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.

Comparative analysis of the organelle genomes of three Rhodiola species provide insights into their structural dynamics and sequence divergences

BACKGROUND

Plant organelle genomes are a valuable resource for evolutionary biology research, yet their genome architectures, evolutionary patterns and environmental adaptations are poorly understood in many lineages. Rhodiola species is a type of flora mainly distributed in highland habitats, with high medicinal value. Here, we assembled the organelle genomes of three Rhodiola species (R. wallichiana, R. crenulata and R. sacra) collected from the Qinghai-Tibet plateau (QTP), and compared their genome structure, gene content, structural rearrangements, sequence transfer and sequence evolution rates.

RESULTS

The results demonstrated the contrasting evolutionary pattern between plastomes and mitogenomes in three Rhodiola species, with the former possessing more conserved genome structure but faster evolutionary rates of sequence, while the latter exhibiting structural diversity but slower rates of sequence evolution. Some lineage-specific features were observed in Rhodiola mitogenomes, including chromosome fission, gene loss and structural rearrangement. Repeat element analysis shows that the repeats occurring between the two chromosomes may mediate the formation of multichromosomal structure in the mitogenomes of Rhodiola, and this multichromosomal structure may have recently formed. The identification of homologous sequences between plastomes and mitogenomes reveals several unidirectional protein-coding gene transfer events from chloroplasts to mitochondria. Moreover, we found that their organelle genomes contained multiple fragments of nuclear transposable elements (TEs) and exhibited different preferences for TEs insertion type. Genome-wide scans of positive selection identified one gene matR from the mitogenome. Since the matR is crucial for plant growth and development, as well as for respiration and stress responses, our findings suggest that matR may participate in the adaptive response of Rhodiola species to environmental stress of QTP.

CONCLUSION

The study analyzed the organelle genomes of three Rhodiola species and demonstrated the contrasting evolutionary pattern between plastomes and mitogenomes. Signals of positive selection were detected in the matR gene of Rhodiola mitogenomes, suggesting the potential role of this gene in Rhodiola adaptation to QTP. Together, the study is expected to enrich the genomic resources and provide valuable insights into the structural dynamics and sequence divergences of Rhodiola species.

Enzyme activity engineering based on sequence co-evolution analysis

The utility of engineering enzyme activity is expanding with the development of biotechnology. Conventional methods have limited applicability as they require high-throughput screening or three-dimensional structures to direct target residues of activity control. An alternative method uses sequence evolution of natural selection. A repertoire of mutations was selected for fine-tuning enzyme activities to adapt to varying environments during the evolution. Here, we devised a strategy called sequence co-evolutionary analysis to control the efficiency of enzyme reactions (SCANEER), which scans the evolution of protein sequences and direct mutation strategy to improve enzyme activity. We hypothesized that amino acid pairs for various enzyme activity were encoded in the evolutionary history of protein sequences, whereas loss-of-function mutations were avoided since those are depleted during the evolution. SCANEER successfully predicted the enzyme activities of beta-lactamase and aminoglycoside 3'-phosphotransferase. SCANEER was further experimentally validated to control the activities of three different enzymes of great interest in chemical production: cis-aconitate decarboxylase, α-ketoglutaric semialdehyde dehydrogenase, and inositol oxygenase. Activity-enhancing mutations that improve substrate-binding affinity or turnover rate were found at sites distal from known active sites or ligand-binding pockets. We provide SCANEER to control desired enzyme activity through a user-friendly webserver.

Profiles of Natural and Designed Protein-Like Sequences Effectively Bridge Protein Sequence Gaps: Implications in Distant Homology Detection

Sequence-based approaches are fundamental to guide experimental investigations in obtaining structural and/or functional insights into uncharacterized protein families. Powerful profile-based sequence search methods rely on a sequence space continuum to identify non-trivial relationships through homology detection. The computational design of protein-like sequences that serve as "artificial linkers" is useful in identifying relationships between distant members of a structural fold. Such sequences act as intermediates and guide homology searches between distantly related proteins. Here, we describe an approach that represents natural intermediate sequences and designed protein-like sequences as HMM (Hidden Markov Models) profiles, to improve the sensitivity of existing search methods. Searches made within the "Profile database" were shown to recognize the parent structural fold for 90% of the search queries at query coverage better than 60%. For 1040 protein families with no available structure, fold associations were made through searches in the database of natural and designed sequence profiles. Most of the associations were made with the Alpha-alpha superhelix, Transmembrane beta-barrels, TIM barrel, and Immunoglobulin-like beta-sandwich folds. For 11 domain families of unknown functions, we provide confident fold associations using the profiles of designed sequences and a consensus from other fold recognition methods. For two DUFs (Domain families of Unknown Functions), we performed detailed functional annotation through comparisons with characterized templates of families of known function.

Genome annotation across species using deep convolutional neural networks

Application of deep neural network is a rapidly expanding field now reaching many disciplines including genomics. In particular, convolutional neural networks have been exploited for identifying the functional role of short genomic sequences. These approaches rely on gathering large sets of sequences with known functional role, extracting those sequences from whole-genome-annotations. These sets are then split into learning, test and validation sets in order to train the networks. While the obtained networks perform well on validation sets, they often perform poorly when applied on whole genomes in which the ratio of positive over negative examples can be very different than in the training set. We here address this issue by assessing the genome-wide performance of networks trained with sets exhibiting different ratios of positive to negative examples. As a case study, we use sequences encompassing gene starts from the RefGene database as positive examples and random genomic sequences as negative examples. We then demonstrate that models trained using data from one organism can be used to predict gene-start sites in a related species, when using training sets providing good genome-wide performance. This cross-species application of convolutional neural networks provides a new way to annotate any genome from existing high-quality annotations in a related reference species. It also provides a way to determine whether the sequence motifs recognised by chromatin-associated proteins in different species are conserved or not.

Modeling methylation dynamics with simultaneous changes in CpG islands

Background

In vertebrate genomes, CpG sites can be clustered into CpG islands, and the amount of methylation in a CpG island can change due to gene regulation processes. Thus, single regulatory events can simultaneously change the methylation states of many CpG sites within a CpG island. This should be taken into account when quantifying the amount of change in methylation, for example in form of a branch length in a phylogeny of cell types.

Results

We propose a probabilistic model (the IWE-SSE model) of methylation dynamics that accounts for simultaneous methylation changes in multiple CpG sites belonging to the same CpG island. We further propose a Markov-chain Monte-Carlo (MCMC) method to fit this model to methylation data from cell type phylogenies and apply this method to available data from murine haematopoietic cells and from human cell lines. Combined with simulation studies, these analyses show that accounting for CpG island wide methylation changes has a strong effect on the inferred branch lengths and leads to a significantly better model fit for the methylation data from murine haematopoietic cells and human cell lines.

Conclusion

The MCMC based parameter estimation method for the IWE-SSE model in combination with our MCMC based inference method allows to quantify the amount of methylation changes at single CpG sites as well as on entire CpG islands. Accounting for changes affecting entire islands can lead to more accurate branch length estimation in the presence of simultaneous methylation change.

Conservative Evolution of Hepatitis B Virus Precore and Core Gene During Immune Tolerant Phase in Intrafamilial Transmission

Hepatitis B virus (HBV) is characterized with high mutations, which is attributed to the lack of proof-reading of the viral reverse transcriptase and host immune pressure. In this study, 31 HBV chronic carriers from 14 families were enrolled to investigate the evolution of the same original HBV sources in different hosts. Sequences of pre-C and C (pre-C/C) genes were analyzed in eight pairs of HBV-infected mothers with longitudinal sera (at an interval of 6.0-7.2 years) and their children (5.5-6.7 years old), and in 15 adults (21-78 years old) from six families with known intrafamilial HBV infection. The pre-C/C sequences had almost no change in eight mothers during 6.0-7.2 years and their children who were in immune tolerant phase. The pre-C/C sequences from the 15 adults of six families, mostly in the immune-clearance phase or the low replicative phase, showed various diversified mutations between individuals from each family. Compared to a reference stain (GQ205441) isolated nearby, the pre-C/C in individuals in immune tolerant phase showed 98.56%-99.52% homology at nucleotide level and 99.5%-100% homology at amino acid level. In contrast, multiple mutations were developed in the immune-clearance phase or the low replicative phase, affecting immune epitopes in core gene and G1896 in pre-C gene. The results indicate that the evolution of new HBV variants is not mainly resulted from the spontaneous error rate of viral reverse transcription, but from the host immune pressure.

Evolutionary conservation of influenza A PB2 sequences reveals potential target sites for small molecule inhibitors

The influenza A basic polymerase protein 2 (PB2) functions as part of a heterotrimer to replicate the viral RNA genome. To investigate novel PB2 antiviral target sites, this work identified evolutionary conserved regions across the PB2 protein sequence amongst all sub-types and hosts, as well as ligand binding hot spots which overlap with highly conserved areas. Fifteen binding sites were predicted in different PB2 domains; some of which reside in areas of unknown function. Virtual screening of ~50,000 drug-like compounds showed binding affinities of up to -10.3kcal/mol. The highest affinity molecules were found to interact with conserved residues including Gln138, Gly222, Ile529, Asn540 and Thr530. A library containing 1738 FDA approved drugs was screened additionally and revealed Paliperidone as a top hit with a binding affinity of -10kcal/mol. Predicted ligands are ideal leads for new antivirals as they were targeted to evolutionary conserved binding sites.

Evidence of selection for an accessible nucleosomal array in human

BACKGROUND

Recently, a physical model of nucleosome formation based on sequence-dependent bending properties of the DNA double-helix has been used to reveal some enrichment of nucleosome-inhibiting energy barriers (NIEBs) nearby ubiquitous human "master" replication origins. Here we use this model to predict the existence of about 1.6 millions NIEBs over the 22 human autosomes.

RESULTS

We show that these high energy barriers of mean size 153 bp correspond to nucleosome-depleted regions (NDRs) in vitro, as expected, but also in vivo. On either side of these NIEBs, we observe, in vivo and in vitro, a similar compacted nucleosome ordering, suggesting an absence of chromatin remodeling. This nucleosomal ordering strongly correlates with oscillations of the GC content as well as with the interspecies and intraspecies mutation profiles along these regions. Comparison of these divergence rates reveals the existence of both positive and negative selections linked to nucleosome positioning around these intrinsic NDRs. Overall, these NIEBs and neighboring nucleosomes cover 37.5 % of the human genome where nucleosome occupancy is stably encoded in the DNA sequence. These 1 kb-sized regions of intrinsic nucleosome positioning are equally found in GC-rich and GC-poor isochores, in early and late replicating regions, in intergenic and genic regions but not at gene promoters.

CONCLUSION

The source of selection pressure on the NIEBs has yet to be resolved in future work. One possible scenario is that these widely distributed chromatin patterns have been selected in human to impair the condensation of the nucleosomal array into the 30 nm chromatin fiber, so as to facilitate the epigenetic regulation of nuclear functions in a cell-type-specific manner.

Codon usage bias as a function of generation time and life expectancy

It has recently been demonstrated that human natural codon usage bias is optimized towards a higher buffering capacity to mutations (measured as the tendency of single point mutations in a DNA sequence to yield the same or similar amino acids) compared to random sequences. In this work, we investigate this phenomenon further by analyzing the natural DNA of four different species (human, mouse, zebrafish and fruit fly) to determine whether such a tolerance to mutations is correlated with the life span and age of sexual maturation for the corresponding organisms. We also propose a new measure to quantify the buffering capacity of a DNA sequence to mutations that takes into account the observed mutation rates within every genome and the effect of the corresponding mutation.Our results suggest there is a propensity for tolerance to mutations that is positively correlated with the life expectancy of the considered organisms. Moreover, random sequences that are constrained to produce the same protein as the naturally occurring sequences are found to be more buffered than completely random sequences while being less buffered than the natural sequences. These results suggest that optimization toward protective mechanisms tolerant to mutations is correlated with both life expectancy and age to sexual maturity at both the levels of codon usage bias and the bias of the natural sequence of codons itself.