AssessORF: combining evolutionary conservation and proteomics to assess prokaryotic gene predictions

A Practical Guide for the Quality Evaluation of Fluobodies/Chromobodies

BACKGROUND

Fluorescent proteins (FPs) are pivotal reagents for flow cytometry analysis or fluorescent microscopy. A new generation of immunoreagents (fluobodies/chromobodies) has been developed by fusing recombinant nanobodies to FPs.

METHODS

We analyzed the quality of such biomolecules by a combination of gel filtration and SDS-PAGE to identify artefacts due to aggregation or material degradation.

RESULTS

In the SDS-PAGE run, unexpected bands corresponding to separate fluobodies were evidenced and characterized as either degradation products or artefacts that systematically resulted in the presence of specific FPs and some experimental conditions. The elimination of N-terminal methionine from FPs did not impair the appearance of FP fragments, whereas the stability and migration characteristics of some FP constructs were strongly affected by heating in loading buffer, which is a step samples undergo before electrophoretic separation.

CONCLUSIONS

In this work, we provide explanations for some odd results observed during the quality control of fluobodies and summarize practical suggestions for the choice of the most convenient FPs to fuse to antibody fragments.

Long-term application of organic fertilizer prompting the dispersal of antibiotic resistance genes and their health risks in the soil plastisphere

Microplastic (MP) pollution is a rapidly growing global environmental concern that has led to the emergence of a new environmental compartment, the plastisphere, which is a hotspot for the accumulation of antibiotic resistance genes (ARGs) and human bacterial pathogens (HBPs). However, studies on the effects of long-term organic fertilizer application on the dispersal of ARGs and virulence factor genes (VFGs) in the plastisphere of farmland soil have been limited. Here, we performed a field culture experiment by burying nylon bags filled with MPs in paddy soil that had been treated with different fertilizers for over 30 years to explore the changes of ARGs and VFGs in soil plastisphere. Our results show that the soil plastisphere amplified the ARG and VFG pollution caused by organic fertilization by 1.5 and 1.4 times, respectively. And it also led to a 2.7-fold increase in the risk of horizontal gene transfer. Meanwhile, the plastisphere tended to promote deterministic process in the community assembly of HBPs, with an increase of 1.4 times. Network analysis found a significant correlation between ARGs, VFGs, and bacteria in plastisphere. Correlation analysis highlight the important role of mobile genetic elements (MGEs) and bacterial communities in shaping the abundance of ARGs and VFGs, respectively. Our findings provide new insights into the health risk associated with the soil plastisphere due ARGs and VFGs derived from organic fertilizers.

Small proteins in bacteria - Big challenges in prediction and identification

Proteins with up to 100 amino acids have been largely overlooked due to the challenges associated with predicting and identifying them using traditional methods. Recent advances in bioinformatics and machine learning, DNA sequencing, RNA and Ribo-seq technologies, and mass spectrometry (MS) have greatly facilitated the detection and characterisation of these elusive proteins in recent years. This has revealed their crucial role in various cellular processes including regulation, signalling and transport, as toxins and as folding helpers for protein complexes. Consequently, the systematic identification and characterisation of these proteins in bacteria have emerged as a prominent field of interest within the microbial research community. This review provides an overview of different strategies for predicting and identifying these proteins on a large scale, leveraging the power of these advanced technologies. Furthermore, the review offers insights into the future developments that may be expected in this field.

INRI-seq enables global cell-free analysis of translation initiation and off-target effects of antisense inhibitors

Ribosome profiling (Ribo-seq) is a powerful method for the transcriptome-wide assessment of protein synthesis rates and the study of translational control mechanisms. Yet, Ribo-seq also has limitations. These include difficulties with the analysis of translation-modulating molecules such as antibiotics, which are often toxic or challenging to deliver into living cells. Here, we have developed in vitro Ribo-seq (INRI-seq), a cell-free method to analyze the translational landscape of a fully customizable synthetic transcriptome. Using Escherichia coli as an example, we show how INRI-seq can be used to analyze the translation initiation sites of a transcriptome of interest. We also study the global impact of direct translation inhibition by antisense peptide nucleic acid (PNA) to analyze PNA off-target effects. Overall, INRI-seq presents a scalable, sensitive method to study translation initiation in a transcriptome-wide manner without the potentially confounding effects of extracting ribosomes from living cells.

A Large-Scale Genome-Based Survey of Acidophilic Bacteria Suggests That Genome Streamlining Is an Adaption for Life at Low pH

The genome streamlining theory suggests that reduction of microbial genome size optimizes energy utilization in stressful environments. Although this hypothesis has been explored in several cases of low-nutrient (oligotrophic) and high-temperature environments, little work has been carried out on microorganisms from low-pH environments, and what has been reported is inconclusive. In this study, we performed a large-scale comparative genomics investigation of more than 260 bacterial high-quality genome sequences of acidophiles, together with genomes of their closest phylogenetic relatives that live at circum-neutral pH. A statistically supported correlation is reported between reduction of genome size and decreasing pH that we demonstrate is due to gene loss and reduced gene sizes. This trend is independent from other genome size constraints such as temperature and G + C content. Genome streamlining in the evolution of acidophilic bacteria is thus supported by our results. The analyses of predicted Clusters of Orthologous Genes (COG) categories and subcellular location predictions indicate that acidophiles have a lower representation of genes encoding extracellular proteins, signal transduction mechanisms, and proteins with unknown function but are enriched in inner membrane proteins, chaperones, basic metabolism, and core cellular functions. Contrary to other reports for genome streamlining, there was no significant change in paralog frequencies across pH. However, a detailed analysis of COG categories revealed a higher proportion of genes in acidophiles in the following categories: "replication and repair," "amino acid transport," and "intracellular trafficking". This study brings increasing clarity regarding the genomic adaptations of acidophiles to life at low pH while putting elements, such as the reduction of average gene size, under the spotlight of streamlining theory.

KEMET - a python tool for KEGG Module evaluation and microbial genome annotation expansion

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KEMET allows evaluating and expanding microbial genome annotations.

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KEMET can identify unannotated genes having partial sequence truncations.

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Annotation expansion can improve contextual draft genome-scale metabolic model reconstruction.

Background

The rapid accumulation of sequencing data from metagenomic studies is enabling the generation of huge collections of microbial genomes, with new challenges for mapping their functional potential. In particular, metagenome-assembled genomes are typically incomplete and harbor partial gene sequences that can limit their annotation from traditional tools. New scalable solutions are thus needed to facilitate the evaluation of functional potential in microbial genomes.

Methods

To resolve annotation gaps in microbial genomes, we developed KEMET, an open-source Python library devised for the analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) functional units. KEMET focuses on the in-depth analysis of metabolic reaction networks to identify missing orthologs through hidden Markov model profiles.

Results

We evaluate the potential of KEMET for expanding functional annotations by simulating the effect of assembly issues on real gene sequences and showing that our approach can identify missing KEGG orthologs. Additionally, we show that recovered gene annotations can sensibly increase the quality of draft genome-scale metabolic models obtained from metagenome-assembled genomes, in some cases reaching the accuracy of models generated from complete genomes.

Conclusions

KEMET therefore allows expanding genome annotations by targeted searches for orthologous sequences, enabling a better qualitative and quantitative assessment of metabolic capabilities in novel microbial organisms.

FindNonCoding: rapid and simple detection of non-coding RNAs in genomes

SUMMARY

Non-coding RNAs are often neglected during genome annotation due to their difficulty of detection relative to protein coding genes. FindNonCoding takes a pattern mining approach to capture the essential sequence motifs and hairpin loops representing a non-coding RNA family and quickly identify matches in genomes. FindNonCoding was designed for ease of use and accurately finds non-coding RNAs with a low false discovery rate.

AVAILABILITY AND IMPLEMENTATION

FindNonCoding is implemented within the DECIPHER package (v2.19.3) for R (v4.1) available from Bioconductor. Pre-trained models of common non-coding RNA families are included for bacteria, archaea and eukarya.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.