial analysis of chemical distribution in a tissue, a laser beam is fired at a matrix-coated sample, transferring energy to the molecules extracted from the tissue. [Extracted from the article]

The oral microbiota has an enormous impact on human health, with oral dysbiosis now linked to many oral and systemic diseases. Recent advancements in sequencing, mass spectrometry, bioinformatics, computational biology, and machine learning are revolutionizing oral microbiome research, enabling analysis at an unprecedented scale and level of resolution using omics approaches. This review contains a comprehensive perspective of the current state-of-the-art tools available to perform genomics, metagenomics, phylogenomics, pangenomics, transcriptomics, proteomics, metabolomics, lipidomics, and multi-omics analysis on (all) microbiomes, and then provides examples of how the techniques have been applied to research of the oral microbiome, specifically. Key findings of these studies and remaining challenges for the field are highlighted. Although the methods discussed here are placed in the context of their contributions to oral microbiome research specifically, they are pertinent to the study of any microbiome, and the intended audience of this includes researchers would simply like to get an introduction to microbial omics and/or an update on the latest omics methods. Continued research of the oral microbiota using omics approaches is crucial and will lead to dramatic improvements in human health, longevity, and quality of life. [ABSTRACT FROM AUTHOR]

Aims Atrial fibrillation (AF), the most prevalent clinical arrhythmia, is associated with atrial remodelling manifesting as acute and chronic alterations in expression, function, and regulation of atrial electrophysiological and Ca2+-handling processes. These AF-induced modifications crosstalk and propagate across spatial scales creating a complex pathophysiological network, which renders AF resistant to existing pharmacotherapies that predominantly target transmembrane ion channels. Developing innovative therapeutic strategies requires a systems approach to disentangle quantitatively the pro-arrhythmic contributions of individual AF-induced alterations. Methods and results Here, we built a novel computational framework for simulating electrophysiology and Ca2+-handling in human atrial cardiomyocytes and tissues, and their regulation by key upstream signalling pathways [i.e. protein kinase A (PKA), and Ca2+/calmodulin-dependent protein kinase II (CaMKII)] involved in AF-pathogenesis. Populations of atrial cardiomyocyte models were constructed to determine the influence of subcellular ionic processes, signalling components, and regulatory networks on atrial arrhythmogenesis. Our results reveal a novel synergistic crosstalk between PKA and CaMKII that promotes atrial cardiomyocyte electrical instability and arrhythmogenic triggered activity. Simulations of heterogeneous tissue demonstrate that this cellular triggered activity is further amplified by CaMKII- and PKA-dependent alterations of tissue properties, further exacerbating atrial arrhythmogenesis. Conclusions Our analysis reveals potential mechanisms by which the stress-associated adaptive changes turn into maladaptive pro-arrhythmic triggers at the cellular and tissue levels and identifies potential anti-AF targets. Collectively, our integrative approach is powerful and instrumental to assemble and reconcile existing knowledge into a systems network for identifying novel anti-AF targets and innovative approaches moving beyond the traditional ion channel-based strategy. [ABSTRACT FROM AUTHOR]

These genes, particularly matrix metalloproteinases (MMPs), can potentially serve as novel prognostic markers and therapeutic targets for controlling IBD and IBD-associated colorectal neoplasia. Inflammatory bowel disease (IBD) encompasses chronic idiopathic relapsing and remitting gastrointestinal autoimmune diseases characterized by chronic inflammatory disorders of complex etiology, posing clinical challenges due to their often therapy-refractory nature. One review of this Special Issue focuses on examining the different phenotypical changes observed in the microvascular endothelium during IBD and presents potential vessel-specific targeted therapy options for the treatment of IBD [[9]]. [Extracted from the article]

The origin of genetic coding is characterised as an event of cosmic significance in which quantum mechanical causation was transcended by constructive computation. Computational causation entered the physico-chemical processes of the pre-biotic world by the incidental satisfaction of a condition of reflexivity between polymer sequence information and system elements able to facilitate their own production through translation of that information. This event, which has previously been modelled in the dynamics of Gene–Replication–Translation systems, is properly described as a process of self-guided self-organisation. The spontaneous emergence of a primordial genetic code between two-letter alphabets of nucleotide triplets and amino acids is easily possible, starting with random peptide synthesis that is RNA-sequence-dependent. The evident self-organising mechanism is the simultaneous quasi-species bifurcation of the populations of information-carrying genes and enzymes with aminoacyl-tRNA synthetase-like activities. This mechanism allowed the code to evolve very rapidly to the ~20 amino acid limit apparent for the reflexive differentiation of amino acid properties using protein catalysts. The self-organisation of semantics in this domain of physical chemistry conferred on emergent molecular biology exquisite computational control over the nanoscopic events needed for its self-construction. [ABSTRACT FROM AUTHOR]

Background: Native bone marrow (BM) mesenchymal stem/stromal cells (BM-MSCs) participate in generating and shaping the skeleton and BM throughout the lifespan. Moreover, BM-MSCs regulate hematopoiesis by contributing to the hematopoietic stem cell niche in providing critical cytokines, chemokines and extracellular matrix components. However, BM-MSCs contain a heterogeneous cell population that remains ill-defined. Although studies on the taxonomy of native BM-MSCs in mice have just started to emerge, the taxonomy of native human BM-MSCs remains unelucidated. Methods: By using single-cell RNA sequencing (scRNA-seq), we aimed to define a proper taxonomy for native human BM non-hematopoietic subsets including endothelial cells (ECs) and mural cells (MCs) but with a focal point on MSCs. To this end, transcriptomic scRNA-seq data were generated from 5 distinct BM donors and were analyzed together with other transcriptomic data and with computational biology analyses at different levels to identify, characterize and classify distinct native cell subsets with relevant biomarkers. Results: We could ascribe novel specific biomarkers to ECs, MCs and MSCs. Unlike ECs and MCs, MSCs exhibited an adipogenic transcriptomic pattern while co-expressing genes related to hematopoiesis support and multilineage commitment potential. Furthermore, by a comparative analysis of scRNA-seq of BM cells from humans and mice, we identified core genes conserved in both species. Notably, we identified MARCKS, CXCL12, PDGFRA, and LEPR together with adipogenic factors as archetypal biomarkers of native MSCs within BM. In addition, our data suggest some complex gene nodes regulating critical biological functions of native BM-MSCs together with a preferential commitment toward an adipocyte lineage. Conclusions: Overall, our taxonomy for native BM non-hematopoietic compartment provides an explicit depiction of gene expression in human ECs, MCs and MSCs at single-cell resolution. This analysis helps enhance our understanding of the phenotype and the complexity of biological functions of native human BM-MSCs. [ABSTRACT FROM AUTHOR]

Artificial intelligence-powered protein structure prediction methods have led to a paradigm-shift in computational structural biology, yet contemporary approaches for predicting the interfacial residues (i.e., sites) of protein-protein interaction (PPI) still rely on experimental structures. Recent studies have demonstrated benefits of employing graph convolution for PPI site prediction, but ignore symmetries naturally occurring in 3-dimensional space and act only on experimental coordinates. Here we present EquiPPIS, an E(3) equivariant graph neural network approach for PPI site prediction. EquiPPIS employs symmetry-aware graph convolutions that transform equivariantly with translation, rotation, and reflection in 3D space, providing richer representations for molecular data compared to invariant convolutions. EquiPPIS substantially outperforms state-of-the-art approaches based on the same experimental input, and exhibits remarkable robustness by attaining better accuracy with predicted structural models from AlphaFold2 than what existing methods can achieve even with experimental structures. Freely available at https://github.com/Bhattacharya-Lab/EquiPPIS, EquiPPIS enables accurate PPI site prediction at scale. Author summary: Predicting how proteins interact and characterizing the interacting residues at the protein-protein interaction interface (i.e., sites) is of central importance to understanding various biological processes actuated by protein-protein interactions (PPI). Despite the remarkable recent progress in protein structure prediction driven by artificial intelligence, existing approaches for PPI site prediction still rely on experimental input. This paper presents an E(3) equivariant graph neural network approach for PPI site prediction that takes into account symmetries naturally occurring in 3-dimensional space and transforms equivariantly with translation, rotation, and reflection. Rigorous experimental validation shows that our method attains substantially improved accuracy and robustness over the existing approaches. Moving beyond what is currently possible with only experimental input, our method enables large-scale PPI site prediction using predicted structural models from AlphaFold2 without compromising on accuracy. An open-source software implementation of EquiPPIS, licensed under the GNU General Public License v3, is freely available at https://github.com/Bhattacharya-Lab/EquiPPIS. [ABSTRACT FROM AUTHOR]

Background: HeberFERON is a co-formulation of α2b and γ interferons, based on their synergism, which has shown its clinical superiority over individual interferons in basal cell carcinomas. In glioblastoma (GBM), HeberFERON has displayed promising preclinical and clinical results. This led us to design a microarray experiment aimed at identifying the molecular mechanisms involved in the distinctive effect of HeberFERON compared to the individual interferons in U-87MG model. Methods: Transcriptional expression profiling including a control (untreated) and three groups receiving α2b-interferon, γ-interferon and HeberFERON was performed using an Illumina HT-12 microarray platform. Unsupervised methods for gene and sample grouping, identification of differentially expressed genes, functional enrichment and network analysis computational biology methods were applied to identify distinctive transcription patterns of HeberFERON. Validation of most representative genes was performed by qPCR. For the cell cycle analysis of cells treated with HeberFERON for 24 h, 48 and 72 h we used flow cytometry. Results: The three treatments show different behavior based on the gene expression profiles. The enrichment analysis identified several mitotic cell cycle related events, in particular from prometaphase to anaphase, which are exclusively targeted by HeberFERON. The FOXM1 transcription factor network that is involved in several cell cycle phases and is highly expressed in GBMs, is significantly down regulated. Flow cytometry experiments corroborated the action of HeberFERON on the cell cycle in a dose and time dependent manner with a clear cellular arrest as of 24 h post-treatment. Despite the fact that p53 was not down-regulated, several genes involved in its regulatory activity were functionally enriched. Network analysis also revealed a strong relationship of p53 with genes targeted by HeberFERON. We propose a mechanistic model to explain this distinctive action, based on the simultaneous activation of PKR and ATF3, p53 phosphorylation changes, as well as its reduced MDM2 mediated ubiquitination and export from the nucleus to the cytoplasm. PLK1, AURKB, BIRC5 and CCNB1 genes, all regulated by FOXM1, also play central roles in this model. These and other interactions could explain a G2/M arrest and the effect of HeberFERON on the proliferation of U-87MG. Conclusions: We proposed molecular mechanisms underlying the distinctive behavior of HeberFERON compared to the treatments with the individual interferons in U-87MG model, where cell cycle related events were highly relevant. [ABSTRACT FROM AUTHOR]

Two well-established numerical representations of the coagulation cascade either initiated by the intrinsic system (Chatterjee et al., PLOS Computational Biology 2010) or the extrinsic system (Butenas et al., Journal of Biological Chemistry, 2004) were compared with thrombin generation assays under realistic pathological conditions. Biochemical modifications such as the omission of reactions not relevant to the case studied, the modification of reactions related to factor XI activation and auto-activation, the adaptation of initial conditions to the thrombin assay system, and the adjustment of some of the model parameters were necessary to align in vitro and in silico data. The modified models are able to reproduce thrombin generation for a range of factor XII, XI, and VIII deficiencies, with the coagulation cascade initiated either extrinsically or intrinsically. The results emphasize that when existing models are extrapolated to experimental parameters for which they have not been calibrated, careful adjustments are required. [ABSTRACT FROM AUTHOR]

Mastitis is known as intramammary inflammation, which has a multifactorial complex phenotype. However, the underlying molecular pathogenesis of mastitis remains poorly understood. In this study, we utilized a combination of RNA-seq and miRNA-seq techniques, along with computational systems biology approaches, to gain a deeper understanding of the molecular interactome involved in mastitis. We retrieved and processed one hundred transcriptomic libraries, consisting of 50 RNA-seq and 50 matched miRNA-seq data, obtained from milk-isolated monocytes of Holstein–Friesian cows, both infected with Streptococcus uberis and non-infected controls. Using the weighted gene co-expression network analysis (WGCNA) approach, we constructed co-expressed RNA-seq-based and miRNA-seq-based modules separately. Module-trait relationship analysis was then performed on the RNA-seq-based modules to identify highly-correlated modules associated with clinical traits of mastitis. Functional enrichment analysis was conducted to understand the functional behavior of these modules. Additionally, we assigned the RNA-seq-based modules to the miRNA-seq-based modules and constructed an integrated regulatory network based on the modules of interest. To enhance the reliability of our findings, we conducted further analyses, including hub RNA detection, protein–protein interaction (PPI) network construction, screening of hub-hub RNAs, and target prediction analysis on the detected modules. We identified a total of 17 RNA-seq-based modules and 3 miRNA-seq-based modules. Among the significant highly-correlated RNA-seq-based modules, six modules showed strong associations with clinical characteristics of mastitis. Functional enrichment analysis revealed that the turquoise module was directly related to inflammation persistence and mastitis development. Furthermore, module assignment analysis demonstrated that the blue miRNA-seq-based module post-transcriptionally regulates the turquoise RNA-seq-based module. We also identified a set of different RNAs, including hub-hub genes, hub-hub TFs (transcription factors), hub-hub lncRNAs (long non-coding RNAs), and hub miRNAs within the modules of interest, indicating their central role in the molecular interactome underlying the pathogenic mechanisms of S. uberis infection. This study provides a comprehensive insight into the molecular crosstalk between immunoregulatory mRNAs, miRNAs, and lncRNAs during S. uberis infection. These findings offer valuable directions for the development of molecular diagnosis and biological therapies for mastitis. [ABSTRACT FROM AUTHOR]

Objective To identify key genes associated with the prognosis of oral squamous cell carcinoma (OSCC) by RNA sequencing integrated bioinformatics. Methods Carcinoma and adjacent tissues of 3 OSCC patients admitted to the Department of Oral and Maxillofacial Surgery, General Hospital of Ningxia Medical University from February to October 2019 were collected. RNA was extracted and DNA libraries were constructed, and Illumina high-throughput sequencing was performed to screen out differentially expressed genes (DEGs) by integrating the data. The biological processes, signal pathways and interaction network of DEGs were analyzed, and the visualization and module analysis of DEGs were conducted. Candidate genes were identified by survival analysis of TCGA database of the module gene with the highest score, and key genes were further screened by GSE41613 data set for survival analysis of candidate genes. The human Protein Atlas database was used to verify the protein expression of key genes in OSCC. Gene set enrichment analysis (GSEA website) was used to predict the possible signaling pathways of key genes in OSCC. Results According to the sequencing data from the three pairs of OSCC, a total of 1818 DEGs were screened out, including 824 upregulated and 994 downregulated genes. They were mainly enriched in mitotic nuclear division, extracellular regions and calcium signaling pathways. TCGA data identified three candidate genes, including ASPM, CENPF and KIF15. The survival analysis of GSE41613 data identified CENPF as the key gene of OSCC. Its expression in OSCC tissues was higher (P＜0. 05). OSCC with higher CENPF expression had worse prognosis (P＜0. 05). The protein expression of CENPF in OSCC was higher. The samples with high expression of CENPF were enriched in gene sets of multiple pathways such as basic transcription factors and cell cycle (P＜0. 05). Conclusion A total of 1 818 DEGs are screened by transcriptional sequencing, among which the prognosis is worse when CENPF is higher in OSCC, which may be related to the occurrence, development and prognosis of OSCC. [ABSTRACT FROM AUTHOR]

Understanding complex disease mechanisms requires a comprehensive understanding of the gene regulatory networks, as complex diseases are often characterized by the dysregulation and dysfunction of molecular networks, rather than abnormalities in single genes. Specifically, the exploration of cell line-specific gene networks can provide essential clues for precision medicine, as this methodology can uncover molecular interplays specific to particular cell line statuses, such as drug sensitivity, cancer progression, etc. In this article, we provide a comprehensive review of computational strategies for cell line-specific gene network analysis: (1) cell line-specific gene regulatory network estimation and analysis of gene networks under varying epithelial–mesenchymal transition (EMT) statuses of cell lines; and (2) an explainable artificial intelligence approach for interpreting the estimated massive multiple EMT-status-specific gene networks. The objective of this review is to help readers grasp the concept of computational network biology, which holds significant implications for precision medicine by offering crucial clues. [ABSTRACT FROM AUTHOR]

Systemic sclerosis, also known as scleroderma or SSc, is a condition characterized by significant heterogeneity in clinical presentation, disease progression, and response to treatment. Consequently, the design of clinical trials to successfully identify effective therapeutic interventions poses a major challenge. Recent advancements in skin molecular profiling technologies and stratification techniques have enabled the identification of patient subgroups that may be relevant for personalized treatment approaches. This narrative review aims at providing an overview of the current status of skin gene expression analysis using computational biology approaches and highlights the benefits of stratifying patients upon their skin gene signatures. Such stratification has the potential to lead toward a precision medicine approach in the management of SSc. [ABSTRACT FROM AUTHOR]

Data and metadata come in various formats, and while ChatGPT will not identify outliers or fix missing data, it can suggest tools for most common tasks and provide code snippets. Tip 5: Use ChatGPT to improve your data visualization Data visualization is an essential component of computational biology research, and ChatGPT can be a valuable tool to assist in creating effective and informative figures. Tip 6: Use ChatGPT to improve your writing While AI-assisted writing in science has been steadily growing [[22]], ChatGPT has made this technology accessible to a much wider range of scientists and researchers. [Extracted from the article]

Inference of gene regulatory networks has been an active area of research for around 20 years, leading to the development of sophisticated inference algorithms based on a variety of assumptions and approaches. With the ever increasing demand for more accurate and powerful models, the inference problem remains of broad scientific interest. The abstract representation of biological systems through gene regulatory networks represents a powerful method to study such systems, encoding different amounts and types of information. In this review, we summarize the different types of inference algorithms specifically based on time-series transcriptomics, giving an overview of the main applications of gene regulatory networks in computational biology. This review is intended to give an updated reference of regulatory networks inference tools to biologists and researchers new to the topic and guide them in selecting the appropriate inference method that best fits their questions, aims, and experimental data. [ABSTRACT FROM AUTHOR]

In the machine learning field, the technique known as ensemble learning aims at combining different base learners in order to increase the quality and the robustness of the predictions. Indeed, this approach has widely been applied to tackle, with success, real world problems from different domains, including computational biology. Nevertheless, despite their potential, ensembles combining results from different base learners have been understudied in the context of gene regulatory network inference. In this paper we applied genetic algorithms and frequent itemset mining, to design small but effective ensembles of gene regulatory network inference methods. These ensembles were evaluated and compared to well-established single and ensemble methods, on both real and synthetic datasets. Results showed that small ensembles, consisting of few but diverse base learners, enhance the exploration of the solution space, and compensate base learners biases, outperforming state-of-the-art methods. Results advocate for the use of such methods as gene regulatory network inference tools. [ABSTRACT FROM AUTHOR]

The bioinformatics discipline seeks to solve problems in biology with computational theories and methods. Formal concept analysis (FCA) is one such theoretical model, based on partial orders. FCA allows the user to examine the structural properties of data based on which subsets of the dataset depend on each other. This article surveys the current literature related to the use of FCA for bioinformatics. The survey begins with a discussion of FCA, its hierarchical advantages, several advanced models of FCA, and lattice management strategies. It then examines how FCA has been used in bioinformatics applications, followed by future prospects of FCA in those areas. The applications addressed include gene data analysis (with next-generation sequencing), biomarkers discovery, protein-protein interaction, disease analysis (including COVID-19, cancer, and others), drug design and development, healthcare informatics, biomedical ontologies, and phylogeny. Some of the most promising prospects of FCA are identifying influential nodes in a network representing protein-protein interactions, determining critical concepts to discover biomarkers, integrating machine learning and deep learning for cancer classification, and pattern matching for next-generation sequencing. [ABSTRACT FROM AUTHOR]

The systematic identification of tumour vulnerabilities through perturbational experiments on cancer models, including genome editing and drug screens, is playing a crucial role in combating cancer. This collective effort is known as the Cancer Dependency Map (DepMap). The 1st European Cancer Dependency Map Symposium (EuroDepMap), held in Milan last May, featured talks, a roundtable discussion, and a poster session, showcasing the latest discoveries and future challenges related to the DepMap. The symposium aimed to facilitate interactions among participants across Europe, encourage idea exchange with leading experts, and present their work and future projects. Importantly, it sparked discussions on future endeavours, such as screening more complex cancer models and accounting for tumour evolution. [ABSTRACT FROM AUTHOR]

Objective: Autophagy is a recirculation system of lysosomal degradation of its own structure, which plays an important role in the development of tumors. The purpose of this study was to explore the functions of autophagy-related genes （ARGs） in gastric cancer （GC） to improve the current prognostic staging system. Methods: The differential survival ARGs in GC was identified by R language, and the risk scoring model of ARGs was constructed by univariate Cox and Lasso regression analysis, and its efficacy was verified by survival analysis and ROC curve. Results: Survival analysis showed that high risk score predicted poor prognosis （all P<0.05）. ROC curve confirmed that the model had reliable predictive performance （AUCmax=0.843）. Univariate and multivariate independent prognostic analysis showed that this score was an independent risk factor for GC （P<0.001）. Pearson correlation analysis showed that the model could reflect multiple types of immune cell infiltration （P<0.001）. Conclusion: This study constructed a risk scoring model based on 5 ARGs labels（ CXCR4, GRID2, HSPB8, IRGM, TSC1）, which can be combined with existing clinicopathology to provide more accurate prognosis for GC patients. [ABSTRACT FROM AUTHOR]

Motivation Aligning reads to a variation graph is a standard task in pangenomics, with downstream applications such as improving variant calling. While the vg toolkit [Garrison et al. (Variation graph toolkit improves read mapping by representing genetic variation in the reference. Nat Biotechnol 2018;36:875–9)] is a popular aligner of short reads, GraphAligner [Rautiainen and Marschall (GraphAligner: rapid and versatile sequence-to-graph alignment. Genome Biol 2020;21:253–28)] is the state-of-the-art aligner of erroneous long reads. GraphAligner works by finding candidate read occurrences based on individually extending the best seeds of the read in the variation graph. However, a more principled approach recognized in the community is to co-linearly chain multiple seeds. Results We present a new algorithm to co-linearly chain a set of seeds in a string labeled acyclic graph, together with the first efficient implementation of such a co-linear chaining algorithm into a new aligner of erroneous long reads to acyclic variation graphs, GraphChainer. We run experiments aligning real and simulated PacBio CLR reads with average error rates 15% and 5%. Compared to GraphAligner , GraphChainer aligns 12–17% more reads, and 21–28% more total read length, on real PacBio CLR reads from human chromosomes 1, 22, and the whole human pangenome. On both simulated and real data, GraphChainer aligns between 95% and 99% of all reads, and of total read length. We also show that minigraph [Li et al. (The design and construction of reference pangenome graphs with minigraph. Genome Biol 2020;21:265–19.)] and minichain [Chandra and Jain (Sequence to graph alignment using gap-sensitive co-linear chaining. In: Proceedings of the 27th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2023). Springer, 2023, 58–73.)] obtain an accuracy of <60% on this setting. Availability and implementation GraphChainer is freely available at https://github.com/algbio/GraphChainer. The datasets and evaluation pipeline can be reached from the previous address. [ABSTRACT FROM AUTHOR]

Mayaro (MAYV), Saint Louis encephalitis (SLEV), and Oropouche (OROV) viruses are neglected members of the three main families of arboviruses with medical relevance that circulate in the Amazon region as etiological agents of outbreaks of febrile illnesses in humans. As enveloped viruses, MAYV, SLEV, and OROV largely depend on their class II fusion proteins (E1, E, and Gc, respectively) for entry into the host cell. Since many aspects of the structural biology of such proteins remain unclear, the present study aimed at physicochemically characterizing them by an in silico approach. The complete amino acid sequences of MAYV E1, SLEV E, and OROV Gc proteins derived by conceptual translation from annotated coding regions in the reference sequence genome of the respective viruses were obtained from the NCBI Protein database in the FASTA format and then submitted to the ClustalO, Protcalc, Pepstats, Predator, Proscan, PCprof, Phyre2, and 3Drefine web servers for the determination of sequence identities, the estimation of residual properties, the prediction of secondary structures, the identification of potential post-translational modifications, the recognition of antigenic propensities, and the modeling/refinement of three-dimensional structures. Sequence identities were 20.44%, 18.82%, and 13.70% between MAYV/SLEV, SLEV/OROV, and MAYV/OROV fusion proteins, respectively. As for the residual properties, MAYV E1 and SLEV E proteins showed a predominance of the non-polar profile (56% and 55% of the residues, respectively), whereas the OROV Gc protein showed a predominance of the polar profile (52% of the residues). Regarding predicted secondary structures, MAYV E1 and SLEV E proteins showed fewer alpha-helices (16.51% and 15.17%, respectively) than beta-sheets (21.79% and 25.15%, respectively), while the opposite was observed in the OROV Gc protein (20.39% alpha-helices and 12.14% beta-sheets). Regarding post-translational modifications, MAYV E1, SLEV E, and OROV Gc proteins showed greater relative potential for protein kinase C phosphorylation, N-myristoylation, and casein kinase II phosphorylation, respectively. Finally, antigenic propensities were higher in the N-terminus half than in the C-terminus half of these three proteins, whose three-dimensional structures revealed three distinctive domains. In conclusion, MAYV E1 and SLEV E proteins were found to share more physicochemical characteristics with each other than the OROV Gc protein, although they are all grouped under the same class of viral fusion proteins. [ABSTRACT FROM AUTHOR]

Computational models are increasingly used in high-impact decision making in science, engineering, and medicine. The National Aeronautics and Space Administration (NASA) uses computational models to perform complex experiments that are otherwise prohibitively expensive or require a microgravity environment. Similarly, the Food and Drug Administration (FDA) and European Medicines Agency (EMA) have began accepting models and simulations as forms of evidence for pharmaceutical and medical device approval. It is crucial that computational models meet a standard of credibility when using them in high-stakes decision making. For this reason, institutes including NASA, the FDA, and the EMA have developed standards to promote and assess the credibility of computational models and simulations. However, due to the breadth of models these institutes assess, these credibility standards are mostly qualitative and avoid making specific recommendations. On the other hand, modeling and simulation in systems biology is a narrower domain and several standards are already in place. As systems biology models increase in complexity and influence, the development of a credibility assessment system is crucial. Here we review existing standards in systems biology, credibility standards in other science, engineering, and medical fields, and propose the development of a credibility standard for systems biology models. [ABSTRACT FROM AUTHOR]

Background: Despite the advancements in multiagent chemotherapy in the past years, up to 10% of Hodgkin's Lymphoma (HL) cases are refractory to treatment and, after remission, patients experience an elevated risk of death from all causes. These complications are dependent on the treatment and therefore an increase in the prognostic accuracy of HL can help improve these outcomes and control treatment-related toxicity. Due to the low incidence of this cancer, there is a lack of works comprehensively assessing the predictability of treatment response, especially by resorting to machine learning (ML) advances and high-throughput technologies. Methods: We present a methodology for predicting treatment response after two courses of Adriamycin, Bleomycin, Vinblastine and Dacarbazine (ABVD) chemotherapy, through the analysis of gene expression profiles using state-of-the-art ML algorithms. We work with expression levels of tumor samples of Classical Hodgkin's Lymphoma patients, obtained through the NanoString's nCounter platform. The presented approach combines dimensionality reduction procedures and hyperparameter optimization of various elected classifiers to retrieve reference predictability levels of refractory response to ABVD treatment using the regulatory profile of diagnostic tumor samples. In addition, we propose a data transformation procedure to map the original data space into a more discriminative one using biclustering, where features correspond to discriminative putative regulatory modules. Results: Through an ensemble of feature selection procedures, we identify a set of 14 genes highly representative of the result of an fuorodeoxyglucose Positron Emission Tomography (FDG-PET) after two courses of ABVD chemotherapy. The proposed methodology further presents an increased performance against reference levels, with the proposed space transformation yielding improvements in the majority of the tested predictive models (e.g. Decision Trees show an improvement of 20pp in both precision and recall). Conclusions: Taken together, the results reveal improvements for predicting treatment response in HL disease by resorting to sophisticated statistical and ML principles. This work further consolidates the current hypothesis on the structural difficulty of this prognostic task, showing that there is still a considerable gap to be bridged for these technologies to reach the necessary maturity for clinical practice. [ABSTRACT FROM AUTHOR]

Predicting inhibitor potency is critical in drug design and development, yet it has remained one of computational biology's biggest unresolved challenges. Here, we show that in the case of the N-myristoyltransferase (NMT), this problem could be traced to the mechanisms by which the NMT enzyme is inhibited. NMT adopts open or closed conformations necessary for orchestrating the different steps of the catalytic process. The results indicate that the potency of the NMT inhibitors is determined by their ability to stabilize the enzyme conformation in the closed state, and that in this state, the small molecules themselves are trapped and locked inside the structure of the enzyme, creating a significant barrier for their dissociation. By using molecular dynamics simulations, we demonstrate that the conformational stabilization of the protein molecule in its closed form is highly correlated with the ligands activity and can be used to predict their potency. Hence, predicting inhibitor potency in silico might depend on modeling the conformational changes of the protein molecule upon binding of the ligand rather than estimating the changes in free binding energy that arise from their interaction. [ABSTRACT FROM AUTHOR]

Background: Chronic lung diseases are characterized by impaired lung function. Given that many diseases have shared clinical symptoms and pathogenesis, identifying shared pathogenesis can help the design of preventive and therapeutic strategies. This study aimed to evaluate the proteins and pathways of chronic obstructive pulmonary disease (COPD), asthma, idiopathic pulmonary fibrosis (IPF), and mustard lung disease (MLD). Methods and results: After collecting the data and determining the gene list of each disease, gene expression changes were examined in comparison to healthy individuals. Protein–protein interaction (PPI) and pathway enrichment analysis were used to evaluate genes and shared pathways of the four diseases. There were 22 shared genes, including ACTB, AHSG, ALB, APO, A1, APO C3, FTH1, GAPDH, GC, GSTP1, HP, HSPB1, IGKC, KRT10, KRT9, LCN1, PSMA2, RBP4, 100A8, S100A9, TF, and UBE2N. The major biological pathways in which these genes are involved are inflammatory pathways. Some of these genes activate different pathways in each disease, leading to the induction or inhibition of inflammation. Conclusion: Identification of the genes and shared pathways of diseases can contribute to identifying pathogenesis pathways and designing preventive and therapeutic strategies. [ABSTRACT FROM AUTHOR]