UNASSIGNED: Grape is of high economic value. Colletotrichum viniferum, a pathogen causing grape ripe rot and leaf spot, threatens grape production and quality.
UNASSIGNED: This study investigates the interplay between C. viniferum by Cytological study and transcriptome sequencing.
UNASSIGNED: Different grapevine germplasms, V. vinifera cv. Thompson Seedless (TS), V. labrusca accession Beaumont (B) and V. piasezkii Liuba-8 (LB-8) were classified as highly sensitive, moderate resistant and resistant to C. viniferum, respectively. Cytological study analysis reveals distinct differences between susceptible and resistant grapes post-inoculation, including faster pathogen development, longer germination tubes, normal appressoria of C. viniferum and absence of white secretions in the susceptible host grapevine. To understand the pathogenic mechanisms of C. viniferum, transcriptome sequencing was performed on the susceptible grapevine \"TS\" identifying 236 differentially expressed C. viniferum genes. These included 56 effectors, 36 carbohydrate genes, 5 P450 genes, and 10 genes involved in secondary metabolism. Fungal effectors are known as pivotal pathogenic factors that modulate plant immunity and affect disease development. Agrobacterium-mediated transient transformation in Nicotiana benthamiana screened 10 effectors (CvA13877, CvA01508, CvA05621, CvA00229, CvA07043, CvA05569, CvA12648, CvA02698, CvA14071 and CvA10999) that inhibited INF1 (infestans 1, P. infestans PAMP elicitor) induced cell death and 2 effectors (CvA02641 and CvA11478) that induced cell death. Additionally, transcriptome analysis of \"TS\" in response to C. viniferum identified differentially expressed grape genes related to plant hormone signaling (TGA, PR1, ETR, and ERF1/2), resveratrol biosynthesis genes (STS), phenylpropanoid biosynthesis genes (PAL and COMT), photosynthetic antenna proteins (Lhca and Lhcb), transcription factors (WRKY, NAC, MYB, ERF, GATA, bHLH and SBP), ROS (reactive oxygen species) clearance genes (CAT, GSH, POD and SOD), and disease-related genes (LRR, RPS2 and GST).
UNASSIGNED: This study highlights the potential functional diversity of C. viniferum effectors. Our findings lay a foundation for further research of infection mechanisms in Colletotrichum and identification of disease response targets in grape.

Computational models of homologous protein groups are essential in sequence bioinformatics. Due to the diversity and rapid evolution of viruses, the grouping of protein sequences from virus genomes is particularly challenging. The low sequence similarities of homologous genes in viruses require specific approaches for sequence- and structure-based clustering. Furthermore, the annotation of virus genomes in public databases is not as consistent and up to date as for many cellular genomes. To tackle these problems, we have developed VOGDB, which is a database of virus orthologous groups. VOGDB is a multi-layer database that progressively groups viral genes into groups connected by increasingly remote similarity. The first layer is based on pair-wise sequence similarities, the second layer is based on the sequence profile alignments, and the third layer uses predicted protein structures to find the most remote similarity. VOGDB groups allow for more sensitive homology searches of novel genes and increase the chance of predicting annotations or inferring phylogeny. VOGD B uses all virus genomes from RefSeq and partially reannotates them. VOGDB is updated with every RefSeq release. The unique feature of VOGDB is the inclusion of both prokaryotic and eukaryotic viruses in the same clustering process, which makes it possible to explore old evolutionary relationships of the two groups. VOGDB is freely available at vogdb.org under the CC BY 4.0 license.

Triticum aestivum is an important crop whose reference genome (International Wheat Genome Sequencing Consortium (IWGSC) RefSeq v2.1) offers a valuable resource for understanding wheat genetic structure, improving agronomic traits, and developing new cultivars. A key aspect of gene model annotation is protein-level evidence of gene expression obtained from proteomics studies, followed up by proteogenomics to physically map proteins to the genome. In this research, we have retrieved the largest recent wheat proteomics datasets publicly available and applied the Basic Local Alignment Search Tool (tBLASTn) algorithm to map the 861,759 identified unique peptides against IWGSC RefSeq v2.1. Of the 92,719 hits, 83,015 unique peptides aligned along 33,612 High Confidence (HC) genes, thus validating 31.4% of all wheat HC gene models. Furthermore, 6685 unique peptides were mapped against 3702 Low Confidence (LC) gene models, and we argue that these gene models should be considered for HC status. The remaining 2934 orphan peptides can be used for novel gene discovery, as exemplified here on chromosome 4D. We demonstrated that tBLASTn could not map peptides exhibiting mid-sequence frame shift. We supply all our proteogenomics results, Galaxy workflow and Python code, as well as Browser Extensible Data (BED) files as a resource for the wheat community via the Apollo Jbrowse, and GitHub repositories. Our workflow could be applied to other proteomics datasets to expand this resource with proteins and peptides from biotically and abiotically stressed samples. This would help tease out wheat gene expression under various environmental conditions, both spatially and temporally.

OBJECTIVE: Roscoea is a Sino-Himalayan alpine genus in pantropical family Zingiberaeae. As traditional Tibetan medicinal plants, many species of this genus are threatened by digging, logging, land clearance, grazing and climate change. Roscoea debilis is an endemic species in the Hengduan Mountains with a narrow distribution range. In this study, the assembled and annotated genome of Roscoea was presented in order to furnish significant resources for comparative and functional genomic investigations. The first complete reference genome of Roscoea is expected to shed light on research on conservation and evolutionary biology.
METHODS: A chromosome-level genome of 1601.04 Mb was obtained for R. debilis by combining Illumina short reads (107.28 Gb) and PacBio Hi-Fi reads (64.08 Gb), achieving high-quality sequencing coverage of roughly 67 × and 40 ×. The assembly was additionally assisted by 271.65 Gb Hi-C data (169 ×), which resulted in a contig N50 of 136.17 Mb and a scaffold N50 of 90.48 Mb. Benchmarking Universal Single-Copy Orthologs (BUSCO) assessment results revealed that most of the core embryophyta genes (98.7%) in the BUSCO dataset (embryophyta_odb10) were successfully identified. Additionally, 96.44% of the genomic sequences were accurately mapped onto twelve pseudochromosomes.

The life cycle of genome builds spans interlocking pillars of assembly, annotation, and comparative genomics to drive biological insights. While tools exist to address each pillar separately, there is a growing need for tools to integrate different pillars of a genome project holistically. For example, comparative approaches can provide quality control of assembly or annotation; genome assembly, in turn, can help to identify artifacts that may complicate the interpretation of genome comparisons. The JCVI library is a versatile Python-based library that offers a suite of tools that excel across these pillars. Featuring a modular design, the JCVI library provides high-level utilities for tasks such as format parsing, graphics generation, and manipulation of genome assemblies and annotations. Supporting genomics algorithms like MCscan and ALLMAPS are widely employed in building genome releases, producing publication-ready figures for quality assessment and evolutionary inference. Developed and maintained collaboratively, the JCVI library emphasizes quality and reusability.

This strain was isolated from traditionally (homemade) fermented Lithuanian cherry tomatoes. The genome consists of 55 contigs with a total size of 3,326,119 bp, an N50 of 170738, and a GC% of 44.3 %. According to the COG annotation, most of these proteins were divided into three categories related to transcription (K category: 307), amino acid transport and metabolism (E category: 222), and carbohydrate transport and metabolism (G category: 268). No genes associated with antimicrobial resistance or virulence factors were identified. The data presented here can be used in comparative genomics to identify antimicrobial resistance genes and virulence factors that may be present in relevant Lactobacillus species. PlasmidFinder server revealed the presence of plasmid pR18 (assessment number JN601038) in the genome that has lincomycin resistance, can be transferred from one bacterium to another, and is one of the most common plasmids in the genera Bacillus and Lactobacillus. The draft genome sequence data have been deposited with NCBI under Bioproject under accession number PRJNA947394.

UNASSIGNED: Nicotine degradation is a new strategy to block nicotine-induced pathology. The potential of human microbiota to degrade nicotine has not been explored.
UNASSIGNED: This study aimed to uncover the genomic potentials of human microbiota to degrade nicotine.
UNASSIGNED: To address this issue, we performed a systematic annotation of Nicotine-Degrading Enzymes (NDEs) from genomes and metagenomes of human microbiota. A total of 26,295 genomes and 1,596 metagenomes for human microbiota were downloaded from public databases and five types of NDEs were annotated with a custom pipeline. We found 959 NdhB, 785 NdhL, 987 NicX, three NicA1, and three NicA2 homologs.
UNASSIGNED: Genomic classification revealed that six phylum-level taxa, including Proteobacteria, Firmicutes, Firmicutes_A, Bacteroidota, Actinobacteriota, and Chloroflexota, can produce NDEs, with Proteobacteria encoding all five types of NDEs studied. Analysis of NicX prevalence revealed differences among body sites. NicX homologs were found in gut and oral samples with a high prevalence but not found in lung samples. NicX was found in samples from both smokers and non-smokers, though the prevalence might be different.
UNASSIGNED: This study represents the first systematic investigation of NDEs from the human microbiota, providing new insights into the physiology and ecological functions of human microbiota and shedding new light on the development of nicotine-degrading probiotics for the treatment of smoking-related diseases.

In this study, the genome of Akkermansia muciniphila ONE (designated AKK ONE) was sequenced, assembled, and analyzed. In addition, the safety of this strain was further evaluated by toxicological studies. The results showed that the AKK ONE genome is contained on a single chromosome with a total length of 2,817,524 bp and an average GC content of 55.48%. In total, 2411, 1131, 1168, 1745, and 1402 genes were annotated to the NR, GO, KEGG, COG, and SwissProt database, respectively. Potential resistance genes, adeF, tetW, ANT(3″)-IIa, and aadA1 were detected. AKK ONE was sensitive to ampicillin, ceftriaxone, cefotaxime, meropenem, tetracycline, and chloramphenicol and resistant to moxifloxacin. No potential virulence-related genes were detected. The PathogenFinder database analysis showed that AKK ONE was a non-potential human pathogen. This strain had good gastroenteric fluid tolerance and a weak ability to colonize the gut. No test item-related adverse effects were observed in the acute and subchronic toxicity test. AKK ONE did not display mutagenic activity either. This strain did not change the hematological and clinical biochemical parameters of mice. The weights of the organs were not affected by AKK ONE treatment. These results support that AKK ONE is safe for use as a probiotic at a dose of 8.28 × 109 CFU/kg bw/day.

Genome annotation has historically ignored small open reading frames (smORFs), which encode a class of proteins shorter than 100 amino acids, collectively referred to as microproteins. This cutoff was established to avoid thousands of false positives due to limitations of pure genomics pipelines. Proteogenomics, a computational approach that combines genomics, transcriptomics, and proteomics, makes it possible to accurately identify these short sequences by overlaying different levels of omics evidence. In this chapter, we showcase the use of μProteInS, a bioinformatics pipeline developed for the identification of unannotated microproteins encoded by smORFs in bacteria. The workflow covers all the steps from quality control and transcriptome assembly to the scoring and post-processing of mass spectrometry data. Additionally, we provide an example on how to apply the pipeline\'s machine learning method to identify high-confidence spectra and pinpoint the most reliable identifications from large datasets.

Microbial cell factories (MCFs) have been leveraged to construct sustainable platforms for value-added compound production. To optimize metabolism and reach optimal productivity, synthetic biology has developed various genetic devices to engineer microbial systems by gene editing, high-throughput protein engineering, and dynamic regulation. However, current synthetic biology methodologies still rely heavily on manual design, laborious testing, and exhaustive analysis. The emerging interdisciplinary field of artificial intelligence (AI) and biology has become pivotal in addressing the remaining challenges. AI-aided microbial production harnesses the power of processing, learning, and predicting vast amounts of biological data within seconds, providing outputs with high probability. With well-trained AI models, the conventional Design-Build-Test (DBT) cycle has been transformed into a multidimensional Design-Build-Test-Learn-Predict (DBTLP) workflow, leading to significantly improved operational efficiency and reduced labor consumption. Here, we comprehensively review the main components and recent advances in AI-aided microbial production, focusing on genome annotation, AI-aided protein engineering, artificial functional protein design, and AI-enabled pathway prediction. Finally, we discuss the challenges of integrating novel AI techniques into biology and propose the potential of large language models (LLMs) in advancing microbial production.