Comprehensive analysis of the overall codon usage patterns in equine infectious anemia virus

Background

Equine infectious anemia virus (EIAV) is an important animal model for understanding the relationship between viral persistence and the host immune response during lentiviral infections. Comparison and analysis of the codon usage model between EIAV and its hosts is important for the comprehension of viral evolution. In our study, the codon usage pattern of EIAV was analyzed from the available 29 full-length EIAV genomes through multivariate statistical methods.

Finding

Effective number of codons (ENC) suggests that the codon usage among EIAV strains is slightly biased. The ENC-plot analysis demonstrates that mutation pressure plays a substantial role in the codon usage pattern of EIAV, whereas other factors such as geographic distribution and host translation selection also take part in the process of EIAV evolution. Comparative analysis of codon adaptation index (CAI) values among EIAV and its hosts suggests that EIAV utilize the translational resources of horse more efficiently than that of donkey.

Conclusion

The codon usage bias in EIAV is slight and mutation pressure is the main factor that affects codon usage variation in EIAV. These results suggest that EIAV genomic biases are the result of the co-evolution of genome composition and the ability to evade the host’s immune response.

     

Genes optimized by evolution for accurate and fast translation encode in Archaea and Bacteria a broad and characteristic spectrum of protein functions

Background

In many microbial genomes, a strong preference for a small number of codons can be observed in genes whose products are needed by the cell in large quantities. This codon usage bias (CUB) improves translational accuracy and speed and is one of several factors optimizing cell growth. Whereas CUB and the overrepresentation of individual proteins have been studied in detail, it is still unclear which high-level metabolic categories are subject to translational optimization in different habitats.

Results

In a systematic study of 388 microbial species, we have identified for each genome a specific subset of genes characterized by a marked CUB, which we named the effectome. As expected, gene products related to protein synthesis are abundant in both archaeal and bacterial effectomes. In addition, enzymes contributing to energy production and gene products involved in protein folding and stabilization are overrepresented. The comparison of genomes from eleven habitats shows that the environment has only a minor effect on the composition of the effectomes. As a paradigmatic example, we detailed the effectome content of 37 bacterial genomes that are most likely exposed to strongest selective pressure towards translational optimization. These effectomes accommodate a broad range of protein functions like enzymes related to glycolysis/gluconeogenesis and the TCA cycle, ATP synthases, aminoacyl-tRNA synthetases, chaperones, proteases that degrade misfolded proteins, protectants against oxidative damage, as well as cold shock and outer membrane proteins.

Conclusions

We made clear that effectomes consist of specific subsets of the proteome being involved in several cellular functions. As expected, some functions are related to cell growth and affect speed and quality of protein synthesis. Additionally, the effectomes contain enzymes of central metabolic pathways and cellular functions sustaining microbial life under stress situations. These findings indicate that cell growth is an important but not the only factor modulating translational accuracy and speed by means of CUB.

     

Comparative chloroplast genomes of <Emphasis Type="Italic">Paris</Emphasis> Sect. <Emphasis Type="Italic">Marmorata</Emphasis>: insights into repeat regions and evolutionary implications

Background

Species of Paris Sect. Marmorata are valuable medicinal plants to synthesize steroidal saponins with effective pharmacological therapy. However, the wild resources of the species are threatened by plundering exploitation before the molecular genetics studies uncover the genomes and evolutionary significance. Thus, the availability of complete chloroplast genome sequences of Sect. Marmorata is necessary and crucial to the understanding the plastome evolution of this section and facilitating future population genetics studies. Here, we determined chloroplast genomes of Sect. Marmorata, and conducted the whole chloroplast genome comparison.

Results

This study presented detailed sequences and structural variations of chloroplast genomes of Sect. Marmorata. Over 40 large repeats and approximately 130 simple sequence repeats as well as a group of genomic hotspots were detected. Inverted repeat contraction of this section was inferred via comparing the chloroplast genomes with the one of P. verticillata. Additionally, almost all the plastid protein coding genes were found to prefer ending with A/U. Mutation bias and selection pressure predominately shaped the codon bias of most genes. And most of the genes underwent purifying selection, whereas photosynthetic genes experienced a relatively relaxed purifying selection.

Conclusions

Repeat sequences and hotspot regions can be scanned to detect the intraspecific and interspecific variability, and selected to infer the phylogenetic relationships of Sect. Marmorata and other species in subgenus Daiswa. Mutation and natural selection were the main forces to drive the codon bias pattern of most plastid protein coding genes. Therefore, this study enhances the understanding about evolution of Sect. Marmorata from the chloroplast genome, and provide genomic insights into genetic analyses of Sect. Marmorata.

     

Entropy-based gene ranking without selection bias for the predictive classification of microarray data

Background

We describe the E-RFE method for gene ranking, which is useful for the identification of markers in the predictive classification of array data. The method supports a practical modeling scheme designed to avoid the construction of classification rules based on the selection of too small gene subsets (an effect known as the selection bias, in which the estimated predictive errors are too optimistic due to testing on samples already considered in the feature selection process).

Results

With E-RFE, we speed up the recursive feature elimination (RFE) with SVM classifiers by eliminating chunks of uninteresting genes using an entropy measure of the SVM weights distribution. An optimal subset of genes is selected according to a two-strata model evaluation procedure: modeling is replicated by an external stratified-partition resampling scheme, and, within each run, an internal K-fold cross-validation is used for E-RFE ranking. Also, the optimal number of genes can be estimated according to the saturation of Zipf's law profiles.

Conclusions

Without a decrease of classification accuracy, E-RFE allows a speed-up factor of 100 with respect to standard RFE, while improving on alternative parametric RFE reduction strategies. Thus, a process for gene selection and error estimation is made practical, ensuring control of the selection bias, and providing additional diagnostic indicators of gene importance.

     

Molecular evolution under increasing transposable element burden in <Emphasis Type="Italic">Drosophila</Emphasis>: A speed limit on the evolutionary arms race

Background

Genome architecture is profoundly influenced by transposable elements (TEs), and natural selection against their harmful effects is a critical factor limiting their spread. Genome defense by the piRNA silencing pathway also plays a crucial role in limiting TE proliferation. How these two forces jointly determine TE abundance is not well understood. To shed light on the nature of factors that predict TE success, we test three distinct hypotheses in the Drosophila genus. First, we determine whether TE abundance and relaxed genome-wide purifying selection on protein sequences are positively correlated. This serves to test the hypothesis that variation in TE abundance in the Drosophila genus can be explained by the strength of natural selection, relative to drift, acting in parallel against mildly deleterious non-synonymous mutations. Second, we test whether increasing TE abundance is correlated with an increased rate of amino-acid evolution in genes encoding the piRNA machinery, as might be predicted by an evolutionary arms race model. Third, we test whether increasing TE abundance is correlated with greater codon bias in genes of the piRNA machinery. This is predicted if increasing TE abundance selects for increased efficiency in the machinery of genome defense.

Results

Surprisingly, we find neither of the first two hypotheses to be true. Specifically, we found that genome-wide levels of purifying selection, measured by the ratio of non-synonymous to synonymous substitution rates (ω), were greater in species with greater TE abundance. In addition, species with greater TE abundance have greater levels of purifying selection in the piRNA machinery. In contrast, it appears that increasing TE abundance has primarily driven adaptation in the piRNA machinery by increasing codon bias.

Conclusions

These results indicate that within the Drosophila genus, a historically reduced strength of selection relative to drift is unlikely to explain patterns of increased TE success across species. Other factors, such as ecological exposure, are likely to contribute to variation in TE abundances within species. Furthermore, constraints on the piRNA machinery may temper the evolutionary arms race that would drive increasing rates of evolution at the amino acid level. In the face of these constraints, selection may act primarily by improving the translational efficiency of the machinery of genome defense through efficient codon usage.

     

Analysis of two large functionally uncharacterized regions in the <Emphasis Type="Italic">Methanopyrus kandleri</Emphasis> AV19 genome

Background

For most sequenced prokaryotic genomes, about a third of the protein coding genes annotated are "orphan proteins", that is, they lack homology to known proteins. These hypothetical genes are typically short and randomly scattered throughout the genome. This trend is seen for most of the bacterial and archaeal genomes published to date.

Results

In contrast we have found that a large fraction of the genes coding for such orphan proteins in the Methanopyrus kandleri AV19 genome occur within two large regions. These genes have no known homologs except from other M. kandleri genes. However, analysis of their lengths, codon usage, and Ribosomal Binding Site (RBS) sequences shows that they are most likely true protein coding genes and not random open reading frames.

Conclusions

Although these regions can be considered as candidates for massive lateral gene transfer, our bioinformatics analysis suggests that this is not the case. We predict many of the organism specific proteins to be transmembrane and belong to protein families that are non-randomly distributed between the regions. Consistent with this, we suggest that the two regions are most likely unrelated, and that they may be integrated plasmids.

     

Codon optimization of <Emphasis Type="Italic">Saccharomyces cerevisiae</Emphasis> mating factor alpha prepro-leader to improve recombinant protein production in <Emphasis Type="Italic">Pichia pastoris</Emphasis>

Objectives

To evaluate different codon optimization parameters on the Saccharomyces cerevisiae-derived mating factor α prepro-leader sequence (MFLS) to improve Candida antarctica lipase B (CAL-B) secretory production in Pichia pastoris.

Results

Codon optimization based on the individual codon usage (ICU) and codon context (CC) design parameters enhanced secretory production of CAL-B to 7 U/ml and 12 U/ml, respectively. Only 3 U/ml was obtained with the wild type sequence while the sequence optimized using both ICU and CC objectives showed intermediate performance of 10 U/ml. These results clearly show that CC is the most relevant parameter for the codon optimization of MFLS in P. pastoris, and there is no synergistic effect achieved by considering both ICU and CC together.

Conclusion

The CC optimized MFLS increased secretory protein production of CAL-B in P. pastoris by fourfold.

     

Deep learning for DNase I hypersensitive sites identification

Background

The DNase I hypersensitive sites (DHSs) are associated with the cis-regulatory DNA elements. An efficient method of identifying DHSs can enhance the understanding on the accessibility of chromatin. Despite a multitude of resources available on line including experimental datasets and computational tools, the complex language of DHSs remains incompletely understood.

Methods

Here, we address this challenge using an approach based on a state-of-the-art machine learning method. We present a novel convolutional neural network (CNN) which combined Inception like networks with a gating mechanism for the response of multiple patterns and longterm association in DNA sequences to predict multi-scale DHSs in Arabidopsis, rice and Homo sapiens.

Results

Our method obtains 0.961 area under curve (AUC) on Arabidopsis, 0.969 AUC on rice and 0.918 AUC on Homo sapiens.

Conclusions

Our method provides an efficient and accurate way to identify multi-scale DHSs sequences by deep learning.

     

Amino acid composition in endothermic vertebrates is biased in the same direction as in thermophilic prokaryotes

Background

Among bacteria and archaea, amino acid usage is correlated with habitat temperatures. In particular, protein surfaces in species thriving at higher temperatures appear to be enriched in amino acids that stabilize protein structure and depleted in amino acids that decrease thermostability. Does this observation reflect a causal relationship, or could the apparent trend be caused by phylogenetic relatedness among sampled organisms living at different temperatures? And do proteins from endothermic and exothermic vertebrates show similar differences?

Results

We find that the observed correlations between the frequencies of individual amino acids and prokaryotic habitat temperature are strongly influenced by evolutionary relatedness between the species analysed; however, a proteome-wide bias towards increased thermostability remains after controlling for phylogeny. Do eukaryotes show similar effects of thermal adaptation? A small shift of amino acid usage in the expected direction is observed in endothermic ('warm-blooded') mammals and chicken compared to ectothermic ('cold-blooded') vertebrates with lower body temperatures; this shift is not simply explained by nucleotide usage biases.

Conclusion

Protein homologs operating at different temperatures have different amino acid composition, both in prokaryotes and in vertebrates. Thus, during the transition from ectothermic to endothermic life styles, the ancestors of mammals and of birds may have experienced weak genome-wide positive selection to increase the thermostability of their proteins.

     

Microcystin-LR regulates circadian clock and antioxidant gene expression in cultured rat cardiomyocytes

Background

Microcystins are waterborne environmental toxins that induce oxidative stress and cause injuries in the heart. On the other hand, many physiological processes, including antioxidant defense, are under precise control by the mammalian circadian clock.

Results

In the present study, we evaluated the effect of microcystin-LR (MC-LR) on the rhythmic expression patterns of circadian and antioxidant genes in rat cardiomyocytes using the serum shock technique. We found that a non-toxic dose (10 μm) of MC-LR decreased the amplitudes of rhythmic patterns of clock genes, while it increased the expression levels of antioxidant genes.

Conclusions

Our results indicate an influence of MC-LR on the circadian clock system and clock-controlled antioxidant genes, which will shed some light on the explanation of heart toxicity induced by MC-LR from the viewpoint of chronobiology.

     

Do universal codon-usage patterns minimize the effects of mutation and translation error?

Background

Do species use codons that reduce the impact of errors in translation or replication? The genetic code is arranged in a way that minimizes errors, defined as the sum of the differences in amino-acid properties caused by single-base changes from each codon to each other codon. However, the extent to which organisms optimize the genetic messages written in this code has been far less studied. We tested whether codon and amino-acid usages from 457 bacteria, 264 eukaryotes, and 33 archaea minimize errors compared to random usages, and whether changes in genome G+C content influence these error values.

Results

We tested the hypotheses that organisms choose their codon usage to minimize errors, and that the large observed variation in G+C content in coding sequences, but the low variation in G+U or G+A content, is due to differences in the effects of variation along these axes on the error value. Surprisingly, the biological distribution of error values has far lower variance than randomized error values, but error values of actual codon and amino-acid usages are actually greater than would be expected by chance.

Conclusion

These unexpected findings suggest that selection against translation error has not produced codon or amino-acid usages that minimize the effects of errors, and that even messages with very different nucleotide compositions somehow maintain a relatively constant error value. They raise the question: why do all known organisms use highly error-minimizing genetic codes, but fail to minimize the errors in the mRNA messages they encode?

     

Derivative component analysis for mass spectral serum proteomic profiles

Background

As a promising way to transform medicine, mass spectrometry based proteomics technologies have seen a great progress in identifying disease biomarkers for clinical diagnosis and prognosis. However, there is a lack of effective feature selection methods that are able to capture essential data behaviors to achieve clinical level disease diagnosis. Moreover, it faces a challenge from data reproducibility, which means that no two independent studies have been found to produce same proteomic patterns. Such reproducibility issue causes the identified biomarker patterns to lose repeatability and prevents it from real clinical usage.

Methods

In this work, we propose a novel machine-learning algorithm: derivative component analysis (DCA) for high-dimensional mass spectral proteomic profiles. As an implicit feature selection algorithm, derivative component analysis examines input proteomics data in a multi-resolution approach by seeking its derivatives to capture latent data characteristics and conduct de-noising. We further demonstrate DCA's advantages in disease diagnosis by viewing input proteomics data as a profile biomarker via integrating it with support vector machines to tackle the reproducibility issue, besides comparing it with state-of-the-art peers.

Results

Our results show that high-dimensional proteomics data are actually linearly separable under proposed derivative component analysis (DCA). As a novel multi-resolution feature selection algorithm, DCA not only overcomes the weakness of the traditional methods in subtle data behavior discovery, but also suggests an effective resolution to overcoming proteomics data's reproducibility problem and provides new techniques and insights in translational bioinformatics and machine learning. The DCA-based profile biomarker diagnosis makes clinical level diagnostic performances reproducible across different proteomic data, which is more robust and systematic than the existing biomarker discovery based diagnosis.

Conclusions

Our findings demonstrate the feasibility and power of the proposed DCA-based profile biomarker diagnosis in achieving high sensitivity and conquering the data reproducibility issue in serum proteomics. Furthermore, our proposed derivative component analysis suggests the subtle data characteristics gleaning and de-noising are essential in separating true signals from red herrings for high-dimensional proteomic profiles, which can be more important than the conventional feature selection or dimension reduction. In particular, our profile biomarker diagnosis can be generalized to other omics data for derivative component analysis (DCA)'s nature of generic data analysis.

     

Does centrifugation matter? Centrifugal force and spinning time alter the plasma metabolome

Background

Centrifugation is an indispensable procedure for plasma sample preparation, but applied conditions can vary between labs.

Aim

Determine whether routinely used plasma centrifugation protocols (1500×g 10 min; 3000×g 5 min) influence non-targeted metabolomic analyses.

Methods

Nuclear magnetic resonance spectroscopy (NMR) and High Resolution Mass Spectrometry (HRMS) data were evaluated with sparse partial least squares discriminant analyses and compared with cell count measurements.

Results

Besides significant differences in platelet count, we identified substantial alterations in NMR and HRMS data related to the different centrifugation protocols.

Conclusion

Already minor differences in plasma centrifugation can significantly influence metabolomic patterns and potentially bias metabolomics studies.

     

Daily variation and effect of dietary folate on urinary pteridines

Introduction

Urinary pteridines are putative molecular biomarkers for noninvasive cancer screening and prognostication. Central to their translational biomarker development is the need to understand the sources and extent of their non-epidemiological variation.

Objectives

This study was designed to characterize the two primary sources of urinary pteridine variance: daily variation and the effect of dietary folate.

Methods

Daily variation was studied by collecting urine specimens (n = 81) three times daily for 3 days. The effect of dietary folate was investigated in a treatment study in which urine specimens (n = 168) were collected daily during a control week and a treatment week during which participants received dietary folate supplements. Measurements of six urinary pteridines were made using high-performance liquid chromatography–tandem mass spectrometry. Coefficients of variation were calculated to characterize daily variance between and within subjects, while nearest neighbor non-parametric analyses were used to identify diurnal patterns and measure dietary folate effects.

Results

Daily variance was approximately 35 % RSD for both within-day and between-day periods for most pteridines. Diurnal patterns in response to circadian rhythms were similarly observed for urinary pteridines. Folate supplementation was shown to alter urinary pteridine profiles in a pathway dependent manner, suggesting that dietary folate may regulate endogenous neopterin and biopterin biosynthesis.

Conclusions

Urinary pteridine levels were found to be responsive to both daily variation and folate supplementation. These findings provide new insights into pteridine biosynthesis and regulation as well as useful information for the design of future clinical translational research.

     

Citrate usage in the leading causes of blindness: new possibilities for the old metabolite

Introduction

Citrate is an old metabolite which is best known for the role in the Krebs cycle. Citrate is widely used in many branches of medicine. In ophthalmology citrate is considered as a therapeutic agent and an useful diagnostic tool—biomarker.

Objectives

To summarize the published literature on citrate usage in the leading causes of blindness and highlight the new possibilities for this old metabolite.

Methods

We conducted a systematic search of the scientific literature about citrate usage in ophthalmology up to January 2018. The reference lists of identified articles were searched for providing in-depth information.

Results

This systematic review included 30 articles. The role of citrate in the leading causes of blindness is presented.

Conclusions

Citrate might help inhibit cataract progression, in case of questions confirm glaucoma diagnosis or improve cornea repair treatment as adjuvant agent (therapy of ulcerating cornea after alkali injury, crosslinking procedure). However, the knowledge about possible citrate usage in ophthalmology is not widely known. Promoting recent scientific knowledge about citrate usage in ophthalmology may not only benefit of medical improvement but may also limit economic costs caused by leading causes of blindness. Further studies on citrate usage in ophthalmology should continuously be the field of scientific interest.

     

Multivariate strategy for the sample selection and integration of multi-batch data in metabolomics

Introduction

Availability of large cohorts of samples with related metadata provides scientists with extensive material for studies. At the same time, recent development of modern high-throughput ‘omics’ technologies, including metabolomics, has resulted in the potential for analysis of large sample sizes. Representative subset selection becomes critical for selection of samples from bigger cohorts and their division into analytical batches. This especially holds true when relative quantification of compound levels is used.

Objectives

We present a multivariate strategy for representative sample selection and integration of results from multi-batch experiments in metabolomics.

Methods

Multivariate characterization was applied for design of experiment based sample selection and subsequent subdivision into four analytical batches which were analyzed on different days by metabolomics profiling using gas-chromatography time-of-flight mass spectrometry (GC–TOF–MS). For each batch OPLS-DA^® was used and its p(corr) vectors were averaged to obtain combined metabolic profile. Jackknifed standard errors were used to calculate confidence intervals for each metabolite in the average p(corr) profile.

Results

A combined, representative metabolic profile describing differences between systemic lupus erythematosus (SLE) patients and controls was obtained and used for elucidation of metabolic pathways that could be disturbed in SLE.

Conclusion

Design of experiment based representative sample selection ensured diversity and minimized bias that could be introduced at this step. Combined metabolic profile enabled unified analysis and interpretation.

     

The role of laterally transferred genes in adaptive evolution

Background

Bacterial genomes develop new mechanisms to tide them over the imposing conditions they encounter during the course of their evolution. Acquisition of new genes by lateral gene transfer may be one of the dominant ways of adaptation in bacterial genome evolution. Lateral gene transfer provides the bacterial genome with a new set of genes that help it to explore and adapt to new ecological niches.

Methods

A maximum likelihood analysis was done on the five sequenced corynebacterial genomes to model the rates of gene insertions/deletions at various depths of the phylogeny.

Results

The study shows that most of the laterally acquired genes are transient and the inferred rates of gene movement are higher on the external branches of the phylogeny and decrease as the phylogenetic depth increases. The newly acquired genes are under relaxed selection and evolve faster than their older counterparts. Analysis of some of the functionally characterised LGTs in each species has indicated that they may have a possible adaptive role.

Conclusion

The five Corynebacterial genomes sequenced to date have evolved by acquiring between 8 – 14% of their genomes by LGT and some of these genes may have a role in adaptation.

     

Soil microalgae modulate grain arsenic accumulation by reducing dimethylarsinic acid and enhancing nutrient uptake in rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.)

Background and aims

Microalgae are ubiquitous in paddy soils. However, their roles in arsenic (As) accumulation and transport in rice plants remains unknown.

Methods

Two green algae and five cyanobacteria were used in pot experiments under continuously flooded conditions to ascertain whether a microalgal inoculation could influence rice growth and rice grain As accumulation in plants grown in As-contaminated soils.

Results

The microalgal inoculation greatly enhanced nutrient uptake and rice growth. The presence of representative microalga Anabaena azotica did not significantly differ the grain inorganic As concentrations but remarkably decreased the rice root and grain DMA concentrations. The translocation of As from roots to grains was also markedly decreased by rice inoculated with A. azotica. This subsequently led to a decrease in the total As concentration in rice grains.

Conclusions

The results of the study indicate that the microalgal inoculation had a strong influence on soil pH, soil As speciation, and soil nutrient bioavailability, which significantly affected the rice growth, nutrient uptake, and As accumulation and translocation in rice plants. The results suggest that algae inoculation can be an effective strategy for improving nutrient uptake and reducing As translocation from roots to grains by rice grown in As-contaminated paddy soils.