Biological Databases Research Articles

Anti-angiogenesis agents plus chemoradiotherapy for locally advanced nasopharyngeal cancer: a systematic review and meta-analysis.

To evaluate literature evidences about the efficacy and safety of anti-angiogenesis agents plus chemoradiotherapy versus chemoradiotherapy in the treatment of locally advanced nasopharyngeal carcinoma. The relevant literature was systematically searched from the date of establishment to April 2023 in PubMed, Embase, Web of Science, The Cochrane Library, Chinese National Knowledge Infrastructure, Chinese Biological Medicine, Wanfang and VIP database. Search terms included: Nasopharyngeal Neoplasms, Angiogenesis inhibitors, Endostar, Anlotinib, Apatinib, Bevacizumab, Sunitinib, Pazopanib, Chemoradiotherapy. The literature was strictly screened according to the inclusion and exclusion criteria, and 8 eligible studies were finally included in our meta-analysis (4 randomized controlled trials and 4 retrospective studies). A total of 642 patients were included, with 316 in the anti-angiogenesis agents plus chemoradiotherapy group and 326 in the chemoradiotherapy group. The results of our meta-analysis showed that compared with chemoradiotherapy group, the complete response rate (RR = 1.35, 95% CI 1.05-1.74, P = 0.02), objective response rate (RR = 1.26, 95% CI 1.12-1.43, P = 0.0002) in the anti-angiogenesis agents plus chemoradiotherapy group were significantly improved. In terms of safety, there was a higher incidence of cardiac arrhythmia (RR = 3.63, 95% CI 1.16-11.37, P = 0.03) and hypertension (RR = 1.85, 95% CI 1.04-3.27, P = 0.004) in the anti-angiogenesis agents plus chemoradiotherapy group, while no statistically significant differences were reported in other adverse reactions (all P > 0.05). Compared with chemoradiotherapy, anti-angiogenesis agents plus chemoradiotherapy could bring more benefits in terms of short-term efficacy, particularly by notably improving both complete response rate and objective response rate, and overall adverse reactions were acceptable. Anti-angiogenesis agents plus chemoradiotherapy may provide a promising direction for the treatment of locally advanced nasopharyngeal carcinoma. https://inplasy.com/inplasy-2023-8-0076/ , registration number INPLASY202380076.

Purple is the new green: biopigments and spectra of Earth-like purple worlds

ABSTRACT With more than 5500 detected exoplanets, the search for life is entering a new era. Using life on Earth as our guide, we look beyond green landscapes to expand our ability to detect signs of surface life on other worlds. While oxygenic photosynthesis gives rise to modern green landscapes, bacteriochlorophyll-based anoxygenic phototrophs can also colour their habitats and could dominate a much wider range of environments on Earth-like exoplanets. Here, we characterize the reflectance spectra of a collection of purple sulfur and purple non-sulfur bacteria from a variety of anoxic and oxic environments. We present models for Earth-like planets where purple bacteria dominate the surface and show the impact of their signatures on the reflectance spectra of terrestrial exoplanets. Our research provides a new resource to guide the detection of purple bacteria and improves our chances of detecting life on exoplanets with upcoming telescopes. Our biological pigment data base for purple bacteria and the high-resolution spectra of Earth-like planets, including ocean worlds, snowball planets, frozen worlds, and Earth analogues, are available online, providing a tool for modellers and observers to train retrieval algorithms, optimize search strategies, and inform models of Earth-like planets, where purple is the new green.

Analytical performance specifications based on biological variation data- considerations, strengths and limitations.

Analytical performance specifications (APS) are typically established through one of three models: (i)outcome studies, (ii) biological variation (BV), or (iii) state-of-the-art. Presently, The APS can, for most measurands that have a stable concentration, be based on BV. BV based APS, defined for imprecision, bias, total allowable error and allowable measurement uncertainty, are applied to many different processes in the laboratory. When calculating APS, it is important to consider the different APS formulae, for what setting they are to be applied and if they are suitable for the intended purpose. In this opinion paper, we elucidate the background, limitations, strengths, and potential intended applications of the different BV based APS formulas. When using BV data to set APS, it is important to consider that all formulae are contingent on accurate and relevant BV estimates. During the last decade, efficient procedures have been established to obtain reliable BV estimates that are presented in the EFLM biological variation database. The database publishes detailed BV data for numerous measurands, global BV estimates derived from meta-analysis of quality-assured studies of similar study design and automatic calculation of BV based APS.

GENETIC ARCHIVES AND LIFELINE: UNRAVELING THE UMBILICAL CORD’S BIOLOGICAL DATABASE

Biological databases are critical components of the field of bioinformatics, which is dedicated to managing, organising, and making sense of the vast and diverse biological data generated by scientific research. These databases play a fundamental role in modern life sciences, allowing researchers, clinicians, and scientists to access, analyse, and interpret a wide range of biological information. These databases serve as invaluable resources, enabling researchers to access, manage, and analyse biological data, accelerating scientific discoveries and enhancing our understanding of life's complexities. There are various types of biological databases that serve as critical resources for researchers and scientists in the field of life sciences. These databases are categorised based on the types of biological data they store and manage. Keywords: Bioinformatics, biological database, genetics, scientific research, genomics, medical advancements, data repositories

Exploring the therapeutic mechanism of potential phytocompounds from Kalanchoe pinnata in the treatment of diabetes mellitus by integrating network pharmacology, molecular docking and simulation approach

Since ancient times, bioactive phytocompounds from different parts of medicinal plants have been used to heal various disease ailments and they are now regarded as a valuable source of disease prevention globally.Kalanchoe pinnatais a member of the Crassulaceae family; it has a long history of usage in traditional ayurvedic treatment. Analysis of bioactive compounds for their potential anti-type-2 diabetes mellitus (T2DM) mechanism along within-vitroandin-silicoapproaches was studied in the present research. The alpha-amylase and alpha-glucosidase inhibitory activity of methanolic extract of Kalanchoe pinnata (α-amylase: IC50 29.50 ± 0.04 μg/ml; α-glucosidase IC50 32.04 ± 0.35 μg/ml) exhibit a high degree of similarity to the standard drug acarbose (IC50 35.82 ± 0.14 μg/ml). Different biological databases were used to list phytocompounds from the plant, and ADME analysis using swissADME was carried out to screen compounds that obeyed the Lipinski rule of 5 and were employed further. STRING and KEGG pathway analysis was performed for gene enrichment analysis followed by network pharmacology to identify key target proteins involved in DM. AMY2A, NOX4, RPS6KA3, ADRA2A, CHRM5, and IL2 were identified as core targets for luteolin, kaempferol, alpha amyrin, stigmasterol compounds by modulating neuroactive ligand interaction, P13-AKT, MAPK, and PPAR signaling pathways. Molecular docking was performed to study the binding affinity among bioactive compounds ofK. pinnataagainst aldose reductase, alpha-amylase, alpha-glucosidase, and dipeptidyl peptidase IV. Alpha-amylase-friedelin [FRI] and alpha-amylase-acarbose [STD] complexes were subjected to molecular simulation for a 200 ns duration that depicted the stability of the compounds and proteins. In the current study, employing dual approach in-silico and in-vitroenzyme assays has yielded a comprehensive and strong understanding of its potential therapeutic properties, making a significant step towards the development of novel anti-diabetic treatment.

BioVDB: biological vector database for high-throughput gene expression meta-analysis.

High-throughput sequencing has created an exponential increase in the amount of gene expression data, much of which is freely, publicly available in repositories such as NCBI's Gene Expression Omnibus (GEO). Querying this data for patterns such as similarity and distance, however, becomes increasingly challenging as the total amount of data increases. Furthermore, vectorization of the data is commonly required in Artificial Intelligence and Machine Learning (AI/ML) approaches. We present BioVDB, a vector database for storage and analysis of gene expression data, which enhances the potential for integrating biological studies with AI/ML tools. We used a previously developed approach called Automatic Label Extraction (ALE) to extract sample labels from metadata, including age, sex, and tissue/cell-line. BioVDB stores 438,562 samples from eight microarray GEO platforms. We show that it allows for efficient querying of data using similarity search, which can also be useful for identifying and inferring missing labels of samples, and for rapid similarity analysis.

Optimization and Performance Analysis of CAT Method for DNA Sequence Similarity Searching and Alignment.

Bioinformatics is a rapidly developing field enabling scientific experiments via computer models and simulations. In recent years, there has been an extraordinary growth in biological databases. Therefore, it is extremely important to propose effective methods and algorithms for the fast and accurate processing of biological data. Sequence comparisons are the best way to investigate and understand the biological functions and evolutionary relationships between genes on the basis of the alignment of two or more DNA sequences in order to maximize the identity level and degree of similarity. This paper presents a new version of the pairwise DNA sequences alignment algorithm, based on a new method called CAT, where a dependency with a previous match and the closest neighbor are taken into consideration to increase the uniqueness of the CAT profile and to reduce possible collisions, i.e., two or more sequence with the same CAT profiles. This makes the proposed algorithm suitable for finding the exact match of a concrete DNA sequence in a large set of DNA data faster. In order to enable the usage of the profiles as sequence metadata, CAT profiles are generated once prior to data uploading to the database. The proposed algorithm consists of two main stages: CAT profile calculation depending on the chosen benchmark sequences and sequence comparison by using the calculated CAT profiles. Improvements in the generation of the CAT profiles are detailed and described in this paper. Block schemes, pseudo code tables, and figures were updated according to the proposed new version and experimental results. Experiments were carried out using the new version of the CAT method for DNA sequence alignment and different datasets. New experimental results regarding collisions, speed, and efficiency of the suggested new implementation are presented. Experiments related to the performance comparison with Needleman-Wunsch were re-executed with the new version of the algorithm to confirm that we have the same performance. A performance analysis of the proposed algorithm based on the CAT method against the Knuth-Morris-Pratt algorithm, which has a complexity of O(n) and is widely used for biological data searching, was performed. The impact of prior matching dependencies on uniqueness for generated CAT profiles is investigated. The experimental results from sequence alignment demonstrate that the proposed CAT method-based algorithm exhibits minimal deviation, which can be deemed negligible if such deviation is considered permissible in favor of enhanced performance. It should be noted that the performance of the CAT algorithm in terms of execution time remains stable, unaffected by the length of the analyzed sequences. Hence, the primary benefit of the suggested approach lies in its rapid processing capabilities in large-scale sequence alignment, a task that traditional exact algorithms would require significantly more time to perform.

Screening of GHSR, GHRHR, GH1 genes in isolated growth hormone deficiency disease in Egyptian patients

Abstract Background Isolated growth hormone deficiency (IGHD) is a hereditary disorder that causes significant short stature. GHD has a reported incidence of 1/4000–1/10,000 births. It is caused by mutations in the major somatotroph axis genes, involving GH1, codes for growth hormone, GHSR, and GHRHR, codes for growth hormone secretagogue receptor and growth hormone-releasing hormone receptor, respectively. Aims of the study The present study aims to examine the clinical phenotype and investigate the genetic etiology of ten Egyptian patients with type I isolated growth hormone insufficiency. Patients and methods Patients recruited for the study were clinically diagnosed by two provocation tests and were subjected to a thorough history, clinical examination, and anthropometric measurements. Sanger sequencing and mutational analysis of the three genes, GH1, GHSR, and GHRHR, was our approach, performed in all enrolled IGHD patients. The variants identified were analyzed using the biological, population, sequence variants, and clinical genetics databases. Prediction of the pathogenicity of the novel variants was done by in silico prediction tools following the American College of Medical Genetics and Genomics (ACMG) guidelines. Results Sanger sequencing revealed a previously reported pathogenic mutation (NM_000823.4: c.1069C > T; p.Arg357Cys) in the GHRHR gene in one patient and a novel frameshift variant (NM_198407.2: c.1043dup; Ser349Leu fs*6) in the GHSR gene in another patient. This is the fourth report highlighting the autosomal dominant inheritance of the GHSR mutation as a cause of isolated growth hormone deficiency. A number of previously reported variants, but of rare frequency, were identified in this study. In our IGHD cases, 90% of the patients were underweight, 50% had anemia, and 80% showed hypovitaminosis D. Conclusion Our findings broaden the mutational spectrum underlying the IGHD in Egyptian patients and point out the importance of mutation screening of the GHSR and GHRHR genes. This study also acknowledges the autosomal dominant mode of inheritance of the GHSR mutation as a cause for dwarfism.

Novel Computational Methods for Cancer Drug Design.

Cancer is a complex and debilitating disease that is one of the leading causes of death in the modern world. Computational methods have contributed to the successful design and development of several drugs. The recent advances in computational methodology, coupled with the avalanche of data being acquired through high throughput genomics, proteomics, and metabolomics, are likely to increase the contribution of computational methods toward the development of more effective treatments for cancer. Recent advances in the application of neural networks for the prediction of the native conformation of proteins have provided structural information regarding the complete human proteome. In addition, advances in machine learning and network pharmacology have provided novel methods for target identification and for the utilization of biological, pharmacological, and clinical databases for the design and development of drugs. This is a review of the key advances in computational methods that have the potential for application in the design and development of drugs for cancer.

DNA marker utilization for the sustainable production of trehalose

Trehalose is a type of sugar that is known by its stability and resilience towards acid and low temperature. Furthermore, trehalose has numerous health benefits and has been used by several industries, including food, cosmetics, and pharmaceuticals. Even though trehalose could be easily produced using trehalose synthase (TreS) enzyme, a sustainable production of trehalose is still a problem. Our work aims to develop an approach to identify a novel trehalose synthase enzyme from various organisms, especially thermophilic bacteria, by implementing a deoxyribonucleic acid (DNA) marker technique. We first collected protein and DNA sequences from public biological databases and subsequently conducted sequence analysis. We then designed degenerate primers based on the conserved regions identified from the sequence analysis. The designed primers were subjected to primer characterization using Oligo Calc software. The primers were further validated via in-silico PCR amplification. In general, our designed primers possess the properties to work optimally. In addition, agarose gel electrophoresis that the primers successfully amplified nucleotides encoding TreS enzyme from all samples. Our findings may serve as a basis to discover the TreS enzyme variants which possess superior attributes, allowing the sustainable production of trehalose.

Analysis of the application of bioinformatics in the medicine

As humans develop, science is also rapidly evolving, and biological science and medical support are vital for humans need. The development of biology has been particularly important. It stretches from the initial understanding of plants and animals to human biology and micro molecular biology, from macro understanding of life to micro molecular biology. Although it has accumulated a lot of knowledge about biology, it still has many unknowns about this giant and precise system. However, in recent years, a new interdisciplinary and emerging discipline has greatly opened up peoples understanding about biological information-bioinformatics. The most fundamental research target of bioinformatics is different sequences. The most important is the amino acid sequence of proteins and the base sequence of DNA. To study sequences and their constituent components, bioinformatics has another major research target - the biological database. This involves analysing the structure, connotation, and function of biological information through basic protein and nucleic acid sequences. Therefore, bioinformatics has made tremendous applications in the medical field. This research mainly analyzes and discusses the advantages and disadvantages of the application of bioinformatics in the medical field.

Poor adherence to and persistence with biologics in generalized pustular psoriasis: A claim-based study using real-world data from two large US databases

BackgroundGeneralized pustular psoriasis (GPP) is a rare skin disease characterized by episodes of widespread sterile pustules. MethodsA retrospective cohort study using data from the US IBM MarketScan Commercial and Optum Clinformatics Data Mart databases between October 1, 2015 and March 31, 2020 was performed to describe adherence and persistence to biologics in patients with GPP. Patients were aged ≥18 years with newly diagnosed GPP (International Classification of Diseases code L40.1) and had ≥1 inpatient or ≥2 outpatient claims. ResultsBiologics were dispensed to 110 of 502 (22%) and 73 of 528 (14%) patients from MarketScan and Optum databases, respectively. The mean proportion of days covered (PDC) (range) was similar in both databases (MarketScan, 65% [8%-100%]; Optum, 59% [8%-99%]), and good adherence (≥80% PDC) was uncommon (MarketScan, 36%; Optum, 24%). Mean (standard deviation) persistence was similar in both databases (MarketScan, 287 [122] days; Optum, 261 [134] days). In Optum, the mean PDC was similar between age categories; good adherence was more common in patients aged 18 to 64 years (28%) versus ≥65 years (13%). Mean persistence was longer in patients aged 18 to 64 years (267 days) versus ≥65 years (242 days). ConclusionsOverall, adherence and persistence were generally poor and varied according to the biologic class, database, and age. Improving adherence may help improve GPP treatment outcomes.

The impact of transitive annotation on the training of taxonomic classifiers.

A common task in the analysis of microbial communities involves assigning taxonomic labels to the sequences derived from organisms found in the communities. Frequently, such labels are assigned using machine learning algorithms that are trained to recognize individual taxonomic groups based on training data sets that comprise sequences with known taxonomic labels. Ideally, the training data should rely on labels that are experimentally verified-formal taxonomic labels require knowledge of physical and biochemical properties of organisms that cannot be directly inferred from sequence alone. However, the labels associated with sequences in biological databases are most commonly computational predictions which themselves may rely on computationally-generated data-a process commonly referred to as "transitive annotation." In this manuscript we explore the implications of training a machine learning classifier (the Ribosomal Database Project's Bayesian classifier in our case) on data that itself has been computationally generated. We generate new training examples based on 16S rRNA data from a metagenomic experiment, and evaluate the extent to which the taxonomic labels predicted by the classifier change after re-training. We demonstrate that even a few computationally-generated training data points can significantly skew the output of the classifier to the point where entire regions of the taxonomic space can be disturbed. We conclude with a discussion of key factors that affect the resilience of classifiers to transitively-annotated training data, and propose best practices to avoid the artifacts described in our paper.

DRGCL: Drug Repositioning via Semantic-enriched Graph Contrastive Learning.

Drug repositioning greatly reduces drug development costs and time by discovering new indications for existing drugs. With the development of technology and large-scale biological databases, computational drug repositioning has increasingly attracted remarkable attention, which can narrow down repositioning candidates. Recently, graph neural networks (GNNs) have been widely used and achieved promising results in drug repositioning. However, the existing GNNs based methods usually focus on modeling the complex drug-disease association graph, but ignore the semantic information on the graph, which may lead to a lack of consistency of global topology information and local semantic information for the learned features. To alleviate the above challenge, we propose a novel drug repositioning model based on graph contrastive learning, termed DRGCL. First, we treat the known drug-disease associations as the topology graph. Second, we select the top- K similar neighbor from drug/disease similarity information to construct the semantic graph rather than use the traditional data augmentation strategy, thereby maximally retaining rich semantic information. Finally, we pull closer to embedding consistency of the different embedding spaces by graph contrastive learning to enhance the topology and semantic feature on the graph. We have evaluated DRGCL on four benchmark datasets and the experiment results show that the proposed DRGCL is superior to the state-of-the-art methods. Especially, the average result of DRGCL is 11.92% higher than that of the second-best method in terms of AUPRC. The case studies further demonstrate the reliability of DRGCL. Experimental datasets and experimental codes can be found in https://github.com/Jiaxiao123/DRGCL.

Machine Learning Mediated Advanced Phage and Antimicrobial Therapy- A Futuristic Approach

The emergence of antimicrobial resistance (AMR) has overwhelmed the contemporary curatives and have turned into one of the major challenges in the biomedical sector. With increasing deaths being associated with AMR every year; early detection of pathogens and development of novel drugs and alternative therapies, have all become ad hoc in diagnosis, prognosis and patient survival. Bacteriophage therapy remains a viable strategy to counteract AMR, yet unduly restrained by phage resistance. Phage infection is a natural phenomenon and can be widely manipulated in vitro using advanced techniques including the CRISPR/Cas systems which renders phage therapy an upper hand in comparison to conventional drugs. Phage identification, host range detection, determination of phage-receptor binding efficiency, adsorption rate, phage genome analysis are crucial stages in phage selection and phage cocktail preparation and moreover pivotal in flourishing phage therapy. The ascent of translational research and omics has allowed the development of quick, reliable and precise strategies for phage-based diagnosis and treatment techniques. However, in vitro evaluation of AMR and phage factors as well as storing, processing and analyzing large laboratory data outputs are expensive, time-consuming and labor-intensive. Machine learning (ML) is a utilitarian strategy to organize, store, analyze data sets and more importantly allows prediction of certain features by recognizing patterns in the data sets. With the huge number of research been carried out around the globe and enormous data sets being published and stored in databases, ML can utilize the available data to perform and guide in developing alternative therapeutics. Several ML based tools have been developed to predict resistance in host, phage grouping for cocktail preparation, resistance and lysogenic genes detection, phage genomic evaluation and to understand phage-host interactions. ML also allows the in silico analysis of large samples (drug/phage) and reduces sample size for in vitro evaluation thereby reducing overall costs, time and labor. The present review summarizes the available ML algorithms and corresponding databases used in AMR and phage research. It also emphasizes the status quo of antimicrobial and phage resistance in the healthcare sector and analyses the role of ML in analyzing biological databases in order to predict possible phage/drug-host interaction patterns, phage susceptibility, suitability of phage strains for therapy and recommends the most efficient drug combinations and treatment strategies.

System biology mediated assessment of molecular mechanism for sinapic acid against breast cancer: via network pharmacology and molecular dynamic simulation

Sinapic acid is a hydroxycinnamic acid widespread in the plant kingdom, known to be a potent anti-oxidant used for the treatment of cancer, infections, oxidative stress, and inflammation. However, the mode of action for its chemotherapeutic properties has yet not been unleashed. Hence, we aimed to identify potential targets to propose a possible molecular mechanism for sinapic acid against breast cancer. We utilized multiple system biology tools and databases like DisGeNET, DIGEP-Pred, Cytoscape, STRING, AutoDock 4.2, AutoDock vina, Schrodinger, and gromacs to predict a probable molecular mechanism for sinapic acid against breast cancer. Targets for the disease breast cancer, were identified via DisGeNET database which were further matched with proteins predicted to be modulated by sinapic acid. In addition, KEGG pathway analysis was used to identify pathways; a protein-pathway network was constructed via Cytoscape. Molecular docking was performed using three different algorithms followed by molecular dynamic simulations and MMPBSA analysis. Moreover, cluster analysis and gene ontology (GO) analysis were performed. A total of 6776 targets were identified for breast cancer; 95.38% of genes predicted to be modulated by sinapic acid were common with genes of breast cancer. The ‘Pathways in cancer’ was predicted to be modulated by most umber of proteins. Further, PRKCA, CASP8, and CTNNB1 were predicted to be the top 3 hub genes. In addition, molecular docking studies revealed CYP3A4, CYP1A1, and SIRT1 to be the lead proteins identified from AutoDock 4.2, AutoDock Vina, and Schrodinger suite Glide respectively. Molecular dynamic simulation and MMPBSA were performed for the complex of sinapic acid with above mentioned proteins which revealed a stable complex throughout simulation. The predictions revealed that the mechanism of sinapic acid in breast cancer may be due to regulation of multiple proteins like CTNNB1, PRKCA, CASP8, SIRT1, and cytochrome enzymes (CYP1A1 & CYP3A4); the majorly regulated pathway was predicted to be ‘Pathways in cancer’. This indicates the rationale for sinapic acid to be used in the treatment of breast cancer. However, these are predictions and need to be validated and looked upon in-depth to confirm the exact mechanism of sinapic acid in the treatment of breast cancer; this is future scope as well as a drawback of the current study.

User verification of Abbott Alinity HQ hematology analyzer

Abstract Objectives This study aims to evaluate the performance characteristics of the Alinity HQ hematology analyzer in a routine laboratory setting. Methods In the study, precision (short-term and long-term precision), accuracy (method comparison with Abbott Cell Dyn Ruby and estimation of bias), confirmation of a background (Limit of Blank, LoB), and carry-over were used to evaluate the performance of Alinity HQ as recommended by ICSH, CLSI guidelines EP15-A3, EP09, EP17A2, and H26-A2. Acceptance criteria were based on manufacturer technical specifications and the EFLM Biological Variation Database. Results According to the short-term precision results, except for mean corpuscular volume (MCV) and mean corpuscular hemoglobin concentration (MCHC), all measurements exhibited coefficient variations (CV) lower than their verification limits. Basophil, eosinophil, and monocyte counts, as well as mean corpuscular hemoglobin (MCH), MCHC, and red cell distribution width standard deviation (RDW-SD), did not meet the allowable imprecision criteria for the long-term precision study. The estimated bias for all analytes was within verification limits. However, the method comparison study showed concentration-dependent variations for MCHC, MCH, and mean platelet volume (MPV) parameters. Furthermore, the correlation of parameters between Alinity HQ and Cell Dyn Ruby ranged from 0.46 to 1.00. The LoB and carry-over studies demonstrated satisfactory performance for the Alinity HQ analyzer. Conclusions Although some parameters had higher CVs than expected and concentration-dependent bias, the overall analytical performance of Alinity HQ was found to be satisfactory. Alinity HQ is an accurate, highly precise analyzer with good analytical performance, suitable for high-volume laboratories.

The 2024 Nucleic Acids Research database issue and the online molecular biology database collection.

The 2024 Nucleic Acids Research database issue contains 180 papers from across biology and neighbouring disciplines. There are 90 papers reporting on new databases and 83 updates from resources previously published in the Issue. Updates from databases most recently published elsewhere account for a further seven. Nucleic acid databases include the new NAKB for structural information and updates from Genbank, ENA, GEO, Tarbase and JASPAR. The Issue's Breakthrough Article concerns NMPFamsDB for novel prokaryotic protein families and the AlphaFold Protein Structure Database has an important update. Metabolism is covered by updates from Reactome, Wikipathways and Metabolights. Microbes are covered by RefSeq, UNITE, SPIRE and P10K; viruses by ViralZone and PhageScope. Medically-oriented databases include the familiar COSMIC, Drugbank and TTD. Genomics-related resources include Ensembl, UCSC Genome Browser and Monarch. New arrivals cover plant imaging (OPIA and PlantPAD) and crop plants (SoyMD, TCOD and CropGS-Hub). The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). Over the last year the NAR online Molecular Biology Database Collection has been updated, reviewing 1060 entries, adding 97 new resources and eliminating 388 discontinued URLs bringing the current total to 1959 databases. It is available at http://www.oxfordjournals.org/nar/database/c/.

Clonal Trafficking of Individual T Cells between Peripheral Blood and Inflamed Synovial Tissue in Patients with Immune-Mediated Refractory Arthritis

Background Rheumatoid arthritis (RA) is the most prevalent form of immune mediated arthritis, which is characterised by immune-mediated destruction of synovial joints, leading to synovitis, acute joint pain, reduced motility, and mortality. Enrichment of CD4+ memory T cell subsets with local clonal expansions in the RA synovial tissue (ST) has been extensively reported in the literature. In addition, emerging evidence suggests that CD8+ T cells with inflammatory phenotypes are also present in increased numbers in the ST. However, little is known about whether these populations represent autoantigen-experienced migratory T cells or locally proliferated populations. Here, with the help of paired single cell T cell receptor (scTCR) and RNA sequencing recognizing individual T cell clones, we aimed to gain insights into the trafficking of T cells between peripheral blood (PB) and inflamed target tissue. Methods We collected ST and PB samples from patients with immune mediated arthritis during surgical synovectomy. The patients had been diagnosed with seronegative juvenile arthritis (Pt1-JIA), seropositive RA (Pt2-SP) and seronegative RA (Pt3-SN), and at the time of sampling they were refractory to treatment. We flow sorted T cells and CD45+ leukocytes from ST and PB and profiled them with 10x Genomics 5´-scRNA+V(D)J-seq. Altogether, we captured 68,610 cells passing quality control thresholds. We integrated the data from all individual samples using scVI and visualised the subpopulation structure using UMAP dimensionality reduction as implemented in the Seurat package. Results Altogether we analysed 34134 CD4+ T cells and 23385 CD8+ T cells from PB and ST. In concordance with previous reports, we observed strong transcriptomic differences between the tissue of origin for both CD4+ and CD8+ T cells. We pooled all CD4+ and CD8+ T cells separately and further grouped all cells with identical CDR3b aa sequences into unique clones. In CD8+ T cells, a high number of clones (n=101, pertaining to 7409 cells) was shared between PB and ST. 8 of these clones were enriched to the ST as compared to PB. Interestingly, Pt2-SP harboured a large clone in the ST consisting of 27% of the CD8+ T cells (CDR3b sequence - CASRGGTSITDTQYF). This clone was also detected in PB with a much smaller frequency of only 2% and in a phenotypically different cluster, indicating local proliferation of specific T cell clones in the synovium. By performing a DE gene analysis between cells with the same TCR but between different tissues of origin, we found that the top upregulated genes in ST were CCL4 and CREM suggesting more activated and inflamed phenotype in ST. In the CD4+ compartment, we found only 12 intersecting TCR clones pertaining to 637 cells. Majority of these clones were coming from PB (458 cells from PB and 179 from ST) and were hyperexpanded in PB as compared to ST. No matches for common viral or bacterial epitopes in the existing VDJ database for any of the intersecting clones was found. We then pooled all T cell clones that were enriched in the ST and found 113 clones (p < 0.05, Fisher's exact test). DE gene analysis showed that ST enriched clones have a more activated profile. Pathway enrichment analysis with hypergeometric testing against the GO Biological Process database identified T cell activation and positive regulation of cell adhesion as the top hits. Using GLIPH2, we used publicly available data from a cohort of JIA patients to check TCR level similarity between the two cohorts. We found common motifs between these two cohorts in some of the hyperexpanded clones from PB and ST, and interestingly, TCRs which were predicted to target similar antigens, were enriched to both expanded and intersecting clones. By performing DE gene analysis, we found that predicted TCRs with similarity were more cytotoxic ( GZMH, GNLY, NKG7, GZMB, GZMA) and they overexpressed the TRBV7-6 gene. Conclusions Our results suggest that the cytokine signalling environment in the ST leads to a widespread activation and inflammatory signature within the T cell compartment. This shift in the phenotype occurs within clonal families and is not due to the selective recruitment of activated cells to inflamed tissue. Additional investigations may elucidate the cellular interactions that promote expansion of specific disease promoting clones in ST and provide a more detailed understanding of the spatiotemporal aspects of this process.

Quantifying Challenges to Development of Generics and Biosimilars in Non-Hodgkin's Lymphoma Therapeutics: The Future of Low-Cost Alternatives from Monoclonal Antibodies to CAR-T

Background: There are numerous monotherapies and combination therapies available to treat Non-Hodgkin lymphoma's (NHL), however relatively few of these varied antineoplastics agents have commercially available, lower-cost generic forms in the United States. High cancer drug prices, in particular for socioeconomically vulnerable and underinsured patients, pose challenges to affordable cancer treatment in the United States. In 2017, FDA initiated the Drug Competition Action Plan (DCAP) that aimed to ensure timely market competition by enabling generic drug development, review, and approval, and streamlining regulatory complexities. This regulatory program was designed to limit so-called “gaming” recognized as a cause of delay to generic drug approvals, a phenomenon that has the further deleterious effect of extending brand monopolies beyond what Congress intended with Hatch-Waxman Amendments (1984) establishing modern generic drug regulatory processes. In 2018, FDA announced analogous Biosimilars Action Plan (BAP). However, to date the efficacy of these measures has not been comprehensively assessed in the NHL development landscape. Methods: A model was created by surveying the FDA Orange Book (drug approval database) and Purple Book (biologic product approval database) for initial approval of drugs/biologics with their initial generic/biosimilar approval. Approvals were classified by drug class and availability of generic/biosimilar for the innovator product with approval from 2002 to 2023 (Fig. 1) and t-test were used to determine significance. Results: Of small molecule drug NHL therapeutics approved in the last two decades, only 37% (19/52; Fig. 2) have available, lower-cost generics, with use in more than one indication and time on market both positively and significantly corresponding with availability of generics for the original innovator drug (P < 0.05). Notably, while generic drug approvals dropped during the 2020-2021 period (possibly due to impact of the COVID-19 pandemic), after 2017 implementation of DCAP more generics for common NHL antineoplastics were approved. Conversely, there are relatively few biologic drugs (total of 41 approvals; Fig. 2) with biosimilars, in part due to retained patents and exclusivity, and perhaps indicating a lack of efficacy in BAP among NHL antineoplastics therapies. For off-patent innovator monoclonal antibodies (mAbs), which make up the majority of these therapies, development of biosimilars is effectively blocked by secondary or “process-related” patents which effectively extend the period of non-competition for complex biologic therapies. It is expected that novel bispecific antibody (biAb) therapeutics and novel therapies, such as CD19 CAR-T will also have a delayed time to low-cost biosimilar availability following a similar trend. Conclusions: With drug price and ever-rising insurance and patient payments a critical area of public health, in particular in the United States, availability of lower cost generics and biosimilars is a critical issue for NHL patients. As we move into the maturation of gene therapies and CAR-T therapeutics, it merits consideration of that additional regulatory programs may be needed to incentivize the development of efficacious and lower cost biosimilars. However, it is critical that controlling competition be carefully balanced with investment in both biologic drug entity and process-related innovations among these relatively complex therapeutic entities.