HAYSTAC: A Bayesian framework for robust and rapid species identification in high-throughput sequencing data

soibean: High-Resolution Taxonomic Identification of Ancient Environmental DNA Using Mitochondrial Pangenome Graphs

Ancient environmental DNA (aeDNA) is becoming a powerful tool to gain insights about past ecosystems, overcoming the limitations of conventional fossil records. However, several methodological challenges remain, particularly for classifying the DNA to species level and conducting phylogenetic analysis. Current methods, primarily tailored for modern datasets, fail to capture several idiosyncrasies of aeDNA, including species mixtures from closely related species and ancestral divergence. We introduce soibean, a novel tool that utilizes mitochondrial pangenomic graphs for identifying species from aeDNA reads. It outperforms existing methods in accurately identifying species from multiple closely related sources within a sample, enhancing phylogenetic analysis for aeDNA. soibean employs a damage-aware likelihood model for precise identification at low coverage with a high damage rate. Additionally, we reconstructed ancestral sequences for soibean's database to handle aeDNA that is highly diverged from modern references. soibean demonstrates effectiveness through simulated data tests and empirical validation. Notably, our method uncovered new empirical results in published datasets, including using porpoise whales as food in a Mesolithic community in Sweden, demonstrating its potential to reveal previously unrecognized findings in aeDNA studies.

Jellyfish Venom Peptides Targeting Human Potassium Channels Identified through Ligand Screening: Morphometric and Molecular Identification of the Species and Antibiotic Potential

The relative lack of marine venom could be attributed to the difficulty in dealing with venomous marine animals. Moreover, the venom of marine animals consists of various bioactive molecules, many of which are proteins with unique properties. In this study, we investigated the potential toxic proteins of jellyfish collected for ligand screening to understand the mechanism of the toxic effects of jellyfish. Since taxonomic identification is problematic due to the lack of proper keys, we conducted morphological and molecular mitochondrial DNA sequencing from COI and ITS regions. The venom extract from nematocysts found along the bell margins was used for protein characterization using SDS-gel electrophoresis and nano-liquid chromatography-tandem mass spectrometry. Ligand screening for the most potent toxin and antibacterial and cytotoxicity assays were carried out. The phylogenetic tree showed distinct clustering from other Catostylus sp. The proteomic analysis revealed venom with many bioactive proteins. Only 13 venom proteins were identified with molecular weights ranging from 4318 to 184,923 Da, exhibiting the venom's complexity. The overall toxin protein composition of Catostylus sp. venom was dominated by potassium channel toxin alpha-KTx. Molecular docking of toxin alpha-KTx 1.13 revealed high specificity towards the human voltage-gated potassium channel Kv3 with a high fitness score and a minimum energy barrier of -17.9 kcal/mol. Disc diffusion and cytotoxicity assays revealed potent antibacterial activity against Klebsiella pneumoniae with no cytotoxicity. Further studies on detailed characterization and therapeutic potentials are warranted.

Equine herpesvirus 4 infected domestic horses associated with Sintashta spoke-wheeled chariots around 4,000 years ago

Equine viral outbreaks have disrupted the socio-economic life of past human societies up until the late 19th century and continue to be of major concern to the horse industry today. With a seroprevalence of 60-80 per cent, equine herpesvirus 4 (EHV-4) is the most common horse pathogen on the planet. Yet, its evolutionary history remains understudied. Here, we screen the sequenced data of 264 archaeological horse remains to detect the presence of EHV-4. We recover the first ancient EHV-4 genome with 4.2× average depth-of-coverage from a specimen excavated in the Southeastern Urals and dated to the Early Bronze Age period, approximately 3,900 years ago. The recovery of an EHV-4 virus outside the upper respiratory tract not only points to an animal particularly infected but also highlights the importance of post-cranial bones in pathogen characterisation. Bayesian phylogenetic reconstruction provides a minimal time estimate for EHV-4 diversification to around 4,000 years ago, a time when modern domestic horses spread across the Central Asian steppes together with spoke-wheeled Sintashta chariots, or earlier. The analyses also considerably revise the diversification time of the two EHV-4 subclades from the 16th century based solely on modern data to nearly a thousand years ago. Our study paves the way for a robust reconstruction of the history of non-human pathogens and their impact on animal health.

Ancient chicken remains reveal the origins of virulence in Marek's disease virus

The pronounced growth in livestock populations since the 1950s has altered the epidemiological and evolutionary trajectory of their associated pathogens. For example, Marek's disease virus (MDV), which causes lymphoid tumors in chickens, has experienced a marked increase in virulence over the past century. Today, MDV infections kill >90% of unvaccinated birds, and controlling it costs more than US$1 billion annually. By sequencing MDV genomes derived from archeological chickens, we demonstrate that it has been circulating for at least 1000 years. We functionally tested the Meq oncogene, one of 49 viral genes positively selected in modern strains, demonstrating that ancient MDV was likely incapable of driving tumor formation. Our results demonstrate the power of ancient DNA approaches to trace the molecular basis of virulence in economically relevant pathogens.

Unlocking the microbial studies through computational approaches: how far have we reached?

The metagenomics approach accelerated the study of genetic information from uncultured microbes and complex microbial communities. In silico research also facilitated an understanding of protein-DNA interactions, protein-protein interactions, docking between proteins and phyto/biochemicals for drug design, and modeling of the 3D structure of proteins. These in silico approaches provided insight into analyzing pathogenic and nonpathogenic strains that helped in the identification of probable genes for vaccines and antimicrobial agents and comparing whole-genome sequences to microbial evolution. Artificial intelligence, more precisely machine learning (ML) and deep learning (DL), has proven to be a promising approach in the field of microbiology to handle, analyze, and utilize large data that are generated through nucleic acid sequencing and proteomics. This enabled the understanding of the functional and taxonomic diversity of microorganisms. ML and DL have been used in the prediction and forecasting of diseases and applied to trace environmental contaminants and environmental quality. This review presents an in-depth analysis of the recent application of silico approaches in microbial genomics, proteomics, functional diversity, vaccine development, and drug design.