Local Genomic Surveillance of Invasive Streptococcus pyogenes in Eastern North Carolina (ENC) in 2022-2023.

The recent increase in Group A Streptococcus (GAS) incidences in several countries across Europe and some areas of the Unites States (U.S.) has raised concerns. To understand GAS diversity and prevalence, we conducted a local genomic surveillance in Eastern North Carolina (ENC) in 2022-2023 with 95 isolates and compared its results to those of the existing national genomic surveillance in the U.S. in 2015-2021 with 13,064 isolates. We observed their epidemiological changes before and during the COVID-19 pandemic and detected a unique sub-lineage in ENC among the most common invasive GAS strain, ST28/emm1. We further discovered a multiple-copy insertion sequence, ISLgar5, in ST399/emm77 and its single-copy variants in some other GAS strains. We discovered ISLgar5 was linked to a Tn5801-like tetM-carrying integrative and conjugative element, and its copy number was associated with an ermT-carrying pRW35-like plasmid. The dynamic insertions of ISLgar5 may play a vital role in genome fitness and adaptation, driving GAS evolution relevant to antimicrobial resistance and potentially GAS virulence.

Predicting Promoters in Multiple Prokaryotes with Prompt.

Promoters are important cis-regulatory elements for the regulation of gene expression, and their accurate predictions are crucial for elucidating the biological functions and potential mechanisms of genes. Many previous prokaryotic promoter prediction methods are encouraging in terms of the prediction performance, but most of them focus on the recognition of promoters in only one or a few bacterial species. Moreover, due to ignoring the promoter sequence motifs, the interpretability of predictions with existing methods is limited. In this work, we present a generalized method Prompt (Promoters in multiple prokaryotes) to predict promoters in 16 prokaryotes and improve the interpretability of prediction results. Prompt integrates three methods including RSK (Regression based on Selected k-mer), CL (Contrastive Learning) and MLP (Multilayer Perception), and employs a voting strategy to divide the datasets into high-confidence and low-confidence categories. Results on the promoter prediction tasks in 16 prokaryotes show that the accuracy (Accuracy, Matthews correlation coefficient) of Prompt is greater than 80% in highly credible datasets of 16 prokaryotes, and is greater than 90% in 12 prokaryotes, and Prompt performs the best compared with other existing methods. Moreover, by identifying promoter sequence motifs, Prompt can improve the interpretability of the predictions. Prompt is freely available at https://github.com/duqimeng/PromptPrompt , and will contribute to the research of promoters in prokaryote.

Microorganisms Involved in Methylmercury Demethylation and Mercury Reduction are Widely Distributed and Active in the Bathypelagic Deep Ocean Waters.

The ocean's mercury (Hg) content has tripled due to anthropogenic activities, and although the dark ocean (>200 m) has become an important Hg reservoir, concentrations of the toxic and bioaccumulative methylmercury (MeHg) are low and therefore very difficult to measure. As a consequence, the current understanding of the Hg cycle in the deep ocean is severely data-limited, and the factors controlling MeHg, as well as its transformation rates, remain largely unknown. By analyzing 52 globally distributed bathypelagic deep-ocean metagenomes and 26 new metatranscriptomes from the Malaspina Expedition, our study reveals the widespread distribution and expression of bacterial-coding genes merA and merB in the global bathypelagic ocean (∼4000 m depth). These genes, associated with HgII reduction and MeHg demethylation, respectively, are particularly prevalent within the particle-attached fraction. Moreover, our results indicate that water mass age and the organic matter composition shaped the structure of the communities harboring merA and merB genes living in different particle size fractions, their abundance, and their expression levels. Members of the orders Corynebacteriales, Rhodobacterales, Alteromonadales, Oceanospirillales, Moraxellales, and Flavobacteriales were the main taxonomic players containing merA and merB genes in the deep ocean. These findings, together with our previous results of pure culture isolates of the deep bathypelagic ocean possessing the metabolic capacity to degrade MeHg, indicated that both methylmercury demethylation and HgII reduction likely occur in the global dark ocean, the largest biome in the biosphere.

Horizontal gene transfer of the Mer operon is associated with large effects on the transcriptome and increased tolerance to mercury in nitrogen-fixing bacteria.

BACKGROUND: Mercury (Hg) is highly toxic and has the potential to cause severe health problems for humans and foraging animals when transported into edible plant parts. Soil rhizobia that form symbiosis with legumes may possess mechanisms to prevent heavy metal translocation from roots to shoots in plants by exporting metals from nodules or compartmentalizing metal ions inside nodules. Horizontal gene transfer has potential to confer immediate de novo adaptations to stress. We used comparative genomics of high quality de novo assemblies to identify structural differences in the genomes of nitrogen-fixing rhizobia that were isolated from a mercury (Hg) mine site that show high variation in their tolerance to Hg. RESULTS: Our analyses identified multiple structurally conserved merA homologs in the genomes of Sinorhizobium medicae and Rhizobium leguminosarum but only the strains that possessed a Mer operon exhibited 10-fold increased tolerance to Hg. RNAseq analysis revealed nearly all genes in the Mer operon were significantly up-regulated in response to Hg stress in free-living conditions and in nodules. In both free-living and nodule environments, we found the Hg-tolerant strains with a Mer operon exhibited the fewest number of differentially expressed genes (DEGs) in the genome, indicating a rapid and efficient detoxification of Hg from the cells that reduced general stress responses to the Hg-treatment. Expression changes in S. medicae while in bacteroids showed that both rhizobia strain and host-plant tolerance affected the number of DEGs. Aside from Mer operon genes, nif genes which are involved in nitrogenase activity in S. medicae showed significant up-regulation in the most Hg-tolerant strain while inside the most Hg-accumulating host-plant. Transfer of a plasmid containing the Mer operon from the most tolerant strain to low-tolerant strains resulted in an immediate increase in Hg tolerance, indicating that the Mer operon is able to confer hyper tolerance to Hg. CONCLUSIONS: Mer operons have not been previously reported in nitrogen-fixing rhizobia. This study demonstrates a pivotal role of the Mer operon in effective mercury detoxification and hypertolerance in nitrogen-fixing rhizobia. This finding has major implications not only for soil bioremediation, but also host plants growing in mercury contaminated soils.

AFITbin: a metagenomic contig binning method using aggregate l-mer frequency based on initial and terminal nucleotides.

BACKGROUND: Using next-generation sequencing technologies, scientists can sequence complex microbial communities directly from the environment. Significant insights into the structure, diversity, and ecology of microbial communities have resulted from the study of metagenomics. The assembly of reads into longer contigs, which are then binned into groups of contigs that correspond to different species in the metagenomic sample, is a crucial step in the analysis of metagenomics. It is necessary to organize these contigs into operational taxonomic units (OTUs) for further taxonomic profiling and functional analysis. For binning, which is synonymous with the clustering of OTUs, the tetra-nucleotide frequency (TNF) is typically utilized as a compositional feature for each OTU. RESULTS: In this paper, we present AFIT, a new l-mer statistic vector for each contig, and AFITBin, a novel method for metagenomic binning based on AFIT and a matrix factorization method. To evaluate the performance of the AFIT vector, the t-SNE algorithm is used to compare species clustering based on AFIT and TNF information. In addition, the efficacy of AFITBin is demonstrated on both simulated and real datasets in comparison to state-of-the-art binning methods such as MetaBAT 2, MaxBin 2.0, CONCOT, MetaCon, SolidBin, BusyBee Web, and MetaBinner. To further analyze the performance of the purposed AFIT vector, we compare the barcodes of the AFIT vector and the TNF vector. CONCLUSION: The results demonstrate that AFITBin shows superior performance in taxonomic identification compared to existing methods, leveraging the AFIT vector for improved results in metagenomic binning. This approach holds promise for advancing the analysis of metagenomic data, providing more reliable insights into microbial community composition and function. AVAILABILITY: A python package is available at: https://github.com/SayehSobhani/AFITBin .

W2V-repeated index: Prediction of enhancers and their strength based on repeated fragments.

Enhancers are crucial in gene expression regulation, dictating the specificity and timing of transcriptional activity, which highlights the importance of their identification for unravelling the intricacies of genetic regulation. Therefore, it is critical to identify enhancers and their strengths. Repeated sequences in the genome are repeats of the same or symmetrical fragments. There has been a great deal of evidence that repetitive sequences contain enormous amounts of genetic information. Thus, We introduce the W2V-Repeated Index, designed to identify enhancer sequence fragments and evaluates their strength through the analysis of repeated K-mer sequences in enhancer regions. Utilizing the word2vector algorithm for numerical conversion and Manta Ray Foraging Optimization for feature selection, this method effectively captures the frequency and distribution of K-mer sequences. By concentrating on repeated K-mer sequences, it minimizes computational complexity and facilitates the analysis of larger K values. Experiments indicate that our method performs better than all other advanced methods on almost all indicators.

The expression analysis of long noncoding RNAs PCAT-1, PCAT-29, and MER11C in bipolar disorder.

Long non-coding RNAs (lncRNAs) are transcripts with a length of usually more than 200 nucleotides (nt) that have promised functions in varied biological processes. lncRNAs participate in the regulation of differentiation, development, and function of the brain. Thus, their dysregulation might play important roles in the etiology of neurological disorders such as BD. In this study, the expression level of PCAT-1, PCAT-29, and MER11C lncRNAs was evaluated in the blood of BD patients compared to the control group. Peripheral blood mononuclear cells of 50 BD type I patients and 50 healthy individuals were isolated. The RNAs were extracted and cDNA was synthesized. Then, the expression level of the desired lncRNAs was measured through Real-Time PCR. The expression levels of PCAT-29 and MER11C lncRNAs were significantly lower in BD patients compared to controls. However, the expression level of PCAT-1 was not significantly different between these two sets of samples. According to the ROC curve, PCAT-29 and MER11C had significant diagnostic power for the differentiation of BD patients from controls. Taken together, our results indicate dysregulation of two lncRNAs in patients with BD and the possible roles of these lncRNAs in the neuropathology of bipolar disorder.

Sea cucumber significance: Drying techniques and India's comprehensive status.

Sea cucumbers, members of the echinoderm class Holothuroidea, are marine invertebrates with ecological significance and substantial commercial value. With approximately 1700 species, these organisms contribute to marine ecosystems through nutrient cycling and face various threats, including overfishing and habitat loss. Despite their importance, they are extensively exploited for diverse applications, from seafood to pharmaceuticals. This study investigates sea cucumbers' nutritional profile and bioactive elements, emphasizing their role as sources of essential compounds with potential health benefits. The demand for sea cucumbers, especially in dried form, is significant, prompting exploration into various drying techniques. Examining the global trade in sea cucumbers highlights their economic importance and the conservation challenges they face. Conservation efforts, such as awareness campaigns and international collaboration, are evaluated as essential steps in combating illicit trade and promoting the sustainable stewardship of sea cucumber populations. PRACTICAL APPLICATION: Around 1700 species of sea cucumbers were identified as vital ecological scavengers in the Holothuroidea class. High commercial value due to their health benefits, particularly their demonstrated inhibitory effect against various types of cancer. "Beche-de-mer" holds a 90% market share and is regarded as a luxury food item in Southeast Asian countries. Due to overexploitation, the species is classified as Schedule I under the Wildlife Protection Act (WPA) in India, prompting the implementation of a blanket ban on their harvesting to ensure its conservation.

Gas6 and Protein S Ligands Cooperate to Regulate MerTK Rhythmic Activity Required for Circadian Retinal Phagocytosis.

Among the myriad of existing tyrosine kinase receptors, the TAM family-abbreviated from Tyro3, Axl, and Mer tyrosine kinase (MerTK)-has been extensively studied with an outstanding contribution from the team of Prof. Greg Lemke. MerTK activity is implicated in a wide variety of functions involving the elimination of apoptotic cells and has recently been linked to cancers, auto-immune diseases, and atherosclerosis/stroke. In the retina, MerTK is required for the circadian phagocytosis of oxidized photoreceptor outer segments by the retinal-pigment epithelial cells, a function crucial for the long-term maintenance of vision. We previously showed that MerTK ligands carry the opposite role in vitro, with Gas6 inhibiting the internalization of photoreceptor outer segments while Protein S acts conversely. Using site-directed mutagenesis and ligand-stimulated phagocytosis assays on transfected cells, we presently demonstrate, for the first time, that Gas6 and Protein S recognize different amino acids on MerTK Ig-like domains. In addition, MerTK's function in retinal-pigment epithelial cells is rhythmic and might thus rely on the respective stoichiometry of both ligands at different times of the day. Accordingly, we show that ligand bioavailability varies during the circadian cycle using RT-qPCR and immunoblots on retinal and retinal-pigment epithelial samples from control and beta5 integrin knockout mice where retinal phagocytosis is arrhythmic. Taken together, our results suggest that Gas6 and Protein S might both contribute to refine the acute regulation of MerTK in time for the daily phagocytic peak.

Investigating the Formation of In Vitro Immunogenic Gluten Peptides after Covalent Modification of Their Structure with Green Tea Phenolic Compounds under Alkaline Conditions.

Celiac disease is an autoimmune disorder triggered by immunogenic gluten peptides produced during gastrointestinal digestion. To prevent the production of immunogenic gluten peptides, the stimulation of covalent-type protein-polyphenol interactions may be promising. In this study, gluten interacted with green tea extract (GTE) at pH 9 to promote the covalent-type gluten-polyphenol interactions, and the number of immunogenic gluten peptides, 19-mer, 26-mer, and 33-mer, was monitored after in vitro digestion. Treatment of gluten with GTE provided an increased antioxidant capacity, decreased amino group content, and increased thermal properties. More importantly, there was a remarkable (up to 73%) elimination of immunogenic gluten peptide release after the treatment of gluten with 2% GTE at 50 °C and pH 9 for 2 h. All of these confirmed that gluten was efficiently modified by GTE polyphenols under the stated conditions. These findings are important in developing new strategies for the development of gluten-free or low-gluten food products with reduced immunogenicity.

Cosine Similarity Estimation Using FracMinHash: Theoretical Analysis, Safety Conditions, and Implementation.

Motivation: The increasing number and volume of genomic and metagenomic data necessitates scalable and robust computational models for precise analysis. Sketching techniques utilizing k -mers from a biological sample have proven to be useful for large-scale analyses. In recent years, FracMinHash has emerged as a popular sketching technique and has been used in several useful applications. Recent studies on FracMinHash proved unbiased estimators for the containment and Jaccard indices. However, theoretical investigations for other metrics, such as the cosine similarity, are still lacking. Theoretical contributions: In this paper, we present a theoretical framework for estimating cosine similarity from FracMinHash sketches. We establish conditions under which this estimation is sound, and recommend a minimum scale factor s for accurate results. Experimental evidence supports our theoretical findings. Practical contributions: We also present frac-kmc, a fast and efficient FracMinHash sketch generator program. frac-kmc is the fastest known FracMinHash sketch generator, delivering accurate and precise results for cosine similarity estimation on real data. We show that by computing FracMinHash sketches using frac-kmc, we can estimate pairwise cosine similarity speedily and accurately on real data. frac-kmc is freely available here: https://github.com/KoslickiLab/frac-kmc/.

Prediction of viral families and hosts of single-stranded RNA viruses based on K-Mer coding from phylogenetic gene sequences.

There are billions of virus species worldwide, and viruses, the smallest parasitic entities, pose a serious threat. Therefore, fighting associated disorders requires an understanding of the genetic structure of viruses. Considering the wide diversity and rapid evolution of viruses, there is a critical need to quickly and accurately classify viral species and their potential hosts to better understand transmission dynamics, facilitating the development of targeted therapies. Recognizing this, this study has investigated the classes of RNA viruses based on their genomic sequences using Machine Learning (ML) and Deep Learning (DL) models. The PhyVirus dataset, consisting of pathogenic Single-stranded RNA viruses of Baltimore group four (+ssRNA) and five (-ssRNA) with different hosts and species, was analyzed. The dataset containing viral gene sequences was analyzed using the K-Mer coding technique, which is based on base words of various lengths. The study used classical ML algorithms (Random Forest, Gradient Boosting and Extra Trees) and the Fully Connected Deep Neural Network, a Deep Learning algorithm, to predict viral families and hosts. Detailed analyses were performed on the classifier performance in scenarios with different train-test ratios and different word lengths (k-values) for K-Mer. The observed results show that Fully Connected Deep Neural Network has a high success rate of 99.60 % in predicting virus families. In predicting virus hosts, the Extra Trees classifier achieved the highest success rate of 81.53 %. This study is considered to be the first classification study in the literature on this dataset, which has a very large family and host diversity consisting of gene sequences of Single-stranded RNA viruses. Our detailed investigations on how varying word lengths based on K-Mer coding in gene sequences affect the classification into viral families and hosts make this study particularly valuable. This study shows that ML and DL methods have the potential to produce valuable results in phylogenetic studies. In addition, the results and high-performance values show that these methods can be successfully used in regenerative applications of gene sequences or in studies such as the elimination of losses in gene sequences.

Prokrustean Graph: A substring index supporting rapid enumeration across a range of k-mer sizes.

Despite the widespread adoption of k -mer-based methods in bioinformatics, a fundamental question persists: How can we quantify the influence of k sizes in applications? With no universal answer available, choosing an optimal k size or employing multiple k sizes remains application-specific, arbitrary, and computationally expensive. The assessment of the primary parameter k is typically empirical, based on the end products of applications which pass complex processes of genome analysis, comparison, assembly, alignment, and error correction. The elusiveness of the problem stems from a limited understanding of the transitions of k -mers with respect to k sizes. Indeed, there is considerable room for improving both practice and theory by exploring k -mer-specific quantities across multiple k sizes. This paper introduces an algorithmic framework built upon a novel substring representation: the Prokrustean graph. The primary functionality of this framework is to extract various k -mer-based quantities across a range of k sizes, but its computational complexity depends only on maximal repeats, not on the k range. For example, counting maximal unitigs of de Bruijn graphs for k = 10 , … , 100 takes just a few seconds with a Prokrustean graph built on a read set of gigabases in size. This efficiency sets the graph apart from other substring indices, such as the FM-index, which are normally optimized for string pattern searching rather than for depicting the substring structure across varying lengths. However, the Prokrustean graph is expected to close this gap, as it can be built using the extended Burrows-Wheeler Transform (eBWT) in a space-efficient manner. The framework is particularly useful in pangenome and metagenome analyses, where the demand for precise multi- k approaches is increasing due to the complex and diverse nature of the information being managed. We introduce four applications implemented with the framework that extract key quantities actively utilized in modern pangenomics and metagenomics.

An alignment-free method for detection of missing regions for phylogenetic analysis.

Phylogenetic tree estimation using conventional approaches usually requires pairwise or multiple sequence alignment. However, sequence alignment has difficulties related to scalability and accuracy in case of long sequences such as whole genomes, low sequence identity, and in presence of genomic rearrangements. To address these issues, alignment-free approaches have been proposed. While these methods have demonstrated promising results, many of these lead to errors when regions are missing from the sequences of one or more species that are trivially detected in alignment-based methods. Here, we present an alignment-free method for detecting missing regions in sequences of species for which phylogeny is to be estimated. It is based on counts of k-mers and can be used to filter out k-mers belonging to regions in one species that are missing in one or more of the other species. We perform experiments with real and simulated datasets containing missing regions and find that it can successfully detect a large fraction of such k-mers and can lead to improvements in the estimated phylogenies. Our method can be used in k-mer based alignment-free phylogeny estimation methods to filter out k-mers corresponding to missing regions.

A k-mer-based pangenome approach for cataloging seed-storage-protein genes in wheat to facilitate genotype-to-phenotype prediction and improvement of end-use quality.

Wheat is a staple food for more than 35% of the world's population, with wheat flour used to make hundreds of baked goods. Superior end-use quality is a major breeding target; however, improving it is especially time-consuming and expensive. Furthermore, genes encoding seed-storage proteins (SSPs) form multi-gene families and are repetitive, with gaps commonplace in several genome assemblies. To overcome these barriers and efficiently identify superior wheat SSP alleles, we developed "PanSK" (Pan-SSP k-mer) for genotype-to-phenotype prediction based on an SSP-based pangenome resource. PanSK uses 29-mer sequences that represent each SSP gene at the pangenomic level to reveal untapped diversity across landraces and modern cultivars. Genome-wide association studies with k-mers identified 23 SSP genes associated with end-use quality that represent novel targets for improvement. We evaluated the effect of rye secalin genes on end-use quality and found that removal of ω-secalins from 1BL/1RS wheat translocation lines is associated with enhanced end-use quality. Finally, using machine-learning-based prediction inspired by PanSK, we predicted the quality phenotypes with high accuracy from genotypes alone. This study provides an effective approach for genome design based on SSP genes, enabling the breeding of wheat varieties with superior processing capabilities and improved end-use quality.

Degradation of organic mercury in high salt environments by a marine aerobic bacterium Alteromonas macleodii KD01.

Mercury (Hg), particularly organic mercury, poses a global concern due to its pronounced toxicity and bioaccumulation. Bioremediation of organic mercury in high-salt wastewater faces challenges due to the growth limitations imposed by elevated Cl- and Na+ concentrations on microorganisms. In this study, an isolated marine bacterium Alteromonas macleodii KD01 was demonstrated to degrade methylmercury (MeHg) efficiently in seawater and then was applied to degrade organic mercury (MeHg, ethylmercury, and thimerosal) in simulated high-salt wastewater. Results showed that A. macleodii KD01 can rapidly degrade organic mercury (within 20 min) even at high concentrations (>10 ng/mL), volatilizing a portion of Hg from the wastewater. Further analysis revealed an increased transcription of organomercury lyase (merB) with rising organic mercury concentrations during the exposure process, suggesting the involvement of mer operon (merA and merB). These findings highlight A. macleodii KD01 as a promising candidate for addressing organic mercury pollution in high-salt wastewater.

Methods for Pangenomic Core Detection.

Computational pangenomics deals with the joint analysis of all genomic sequences of a species. It has already been successfully applied to various tasks in many research areas. Further advances in DNA sequencing technologies constantly let more and more genomic sequences become available for many species, leading to an increasing attractiveness of pangenomic studies. At the same time, larger datasets also pose new challenges for data structures and algorithms that are needed to handle the data. Efficient methods oftentimes make use of the concept of k-mers.Core detection is a common way of analyzing a pangenome. The pangenome's core is defined as the subset of genomic information shared among all individual members. Classically, it is not only determined on the abstract level of genes but can also be described on the sequence level.In this chapter, we provide an overview of k-mer-based methods in the context of pangenomics studies. We first revisit existing software solutions for k-mer counting and k-mer set representation. Afterward, we describe the usage of two k-mer-based approaches, Pangrowth and Corer, for pangenomic core detection.

Reference-free inferring of transcriptomic events in cancer cells on single-cell data.

BACKGROUND: Cancerous cells' identity is determined via a mixture of multiple factors such as genomic variations, epigenetics, and the regulatory variations that are involved in transcription. The differences in transcriptome expression as well as abnormal structures in peptides determine phenotypical differences. Thus, bulk RNA-seq and more recent single-cell RNA-seq data (scRNA-seq) are important to identify pathogenic differences. In this case, we rely on k-mer decomposition of sequences to identify pathogenic variations in detail which does not need a reference, so it outperforms more traditional Next-Generation Sequencing (NGS) analysis techniques depending on the alignment of the sequences to a reference. RESULTS: Via our alignment-free analysis, over esophageal and glioblastoma cancer patients, high-frequency variations over multiple different locations (repeats, intergenic regions, exons, introns) as well as multiple different forms (fusion, polyadenylation, splicing, etc.) could be discovered. Additionally, we have analyzed the importance of less-focused events systematically in a classic transcriptome analysis pipeline where these events are considered as indicators for tumor prognosis, tumor prediction, tumor neoantigen inference, as well as their connection with respect to the immune microenvironment. CONCLUSIONS: Our results suggest that esophageal cancer (ESCA) and glioblastoma processes can be explained via pathogenic microbial RNA, repeated sequences, novel splicing variants, and long intergenic non-coding RNAs (lincRNAs). We expect our application of reference-free process and analysis to be helpful in tumor and normal samples differential scRNA-seq analysis, which in turn offers a more comprehensive scheme for major cancer-associated events.

MERTK in the rat trigeminal system: a potential novel target for cluster headache?

The trigeminal system is key to the pathophysiology of migraine and cluster headache, two primary headache disorders that share many features. Recently, MER proto-oncogene tyrosine kinase (MERTK), a cell surface receptor, was strongly associated with cluster headache through genetic studies. Further, the MERTK ligand galectin-3 has been found to be elevated in serum of migraine patients. In this study, MERTK and MERTK ligands were investigated in key tissue to better understand their potential implication in the pathophysiology of primary headache disorders. Immunohistochemistry was used to map MERTK and galectin-3 expression in rat trigeminal ganglia. RT-qPCR was used to assess MERTK gene expression in blood, and ELISA immunoassays were used for MERTK ligand quantification in serum from study participants with and without cluster headache. MERTK gene expression was elevated in blood samples from study participants with cluster headache compared to controls. In addition, MERTK ligand galectin-3 was found at increased concentration in the serum of study participants with cluster headache, whereas the levels of MERTK ligands growth arrest specific 6 and protein S unaffected. MERTK and galectin-3 were both expressed in rat trigeminal ganglia. Galectin-3 was primarily localized in smaller neurons and to a lesser extent in C-fibres, while MERTK was found in satellite glia cells and in the outer membrane of Schwann cells. Interestingly, a strong MERTK signal was found specifically in the region proximal to the nodes of Ranvier. The overexpression of MERTK and galectin-3 in tissue from study participants with cluster headache, as well as the presence of MERTK in rat peripheral satellite glia cells and Schwann cells in the trigeminal ganglia, further highlights MERTK signalling as an interesting potential future therapeutic target in primary headache.

Enhanced Compression of k-Mer Sets with Counters via de Bruijn Graphs.

An essential task in computational genomics involves transforming input sequences into their constituent k-mers. The quest for an efficient representation of k-mer sets is crucial for enhancing the scalability of bioinformatic analyses. One widely used method involves converting the k-mer set into a de Bruijn graph (dBG), followed by seeking a compact graph representation via the smallest path cover. This study introduces USTAR* (Unitig STitch Advanced constRuction), a tool designed to compress both a set of k-mers and their associated counts. USTAR leverages the connectivity and density of dBGs, enabling a more efficient path selection for constructing the path cover. The efficacy of USTAR is demonstrated through its application in compressing real read data sets. USTAR improves the compression achieved by UST (Unitig STitch), the best algorithm, by percentages ranging from 2.3% to 26.4%, depending on the k-mer size, and it is up to 7× times faster.