Sequences Clustered Research Articles

Profound reduction of HIV-1 reservoir cells over three decades of antiretroviral therapy started in early infancy.

HIV-1 reservoir cells persist indefinitely during suppressive antiretroviral therapy (ART) in individuals who acquire infection in adulthood, but little is known about the longitudinal evolution of viral reservoir cells during long-term ART started during early infancy. We studied two fraternal twins who acquired HIV-1 perinatally, started ART at week 10 after birth and remained on ART for 28 years. We observed that the frequency of genome intact proviruses, determined by single-genome near full-length proviral sequencing, declined by approximately 4,000- to 13,000-fold during this period, indicating enhanced decay rates of intact proviruses even after adjusting for dilution effects from somatic growth. Despite analyzing more than one billion PBMC after 28 years of ART in each participant, no intact proviruses were detected in one participant, and one intact provirus was isolated in the other. The longitudinal decline of defective proviruses in the two participants was more similar to proviral decay kinetics reported in individuals who started ART during adulthood; moreover, clonal sequence clusters were readily detectable for defective proviruses but not for intact proviruses after 28 years of ART in the two twins. Together, these data suggest decreased long-term stability and increased immunological vulnerability of intact proviruses during long-term ART started in early infancy.

Missing microbial eukaryotes and misleading meta-omic conclusions.

Meta-omics is commonly used for large-scale analyses of microbial eukaryotes, including species or taxonomic group distribution mapping, gene catalog construction, and inference on the functional roles and activities of microbial eukaryotes in situ. Here, we explore the potential pitfalls of common approaches to taxonomic annotation of protistan meta-omic datasets. We re-analyze three environmental datasets at three levels of taxonomic hierarchy in order to illustrate the crucial importance of database completeness and curation in enabling accurate environmental interpretation. We show that taxonomic membership of sequence clusters estimates community composition more accurately than returning exact sequence labels, and overlap between clusters can address database shortcomings. Clustering approaches can be applied to diverse environments while continuing to exploit the wealth of annotation data collated in databases, and selecting and evaluating these databases is a critical part of correctly annotating protistan taxonomy in environmental datasets. We argue that ongoing curation of genetic resources is crucial in accurately annotating protists in in situ meta-omic datasets. Moreover, we propose that precise taxonomic annotation of meta-omic data is a clustering problem rather than a feasible alignment problem.

The physical properties of cluster chains

We explore the kinematics and star formation history of the Scorpius Centaurus (Sco-Cen) OB association following the initial identification of sequential, linearly aligned chains of clusters. Building upon our characterization of the Corona Australis (CrA) chain, we now analyze two additional major cluster chains that exhibit similar characteristics: the Lower Centaurus Crux (LCC) and Upper Scorpius (Upper Sco) chains. All three cluster chains display distinct sequential patterns in 1) the 3D spatial distribution, 2) age, 3) velocity, and 4) mass. The Upper-Sco chain is the most massive and complex cluster chain, possibly consisting of two or more overlapping subchains. We discuss the possible formation of cluster chains and argue for a scenario where feedback from the most massive star formation episode 15 Myr ago initiated the formation of these spatio-temporal cluster sequences. Our results identify cluster chains as a distinct type of stellar structure with well-defined physical properties, formed in environments capable of sustaining stellar feedback over timescales of 5--10 Myr. We find that around 40<!PCT!> of the stellar population in Sco-Cen formed due to triggered star formation, with 35<!PCT!> forming along the three cluster chains. We conclude that cluster chains could be common structures in OB associations, particularly in regions that have similar natal environments as Sco-Cen. Beyond their significance for star formation and stellar feedback, they appear to be promising laboratories for chemical enrichment and the transport of elements from one generation to the next in the same star-forming region.

A multi-species benchmark for training and validating mass spectrometry proteomics machine learning models

Training machine learning models for tasks such as de novo sequencing or spectral clustering requires large collections of confidently identified spectra. Here we describe a dataset of 2.8 million high-confidence peptide-spectrum matches derived from nine different species. The dataset is based on a previously described benchmark but has been re-processed to ensure consistent data quality and enforce separation of training and test peptides.

The HST Large Programme on ω Centauri

We present a study of the white dwarf (WD) cooling sequence (CS) in the globular cluster (GC) Omega Centauri (or NGC 5139; hereafter, ω Cen), the primary goal of a dedicated Hubble Space Telescope (HST) programme. We find that the peak at the termination of the WD CS is located at mF606W = 30.1 ± 0.2 (equivalent to V ∼ 31). The brighter part of ω Cen’s WD CS is consistent with the presence of massive He-core WDs, in agreement with previous HST analyses with ultraviolet and blue filters. Comparative analyses of the WD luminosity function (LF) and theoretical counterparts show that a single-age population for the cluster is compatible with the data. However, an analysis of only the WD LF cannot entirely exclude the possibility of an age range, due to uncertainties in the present-day WD mass function, with a star formation history potentially spanning up to 5 billion years, predominantly comprising stars about 13 Gyr old, with a minority potentially as young as 8 Gyr. This underscores the need for global spectroscopic and photometric investigations that simultaneously include both the WD populations and the previous evolutionary phases, in order to fully understand the cluster’s diverse chemical compositions and ages.

Virulence perspective genomic research unlocks the secrets of Rhizoctonia solani associated with banded sheath blight in Barnyard Millet (Echinochloa frumentacea).

Banded sheath blight (Bsb) disease, caused by Rhizoctonia solani, is an emerging problem in barnyard millet cultivation. One of the significant goals of pathogenomic research is to identify genes responsible for pathogenicity in the fungus. A virulence profiling-based approach was employed and six R. solani isolates were collected from various ecological zones of India. The morphological parameters and virulence of all of the six R. solani isolates were investigated. The most virulent strain was designated as RAP2 and its genome has been sequenced, assembled, and annotated. The RAP2 genome is 43.63 megabases in size and comprises 10.95% repetitive DNA, within which 46% are retroelements, 8% are DNA transposons, and 46% are unidentified DNA. The Gene Ontology (GO) annotation of RAP2 proteins revealed that "phosphorylation", "membrane", and "ATP binding" have the highest gene enrichment in the "biological process", "cellular component" and "molecular function" domains, respectively. The genome comprises a majority of secretory proteins in the pectin lyase fold/virulence factor superfamily, which break down plant cell wall polymers to extract saccharides. The RAP2 genome is comparable to R. solani, which infects maize and rice, but it diverges further from soybean in terms of nucleotide-level genetic similarity. Orthologous clustering of RAP2 protein sequences with R. solani infecting maize, rice, and soybean yields 5606 proteins shared across all genomes. GO analysis of 25 proteins specific to the RAP2 genome found enrichment in the ethylene response, which can cause spore germination and infection in host plants. Interestingly, a 28-bp deletion in the RAP2 strain's cutinase domain was discovered in the cutinase protein, which might be important in the infection process, perhaps rendering the enzyme inactive or allowing the pathogen to infect barnyard millet while avoiding host defense. This study sheds light on the genetic makeup of R. solani, allowing researchers to discover critical genes related with pathogenicity as well as potential targets for fungicide development.

A genome-based survey of invasive pneumococci in Norway over four decades reveals lineage-specific responses to vaccination

BackgroundStreptococcus pneumoniae is a major cause of mortality globally. The introduction of pneumococcal conjugate vaccines (PCVs) has reduced the incidence of the targeted serotypes significantly, but expansion of non-targeted serotypes, serotype replacement, and incomplete vaccine-targeting contribute to pneumococcal disease in the vaccine era. Here, we characterize the changing population genetic landscape of S. pneumoniae in Norway over a 41-year period (1982–2022).MethodsSince 2018, all cases of invasive pneumococcal disease have undergone whole-genome sequencing (WGS) at the Norwegian Institute of Public Health. In order to characterize the changing population over time, historical isolates were re-cultured and sequenced, resulting in a historical WGS dataset. Isolates were assigned to global pneumococcal sequence clusters (GPSCs) using PathogenWatch and assigned to serotypes using in silico (SeroBA) and in vitro methods (Quellung reaction). Temporal phylogenetic analyses were performed on GPSCs of particular interest.ResultsThe availability of WGS data allowed us to study capsular variation at the level of individual lineages. We detect highly divergent fates for different GPSCs following the introduction of PCVs. For two out of eight major GPSCs, we identified multiple instances of serotype switching from vaccine types to non-vaccine types. Dating analyses suggest that most instances of serotype switching predated the introduction of PCVs, but expansion occurred after their introduction. Furthermore, selection for penicillin non-susceptibility was not a driving force for the changing serotype distribution within the GPSCs over time.ConclusionsPCVs have been major shapers of the Norwegian disease-causing pneumococcal population, both at the level of serotype distributions and the underlying lineage dynamics. Overall, the introduction of PCVs has reduced the incidence of invasive disease. However, some GPSCs initially dominated by vaccine types escaped the effect of vaccination through expansion of non-vaccine serotypes. Close monitoring of circulating lineages and serotypes will be key for ensuring optimal vaccination coverage going forward.

Unraveling the Biosynthetic Logic Behind the Production of Ramoplanin and Related Lipodepsipeptide Antibiotics

This review focuses on the genetic and biotechnological aspects of the biosynthesis of ramoplanin (Rmp), enduracidin (End), and other related lipodepsipeptide antibiotics, herein named collectively ramoplanin and ramoplanin-related lipodepsipeptide (RRLDPs). These compounds exhibit a promising antimicrobial activity against Gram-positive bacterial pathogens, showing no cross-resistance with vancomycin. Rmp is in clinical development for human treatment and End has been used as animal growth promoter for decades. Other RRLDPs as ramoplanose and janiemycin had been poorly investigated in the past, whereas new molecules as chersinamycin have been recently discovered, attracting a renewed interest in this class of antibiotics. Nowadays, sequence and annotation of the biosynthetic gene clusters (BGCs) of Rmp, End, and several other RRLDPs are available, and researchers are focused on understanding the biosynthetic logic behind the production of these compounds. Interestingly, producers of Rmp and chersinamycin belong to the so-called “non-common” actinomycetes from the family Micromonosporaceae, whereas End is produced by different members of the genus Streptomyces. To the best of our knowledge, no reviews summarize and systematize the current information on the biosynthesis of RRLDPs. Therefore, in this review, we aim to fill this gap. We first describe and compare the BGCs for known RRLDPs, giving an insight on how they were discovered and developed. Next, we review the biosynthetic pathways of these antibiotics, as well as the regulation of their biosynthesis. Then, we focus on the production processes of RRLDPs, demonstrating how cultivation and nutritional factors influence their production. Finally, we provide a short outline of future directions in studying RRLDPs.

Interpretable GWAS by linking clinical phenotypes to quantifiable immune repertoire components

Bridging the gap between genotype and phenotype in GWAS studies is challenging. A multitude of genetic variants have been associated with immune-related diseases, including cancer, yet the interpretability of most variants remains low. Here, we investigate the quantitative components in the T cell receptor (TCR) repertoire, the frequency of clusters of TCR sequences predicted to have common antigen specificity, to interpret the genetic associations of diverse human diseases. We first developed a statistical model to predict the TCR components using variants in the TRB and HLA loci. Applying this model to over 300,000 individuals in the UK Biobank data, we identified 2309 associations between TCR abundances and various immune diseases. TCR clusters predicted to be pathogenic for autoimmune diseases were significantly enriched for predicted autoantigen-specificity. Moreover, four TCR clusters were associated with better outcomes in distinct cancers, where conventional GWAS cannot identify any significant locus. Collectively, our results highlight the integral role of adaptive immune responses in explaining the associations between genotype and phenotype.

Prediction of post-PCV13 pneumococcal evolution using invasive disease data enhanced by inverse-invasiveness weighting.

After introducing pneumococcal conjugate vaccines (PCVs), serotype replacement occurred in Streptococcus pneumoniae. Predicting which pneumococcal strains will become common in carriage after vaccination can enhance vaccine design, public health interventions, and understanding of pneumococcal evolution. Invasive pneumococcal isolates were collected during 1998-2018 by the Active Bacterial Core surveillance (ABCs). Carriage data from Massachusetts (MA) and Southwest United States were used to calculate weights. Using pre-vaccine data, serotype-specific inverse-invasiveness weights were defined as the ratio of the proportion of the serotype in carriage to the proportion in invasive data. Genomic data were processed under bioinformatic pipelines to define genetically similar sequence clusters (i.e., strains), and accessory genes (COGs) present in 5-95% of isolates. Weights were applied to adjust observed strain proportions and COG frequencies. The negative frequency-dependent selection (NFDS) model predicted strain proportions by calculating the post-vaccine strain composition in the weighted invasive disease population that would best match pre-vaccine COG frequencies. Inverse-invasiveness weighting increased the correlation of COG frequencies between invasive and carriage data in linear or logit scale for pre-vaccine, post-PCV7, and post-PCV13; and between different epochs in the invasive data. Weighting the invasive data significantly improved the NFDS model's accuracy in predicting strain proportions in the carriage population in the post-PCV13 epoch, with the adjusted R2 increasing from 0.254 before weighting to 0.545 after weighting. The weighting system adjusted invasive disease data to better represent the pneumococcal carriage population, allowing the NFDS mechanism to predict strain proportions in carriage in the post-PCV13 epoch. Our methods enrich the value of genomic sequences from invasive disease surveillance.IMPORTANCEStreptococcus pneumoniae, a common colonizer in the human nasopharynx, can cause invasive diseases including pneumonia, bacteremia, and meningitis mostly in children under 5 years or older adults. The PCV7 was introduced in 2000 in the United States within the pediatric population to prevent disease and reduce deaths, followed by PCV13 in 2010, PCV15 in 2022, and PCV20 in 2023. After the removal of vaccine serotypes, the prevalence of carriage remained stable as the vacated pediatric ecological niche was filled with certain non-vaccine serotypes. Predicting which pneumococcal clones, and which serotypes, will be most successful in colonization after vaccination can enhance vaccine design and public health interventions, while also improving our understanding of pneumococcal evolution. While carriage data, which are collected from the pneumococcal population that is competing to colonize and transmit, are most directly relevant to evolutionary studies, invasive disease data are often more plentiful. Previously, evolutionary models based on negative frequency-dependent selection (NFDS) on the accessory genome were shown to predict which non-vaccine strains and serotypes were most successful in colonization following the introduction of PCV7. Here, we show that an inverse-invasiveness weighting system applied to invasive disease surveillance data allows the NFDS model to predict strain proportions in the projected carriage population in the post-PCV13/pre-PCV15 and pre-PCV20 epoch. The significance of our research lies in using a sample of invasive disease surveillance data to extend the use of NFDS as an evolutionary mechanism to predict post-PCV13 population dynamics. This has shown that we can correct for biased sampling that arises from differences in virulence and can enrich the value of genomic data from disease surveillance and advance our understanding of how NFDS impacts carriage population dynamics after both PCV7 and PCV13 vaccination.

Research on Sequence Clustering and Alignment in DNA Storage Based on the K-Means Model

Research on Sequence Clustering and Alignment in DNA Storage Based on the K-Means Model

A novel physics-guided spatial-temporal data mining method with external and internal causal attention for drilling risk evaluation

As drilling technology advances and operations extend into more complex geological environments, evaluating drilling risks has become increasingly complex, challenging the effectiveness of traditional methods. The novel physics-guided spatial-temporal data mining method that integrates external and internal causal attention mechanisms for drilling risk evaluation is proposed to address this issue. Firstly, a risk calibration method based on spatial-temporal sequence clustering is designed. This method dynamically calibrates drilling risks by mining subtle changes in sign data during drilling. Secondly, an expert experience extraction method based on the correlation of drilling risk signs and a fuzzy inference system is established. Kendall's tau is used to quantify the correlation between drilling risk signs. The fuzzy inference system is employed to convert fuzzy and difficult-to-quantify expert experience into computable and interpretable rules. In order to improve the flexibility and adaptability of the fuzzy inference system, an expert experience rules base is also constructed. Subsequently, a spatial-temporal data mining model integrating both external and internal causal attention mechanisms (STMIEICAM) is constructed. The external causal attention mechanism (ECAM) quantified the correlation between signs and risk. The internal causal attention mechanism (ICAM) improved the model's ability to capture and quantify the features of spatial-temporal sequences. Finally, the physical knowledge from the fuzzy inference system and well-site is embedded into the STMIEICAM model, forming a physics-guided spatial-temporal data mining model integrating both external and internal causal attention mechanisms (PG-STMIEICAM) that enables graded evaluation of drilling risks. The proposed method was applied to overflow risk evaluation in an oil field to validate its effectiveness. The results demonstrate that the method not only excels in uncovering hidden relationships within the data but also integrates expert knowledge, achieving accurate evaluation of drilling risks.

Change and Inequality in German Teenagers’ Typical Time-Use Sequences Between 1991 and 2013

How children spend their time is widely regarded as a key factor shaping their development and wellbeing, as well as the intergenerational transmission of socio-economic and gender inequalities. While there is much evidence on the aggregate amount of time teenagers spent on different activities, we know little about how teenagers’ activities unfold during a typical day. We use social sequence analysis and data collected in 1991/92, 2001/02 and 2012/13 via high-frequency time-use diaries of teenagers aged 12-16 in Germany to provide a fine-grained description of teenagers’ everyday time-use sequences. We identify and visualize a typology of teenagers’ time-use during weekdays and weekend days, respectively, which groups teenagers into clusters of typical time-use sequences. These typical time-use sequences change in prevalence over time, and are stratified by teenagers’ socio-economic background, gender, and age. Our findings reveal the sequential structure of teenagers’ time use, provide new evidence corroborating theories of the social stratification of teenagers’ time use, and complement existing evidence on teenagers’ aggregate time-use. Our identification of teenagers’ typical time-use sequences forms the basis for future research to examine their causes and consequences, as well as their role in the reproduction of social inequalities.

Change and Inequality in German Teenagers’ Typical Time-Use Sequences Between 1991 and 2013. Online Appendix

How children spend their time is widely regarded as a key factor shaping their development and wellbeing, as well as the intergenerational transmission of socio-economic and gender inequalities. While there is much evidence on the aggregate amount of time teenagers spent on different activities, we know little about how teenagers’ activities unfold during a typical day. We use social sequence analysis and data collected in 1991/92, 2001/02 and 2012/13 via high-frequency time-use diaries of teenagers aged 12-16 in Germany to provide a fine-grained description of teenagers’ everyday time-use sequences. We identify and visualize a typology of teenagers’ time-use during weekdays and weekend days, respectively, which groups teenagers into clusters of typical time-use sequences. These typical time-use sequences change in prevalence over time, and are stratified by teenagers’ socio-economic background, gender, and age. Our findings reveal the sequential structure of teenagers’ time use, provide new evidence corroborating theories of the social stratification of teenagers’ time use, and complement existing evidence on teenagers’ aggregate time-use. Our identification of teenagers’ typical time-use sequences forms the basis for future research to examine their causes and consequences, as well as their role in the reproduction of social inequalities.

SimpleMetaPipeline: Breaking the bioinformatics bottleneck in metabarcoding

Abstract The democratisation of next‐generation sequencing has vastly increased the availability of sequencing data from metabarcoding. However, to effectively prepare these metabarcoding data for subsequent analysis, researchers must consistently apply several different bioinformatic tools—including those which denoise reads, cluster sequences and assign taxonomic identities. This often creates a bioinformatics bottleneck in workflows for non‐specialists due to obstacles around: (a) integrating different tools, (b) the inability to easily modify and rerun bioinformatic pipelines involving non‐scripted (‘point‐and‐click’) elements and (c) the multiple outputs that may be required of a single dataset (e.g. amplicon sequence variants [ASVs] and operational taxonomic units [OTUs]), which often results in users running pipelines multiple times. Here, we introduce SimpleMetaPipeline, an open‐source bioinformatics pipeline implemented in R, which addresses these obstacles. SimpleMetaPipeline integrates the most robust and commonly used existing bioinformatic tools in a single reproducible pipeline, with a streamlined choice of parameters, to generate a sequence data table containing alternative clustering and assignment options. SimpleMetaPipeline accepts demultiplexed paired‐end and single reads from multiple sequencing runs. We describe the pipeline and demonstrate how alternative annotations enable the easy implementation of multi‐algorithm agreement tests to strengthen inferences. SimpleMetaPipeline represents a valuable addition to the existing library of pipelines, providing easy and reproducible bioinformatics, including a range of commonly desired clustering and assignment options, such as OTUs and ASVs.

Serial single-cell RNA sequencing unveils drug resistance and metastatic traits in stage IV breast cancer

Metastasis is a complex process that remains poorly understood at the molecular levels. We profiled single-cell transcriptomic, genomic, and epigenomic changes associated with cancer cell progression, chemotherapy resistance, and metastasis from a Stage IV breast cancer patient. Pretreatment- and posttreatment-specimens from the primary tumor and distant metastases were collected for single-cell RNA sequencing and subsequent cell clustering, copy number variation (CNV) estimation, transcriptomic factor estimation, and pseudotime analyses. CNV analysis revealed that a small population of pretreatment cancer cells resisted chemotherapy and expanded. New clones including Metastatic Precursor Cells (MPCs), emerged in the posttreatment primary tumors in CNV similar to metastatic cells. MPCs exhibited expression profiles indicative of epithelial–mesenchymal transition. Comparison of MPCs with metastatic cancer cells also revealed dynamic changes in transcription factors and calcitonin pathway gene expression. These findings demonstrate the utility of single-patient clinical sample analysis for understanding tumor drug resistance, regrowth, and metastasis.

AptamerRunner: An accessible aptamer structure prediction and clustering algorithm for visualization of selected aptamers

Aptamers are short single-stranded DNA or RNA molecules with high affinity and specificity for targets and are generated using the iterative Systematic Evolution of Ligands by EXponential enrichment (SELEX) process. Next-generation sequencing (NGS) revolutionized aptamer selections by allowing a more comprehensive analysis of SELEX-enriched aptamers as compared to Sanger sequencing. The current challenge with aptamer NGS datasets is identifying a diverse cohort of candidate aptamers with the highest likelihood of successful experimental validation. Herein we present AptamerRunner, an aptamer sequence and/or structure clustering algorithm that synergistically integrates computational analysis with visualization and expertise-directed decision making. The visual integration of networked aptamers with ranking data, such as fold enrichment or scoring algorithm results, represents a significant advancement over existing clustering tools by providing a natural context to depict groups of aptamers from which ranked or scored candidates can be chosen for experimental validation. The inherent flexibility, user-friendly design, and prospects for future enhancements with AptamerRunner has broad-reaching implications for aptamer researchers across a wide range of disciplines.

Description of the complete rDNA repeat unit structure of Coturnixjaponica Temminck et Schlegel, 1849 (Aves).

Ribosomal RNA (18S, 5.8S, 28S) gene clusters in genomes form regions that consist of multiple tandem repeats. They are located on a single or several pairs of chromosomes and play an important role in the formation of the nucleolus responsible for the assembly of ribosome subunits. The rRNA gene cluster sequences are widely used for taxonomic studies, however at present, complete information on the avian rDNA repeat unit structure including intergenic spacer sequence is available only for the chicken (Gallusgallusdomesticus Linnaeus, 1758). The GC enrichment and high-order repeats peculiarities within the intergenic spacer described for the chicken rDNA cluster may be responsible for these failures. The karyotype of the Japanese quail (Coturnixjaponica Temminck et Schlegel, 1849) deserves close attention because, unlike most birds, it has three pairs of nucleolar organizer bearing chromosomes, two of which are microchromosomes enriched in repeating elements and heterochromatin that carry translocated terminal nucleolar organizers. Here we assembled and annotated the complete Japanese quail ribosomal gene cluster sequence of 21166 base pairs (GenBank under the registration tag BankIt2509210 CoturnixOK523374). This is the second deciphered avian rDNA cluster after the chicken. Despite the revealed high similarity with the chicken corresponding sequence, it has a number of specific features, which include a slightly lower degree of GC content and the presence of bendable elements in the content of both the transcribed spacer I and the non-transcribed intergenic spacer.

The neglected giants: Uncovering the prevalence and functional groups of huge proteins in proteomes.

An often-overlooked aspect of biology is formed by the outliers of the protein length distribution, specifically those proteins with more than 5000 amino acids, which we refer to as huge proteins (HPs). By examining UniprotKB, we discovered more than 41 000 HPs throughout the tree of life, with the majority found in eukaryotes. Notably, the phyla with the highest propensity for huge proteins are Apicomplexa and Fornicata. Moreover, we observed that certain bacteria, such as Elusomicrobia or Planctomycetota, have a higher tendency for encoding huge proteins, even more than the average eukaryote. To investigate if these macro-polypeptides represent "real" proteins, we explored several indirect metrics. Additionally, orthology analyses reveals thousands of clusters of homologous sequences of HPs, revealing functional groups related to key cellular processes such as cytoskeleton organization and functioning as chaperones or as E3-ubiquitin ligases in eukaryotes. In the case of bacteria, the major clusters have functions related to Non-Ribosomomal peptide synthesis/Polyketide synthesis, followed by pathogen-host attachment or recognition surface proteins. Further exploration of the annotations for each HPs supported the previously identified functional groups. These findings underscore the need for further investigation of the cellular and ecological roles of these HPs and their potential impact on biology and biotechnology.

Molecular epidemiology of Streptococcus pneumoniae isolates causing invasive and noninvasive infection in Ethiopia

Streptococcus pneumoniae, a medically important opportunistic bacterial pathogen of the upper respiratory tract, is a major public health concern, causing a wide range of pneumococcal illnesses, both invasive and noninvasive. It is associated with significant global morbidity and mortality, including pneumonia, meningitis, sepsis, and acute otitis media. The major purpose of this study was to determine the molecular epidemiology of Streptococcus pneumoniae strains that cause invasive and noninvasive infections in Ethiopia. A prospective study was undertaken in two regional hospitals between January 2018 and December 2019. Whole-genome sequencing was used to analyze all isolates. Serotypes and multilocus sequence types (MLST) were derived from genomic data. The E-test was used for antimicrobial susceptibility testing. Patient samples obtained 54 Streptococcus pneumoniae isolates, 33 from invasive and 21 from noninvasive specimens. Our findings identified 32 serotypes expressed by 25 Global Pneumococcal Sequence Clusters (GPSCs) and 42 sequence types (STs), including 21 new STs. The most common sequence types among the invasive isolates were ST3500, ST5368, ST11162, ST15425, ST15555, ST15559, and ST15561 (2/33, 6% each). These sequence types were linked to serotypes 8, 7 C, 15B/C, 16 F, 10 A, 15B, and 6 A, respectively. Among the noninvasive isolates, only ST15432, associated with serotype 23 A, had numerous isolates (4/21, 19%). Serotype 14 was revealed as the most resistant strain to penicillin G, whereas isolates from serotypes 3, 8, 7 C, and 10 A were resistant to erythromycin. Notably, all serotype 6 A isolates were resistant to both erythromycin and penicillin G. Our findings revealed an abnormally significant number of novel STs, as well as extremely diversified serotypes and sequence types, implying that Ethiopia may serve as a breeding ground for novel STs. Recombination can produce novel STs that cause capsular switching. This has the potential to influence how immunization campaigns affect the burden of invasive pneumococcal illness. The findings highlight the importance of continuous genetic surveillance of the pneumococcal population as a vital step toward enhancing future vaccine design.