PASE: a novel method for functional prediction of amino acid substitutions based on physicochemical properties

Pre-extinction Demographic Stability and Genomic Signatures of Adaptation in the Woolly Rhinoceros

Ancient DNA has significantly improved our understanding of the evolution and population history of extinct megafauna. However, few studies have used complete ancient genomes to examine species responses to climate change prior to extinction. The woolly rhinoceros (Coelodonta antiquitatis) was a cold-adapted megaherbivore widely distributed across northern Eurasia during the Late Pleistocene and became extinct approximately 14 thousand years before present (ka BP). While humans and climate change have been proposed as potential causes of extinction [1-3], knowledge is limited on how the woolly rhinoceros was impacted by human arrival and climatic fluctuations [2]. Here, we use one complete nuclear genome and 14 mitogenomes to investigate the demographic history of woolly rhinoceros leading up to its extinction. Unlike other northern megafauna, the effective population size of woolly rhinoceros likely increased at 29.7 ka BP and subsequently remained stable until close to the species' extinction. Analysis of the nuclear genome from a ∼18.5-ka-old specimen did not indicate any increased inbreeding or reduced genetic diversity, suggesting that the population size remained steady for more than 13 ka following the arrival of humans [4]. The population contraction leading to extinction of the woolly rhinoceros may have thus been sudden and mostly driven by rapid warming in the Bølling-Allerød interstadial. Furthermore, we identify woolly rhinoceros-specific adaptations to arctic climate, similar to those of the woolly mammoth. This study highlights how species respond differently to climatic fluctuations and further illustrates the potential of palaeogenomics to study the evolutionary history of extinct species.

Cloning, nucleotide sequencing, and bioinformatics analyses of growth hormone mRNA of Assaf sheep and Boer goats reared in Egypt

BACKGROUND

Identification of molecular characterization of genes underlying livestock productive traits may allow applying advanced biotechnology techniques to improve animal productivity. Growth hormone (GH) controls body growth rate, milk production, reproduction as well as carbohydrate, lipid, and protein metabolism. Therefore, the present study aims to investigate the genetic variations of growth hormone cDNA sequences between Assaf sheep (As_GH) and Boer goat (Bo_GH) that mainly used for genetic improvement in Egypt using bioinformatics analysis. Growth hormone cDNA was isolated from the pituitary gland tissue of Assaf sheep Boer goat and subcloned into pTZ57R/T cloning vector for sequencing.

RESULTS

Molecular weight of As_GH cDNA was 665 bp and was 774 bp for Bo_GH cDNA. The complete coding sequences (CDS) of As_GH and Bo_GH were registered in the GenBank database under accession number (AC: MH128986 and AC: MG744290, respectively). High homology percentage was observed (99.5%) between AS_GH and Bo_GH protein sequences with one different amino acid in the As_GH protein sequence (Arg194). The protein sequence of As_GH has only one motif signature; Somatotropin_1 from 79 to 112 aa compared to Bo_GH protein sequences and GenBank database that had two motifs signature. The growth hormone cDNA sequence of Assaf sheep has a unique three single nucleotide polymorphisms (SNPs) (A637A638G639) that encodes for arginine (Arg194); this insertion mutation (AAG) was not found in the growth hormone cDNA sequences of Boer goat in the present study and GenBank database breeds. This mutation can be used to develop SNPs markers for Assaf sheep.

CONCLUSIONS

GH sequence of Assaf and Boer goat is highly conserved and the homogeny in the codon region (99.5%). The Assaf sheep GH sequence has a unique three SNPs that may be used to develop SNPs markers for such breed. Further studies are needed to investigate the genetic variations of growth hormone gene in different sheep and goat breeds in Egypt and document the relationship between these variations and the productive performance of animals.

Protein function prediction from protein-protein interaction network using gene ontology based neighborhood analysis and physico-chemical features

Protein Function Prediction from Protein-Protein Interaction Network (PPIN) and physico-chemical features using the Gene Ontology (GO) classification are indeed very useful for assigning biological or biochemical functions to a protein. They also lead to the identification of those significant proteins which are responsible for the generation of various diseases whose drugs are still yet to be discovered. So, the prediction of GO functional terms from PPIN and sequence is an important field of study. In this work, we have proposed a methodology, Multi Label Protein Function Prediction (ML_PFP) which is based on Neighborhood analysis empowered with physico-chemical features of constituent amino acids to predict the functional group of unannotated protein. A protein does not perform functions in isolation rather it performs functions in a group by interacting with others. So a protein is involved in many functions or, in other words, may be associated with multiple functional groups or labels or GO terms. Though functional group of other known interacting partner protein and its physico-chemical features provide useful information, assignment of multiple labels to unannotated protein is a very challenging task. Here, we have taken Homo sapiens or Human PPIN as well as Saccharomyces cerevisiae or yeast PPIN along with their GO terms to predict functional groups or GO terms of unannotated proteins. This work has become very challenging as both Human and Yeast protein dataset are voluminous and complex in nature and multi-label functional groups assignment has also added a new dimension to this challenge. Our algorithm has been observed to achieve a better performance in Cellular Function, Molecular Function and Biological Process of both yeast and human network when compared with the other existing state-of-the-art methodologies which will be discussed in detail in the results section.

A Meta-Analysis Based Method for Prioritizing Candidate Genes Involved in a Pre-specific Function

The identification of genes associated with a given biological function in plants remains a challenge, although network-based gene prioritization algorithms have been developed for Arabidopsis thaliana and many non-model plant species. Nevertheless, these network-based gene prioritization algorithms have encountered several problems; one in particular is that of unsatisfactory prediction accuracy due to limited network coverage, varying link quality, and/or uncertain network connectivity. Thus, a model that integrates complementary biological data may be expected to increase the prediction accuracy of gene prioritization. Toward this goal, we developed a novel gene prioritization method named RafSee, to rank candidate genes using a random forest algorithm that integrates sequence, evolutionary, and epigenetic features of plants. Subsequently, we proposed an integrative approach named RAP (Rank Aggregation-based data fusion for gene Prioritization), in which an order statistics-based meta-analysis was used to aggregate the rank of the network-based gene prioritization method and RafSee, for accurately prioritizing candidate genes involved in a pre-specific biological function. Finally, we showcased the utility of RAP by prioritizing 380 flowering-time genes in Arabidopsis. The "leave-one-out" cross-validation experiment showed that RafSee could work as a complement to a current state-of-art network-based gene prioritization system (AraNet v2). Moreover, RAP ranked 53.68% (204/380) flowering-time genes higher than AraNet v2, resulting in an 39.46% improvement in term of the first quartile rank. Further evaluations also showed that RAP was effective in prioritizing genes-related to different abiotic stresses. To enhance the usability of RAP for Arabidopsis and non-model plant species, an R package implementing the method is freely available at http://bioinfo.nwafu.edu.cn/software.

Using Targeted Resequencing for Identification of Candidate Genes and SNPs for a QTL Affecting the pH Value of Chicken Meat

Using targeted genetical genomics, a quantitative trait locus (QTL) affecting the initial postmortem pH value of chicken breast muscle (Pectoralis major) on chromosome 1 (GGA1) recently was fine-mapped. Thirteen genes were present in the QTL region of approximately 1 Mb. In this study, 10 birds that were inferred to be homozygous for either the high (QQ) or low (qq) QTL allele were selected for resequencing. After enrichment for 1 Mb around the QTL region, >500 × coverage for the QTL region in each of the 10 birds was obtained. In total 5056 single-nucleotide polymorphisms (SNPs) were identified for which the genotypes were consistent with one of the QTL genotypes. We used custom tools to identify putative causal mutations in the mapped QTL region from these SNPs. Four nonsynonymous SNPs differentiating the two QTL genotype groups were identified within four local genes (PRDX4, EIF2S3, PCYT1B, and E1BTD2). Although these are likely candidate SNPs to explain the QTL effect, 54 additional consensus SNPs were detected within gene-related regions (untranslated regions, splicing sites CpG island, and promoter regions) for the QQ birds and 71 for the qq birds. These could also play a role explaining the observed QTL effect. The results provide an important step for prioritizing among a large amount of candidate mutations and significantly contribute to the understanding of the genetic mechanisms affecting the initial postmortem pH value of chicken muscle.

Natural CMT2 variation is associated with genome-wide methylation changes and temperature seasonality

As Arabidopsis thaliana has colonized a wide range of habitats across the world it is an attractive model for studying the genetic mechanisms underlying environmental adaptation. Here, we used public data from two collections of A. thaliana accessions to associate genetic variability at individual loci with differences in climates at the sampling sites. We use a novel method to screen the genome for plastic alleles that tolerate a broader climate range than the major allele. This approach reduces confounding with population structure and increases power compared to standard genome-wide association methods. Sixteen novel loci were found, including an association between Chromomethylase 2 (CMT2) and temperature seasonality where the genome-wide CHH methylation was different for the group of accessions carrying the plastic allele. Cmt2 mutants were shown to be more tolerant to heat-stress, suggesting genetic regulation of epigenetic modifications as a likely mechanism underlying natural adaptation to variable temperatures, potentially through differential allelic plasticity to temperature-stress.

Identification of candidate genes and mutations in QTL regions for chicken growth using bioinformatic analysis of NGS and SNP-chip data

Mapping of chromosomal regions harboring genetic polymorphisms that regulate complex traits is usually followed by a search for the causative mutations underlying the observed effects. This is often a challenging task even after fine mapping, as millions of base pairs including many genes will typically need to be investigated. Thus to trace the causative mutation(s) there is a great need for efficient bioinformatic strategies. Here, we searched for genes and mutations regulating growth in the Virginia chicken lines - an experimental population comprising two lines that have been divergently selected for body weight at 56 days for more than 50 generations. Several quantitative trait loci (QTL) have been mapped in an F2 intercross between the lines, and the regions have subsequently been replicated and fine mapped using an Advanced Intercross Line. We have further analyzed the QTL regions where the largest genetic divergence between the High-Weight selected (HWS) and Low-Weight selected (LWS) lines was observed. Such regions, covering about 37% of the actual QTL regions, were identified by comparing the allele frequencies of the HWS and LWS lines using both individual 60K SNP chip genotyping of birds and analysis of read proportions from genome resequencing of DNA pools. Based on a combination of criteria including significance of the QTL, allele frequency difference of identified mutations between the selected lines, gene information on relevance for growth, and the predicted functional effects of identified mutations we propose here a subset of candidate mutations of highest priority for further evaluation in functional studies. The candidate mutations were identified within the GCG, IGFBP2, GRB14, CRIM1, FGF16, VEGFR-2, ALG11, EDN1, SNX6, and BIRC7 genes. We believe that the proposed method of combining different types of genomic information increases the probability that the genes underlying the observed QTL effects are represented among the candidate mutations identified.

A gene-specific method for predicting hemophilia-causing point mutations

A fundamental goal of medical genetics is the accurate prediction of genotype-phenotype correlations. As an approach to develop more accurate in silico tools for prediction of disease-causing mutations of structural proteins, we present a gene- and disease-specific prediction tool based on a large systematic analysis of missense mutations from hemophilia A (HA) patients. Our HA-specific prediction tool, HApredictor, showed disease prediction accuracy comparable to other publicly available prediction software. In contrast to those methods, its performance is not limited to non-synonymous mutations. Given the role of synonymous mutations in disease and drug codon optimization, we propose that utilizing a gene- and disease-specific method can be highly useful to make functional predictions possible even for synonymous mutations. Incorporating computational metrics at both nucleotide and amino acid levels along with multiple protein sequence/structure alignment significantly improved the predictive performance of our tool. HApredictor is freely available for download at http://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/HA_Predict/index.htm.