Taxonomic Classification Research Articles

Constructing phylogenetic trees for microbiome data analysis: A mini-review

As next-generation sequencing technologies advance rapidly and the cost of metagenomic sequencing continues to decrease, researchers now face an unprecedented volume of microbiome data. This surge has stimulated the development of scalable microbiome data analysis methods and necessitated the incorporation of phylogenetic information into microbiome analysis for improved accuracy. Tools for constructing phylogenetic trees from 16S rRNA sequencing data are well-established, as the highly conserved regions of the 16S gene are limited, simplifying the identification of marker genes. In contrast, metagenomic and whole genome shotgun (WGS) sequencing involve sequencing from random fragments of the entire gene, making identification of consistent marker genes challenging owing to the vast diversity of genomic regions, resulting in a scarcity of robust tools for constructing phylogenetic trees. Although bacterial sequence tree construction tools exist for upstream bioinformatics, many downstream researchers—those integrating these trees into statistical models or machine learning—are either unaware of these tools or find them difficult to use due to the steep learning curve of processing raw sequences. This is compounded by the fact that public datasets often lack phylogenetic trees, providing only abundance tables and taxonomic classifications. To address this, we present a comprehensive review of phylogenetic tree construction techniques for microbiome data (16S rRNA or whole-genome shotgun sequencing). We outline the strengths and limitations of current methods, offering expert insights and step-by-step guidance to make these tools more accessible and widely applicable in quantitative microbiome data analysis.

Expression of Concern for Nazir et al., “Characterization, taxonomic classification, and genomic analysis of two newly isolated bacteriophages with potential to infect Escherichia coli ”

Expression of Concern for Nazir et al., “Characterization, taxonomic classification, and genomic analysis of two newly isolated bacteriophages with potential to infect <i>Escherichia coli</i> ”

Sex-Induced Changes in Microbial Eukaryotes and Prokaryotes in Gastrointestinal Tract of Simmental Cattle

This study investigates gender-based differences in the gut microbiota of Simmental cattle, focusing on bacterial, archaeal, and fungal communities. Fecal samples were collected and analyzed using high-throughput sequencing, with taxonomic classification performed through the SILVA and UNITE databases. Alpha and beta diversity metrics were assessed, revealing significant differences in the diversity and composition of archaeal communities between males and females. Notably, females exhibited higher alpha diversity in archaea, while beta diversity analyses indicated distinct clustering of bacterial and archaeal communities by gender. The study also identified correlations between specific microbial taxa and hematological parameters, with Treponema and Methanosphaera showing gender-specific associations that may influence cattle health and productivity. These findings highlight the importance of considering gender in microbiota-related research and suggest that gender-specific management strategies could optimize livestock performance. Future research should explore the role of sex hormones in shaping these microbial differences.

A step-by-step procedure for analysing the 16S rRNA-based microbiome diversity using QIIME 2 and comprehensive PICRUSt2 illustration for functional prediction.

Short-read sequencing technology has emerged as a preferred tool to analyse the bacterial composition of a niche by targeting hypervariable regions of the 16S rRNA gene. It targets the short hypervariable regions of the 16S rRNA gene and uncovers the taxonomic profile and their associated pathways. QIIME 2 is preferred and ready-to-use pipelines that perform stepwise analysis of massive short reads of 16S rRNA genes. This wrapper comprises several tools that include quality checking, denoising, taxonomic classification, alignment, and diversity analysis. However, it demands huge bioinformatic analysis practices which are quite challenging to many microbiologists working in the field of traditional microbiology. This paper, therefore, aimsto make microbiologists familiar with the steps of computational analysis for processing 16S rRNA-based sequences. Here, we are presenting stepwise processing of NGS sequences using the QIIME 2 platform along with their analyses, which include installing QIIME 2, importing and processing data, quality checks, taxonomy assignments, and diversity analysis. Besides, the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) has also been illustrated to understand the correlation between metabolic and physiological footprints of the different species observed during microbiome analysis. Therefore, this paper can be used as a handy toolkit for those researchers who are less familiar with its associated bioinformatic analysis.

Unveiling bat-borne viruses: a comprehensive classification and analysis of virome evolution.

Bats (Order Chiroptera) are an important reservoir of emerging zoonotic microbes, including viruses of public health concern such as henipaviruses, lyssaviruses, and SARS-related coronaviruses. Despite the continued discovery of new viruses in bat populations, a significant proportion of these viral agents remain uncharacterized, highlighting the imperative for additional research aimed at elucidating their evolutionary relationship and taxonomic classification. In order to delve deeper into the viral reservoir hosted by bats, the present study employed Next Generation Sequencing (NGS) technology to analyze 13,105 swab samples obtained from various locations in China. Analysis of 378 sample pools revealed the presence of 846 vertebrate-associated viruses. Subsequent thorough examination, adhering to the International Committee on Taxonomy of Viruses (ICTV) criteria for virus classification, identified a total of 120 putative viral species with the potential to emerge as novel viruses, comprising a total of 294 viral strains. Phylogenetic analysis of conserved genomic regions indicated the novel virus exhibited a diverse array of viral lineages and branches, some of which displayed close genetic relationships to known human and livestock pathogens, such as poxviruses and pestiviruses. This study investigates the breadth of DNA and RNA viruses harbored by bats, delineating several novel evolutionary lineages and offering significant contributions to virus taxonomy. Furthermore, the identification of hitherto unknown viruses with relevance to human and livestock health underscores the importance of this study in encouraging infectious disease monitoring and management efforts in both public health and veterinary contexts. Video Abstract.

A Whole-Genome Survey and the Mitochondrial Genome of Acanthocepola indica Provide Insights into Its Phylogenetic Relationships in Priacanthiformes

Acanthocepola indica, a deep-sea snake fish, is primarily found in the Indo-west Pacific region, including India, Korea, Japan, and the South China Sea. The taxonomic classification of A. indica based on morphological characteristics remains inaccurate and unclear. In this study, we utilized next-generation sequencing to generate comprehensive genomic data for A. indica. The estimated genome size of A. indica was 422.95 Mb, with a heterozygosity ratio of 1.02% and a sequence repeat ratio of 22.43%. Our analysis suggested that A. indica is diploid, and the draft genome assembly consists of 1,059,784 contigs with a contig N50 of 1942 bp. We identified a total of 444,728 simple sequence repeats in the genome of A. indica. Furthermore, we successfully assembled the complete mitochondrial genome (16,439 bp) of A. indica, which included 13 protein-coding genes, 22 tRNA genes and 2 rRNA genes. Phylogenetic analysis based on mitochondrial genomes revealed that A. indica is closely related to Acanthocepola krusensternii and Cepola schlegelii, providing evidence that the family Cepolidae belongs to the order Priacanthiformes. Population size dynamics analysis indicated that A. indica experienced a bottleneck effect during the Pleistocene Glacial Epoch, likely due to the changes in glacial cycles and sea level fluctuations since ~800 Kya.

Bacterial diversity in rice field soil and sludge soil samples: A comparative metagenomics-based study

Abstract Soil contains several organic and inorganic substances. It also has a large number of microbial diversity including bacteria. This microbial community affects the physicochemical properties of soil. This bacterial bacteria diversity is also involved in biomass degradation and plant growth promotion. Metagenomic sequencing is used to analyze microbial diversity in environmental samples. The metagenomic profiling of bacterial populations in the sludge and rice field soil was done at phylum, class, order, family, genus, and species levels. The metagenomic study revealed that Proteobacteria, Bacteroidetes, Chloroflexi, Acidobacteria and other unknown bacteria were reported in 16S rRNA Illumina MiSeq in the sludge and rice soil samples. We compared the taxonomic classification of bacterial diversity found in sewage soils with that of rice field soils using metagenomic sequencing analysis of the V3-V4 region of the 16S rRNA. In rice soil, the most abundant group was Proteobacteria followed by Chloroflexi, Acidobacteria, Actinobacteria, and Bacteroidetes which increase the soil nutrient and influence the production of the plant. Bacteroidetes are the most dominant group in sludge soil than rice soil. Consequently, the core microbiome analysis indicated that Sorangium, Geobacter, Azoarcus, and Anaeromyxobacter bacterial genera were more abundant in both soil samples.&amp;#xD;

The Role of Immunohistochemistry as a Surrogate Marker in Molecular Subtyping and Classification of Bladder Cancer

Background/Objectives: Bladder cancer (BC) is a highly heterogeneous disease, presenting clinical challenges, particularly in predicting patient outcomes and selecting effective treatments. Molecular subtyping has emerged as an essential tool for understanding the biological diversity of BC; however, its implementation in clinical practice remains limited due to the high costs and complexity of genomic techniques. This review examines the role of immunohistochemistry (IHC) as a surrogate marker for molecular subtyping in BC, highlighting its potential to bridge the gap between advanced molecular classifications and routine clinical application; Methods: We explore the evolution of taxonomic classification in BC, with a particular focus on cytokeratin (KRT) expression patterns in normal urothelium, which are key to identifying basal and luminal subtypes. Furthermore, we emphasise the need for consensus on IHC markers to reliably define these subtypes, facilitating wider and standardised clinical use. The review also analyses the application of IHC in both muscle-invasive (MIBC) and non-muscle-invasive bladder cancer (NMIBC), with particular attention to the less extensively studied NMIBC cases. We discuss the practical advantages of IHC for subtyping, including its cost effectiveness and feasibility in standard pathology laboratories, alongside ongoing challenges such as the requirement for standardised protocols and external validation across diverse clinical settings; Conclusions: While IHC has limitations, it offers a viable alternative for laboratories lacking access to advanced molecular techniques. Further research is required to determine the optimal combination of markers, establish a consensus diagnostic algorithm, and validate IHC through large-scale trials. This will ultimately enhance diagnostic accuracy, guide treatment decisions, and improve patient outcomes.

Uncovering novel polyhydroxyalkanoate biosynthesis genes and unique pathway in yeast hanseniaspora valbyensis for sustainable bioplastic production

This study delves into the exploration of polyhydroxyalkanoate (PHA) biosynthesis genes within wild-type yeast strains, spotlighting the exceptional capabilities of isolate DMG-2. Through meticulous screening, DMG-2 emerged as a standout candidate, showcasing vivid red fluorescence indicative of prolific intracellular PHA granules. Characterization via FTIR spectroscopy unveiled a diverse biopolymer composition within DMG-2, featuring distinct functional groups associated with PHA and polyphosphate. Phylogenetic analysis placed DMG-2 within the Hanseniaspora valbyensis NRRL Y-1626 group, highlighting its distinct taxonomic classification. Subsequent investigation into DMG-2’s PHA biosynthesis genes yielded promising outcomes, with successful cloning and efficient PHA accumulation confirmed in transgenic E. coli cells. Protein analysis of ORF1 revealed its involvement in sugar metabolism, supported by its cellular localization and identification of functional motifs. Genomic analysis revealed regulatory elements within ORF1, shedding light on potential splice junctions and transcriptional networks influencing PHA synthesis pathways. Spectroscopic analysis of the biopolymer extracted from transgenic E. coli DMG2-1 provided insights into its co-polymer nature, comprising segments of PHB, PHV, and polyphosphate. GC-MS analysis further elucidated the intricate molecular architecture of the polymer. In conclusion, this study represents a pioneering endeavor in exploring PHA biosynthesis genes within yeast cells, with isolate DMG-2 demonstrating remarkable potential. The findings offer valuable insights for advancing sustainable bioplastic production and hold significant implications for biotechnological applications.

ITSxpress version 2: software to rapidly trim internal transcribed spacer sequences with quality scores for amplicon sequencing.

The nuclear ribosomal RNA (rRNA) internal transcribed spacer (ITS) regions are commonly used to identify fungi and other eukaryotic taxa in amplicon sequencing. The highly conserved rRNA regions flanking the ITS are often trimmed before being used for taxonomic assignment. The Python software package ITSxpress rapidly trims single-end or paired-end sequences in FASTQ format for use in amplicon sequence variant clustering methods like DADA2. This new major release of ITSxpress improves the paired-end merging method, simplifies installation of the QIIME 2 ITSxpress plugin, removes major dependencies, adds use cases, and is compatible with newer compression formats. This article discusses the modifications to ITSxpress that improve the output and user experience, leading to a major version increase.IMPORTANCEITSxpress is a sequence trimming method applied to internal transcribed spacer (ITS) amplicon sequences before calling amplicon sequence variants (ASVs). The ITS region is used to understand the composition of eukaryotic microbial communities. ITS sequences provide good taxonomic resolution due to their hypervariability, but are flanked by conserved regions that allow their primers to be more universal. Amplicons generated with such primers contain regions with different evolutionary rates, and trimming these conserved regions results in better taxonomic classification and a more valid set of ASVs. This package can be used for most amplicon sequencing methods including for newer long-read sequencing formats, such as PacBio. ITSxpress can be installed from Bioconda, used as a Docker image, or installed from source code. The package works well with high-performance computing clusters or laptops due to its low-resource requirements.

Deep Lead Optimization: Leveraging Generative AI for Structural Modification.

The integration of deep learning-based molecular generation models into drug discovery has garnered significant attention for its potential to expedite the development process. Central to this is lead optimization, a critical phase where existing molecules are refined into viable drug candidates. As various methods for deep lead optimization continue to emerge, it is essential to classify these approaches more clearly. We categorize lead optimization methods into two main types: goal-directed and structure-directed. Our focus is on structure-directed optimization, which, while highly relevant to practical applications, is less explored compared to goal-directed methods. Through a systematic review of conventional computational approaches, we identify four tasks specific to structure-directed optimization: fragment replacement, linker design, scaffold hopping, and side-chain decoration. We discuss the motivations, training data construction, and current developments for each of these tasks. Additionally, we use classical optimization taxonomy to classify both goal-directed and structure-directed methods, highlighting their challenges and future development prospects. Finally, we propose a reference protocol for experimental chemists to effectively utilize Generative AI (GenAI)-based tools in structural modification tasks, bridging the gap between methodological advancements and practical applications.

SpecGMM: Integrating Spectral Analysis and Gaussian Mixture Models for taxonomic classification and identification of discriminative DNA regions

Abstract Motivation Genomic Signal Processing, which transforms biomolecular sequences into discrete signals for spectral analysis, has provided valuable insights into DNA sequence, structure, and evolution. However, challenges persist with spectral representations of variable-length sequences for tasks like species classification and in interpreting these spectra to identify discriminative DNA regions. Results We introduce SpecGMM, a novel framework that integrates sliding window-based Spectral analysis with a Gaussian Mixture Model to transform variable-length DNA sequences into fixed-dimensional spectral representations for taxonomic classification. SpecGMM’s hyperparameters were selected using a dataset of plant sequences, and applied unchanged across diverse datasets, including mitochondrial DNA, viral and bacterial genome, and 16S rRNA sequences. Across these datasets, SpecGMM outperformed a baseline method, with 9.45% average and 35.55% maximum improvement in test accuracies for a Linear Discriminant classifier. Regarding interpretability, SpecGMM revealed discriminative hypervariable regions in 16S rRNA sequences–particularly V3/V4 for discriminating higher taxa and V2/V3 for lower taxa–corroborating their known classification relevance. SpecGMM’s spectrogram video analysis helped visualize species–specific DNA signatures. SpecGMM thus provides a robust and interpretable method for spectral DNA analysis, opening new avenues in genomic signal processing research. Availability and implementation SpecGMM’s source code is available at https://github.com/BIRDSgroup/SpecGMM.

Seven mitochondrial genomes of tribe Hylurgini (Coleoptera: Curculionidae: Scolytinae) in Eurasia and their phylogenetic analysis.

The Hylurgini tribe (Coleoptera: Curculionidae: Scolytinae) comprises commercially significant bark beetles, including invasive species within the genera Dendroctonus and Hylurgus. These invasive species coexist with native Tomicus species of Hylurgini and cooperatively infest host trees in China. However, we lack sufficient mitochondrial genome data of Hylurgini to conduct phylogenetic studies, clarify the phylogenetic relationships of the above species, and improve the understanding of niche divergence and common hazards. Here, we sequenced and analyzed the mitochondrial genomes of seven Hylurgini species, including Dendroctonus valens, Hylurgus ligniperda, Hylurgus micklitzi, Tomicus piniperda, Tomicus brevipilosus, Tomicus minor and Tomicus yunnanensis. All sequenced mitochondrial genomes ranged from 15,339 bp to 17,545 bp in length, and their AT contents ranged from 73.24% to 78.81%. The structure of the seven mitochondrial genomes was consistent with that of ancestral insects. Based on 13 protein-coding genes from the reported mitochondrial genomes of 29 species of bark beetles, we constructed phylogenetic trees using maximum likelihood and Bayesian inference methods. The topology of the two phylogenetic trees was almost consistent. The findings elucidated the taxonomy classification of Hylurgini and the evolutionary connections of its sister taxa within the Scolytinae. This study offers insights for examining the evolutionary connections between invasive and native bark beetles, as well as the molecular identification and detection of newly invading species.

Phyllanthus-derived Naturally-occurring Products: An Overview of their Effects against Viruses in Cell Models

Background: Infectious diseases are considered a global public health problem, with viruses being the predominant infectious agents afflicting the human population. The most used control alternatives are the search and development of vaccines and drugs. Nevertheless, their efficacy has limitations related to the immune response stimulation, resistance mechanisms, costeffort ratio, development, and production. An alternative to these drawbacks is the search for compounds isolated from plants with antiviral and/or virucidal properties. The genus Phyllanthus is a plant group producing compounds that gathers an antiviral and virucidal spectrum on different biological models. However, there is no complete review of their properties against viruses in cell models. Objective: To compile and analyse the more relevant information on the antiviral and virucidal activity in cell models, phytochemical composition, and generalities of the genus Phyllanthus. Method: The information was assembled from a general search for articles in various databanks, and the information was organized, tabulated, and discussed. Results: The taxonomic classification of the genus Phyllanthus showed discrepancies between different authors and publications. The antiviral and virucidal effects of Phyllanthus naturallyoccurring compounds on cell models showed a broad spectrum and a high chemical diversity mainly related to phenols and polyphenols. Conclusion: Antiviral and virucidal properties of Phyllanthus-derived compounds showed promising results as controlling agents against viral infections in different cell models, particularly in the viral replication and translation processes. Further studies are required to elucidate the specific mechanisms involved in these natural alternatives to expand their efficient and effective applications.

A Survey on Hyperlink Prediction.

As a natural extension of link prediction on graphs, hyperlink prediction aims for the inference of missing hyperlinks in hypergraphs, where a hyperlink can connect more than two nodes. Hyperlink prediction has applications in a wide range of systems, from chemical reaction networks and social communication networks to protein-protein interaction networks. In this article, we provide a systematic and comprehensive survey on hyperlink prediction. We adopt a classical taxonomy from link prediction to classify the existing hyperlink prediction methods into four categories: similarity-based, probability-based, matrix optimization-based, and deep learning-based methods. To compare the performance of methods from different categories, we perform a benchmark study on various hypergraph applications using representative methods from each category. Notably, deep learning-based methods prevail over other methods in hyperlink prediction.

BakRep - a searchable large-scale web repository for bacterial genomes, characterizations and metadata.

Bacteria are fascinating research objects in many disciplines for countless reasons, and whole-genome sequencing (WGS) has become the paramount methodology to advance our microbiological understanding. Meanwhile, access to cost-effective sequencing platforms has accelerated bacterial WGS to unprecedented levels, introducing new challenges in terms of data accessibility, computational demands, heterogeneity of analysis workflows and, thus, ultimately its scientific usability. To this end, a previous study released a uniformly processed set of 661 405 bacterial genome assemblies obtained from the European Nucleotide Archive as of November 2018. Building on these accomplishments, we conducted further genome-based analyses like taxonomic classification, multilocus sequence typing and annotation of all genomes. Here, we present BakRep, a searchable large-scale web repository of these genomes enriched with consistent genome characterizations and original metadata. The platform provides a flexible search engine combining taxonomic, genomic and metadata information, as well as interactive elements to visualize genomic features. Furthermore, all results can be downloaded for offline analyses via an accompanying command line tool. The web repository is accessible via https://bakrep.computational.bio.

Shape matters: investigating the utility of geometric morphometric techniques in the deep-sea isopod family Macrostylidae (Isopoda: Asellota)

Accurate taxonomic classification of deep-sea taxa is often impeded by the presence of highly morphologically similar but genetically distinct species. This issue is particularly pronounced in the isopods of the deep-sea family Macrostylidae, which exhibit remarkably low morphological variation despite significant genetic diversity. In this study, we present the first application of geometric morphometric techniques to 41 specimens across five species of deep-sea macrostylid isopods collected from Icelandic waters. Our results suggest that geometric morphometric techniques can effectively discriminate between macrostylid species. These techniques, hence, promise to be an important addition to the toolset of macrostylid taxonomists.

High prevalence of cardiotropic viruses in endomyocardial biopsies from patients with inflammatory cardiomyopathy demonstrated by a novel targeted NGS approach

Abstract Background Viral infection of the heart muscle is a common cause of myocarditis and viral persistence can lead to chronic inflammatory cardiomyopathies (DCMi). A number of viruses including parvovirus B19 (B19V), adenovirus (AdV), cytomegalovirus (CMV), Epstein-Barr virus (EBV), herpes virus 6 (HHV-6) and enteroviruses (EV) are known to infect the myocardium, but their prevalence and role in persisting DCMi are not well understood. Virological etiologies can only be determined via endomyocardial biopsy (EMB), but the low tissue amount limits the number of conventional viral nucleic acid tests (virus-specific PCR) that can be performed in parallel. This study aimed to detect known and novel cardiotropic viruses in EMB of DCMi patients using a targeted next generation sequencing (NGS) approach. Methods A cohort of 33 patients with inflammatory cardiomyopathy (DCMi) (LVEF=29±12%) were retrospectively analyzed and compared to previous results of virus-specific PCR. DNA and RNA extracted from EMB were used for sequencing library preparation, and viral nucleic acids were enriched via a custom myBaits hybridization capture approach. Enriched libraries were sequenced on an Illumina MiSeq-platform in paired-end mode (500.000 reads/sample), and reads were taxonomically classified via Kraken2 and mapped against viral reference genomes using BWA-MEM2 or HiSat2, and subsequently deduplicated. Viral reads detected via both Kraken2 and mapping were classified as valid. Results Targeted NGS confirmed 19 out of 22 (86%) of the previous PCR-based viral detections. However, NGS was able to detect B19V DNA and RNA in an additional 11 and 15 patient samples respectively; CMV DNA/RNA in additional 5 and 4 samples respectively; EBV DNA/RNA in additional 5 and 1 samples respectively; and HHV-6 DNA/RNA in additional 3 and 1 samples respectively. Furthermore, viral reads of adenovirus type 2, adenovirus-associated virus type 2 (AAV-2), herpes viruses 7 and 8, and human parvovirus 4 were also found among the cohort. Thus, targeted NGS was able to identify 82 viral infections in a cohort of 33 DCMi patients, while only 22 were found using routine PCR tests. For routinely tested B19V, the use of the new NGS diagnostic approach improved sensitivity by 42% and specificity by 72%. Conclusions The presented NGS method represents a powerful new tool for the diagnosis of viral and inflammatory heart disease. The sensitivity is significantly higher than with conventional PCR approaches and the taxonomic classification is more specific. This enables the detection of previously false-negative routine viruses as well as potentially new cardiotropic pathogens from limited bioptic patient material – thus significantly increasing the diagnostic yield of EMB. In addition, our study demonstrates a high prevalence of non-enteroviral cardiotropic viruses in DCMi patients, shedding new light on the etiology of this heterogenous diseases and paving the way for more specific future treatments.

Rumen fermentation shifts and microbial dynamics in mid-lactating holstein dairy cows experiencing heat stress and subsequent recovery periods.

In this study, we investigated the effects of heat stress (HS) on rumen fermentation, blood parameters, and ruminal microbial communities in mid-lactating Holstein dairy cows in Korea. Our study involved 12 mid-lactation Holstein dairy cows aged 55.54 months with 2.5 ± 0.65 parities and 100 to 200 days in milking (DIM), fed a total mixed ratio (TMR) diet. Samples were collected during HS (temperature-humidity index (THI) = 81.69) and recovery (RC) period (THI 69.84). The samples were analyzed for rumen fermentation, blood parameters, heat shock proteins, and microbial communities in dairy cows. The milk yield, milk fat, milk protein, and milk urea nitrogen (MUN) levels differed significantly between two -time points (p < 0.05). Rumen pH and volatile fatty acid (VFA) concentrations, the pH was not significantly different (p=0.619) between HS and RC periods; however, the ammonia nitrogen (NH3-N) levels increased during HS period ), however, there was no significant difference (p>0.05). Blood total protein significantly increased during HS period compared with that during RC period (p < 0.05), while no significant differences were observed in other parameters between the two periods. HSP27, HSP70, and HSP90 increased in dairy cows under HS conditions compared with those during the RC period. Taxonomic classification revealed that Firmicutes and Bacteroidetes dominated the bacterial community. PERMANOVA and PERMDISP showed significant differences in rumen bacterial diversity between HS and RC periods, based on Unifrac metrics (p=0.044 and p=0.015, respectively), indicating taxonomic variations. Microbial networks with correlations of > 0.8 (p < 0.05) showed a complex structure with equal positive and negative connections, indicating Anaerohabdus furcosa and Ruminiclostridium cellobioparum as key species during the HS and RC periods respectively. Heat stress significantly impacts Holstein dairy cows' physiological and metabolic processes, altering rumen fermentation, blood biochemistry, and gut microbiota during mid-lactation.

Comparative analysis of complete chloroplast genomes of 14 Asteraceae species.

The Asteraceae family, the largest and one of the most diverse families of angiosperms, presents significant challenges in taxonomic classification and systematic research due to its vast species diversity and morphological complexity. A comprehensive understanding of the chloroplast genomes within this family is essential for refining taxonomic classifications and advancing phylogenetic studies. In this study, we sequenced the complete chloroplast genomes of 14 Asteraceae species and conducted a thorough bioinformatic analysis of their characteristics. The chloroplast genomes, ranging from 150,907bp to 152,858bp, exhibit a typical quadripartite structure: a large single-copy (LSC) region (83,044bp to 84,625bp), a small single-copy (SSC) region (18,223bp to 18,673bp), and a pair of inverted repeats (IRs) (24,806bp to 25,201bp). These genomes encode 87 to 89 protein-coding genes (PCGs), 36 to 37 tRNA genes, and 8 rRNA genes, with high conservation in size, structure, gene content, and order. Comparative analysis with other Asteraceae species' chloroplast genomes revealed notable similarities and structural variations, particularly in the IR regions. Nucleotide polymorphism analysis identified four genes-trnY-GUA, trnE-UUC, ycf1, and rrn23-with higher Pi values, suggesting potential hotspots for evolutionary studies. Phylogenetic analysis using maximum likelihood (ML) and Bayesian inference (BI) approaches provided new insights, proposing the reclassification of Himalaiella auriculata and Jacobaea raphanifolia as independent genera, distinct from Saussurea and Senecio. This study presents a comprehensive analysis of the chloroplast genome structures and phylogenetic relationships of 14 Asteraceae species, offering critical data for future molecular identification, evolutionary biology, and population genetics research. The findings hold significant implications for the ongoing refinement of Asteraceae taxonomic classifications and enhance our understanding of the evolutionary dynamics within this complex family.