How can quantum computing be applied in clinical trial design and optimization?

Clinical trials are necessary for assessing the safety and efficacy of treatments. However, trial timelines are severely delayed with minimal success due to a multitude of factors, including imperfect trial site selection, cohort recruitment challenges, lack of efficacy, absence of reliable biomarkers, etc. Each of these factors possesses a unique computational challenge, such as data management, trial simulations, statistical analyses, and trial optimization. Recent advancements in quantum computing offer a promising opportunity to overcome these hurdles. In this opinion we uniquely explore the application of quantum optimization and quantum machine learning (QML) to the design and execution of clinical trials. We examine the current capabilities and limitations of quantum computing and outline its potential to streamline clinical trials.

Generalized open-source workflows for atomistic molecular dynamics simulations of viral helicases.

Viral helicases are promising targets for the development of antiviral therapies. Given their vital function of unwinding double-stranded nucleic acids, inhibiting them blocks the viral replication cycle. Previous studies have elucidated key structural details of these helicases, including the location of substrate binding sites, flexible domains, and the discovery of potential inhibitors. Here we present a series of new Galaxy tools and workflows for performing and analyzing molecular dynamics simulations of viral helicases. We first validate them by demonstrating recapitulation of data from previous simulations of Zika (NS3) and SARS-CoV-2 (NSP13) helicases in apo and complex with inhibitors. We further demonstrate the utility and generalizability of these Galaxy workflows by applying them to new cases, proving their usefulness as a widely accessible method for exploring antiviral activity.

Homozygous EPRS1 missense variant causing hypomyelinating leukodystrophy-15 alters variant-distal mRNA m6A site accessibility.

Hypomyelinating leukodystrophy (HLD) is an autosomal recessive disorder characterized by defective central nervous system myelination. Exome sequencing of two siblings with severe cognitive and motor impairment and progressive hypomyelination characteristic of HLD revealed homozygosity for a missense single-nucleotide variant (SNV) in EPRS1 (c.4444 C > A; p.Pro1482Thr), encoding glutamyl-prolyl-tRNA synthetase, consistent with HLD15. Patient lymphoblastoid cell lines express markedly reduced EPRS1 protein due to dual defects in nuclear export and cytoplasmic translation of variant EPRS1 mRNA. Variant mRNA exhibits reduced METTL3 methyltransferase-mediated writing of N6-methyladenosine (m6A) and reduced reading by YTHDC1 and YTHDF1/3 required for efficient mRNA nuclear export and translation, respectively. In contrast to current models, the variant does not alter the sequence of m6A target sites, but instead reduces their accessibility for modification. The defect was rescued by antisense morpholinos predicted to expose m6A sites on target EPRS1 mRNA, or by m6A modification of the mRNA by METTL3-dCas13b, a targeted RNA methylation editor. Our bioinformatic analysis predicts widespread occurrence of SNVs associated with human health and disease that similarly alter accessibility of distal mRNA m6A sites. These results reveal a new RNA-dependent etiologic mechanism by which SNVs can influence gene expression and disease, consequently generating opportunities for personalized, RNA-based therapeutics targeting these disorders.

A Perspective on Protein Structure Prediction Using Quantum Computers.

Despite the recent advancements by deep learning methods such as AlphaFold2, in silico protein structure prediction remains a challenging problem in biomedical research. With the rapid evolution of quantum computing, it is natural to ask whether quantum computers can offer some meaningful benefits for approaching this problem. Yet, identifying specific problem instances amenable to quantum advantage and estimating the quantum resources required are equally challenging tasks. Here, we share our perspective on how to create a framework for systematically selecting protein structure prediction problems that are amenable for quantum advantage, and estimate quantum resources for such problems on a utility-scale quantum computer. As a proof-of-concept, we validate our problem selection framework by accurately predicting the structure of a catalytic loop of the Zika Virus NS3 Helicase, on quantum hardware.

Sphingolipid biosynthesis is essential for metabolic rewiring during TH17 cell differentiation.

T helper 17 (TH17) cells are implicated in autoimmune diseases, and several metabolic processes are shown to be important for their development and function. In this study, we report an essential role for sphingolipids synthesized through the de novo pathway in TH17 cell development. Deficiency of SPTLC1, a major subunit of serine palmitoyl transferase enzyme complex that catalyzes the first and rate-limiting step of de novo sphingolipid synthesis, impaired glycolysis in differentiating TH17 cells by increasing intracellular reactive oxygen species (ROS) through enhancement of nicotinamide adenine dinucleotide phosphate oxidase 2 activity. Increased ROS leads to impaired activation of mammalian target of rapamycin C1 and reduced expression of hypoxia-inducible factor 1-alpha and c-Myc-induced glycolytic genes. SPTLCI deficiency protected mice from developing experimental autoimmune encephalomyelitis and experimental T cell transfer colitis. Our results thus show a critical role for de novo sphingolipid biosynthetic pathway in shaping adaptive immune responses with implications in autoimmune diseases.

Contemporary Antiretroviral Therapy Dysregulates Iron Transport and Augments Mitochondrial Dysfunction in HIV-Infected Human Microglia and Neural-Lineage Cells.

HIV-associated cognitive dysfunction during combination antiretroviral therapy (cART) involves mitochondrial dysfunction, but the impact of contemporary cART on chronic metabolic changes in the brain and in latent HIV infection is unclear. We interrogated mitochondrial function in a human microglia (hµglia) cell line harboring inducible HIV provirus and in SH-SY5Y cells after exposure to individual antiretroviral drugs or cART, using the MitoStress assay. cART-induced changes in protein expression, reactive oxygen species (ROS) production, mitochondrial DNA copy number, and cellular iron were also explored. Finally, we evaluated the ability of ROS scavengers or plasmid-mediated overexpression of the antioxidant iron-binding protein, Fth1, to reverse mitochondrial defects. Contemporary antiretroviral drugs, particularly bictegravir, depressed multiple facets of mitochondrial function by 20-30%, with the most pronounced effects in latently infected HIV+ hµglia and SH-SY5Y cells. Latently HIV-infected hµglia exhibited upregulated glycolysis. Increases in total and/or mitochondrial ROS, mitochondrial DNA copy number, and cellular iron accompanied mitochondrial defects in hµglia and SH-SY5Y cells. In SH-SY5Y cells, cART reduced mitochondrial iron-sulfur-cluster-containing supercomplex and subunit expression and increased Nox2 expression. Fth1 overexpression or pre-treatment with N-acetylcysteine prevented cART-induced mitochondrial dysfunction. Contemporary cART impairs mitochondrial bioenergetics in hµglia and SH-SY5Y cells, partly through cellular iron accumulation; some effects differ by HIV latency.

Design and Synthesis of Thiazole Scaffold-Based Small Molecules as Anticancer Agents Targeting the Human Lactate Dehydrogenase A Enzyme.

A new series of thiazole central scaffold-based small molecules of hLDHA inhibitors were designed using an in silico approach. Molecular docking analysis of designed molecules with hLDHA (PDB ID: 1I10) demonstrates that Ala 29, Val 30, Arg 98, Gln 99, Gly 96, and Thr 94 possessed strong interaction with the compounds. Compounds 8a, 8b, and 8d showed good binding affinity (-8.1 to -8.8 kcal/mol), whereas an additional interaction of NO2 at the ortho position in compounds 8c with Gln 99 through hydrogen bonding enhanced the affinity to -9.8 kcal/mol. Selected high-scored compounds were synthesized and screened for hLDHA inhibitory activities and in vitro anticancer activity in six cancer cell lines. Biochemical enzyme inhibition assays showed the highest hLDHA inhibitory activity observed with compounds 8b, 8c, and 8l. Compounds 8b, 8c, 8j, 8l, and 8m depicted significant anticancer activities, exhibiting IC50 values in the range of 1.65-8.60 µM in HeLa and SiHa cervical cancer cell lines. Compounds 8j and 8m exhibited notable anticancer activity with IC50 values of 7.90 and 5.15 µM, respectively, in liver cancer cells (HepG2). Interestingly, compounds 8j and 8m did not induce noticeable toxicity in the human embryonic kidney cells (HEK293). Insilico absorption, distribution, metabolism, and excretion profiling demonstrates that the compounds possess drug-likeness, and results may pave the way for the development of novel thiazole-based biologically active small molecules for therapeutics.

Phosphorylation-mediated regulation of the Bacillus anthracis phosphoglycerate mutase by the Ser/Thr protein kinase PrkC.

Bacillus anthracis Ser/Thr protein kinase PrkC is necessary for phenotypic memory and spore germination, and the loss of PrkC-dependent phosphorylation events affect the spore development. During sporulation, Bacillus sp. can store 3-Phosphoglycerate (3-PGA) that will be required at the onset of germination when ATP will be necessary. The Phosphoglycerate mutase (Pgm) catalyzes the isomerization of 2-PGA and 3-PGA and is important for spore germination as a key metabolic enzyme that maintains 3-PGA pool at later events. Therefore, regulation of Pgm is important for an efficient spore germination process and metabolic switching. While the increased expression of Pgm in B. anthracis decreases spore germination efficiency, it remains unexplored if PrkC could directly influence Pgm activity. Here, we report the phosphorylation and regulation of Pgm by PrkC and its impact on Pgm stability and catalytic activity. Mass spectrometry revealed Pgm phosphorylation on seven threonine residues. In silico mutational analysis highlighted the role of Thr459 residue towards metal and substrate binding. Altogether, we demonstrated that PrkC-mediated Pgm phosphorylation negatively regulates its activity that is essential to maintain Pgm in its apo-like isoform before germination. This study advances the role of Pgm regulation that represents an important switch for B. anthracis resumption of metabolism and spore germination.

Genomics to Notebook (g2nb): extending the electronic notebook to address the challenges of bioinformatics analysis.

We present Genomics to Notebook (g2nb), an environment that combines the JupyterLab notebook system with widely-used bioinformatics platforms. Galaxy, GenePattern, and the JavaScript versions of IGV and Cytoscape are currently available within g2nb. The analyses and visualizations within those platforms are presented as cells in a notebook, making thousands of genomics methods available within the notebook metaphor and allowing notebooks to contain workflows utilizing multiple software packages on remote servers, all without the need for programming. The g2nb environment is, to our knowledge, the only notebook-based system that incorporates multiple bioinformatics analysis platforms into a notebook interface.

CNV-ClinViewer: enhancing the clinical interpretation of large copy-number variants online.

MOTIVATION: Pathogenic copy-number variants (CNVs) can cause a heterogeneous spectrum of rare and severe disorders. However, most CNVs are benign and are part of natural variation in human genomes. CNV pathogenicity classification, genotype-phenotype analyses, and therapeutic target identification are challenging and time-consuming tasks that require the integration and analysis of information from multiple scattered sources by experts. RESULTS: Here, we introduce the CNV-ClinViewer, an open-source web application for clinical evaluation and visual exploration of CNVs. The application enables real-time interactive exploration of large CNV datasets in a user-friendly designed interface and facilitates semi-automated clinical CNV interpretation following the ACMG guidelines by integrating the ClassifCNV tool. In combination with clinical judgment, the application enables clinicians and researchers to formulate novel hypotheses and guide their decision-making process. Subsequently, the CNV-ClinViewer enhances for clinical investigators' patient care and for basic scientists' translational genomic research. AVAILABILITY AND IMPLEMENTATION: The web application is freely available at https://cnv-ClinViewer.broadinstitute.org and the open-source code can be found at https://github.com/LalResearchGroup/CNV-clinviewer.

Galaxy Training: A powerful framework for teaching!

There is an ongoing explosion of scientific datasets being generated, brought on by recent technological advances in many areas of the natural sciences. As a result, the life sciences have become increasingly computational in nature, and bioinformatics has taken on a central role in research studies. However, basic computational skills, data analysis, and stewardship are still rarely taught in life science educational programs, resulting in a skills gap in many of the researchers tasked with analysing these big datasets. In order to address this skills gap and empower researchers to perform their own data analyses, the Galaxy Training Network (GTN) has previously developed the Galaxy Training Platform (https://training.galaxyproject.org), an open access, community-driven framework for the collection of FAIR (Findable, Accessible, Interoperable, Reusable) training materials for data analysis utilizing the user-friendly Galaxy framework as its primary data analysis platform. Since its inception, this training platform has thrived, with the number of tutorials and contributors growing rapidly, and the range of topics extending beyond life sciences to include topics such as climatology, cheminformatics, and machine learning. While initially aimed at supporting researchers directly, the GTN framework has proven to be an invaluable resource for educators as well. We have focused our efforts in recent years on adding increased support for this growing community of instructors. New features have been added to facilitate the use of the materials in a classroom setting, simplifying the contribution flow for new materials, and have added a set of train-the-trainer lessons. Here, we present the latest developments in the GTN project, aimed at facilitating the use of the Galaxy Training materials by educators, and its usage in different learning environments.

Evaluating homophily in networks via HONTO (HOmophily Network TOol): a case study of chromosomal interactions in human PPI networks.

SUMMARY: It has been observed in different kinds of networks, such as social or biological ones, a typical behavior inspired by the general principle 'similarity breeds connections'. These networks are defined as homophilic as nodes belonging to the same class preferentially interact with each other. In this work, we present HONTO (HOmophily Network TOol), a user-friendly open-source Python3 package designed to evaluate and analyze homophily in complex networks. The tool takes in input from the network along with a partition of its nodes into classes and yields a matrix whose entries are the homophily/heterophily z-score values. To complement the analysis, the tool also provides z-score values of nodes that do not interact with any other node of the same class. Homophily/heterophily z-scores values are presented as a heatmap allowing a visual at-a-glance interpretation of results. AVAILABILITY AND IMPLEMENTATION: Tool's source code is available at https://github.com/cumbof/honto under the MIT license, installable as a package from PyPI (pip install honto) and conda-forge (conda install -c conda-forge honto), and has a wrapper for the Galaxy platform available on the official Galaxy ToolShed (Blankenberg et al., 2014) at https://toolshed.g2.bx.psu.edu/view/fabio/honto.

Analysis of nested alternate open reading frames and their encoded proteins.

Transcriptional and post-transcriptional mechanisms diversify the proteome beyond gene number, while maintaining a sequence relationship between original and altered proteins. A new mechanism breaks this paradigm, generating novel proteins by translating alternative open reading frames (Alt-ORFs) within canonical host mRNAs. Uniquely, 'alt-proteins' lack sequence homology with host ORF-derived proteins. We show global amino acid frequencies, and consequent biochemical characteristics of Alt-ORFs nested within host ORFs (nAlt-ORFs), are genetically-driven, and predicted by summation of frequencies of hundreds of encompassing host codon-pairs. Analysis of 101 human nAlt-ORFs of length ≥150 codons confirms the theoretical predictions, revealing an extraordinarily high median isoelectric point (pI) of 11.68, due to anomalous charged amino acid levels. Also, nAlt-ORF proteins exhibit a >2-fold preference for reading frame 2 versus 3, predicted mitochondrial and nuclear localization, and elevated codon adaptation index indicative of natural selection. Our results provide a theoretical and conceptual framework for exploration of these largely unannotated, but potentially significant, alternative ORFs and their encoded proteins.

Delivery of ceramide phosphoethanolamine lipids to the cleavage furrow through the endocytic pathway is essential for male meiotic cytokinesis.

Cell division, wherein 1 cell divides into 2 daughter cells, is fundamental to all living organisms. Cytokinesis, the final step in cell division, begins with the formation of an actomyosin contractile ring, positioned midway between the segregated chromosomes. Constriction of the ring with concomitant membrane deposition in a specified spatiotemporal manner generates a cleavage furrow that physically separates the cytoplasm. Unique lipids with specific biophysical properties have been shown to localize to intercellular bridges (also called midbody) connecting the 2 dividing cells; however, their biological roles and delivery mechanisms remain largely unknown. In this study, we show that ceramide phosphoethanolamine (CPE), the structural analog of sphingomyelin, has unique acyl chain anchors in Drosophila spermatocytes and is essential for meiotic cytokinesis. The head group of CPE is also important for spermatogenesis. We find that aberrant central spindle and contractile ring behavior but not mislocalization of phosphatidylinositol phosphates (PIPs) at the plasma membrane is responsible for the male meiotic cytokinesis defect in CPE-deficient animals. Further, we demonstrate the enrichment of CPE in multivesicular bodies marked by Rab7, which in turn localize to cleavage furrow. Volume electron microscopy analysis using correlative light and focused ion beam scanning electron microscopy shows that CPE-enriched Rab7 positive endosomes are juxtaposed on contractile ring material. Correlative light and transmission electron microscopy reveal Rab7 positive endosomes as a multivesicular body-like organelle that releases its intraluminal vesicles in the vicinity of ingressing furrows. Genetic ablation of Rab7 or Rab35 or expression of dominant negative Rab11 results in significant meiotic cytokinesis defects. Further, we show that Rab11 function is required for localization of CPE positive endosomes to the cleavage furrow. Our results imply that endosomal delivery of CPE to ingressing membranes is crucial for meiotic cytokinesis.

GenomicSuperSignature facilitates interpretation of RNA-seq experiments through robust, efficient comparison to public databases.

Millions of transcriptomic profiles have been deposited in public archives, yet remain underused for the interpretation of new experiments. We present a method for interpreting new transcriptomic datasets through instant comparison to public datasets without high-performance computing requirements. We apply Principal Component Analysis on 536 studies comprising 44,890 human RNA sequencing profiles and aggregate sufficiently similar loading vectors to form Replicable Axes of Variation (RAV). RAVs are annotated with metadata of originating studies and by gene set enrichment analysis. Functionality to associate new datasets with RAVs, extract interpretable annotations, and provide intuitive visualization are implemented as the GenomicSuperSignature R/Bioconductor package. We demonstrate the efficient and coherent database search, robustness to batch effects and heterogeneous training data, and transfer learning capacity of our method using TCGA and rare diseases datasets. GenomicSuperSignature aids in analyzing new gene expression data in the context of existing databases using minimal computing resources.

Expanding the Galaxy's reference data.

Summary: Properly and effectively managing reference datasets is an important task for many bioinformatics analyses. Refgenie is a reference asset management system that allows users to easily organize, retrieve and share such datasets. Here, we describe the integration of refgenie into the Galaxy platform. Server administrators are able to configure Galaxy to make use of reference datasets made available on a refgenie instance. In addition, a Galaxy Data Manager tool has been developed to provide a graphical interface to refgenie's remote reference retrieval functionality. A large collection of reference datasets has also been made available using the CVMFS (CernVM File System) repository from GalaxyProject.org, with mirrors across the USA, Canada, Europe and Australia, enabling easy use outside of Galaxy. Availability and implementation: The ability of Galaxy to use refgenie assets was added to the core Galaxy framework in version 22.01, which is available from https://github.com/galaxyproject/galaxy under the Academic Free License version 3.0. The refgenie Data Manager tool can be installed via the Galaxy ToolShed, with source code managed at https://github.com/BlankenbergLab/galaxy-tools-blankenberg/tree/main/data_managers/data_manager_refgenie_pull and released using an MIT license. Access to existing data is also available through CVMFS, with instructions at https://galaxyproject.org/admin/reference-data-repo/. No new data were generated or analyzed in support of this research.

PDAUG: a Galaxy based toolset for peptide library analysis, visualization, and machine learning modeling.

BACKGROUND: Computational methods based on initial screening and prediction of peptides for desired functions have proven to be effective alternatives to lengthy and expensive biochemical experimental methods traditionally utilized in peptide research, thus saving time and effort. However, for many researchers, the lack of expertise in utilizing programming libraries, access to computational resources, and flexible pipelines are big hurdles to adopting these advanced methods. RESULTS: To address the above mentioned barriers, we have implemented the peptide design and analysis under Galaxy (PDAUG) package, a Galaxy-based Python powered collection of tools, workflows, and datasets for rapid in-silico peptide library analysis. In contrast to existing methods like standard programming libraries or rigid single-function web-based tools, PDAUG offers an integrated GUI-based toolset, providing flexibility to build and distribute reproducible pipelines and workflows without programming expertise. Finally, we demonstrate the usability of PDAUG in predicting anticancer properties of peptides using four different feature sets and assess the suitability of various ML algorithms. CONCLUSION: PDAUG offers tools for peptide library generation, data visualization, built-in and public database peptide sequence retrieval, peptide feature calculation, and machine learning (ML) modeling. Additionally, this toolset facilitates researchers to combine PDAUG with hundreds of compatible existing Galaxy tools for limitless analytic strategies.

Evaluation of vertical and horizontal distribution of particulate matter near an urban roadway using an unmanned aerial vehicle.

Measurement of traffic emissions has gained a lot of interest in recent times due to its contribution to urban pollution. This paper reports the outcome from an unmanned aerial vehicle (UAV) based measurement of PM concentration near an urban roadway at Kolkata, India. A total of 54 flights were carried out for simultaneous measurements of PM1, PM2.5 and PM10 mass concentration and meteorological parameters in vertical as well as in horizontal direction. Results for the vertical flight up to 100 m showed that the PM1, PM2.5 and PM10 concentrations at higher altitudes are less (mean; 24.6, 39.9 and 103.8 µg m-3) compared to the respective ground level concentrations (mean; 26.3, 50.4 and 201.9 µg m-3). For all the three particle sizes, the majority of the cases of higher PM concentration at higher altitudes happened during the evening flight. Low mixing height and low wind speed are suggested to be the reasons for the poor dispersion of pollutants in the evening. While there was a 7-10% fall of fine particles (PM1 and PM2.5) mass concentrations up to 90 m away from the road, no trend could be seen for PM10. The random forest model to predict the UAV/Ground concentration ratio showed high accuracy (R2 = 0.82-0.95) for all three particle sizes. This is an important finding from this study, which shows how UAV measurement data can be used to generate models that can predict the higher altitude concentrations from the ground based measurements.

Re-Expression of ERα and AR in Receptor Negative Endocrine Cancers via GSK3 Inhibition.

DNA methylation, catalyzed by DNA methyltransferase (DNMT), is a well-characterized epigenetic modification in cancer cells. In particular, promoter hypermethylation of AR and ESR1 results in loss of expression on Androgen Receptor (AR) and Estrogen Receptor (ER), respectively, and is associated with a hormone refractory state. We now report that Glycogen Synthase Kinase 3 (GSK3) phosphorylates DNMT1 at S714, which is localized to a 62 amino acid region referred to as auto-inhibitory linker, which functions to occlude the DNA from the active site of DNMT1 to prevent the methylation of unmethylated DNA. Molecular Dynamics simulation indicates that phosphorylation at S714 resulted in conformational rearrangement of the autoinhibitory domain that inactivated its ability to block the methylation of unmethylated DNA and resulted in enhanced DNA binding. Treatment with a novel and more selective inhibitor of GSK3 resulted in decreased methylation of the promoter region of genes encoding the Androgen Receptor (AR) and Estrogen Receptor alpha (ERa) and re-expression of the AR and ERa in AR negative prostate cancer and ER negative breast cancer cells, respectively. As a result, concurrent treatment with the GSK3 inhibitor resulted in responsiveness of AR negative prostate cancer and ER negative breast cancer cells to inhibitors of the AR or ER, respectively, in in vitro and in vivo experimental models.

SimText: a text mining framework for interactive analysis and visualization of similarities among biomedical entities.

SUMMARY: Literature exploration in PubMed on a large number of biomedical entities (e.g. genes, diseases or experiments) can be time-consuming and challenging, especially when assessing associations between entities. Here, we describe SimText, a user-friendly toolset that provides customizable and systematic workflows for the analysis of similarities among a set of entities based on text. SimText can be used for (i) text collection from PubMed and extraction of words with different text mining approaches, and (ii) interactive analysis and visualization of data using unsupervised learning techniques in an interactive app. AVAILABILITY AND IMPLEMENTATION: We developed SimText as an open-source R software and integrated it into Galaxy (https://usegalaxy.eu), an online data analysis platform with supporting self-learning training material available at https://training.galaxyproject.org. A command-line version of the toolset is available for download from GitHub (https://github.com/dlal-group/simtext) or as Docker image (https://hub.docker.com/r/dlalgroup/simtext/tags.). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.