Orphan Genes Shared by Pathogenic Genomes Are More Associated with Bacterial Pathogenicity

Orphan genes (also known as ORFans [i.e., orphan open reading frames]) are new genes that enable an organism to adapt to its specific living environment. Our focus in this study is to compare ORFans between pathogens (P) and nonpathogens (NP) of the same genus. Using the pangenome idea, we have identified 130,169 ORFans in nine bacterial genera (505 genomes) and classified these ORFans into four groups: (i) SS-ORFans (P), which are only found in a single pathogenic genome; (ii) SS-ORFans (NP), which are only found in a single nonpathogenic genome; (iii) PS-ORFans (P), which are found in multiple pathogenic genomes; and (iv) NS-ORFans (NP), which are found in multiple nonpathogenic genomes. Within the same genus, pathogens do not always have more genes, more ORFans, or more pathogenicity-related genes (PRGs)—including prophages, pathogenicity islands (PAIs), virulence factors (VFs), and horizontal gene transfers (HGTs)—than nonpathogens. Interestingly, in pathogens of the nine genera, the percentages of PS-ORFans are consistently higher than those of SS-ORFans, which is not true in nonpathogens. Similarly, in pathogens of the nine genera, the percentages of PS-ORFans matching the four types of PRGs are also always higher than those of SS-ORFans, but this is not true in nonpathogens. All of these findings suggest the greater importance of PS-ORFans for bacterial pathogenicity

Identification and investigation of ORFans in the viral world

<p>Abstract</p> <p>Background</p> <p>Genome-wide studies have already shed light into the evolution and enormous diversity of the viral world. Nevertheless, one of the unresolved mysteries in comparative genomics today is the abundance of ORFans – ORFs with no detectable sequence similarity to any other ORF in the databases. Recently, studies attempting to understand the origin and functions of bacterial ORFans have been reported. Here we present a first genome-wide identification and analysis of ORFans in the viral world, with focus on bacteriophages.</p> <p>Results</p> <p>Almost one-third of all ORFs in 1,456 complete virus genomes correspond to ORFans, a figure significantly larger than that observed in prokaryotes. Like prokaryotic ORFans, viral ORFans are shorter and have a lower GC content than non-ORFans. Nevertheless, a statistically significant lower GC content is found only on a minority of viruses. By focusing on phages, we find that 38.4% of phage ORFs have no homologs in other phages, and 30.1% have no homologs neither in the viral nor in the prokaryotic world. Phages with different host ranges have different percentages of ORFans, reflecting different sampling status and suggesting various diversities. Similarity searches of the phage ORFeome (ORFans and non-ORFans) against prokaryotic genomes shows that almost half of the phage ORFs have prokaryotic homologs, suggesting the major role that horizontal transfer plays in bacterial evolution. Surprisingly, the percentage of phage ORFans with prokaryotic homologs is only 18.7%. This suggests that phage ORFans play a lesser role in horizontal transfer to prokaryotes, but may be among the major players contributing to the vast phage diversity.</p> <p>Conclusion</p> <p>Although the current sampling of viral genomes is extremely low, ORFans and near-ORFans are likely to continue to grow in number as more genomes are sequenced. The abundance of phage ORFans may be partially due to the expected vast viral diversity, and may be instrumental in understanding viral evolution. The functions, origins and fates of the majority of viral ORFans remain a mystery. Further computational and experimental studies are likely to shed light on the mechanisms that have given rise to so many bacterial and viral ORFans.</p

On the origin of microbial ORFans: quantifying the strength of the evidence for viral lateral transfer

BACKGROUND: The origin of microbial ORFans, ORFs having no detectable homology to other ORFs in the databases, is one of the unexplained puzzles of the post-genomic era. Several hypothesis on the origin of ORFans have been suggested in the last few years, most of which based on selected, relatively small, subsets of ORFans. One of the hypotheses for the origin of ORFans is that they have been acquired thru lateral transfer from viruses. Here we carry out a comprehensive, genome-wide study on the origins of ORFans to quantify the strength of current evidence supporting this hypothesis. RESULTS: We performed similarity searches by querying all current ORFans against the public virus protein database. Surprisingly, we found that only 2.8% of all microbial ORFans have detectable homologs in viruses, while the percentage of non-ORFans with detectable homologs in viruses is 7.9%, a significantly higher figure. This suggests that the current evidence for the origin of ORFans from lateral transfer from viruses is at best weak. However, an analysis of individual genomes revealed a number of organisms with much higher percentages, many of them belonging to the Firmicutes and Gamma-proteobacteria. We provide evidence suggesting that the current virus database may be biased towards those viruses attacking Firmicutes and Gamma-proteobacteria. CONCLUSION: We conclude that as more viral genomes are sequenced, more microbial ORFans will find homologs in viruses, but this trend may vary much for individual genomes. Thus, lateral transfer from viruses alone is unlikely to explain the origin of the majority of ORFans in the majority of prokaryotes and consequently, other, not necessarily exclusive, mechanisms are likely to better explain the origin of the increasing number of ORFans

Antimicrobial peptide identification using multi-scale convolutional network

Background: Antibiotic resistance has become an increasingly serious problem in the past decades. As an alternative choice, antimicrobial peptides (AMPs) have attracted lots of attention. To identify new AMPs, machine learning methods have been commonly used. More recently, some deep learning methods have also been applied to this problem. Results: In this paper, we designed a deep learning model to identify AMP sequences. We employed the embedding layer and the multi-scale convolutional network in our model. The multi-scale convolutional network, which contains multiple convolutional layers of varying filter lengths, could utilize all latent features captured by the multiple convolutional layers. To further improve the performance, we also incorporated additional information into the designed model and proposed a fusion model. Results showed that our model outperforms the state-of-the-art models on two AMP datasets and the Antimicrobial Peptide Database (APD)3 benchmark dataset. The fusion model also outperforms the state-of-the-art model on an anti-inflammatory peptides (AIPs) dataset at the accuracy. Conclusions: Multi-scale convolutional network is a novel addition to existing deep neural network (DNN) models. The proposed DNN model and the modified fusion model outperform the state-of-the-art models for new AMP discovery. The source code and data are available at https://github.com/zhanglabNKU/APIN

Genomic Arrangement of Regulons in Bacterial Genomes

Regulons, as groups of transcriptionally co-regulated operons, are the basic units of cellular response systems in bacterial cells. While the concept has been long and widely used in bacterial studies since it was first proposed in 1964, very little is known about how its component operons are arranged in a bacterial genome. We present a computational study to elucidate of the organizational principles of regulons in a bacterial genome, based on the experimentally validated regulons of E. coli and B. subtilis. Our results indicate that (1) genomic locations of transcriptional factors (TFs) are under stronger evolutionary constraints than those of the operons they regulate so changing a TF’s genomic location will have larger impact to the bacterium than changing the genomic position of any of its target operons; (2) operons of regulons are generally not uniformly distributed in the genome but tend to form a few closely located clusters, which generally consist of genes working in the same metabolic pathways; and (3) the global arrangement of the component operons of all the regulons in a genome tends to minimize a simple scoring function, indicating that the global arrangement of regulons follows simple organizational principles.DOE of the US, BioEnergy Science Center grant (DE-PS02-06ER64304) NSF of the US, DEB-0830024

BERMAD: batch effect removal for single-cell RNA-seq data using a multi-layer adaptation autoencoder with dual-channel framework

Motivation: Removal of batch effect between multiple datasets from different experimental platforms has become an urgent problem, since single-cell RNA sequencing (scRNA-seq) techniques developed rapidly. Although there have been some methods for this problem, most of them still face the challenge of under-correction or over-correction. Specifically, handling batch effect in highly nonlinear scRNA-seq data requires a more powerful model to address under-correction. In the meantime, some previous methods focus too much on removing difference between batches, which may disturb the biological signal heterogeneity of datasets generated from different experiments, thereby leading to over-correction. Results: In this article, we propose a novel multi-layer adaptation autoencoder with dual-channel framework to address the under-correction and over-correction problems in batch effect removal, which is called BERMAD and can achieve better results of scRNA-seq data integration and joint analysis. First, we design a multi-layer adaptation architecture to model distribution difference between batches from different feature granularities. The distribution matching on various layers of autoencoder with different feature dimensions can result in more accurate batch correction outcome. Second, we propose a dual-channel framework, where the deep autoencoder processing each single dataset is independently trained. Hence, the heterogeneous information that is not shared between different batches can be retained more completely, which can alleviate over-correction. Comprehensive experiments on multiple scRNA-seq datasets demonstrate the effectiveness and superiority of our method over the state-of-the-art methods

A survey of plant and algal genomes and transcriptomes reveals new insights into the evolution and function of the cellulose synthase superfamily

Background Enzymes of the cellulose synthase (CesA) family and CesA-like (Csl) families are responsible for the synthesis of celluloses and hemicelluloses, and thus are of great interest to bioenergy research. We studied the occurrences and phylogenies of CesA/Csl families in diverse plants and algae by comprehensive data mining of 82 genomes and transcriptomes. Results We found that 1) charophytic green algae (CGA) have orthologous genes in CesA, CslC and CslD families; 2) liverwort genes are found in the CesA, CslA, CslC and CslD families; 3) The fern Pteridium aquilinum not only has orthologs in these conserved families but also in the CslB, CslH and CslE families; 4) basal angiosperms, e.g. Aristolochia fimbriata, have orthologs in these families too; 5) gymnosperms have genes forming clusters ancestral to CslB/H and to CslE/J/G respectively; 6) CslG is found in switchgrass and basal angiosperms; 7) CslJ is widely present in dicots and monocots; 8) CesA subfamilies have already diversified in ferns. Conclusions We speculate that: (i) ferns and horsetails might both have CslH enzymes, responsible for the synthesis of mixed-linkage glucans and (ii) CslD and similar genes might be responsible for the synthesis of mannans in CGA. Our findings led to a more detailed model of cell wall evolution and suggested that gene loss played an important role in the evolution of Csl families. We also demonstrated the usefulness of transcriptome data in the study of plant cell wall evolution and diversity

dbAPIS: a database of anti-prokaryotic immune system genes

Anti-prokary otic immune sy stem (APIS) proteins, typically encoded b y phages, prophages, and plasmids, inhibit prokaryotic immune systems (e.g. restriction modification, to xin-antito xin, CRISPR-Cas). A gro wing number of APIS genes ha v e been characterized and dispersed in the literature. Here w e de v eloped dbAPIS ( https:// bcb.unl.edu/ dbAPIS ), as the first literature curated data repository for experimentally verified APIS genes and their associated protein f amilies. T he k e y features of dbAPIS include: (i) e xperimentally v erified APIS genes with their protein sequences, functional annotation, PDB or AlphaFold predicted str uct ures, genomic context, sequence and str uct ural homologs from different microbiome / virome databases; (ii) classification of APIS proteins into sequence-based families and construction of hidden Mark o v models (HMMs); (iii) user-friendly web interface for data browsing by the inhibited immune system types or by the hosts, and functions for searching and batch downloading of pre-computed data; (iv) Inclusion of all types of APIS proteins (e x cept f or anti-CRISPRs) that inhibit a v ariety of prokary otic defense systems (e.g. RM, TA, CB A SS , Thoeris, Gabija). The current release of dbAPIS contains 41 verified APIS proteins and ∼4400 sequence homologs of 92 families and 38 clans. dbAPIS will facilitate the discovery of novel anti-defense genes and genomic islands in phages, by providing a user-friendly data repository and a web resource for an easy homology search against known APIS proteins

PlantCAZyme: a database for plant carbohydrate-active enzymes

PlantCAZyme is a database built upon dbCAN (database for automated carbohydrate active enzyme annotation), aiming to provide pre-computed sequence and annotation data of carbohydrate active enzymes (CAZymes) to plant carbohydrate and bioenergy research communities. The current version contains data of 43 790 CAZymes of 159 protein families from 35 plants (including angiosperms, gymnosperms, lycophyte and bryophyte mosses) and chlorophyte algae with fully sequenced genomes. Useful features of the database include: (i) a BLAST server and a HMMER server that allow users to search against our pre-computed sequence data for annotation purpose, (ii) a download page to allow batch downloading data of a specific CAZyme family or species and (iii) protein browse pages to provide an easy access to the most comprehensive sequence and annotation data