HMM-ModE: implementation, benchmarking and validation with HMMER3

Approaches to increase the validity of gene family identification using manual homology search tools

Identifying homologs is an important process in the analysis of genetic patterns underlying traits and evolutionary relationships among species. Analysis of gene families is often used to form and support hypotheses on genetic patterns such as gene presence, absence, or functional divergence which underlie traits examined in functional studies. These analyses often require precise identification of all members in a targeted gene family. Manual pipelines where homology search and orthology assignment tools are used separately are the most common approach for identifying small gene families where accurate identification of all members is important. The ability to curate sequences between steps in manual pipelines allows for simple and precise identification of all possible gene family members. However, the validity of such manual pipeline analyses is often decreased by inappropriate approaches to homology searches including too relaxed or stringent statistical thresholds, inappropriate query sequences, homology classification based on sequence similarity alone, and low-quality proteome or genome sequences. In this article, we propose several approaches to mitigate these issues and allow for precise identification of gene family members and support for hypotheses linking genetic patterns to functional traits.

Proteome-Wide Detection and Annotation of Receptor Tyrosine Kinases (RTKs): RTK-PRED and the TyReK Database

Receptor tyrosine kinases (RTKs) form a highly important group of protein receptors of the eukaryotic cell membrane. They control many vital cellular functions and are involved in the regulation of complex signaling networks. Mutations in RTKs have been associated with different types of cancers and other diseases. Although they are very important for proper cell function, they have been experimentally studied in a limited range of eukaryotic species. Currently, there is no available database for RTKs providing information about their function, expression, and interactions. Therefore, the identification of RTKs in multiple organisms, the documentation of their characteristics, and the collection of related information would be very useful. In this paper, we present a novel RTK detection pipeline (RTK-PRED) and the Receptor Tyrosine Kinases Database (TyReK-DB). RTK-PRED combines profile HMMs with transmembrane topology prediction to identify and classify potential RTKs. Proteins of all eukaryotic reference proteomes of the UniProt database were used as input in RTK-PRED leading to a filtered dataset of 20,478 RTKs. Based on the information collected for these RTKs from multiple databases, the relational TyReK database was created.

Implementation of homology based and non-homology based computational methods for the identification and annotation of orphan enzymes: using Mycobacterium tuberculosis H37Rv as a case study

Background

Homology based methods are one of the most important and widely used approaches for functional annotation of high-throughput microbial genome data. A major limitation of these methods is the absence of well-characterized sequences for certain functions. The non-homology methods based on the context and the interactions of a protein are very useful for identifying missing metabolic activities and functional annotation in the absence of significant sequence similarity. In the current work, we employ both homology and context-based methods, incrementally, to identify local holes and chokepoints, whose presence in the Mycobacterium tuberculosis genome is indicated based on its interaction with known proteins in a metabolic network context, but have not been annotated. We have developed two computational procedures using network theory to identify orphan enzymes (‘Hole finding protocol’) coupled with the identification of candidate proteins for the predicted orphan enzyme (‘Hole filling protocol’). We propose an integrated interaction score based on scores from the STRING database to identify candidate protein sequences for the orphan enzymes from M. tuberculosis, as a case study, which are most likely to perform the missing function.

Results

The application of an automated homology-based enzyme identification protocol, ModEnzA, on M. tuberculosis genome yielded 56 novel enzyme predictions. We further predicted 74 putative local holes, 6 choke points, and 3 high confidence local holes in the genome using ‘Hole finding protocol’. The ‘Hole-filling protocol’ was validated on the E. coli genome using artificial in-silico enzyme knockouts where our method showed 25% increased accuracy, compared to other methods, in assigning the correct sequence for the knocked-out enzyme amongst the top 10 ranks. The method was further validated on 8 additional genomes.

Conclusions

We have developed methods that can be generalized to augment homology-based annotation to identify missing enzyme coding genes and to predict a candidate protein for them. For pathogens such as M. tuberculosis, this work holds significance in terms of increasing the protein repertoire and thereby, the potential for identifying novel drug targets.

Systematic Identification and Classification of β-Lactamases Based on Sequence Similarity Criteria: β-Lactamase Annotation

β-lactamases, the enzymes responsible for resistance to β-lactam antibiotics, are widespread among prokaryotic genera. However, current β-lactamase classification schemes do not represent their present diversity. Here, we propose a workflow to identify and classify β-lactamases. Initially, a set of curated sequences was used as a model for the construction of profiles Hidden Markov Models (HMM), specific for each β-lactamase class. An extensive, nonredundant set of β-lactamase sequences was constructed from 7 different resistance proteins databases to test the methodology. The profiles HMM were improved for their specificity and sensitivity and then applied to fully assembled genomes. Five hierarchical classification levels are described, and a new class of β-lactamases with fused domains is proposed. Our profiles HMM provide a better annotation of β-lactamases, with classes and subclasses defined by objective criteria such as sequence similarity. This classification offers a solid base to the elaboration of studies on the diversity, dispersion, prevalence, and evolution of the different classes and subclasses of this critical enzymatic activity.

Lactobacillus plantarum LP‑Onlly alters the gut flora and attenuates colitis by inducing microbiome alteration in interleukin‑10 knockout mice

The association between inflammatory bowel disease (IBD) and gut microbes has been widely investigated. Our previous study demonstrated that Lactobacillus plantarum LP‑Onlly (LP) applied as a probiotic altered the gut flora and attenuated colitis in interleukin (IL)‑10 knockout (IL‑10‑/‑) mice. In the present study, metagenome sequencing was performed to investigate the gut microbiome in IL‑10‑/‑mice and the influence of oral administration of LP on microbial composition. Metagenomics sequencing was performed to investigate the influence of IBD on the gut microbiome with and without LP treatment. The alteration of the abundances of various taxonomic and functional groups were investigated across these gut microbiomes. The present study demonstrates that Akkermansia muciniphila was significantly enriched in IL‑10‑/‑ mice, and bacteroides were significantly increased following LP administration. In addition, the phylum Bacteroidetes and Firmicutes were significantly influenced by LP administration. Further characterization of functional capacity revealed that in the gut metagenomes of IL‑10‑/‑mice, genes encoding cell cycle control, replication, recombination, repair and cell envelope biogenesis were decreased, but intracellular trafficking, secretion, and vesicular transport were increased. The present findings indicate that the gut metagenome is associated with IBD, and oral administration of LP contributes to prevention of gut inflammation, providing insight into the treatment of IBD.

The genome and phenome of the green alga Chloroidium sp. UTEX 3007 reveal adaptive traits for desert acclimatization

To investigate the phenomic and genomic traits that allow green algae to survive in deserts, we characterized a ubiquitous species, Chloroidium sp. UTEX 3007, which we isolated from multiple locations in the United Arab Emirates (UAE). Metabolomic analyses of Chloroidium sp. UTEX 3007 indicated that the alga accumulates a broad range of carbon sources, including several desiccation tolerance-promoting sugars and unusually large stores of palmitate. Growth assays revealed capacities to grow in salinities from zero to 60 g/L and to grow heterotrophically on >40 distinct carbon sources. Assembly and annotation of genomic reads yielded a 52.5 Mbp genome with 8153 functionally annotated genes. Comparison with other sequenced green algae revealed unique protein families involved in osmotic stress tolerance and saccharide metabolism that support phenomic studies. Our results reveal the robust and flexible biology utilized by a green alga to successfully inhabit a desert coastline.

DOI:http://dx.doi.org/10.7554/eLife.25783.001

Single-celled green algae, also known as green microalgae, play an important role for the world’s ecosystems, in part, because they can harness energy from sunlight to produce carbon-rich compounds. Microalgae are also important for biotechnology and people have harnessed them to make food, fuel and medicines. Green microalgae live in many types of habitats from streams to oceans, and they can also be found on the land, including in deserts. Like plants that live in the desert, these microalgae have likely evolved specific traits that allow them to live in these hot and dry regions. Yet, fewer scientists have studied microalgae compared to land plants, and until now it was not well understood how microalgae could survive in the desert.

Nelson et al. analyzed green microalgae from different locations around the United Arab Emirates and found that one microalga, known as Chloroidium, is one of the most dominant algae in this area. This included samples from beaches, mangroves, desert oases, buildings and public fresh water sources. Chloroidium has a unique set of genes and proteins and grew particularly well in freshwater and saltwater. Rather than just harnessing sunlight, the microalgae were able to consume over 40 different varieties of carbon sources to produce energy. The microalgae also accumulated oily molecules with a similar composition to palm oil, which may help this species to survive in desert regions.

A next step will be to develop biotechnological assets based on the information obtained. In the future, microalgae could be used to make an oil that represents an alternative to palm oil; this would reduce the demand for palm tree plantations, which pose a major threat to the natural environment. Moreover, understanding how microalgae can colonize a desert region will help us to understand the effects of climate change in the region.

DOI:http://dx.doi.org/10.7554/eLife.25783.002

Identification of putative Z-ring-associated proteins, involved in cell division in human pathogenic bacteria Helicobacter pylori

Cell division in bacteria is initiated by FtsZ, which forms a Z ring at the middle of the cell, between the nucleoids. The Z ring is stabilized by Z ring-associated proteins (Zaps), which crosslink the FtsZ filaments and provide strength. The deletion of Zaps leads to the elongation phenotype with an abnormal Z ring. The components of cell division in Helicobacter pylori are similar to other gram negative bacteria except for the absence of few components including Zaps. Here, we used HHsearch to identify homologs of the missing cell division proteins and got potential hits for ZapA and ZapB, as well as for few other cell division proteins. We further validated the function of the putative ZapA homolog by genetic complementation, immuno-colocalization and biochemical analysis.