Gene co-expression networks from RNA sequencing of dairy cattle identifies genes and pathways affecting feed efficiency

Background

Selection for feed efficiency is crucial for overall profitability and sustainability in dairy cattle production. Key regulator genes and genetic markers derived from co-expression networks underlying feed efficiency could be included in the genomic selection of the best cows. The present study identified co-expression networks associated with high and low feed efficiency and their regulator genes in Danish Holstein and Jersey cows.RNA-sequencing data from Holstein and Jersey cows with high and low residual feed intake (RFI) and treated with two diets (low and high concentrate) were used. Approximately 26 million and 25 million pair reads were mapped to bovine reference genome for Jersey and Holstein breed, respectively. Subsequently, the gene count expressions data were analysed using a Weighted Gene Co-expression Network Analysis (WGCNA) approach. Functional enrichment analysis from Ingenuity® Pathway Analysis (IPA®), ClueGO application and STRING of these modules was performed to identify relevant biological pathways and regulatory genes.

Results

WGCNA identified two groups of co-expressed genes (modules) significantly associated with RFI and one module significantly associated with diet. In Holstein cows, the salmon module with module trait relationship (MTR)?=?0.7 and the top upstream regulators ATP7B were involved in cholesterol biosynthesis, steroid biosynthesis, lipid biosynthesis and fatty acid metabolism. The magenta module has been significantly associated (MTR?=?0.51) with the treatment diet involved in the triglyceride homeostasis. In Jersey cows, the lightsteelblue1 (MTR?=???0.57) module controlled by IFNG and IL10RA was involved in the positive regulation of interferon-gamma production, lymphocyte differentiation, natural killer cell-mediated cytotoxicity and primary immunodeficiency.

Conclusion

The present study provides new information on the biological functions in liver that are potentially involved in controlling feed efficiency. The hub genes and upstream regulators (ATP7b, IFNG and IL10RA) involved in these functions are potential candidate genes for the development of new biomarkers. However, the hub genes, upstream regulators and pathways involved in the co-expressed networks were different in both breeds. Hence, additional studies are required to investigate and confirm these findings prior to their use as candidate genes.

     

Identification of biomarkers of chromophobe renal cell carcinoma by weighted gene co-expression network analysis

Background

Chromophobe renal cell carcinoma (ChRCC) is the second common subtype of non-clear cell renal cell carcinoma (nccRCC), which accounting for 4–5% of renal cell carcinoma (RCC). However, there is no effective bio-marker to predict clinical outcomes of this malignant disease. Bioinformatic methods may provide a feasible potential to solve this problem.

Methods

In this study, differentially expressed genes (DEGs) of ChRCC samples on The Cancer Genome Atlas database were filtered out to construct co-expression modules by weighted gene co-expression network analysis and the key module were identified by calculating module-trait correlations. Functional analysis was performed on the key module and candidate hub genes were screened out by co-expression and MCODE analysis. Afterwards, real hub genes were filter out in an independent dataset GSE15641 and validated by survival analysis.

Results

Overall 2215 DEGs were screened out to construct eight co-expression modules. Brown module was identified as the key module for the highest correlations with pathologic stage, neoplasm status and survival status. 29 candidate hub genes were identified. GO and KEGG analysis demonstrated most candidate genes were enriched in mitotic cell cycle. Three real hub genes (SKA1, ERCC6L, GTSE-1) were selected out after mapping candidate genes to GSE15641 and two of them (SKA1, ERCC6L) were significantly related to overall survivals of ChRCC patients.

Conclusions

In summary, our findings identified molecular markers correlated with progression and prognosis of ChRCC, which might provide new implications for improving risk evaluation, therapeutic intervention, and prognosis prediction in ChRCC patients.

     

Identification of cancer subtypes from single-cell RNA-seq data using a consensus clustering method

Background

Human cancers are complex ecosystems composed of cells with distinct molecular signatures. Such intratumoral heterogeneity poses a major challenge to cancer diagnosis and treatment. Recent advancements of single-cell techniques such as scRNA-seq have brought unprecedented insights into cellular heterogeneity. Subsequently, a challenging computational problem is to cluster high dimensional noisy datasets with substantially fewer cells than the number of genes.

Methods

In this paper, we introduced a consensus clustering framework conCluster, for cancer subtype identification from single-cell RNA-seq data. Using an ensemble strategy, conCluster fuses multiple basic partitions to consensus clusters.

Results

Applied to real cancer scRNA-seq datasets, conCluster can more accurately detect cancer subtypes than the widely used scRNA-seq clustering methods. Further, we conducted co-expression network analysis for the identified melanoma subtypes.

Conclusions

Our analysis demonstrates that these subtypes exhibit distinct gene co-expression networks and significant gene sets with different functional enrichment.

     

Evaluation of metabolomics profiles of grain from maize hybrids derived from near-isogenic GM positive and negative segregant inbreds demonstrates that observed differences cannot be attributed unequivocally to the GM trait

Introduction

Past studies on plant metabolomes have highlighted the influence of growing environments and varietal differences in variation of levels of metabolites yet there remains continued interest in evaluating the effect of genetic modification (GM).

Objectives

Here we test the hypothesis that metabolomics differences in grain from maize hybrids derived from a series of GM (NK603, herbicide tolerance) inbreds and corresponding negative segregants can arise from residual genetic variation associated with backcrossing and that the effect of insertion of the GM trait is negligible.

Methods

Four NK603-positive and negative segregant inbred males were crossed with two different females (testers). The resultant hybrids, as well as conventional comparator hybrids, were then grown at three replicated field sites in Illinois, Minnesota, and Nebraska during the 2013 season. Metabolomics data acquisition using gas chromatography–time of flight-mass spectrometry (GC–TOF-MS) allowed the measurement of 367 unique metabolite features in harvested grain, of which 153 were identified with small molecule standards. Multivariate analyses of these data included multi-block principal component analysis and ANOVA-simultaneous component analysis. Univariate analyses of all 153 identified metabolites was conducted based on significance testing (α = 0.05), effect size evaluation (assessing magnitudes of differences), and variance component analysis.

Results

Results demonstrated that the largest effects on metabolomic variation were associated with different growing locations and the female tester. They further demonstrated that differences observed between GM and non-GM comparators, even in stringent tests utilizing near-isogenic positive and negative segregants, can simply reflect minor genomic differences associated with conventional back-crossing practices.

Conclusion

The effect of GM on metabolomics variation was determined to be negligible and supports that there is no scientific rationale for prioritizing GM as a source of variation.

     

A network based covariance test for detecting multivariate eQTL in saccharomyces cerevisiae

Background

Expression quantitative trait locus (eQTL) analysis has been widely used to understand how genetic variations affect gene expressions in the biological systems. Traditional eQTL is investigated in a pair-wise manner in which one SNP affects the expression of one gene. In this way, some associated markers found in GWAS have been related to disease mechanism by eQTL study. However, in real life, biological process is usually performed by a group of genes. Although some methods have been proposed to identify a group of SNPs that affect the mean of gene expressions in the network, the change of co-expression pattern has not been considered. So we propose a process and algorithm to identify the marker which affects the co-expression pattern of a pathway. Considering two genes may have different correlations under different isoforms which is hard to detect by the linear test, we also consider the nonlinear test.

Results

When we applied our method to yeast eQTL dataset profiled under both the glucose and ethanol conditions, we identified a total of 166 modules, with each module consisting of a group of genes and one eQTL where the eQTL regulate the co-expression patterns of the group of genes. We found that many of these modules have biological significance.

Conclusions

We propose a network based covariance test to identify the SNP which affects the structure of a pathway. We also consider the nonlinear test as considering two genes may have different correlations under different isoforms which is hard to detect by linear test.

     

Impact of plant growth-promoting bacteria on grain yield,protein content,and urea-<Superscript>15</Superscript> N recovery by maize in a Cerrado Oxisol

Background and aims

The inoculation of cereal crops with plant growth-promoting bacteria (PGPB) is a potential strategy to improve fertilizer-N acquisition by crops in soils with low capacity to supply N. A study was conducted to assess the impact of three inoculants on grain yield, protein content, and urea-¹⁵ N recovery in maize (Zea mays L.) under Cerrado soil and climate conditions.

Methods

The main treatments included inoculants containing (i) Azospirillum brasilense strain Sp245, (ii) A. brasilense strains AbV5 + AbV6, (iii) Herbaspirillum seropedicae strain ZAE94, and (iv) a non-inoculated control. The subtreatments were (i) urea-N fertilization (100 kg N ha^?1) at 30 days after sowing and (ii) no N addition at the stage. To determine fertilizer-N recovery, ¹⁵N–labelled urea was applied in microplots.

Results

Inoculants carrying A. brasilense improved urea-¹⁵ N acquisition efficiency in maize and also improved grain yield compared to the non-inoculated control, while urea-N fertilization enhanced grain quality by providing higher protein content.

Conclusion

Our results suggest that the inoculation of maize grains with PGPB represents a strategy to improve fertilizer-N recovery and maize yield in Cerrado soil with a low capacity to supply N.

     

The rhizosheath – a potential trait for future agricultural sustainability occurs in orders throughout the angiosperms

Aims

Phosphorus (P) is frequently limiting crop production in agroecosystems. Large progress was achieved in understanding root traits associated with P acquisition efficiency (PAE, i.e. P uptake achieved under low P conditions). Most former studies were performed in controlled environments, and avoided the complexity of soil-root interactions. This may lead to an oversimplification of the root-soil relations. The aim of the present study was, therefore, to identify the dominant root and rhizosphere-related traits determining PAE, in contrasting soil conditions in the field.

Methods

Twenty-three maize hybrids were grown at two contrasting P levels of a long-term P-fertilizer trial in two adjacent soil types: alkaline and neutral. Bulk soil, rhizosphere and root parameters were studied in relation to plant P acquisition.

Results

Soil type had robust effect on PAE. Hybrids’ performance in one soil type was not related to that in the other soil type. In the neutral soil, roots exhibited higher specific root length, higher root/shoot ratio but lower PAE. Best performing hybrids in the neutral soil were characterized by top soil exploration, i.e., greater root surface and topsoil foraging. In contrast, in the alkaline soil, PAE and foraging traits were not correlated, P availability in the rhizosphere was greater than the bulk soil and phosphatase activity was higher, suggesting a ‘mining strategy’ in that case (i.e. traits that facilitate elevated P availability).

Conclusions

These results indicate the key role of environmental factors for roots traits determining high PAE. The study highlights the need to consider soil properties when breeding for high PAE, as various soil types are likely to require different crop ideotypes.

     

Phosphorus availability from bone char in a P-fixing soil influenced by root-mycorrhizae-biochar interactions

Aims

The objectives of this study were to evaluate (1) the fertilizer potential of bone char, (2) the effects of wood biochar on plant-available phosphorus (P), and (3) the role of root-mycorrhizae-biochar interactions in plant P acquisition from a P-fixing soil.

Methods

Incubation and pot experiments were conducted with a P-fixing soil and maize with or without root hairs and arbuscular mycorrhizae (AM) inoculation. Olsen-, resin-P and plant P accumulation were used to estimate P availability from bone char, co-pyrolyzed bone char-wood biochar, and separate bone char and wood biochar additions produced at 60, 350 and 750 °C, and Triple Superphosphate (TSP).

Results

Maize inoculated with AM showed similar P accumulation when fertilized with either 750 °C bone char or TSP. Pyrolyzing bone did not increase extractable P in soil in comparison to unpyrolyzed bone, apart from a 67 % increase in resin-extractable P after additions of bone char pyrolyzed at 350 °C. Despite greater Olsen-P extractability, co-pyrolysis of bone with wood reduced maize P uptake. Wood biochars reduced resin-P from bone char by 14–26 %, whereas oven-dried wood increased resin-P by 23 %.

Conclusions

Bone char is an effective P fertilizer, especially if root-AM interactions are simultaneously considered. Biochar influences plant access to soil P and requires careful management to improve P availability.

     

Suppressive effect of microRNA319 expression on rice plant height

Key message

miR319 was identified as a dwarf-inducing gene from Shiokari and its dwarf near isogenic line, and its transgenic rice showed a reduced plant height. This finding reveals the potential application of miR319 in future molecular breeding.

Abstract

It is well known that microRNAs (miRNAs) play important roles in plant physiology, especially in development and stress responses. However, little is known about the role of miRNAs in plant height. In this study, the rice cultivar Shiokari and its dwarf near isogenic line Shiokari-d6 were analysed to identify and characterize plant height-associated miRNAs. This anatomic and morphological investigation revealed that the major cause of the shorter height of Shiokari-d6 is the significantly dis-elongated internodes, particularly the second internode and those underneath it. The results of miRNA microarray profiling and real-time RT-PCR indicated that miR319 is expressed at a significantly higher level in Shiokari-d6 than in Shiokari. Transgenic rice overexpressing miR319 in Oryza sativa L. cv. Tainung 67 generated through Agrobacterium-mediated transformation had a stable dwarf phenotype regardless of whether the plants were from the T1 or T2 generation. We also found that the internodes of miR319-overexpressing rice are shortened, particularly the third internode and those underneath it. Furthermore, we identified three putative miR319 target genes that were previously uncharacterized with expression levels that were negatively correlated with the expression of miR319. In conclusion, miR319 is the first miRNA proposed to be involved in plant height regulation, and its function may influence the elongation of internodes, which leads to decreased plant height.

     

Assembly of seed-associated microbial communities within and across successive plant generations

Background and aims

Seeds are involved in the transmission of microorganisms from one plant generation to another and consequently may act as the initial inoculum source for the plant microbiota. In this work, we assessed the structure and composition of the seed microbiota of radish (Raphanus sativus) across three successive plant generations.

Methods

Structure of seed microbial communities were estimated on individual plants through amplification and sequencing of genes that are markers of taxonomic diversity for bacteria (gyrB) and fungi (ITS1). The relative contribution of dispersal and ecological drift in inter-individual fluctuations were estimated with a neutral community model.

Results

Seed microbial communities of radish display a low heritability across plant generations. Fluctuations in microbial community profiles were related to changes in community membership and composition across plant generations, but also to variation between individual plants. Ecological drift was an important driver of the structure of seed bacterial communities, while dispersal was involved in the assembly of the fungal fraction of the seed microbiota.

Conclusions

These results provide a first glimpse of the governing processes driving the assembly of the seed microbiota.

     

Crop metabolomics: from diagnostics to assisted breeding

Background

Until recently, plant metabolomics have provided a deep understanding on the metabolic regulation in individual plants as experimental units. The application of these techniques to agricultural systems subjected to more complex interactions is a step towards the implementation of translational metabolomics in crop breeding.

Aim of Review

We present here a review paper discussing advances in the knowledge reached in the last years derived from the application of metabolomic techniques that evolved from biomarker discovery to improve crop yield and quality.

Key Scientific Concepts of Review

Translational metabolomics applied to crop breeding programs.