Does epigenetic polymorphism contribute to phenotypic variances in Jatropha curcas L.?

<p>Abstract</p> <p>Background</p> <p>There is a growing interest in <it>Jatropha curcas </it>L. (jatropha) as a biodiesel feedstock plant. Variations in its morphology and seed productivity have been well documented. However, there is the lack of systematic comparative evaluation of distinct collections under same climate and agronomic practices. With the several reports on low genetic diversity in jatropha collections, there is uncertainty on genetic contribution to jatropha morphology.</p> <p>Result</p> <p>In this study, five populations of jatropha plants collected from China (CN), Indonesia (MD), Suriname (SU), Tanzania (AF) and India (TN) were planted in one farm under the same agronomic practices. Their agronomic traits (branching pattern, height, diameter of canopy, time to first flowering, dormancy, accumulated seed yield and oil content) were observed and tracked for two years. Significant variations were found for all the agronomic traits studied. Genetic diversity and epigenetic diversity were evaluated using florescence Amplified Fragment Length Polymorphism (fAFLP) and methylation sensitive florescence AFLP (MfAFLP) methods. Very low level of genetic diversity was detected (polymorphic band <0.1%) within and among populations. In contrast, intermediate but significant epigenetic diversity was detected (25.3% of bands were polymorphic) within and among populations. More than half of CCGG sites surveyed by MfAFLP were methylated with significant difference in inner cytosine and double cytosine methylation among populations. Principal coordinates analysis (PCoA) based on Nei's epigenetic distance showed Tanzania/India group distinct from China/Indonesia/Suriname group. Inheritance of epigenetic markers was assessed in one F1 hybrid population between two morphologically distinct parent plants and one selfed population. 30 out of 39 polymorphic markers (77%) were found heritable and followed Mendelian segregation. One epiallele was further confirmed by bisulphite sequencing of its corresponding genomic region.</p> <p>Conclusion</p> <p>Our study confirmed climate and practice independent differences in agronomic performance among jatropha collections. Such agronomic trait variations, however, were matched by very low genetic diversity and medium level but significant epigenetic diversity. Significant difference in inner cytosine and double cytosine methylation at CCGG sites was also found among populations. Most epigenetic differential markers can be inherited as epialleles following Mendelian segregation. These results suggest possible involvement of epigenetics in jatropha development.</p

Expression of fatty acid and lipid biosynthetic genes in developing endosperm of Jatropha curcas

BACKGROUND: Temporal and spatial expression of fatty acid and lipid biosynthetic genes are associated with the accumulation of storage lipids in the seeds of oil plants. In jatropha (Jatropha curcas L.), a potential biofuel plant, the storage lipids are mainly synthesized and accumulated in the endosperm of seeds. Although the fatty acid and lipid biosynthetic genes in jatropha have been identified, the expression of these genes at different developing stages of endosperm has not been systemically investigated. RESULTS: Transmission electron microscopy study revealed that the oil body formation in developing endosperm of jatropha seeds initially appeared at 28 days after fertilization (DAF), was actively developed at 42 DAF and reached to the maximum number and size at 56 DAF. Sixty-eight genes that encode enzymes, proteins or their subunits involved in fatty acid and lipid biosynthesis were identified from a normalized cDNA library of jatropha developing endosperm. Gene expression with quantitative reverse-transcription polymerase chain reaction analysis demonstrated that the 68 genes could be collectively grouped into five categories based on the patterns of relative expression of the genes during endosperm development. Category I has 47 genes and they displayed a bell-shaped expression pattern with the peak expression at 28 or 42 DAF, but low expression at 14 and 56 DAF. Category II contains 8 genes and expression of the 8 genes was constantly increased from 14 to 56 DAF. Category III comprises of 2 genes and both genes were constitutively expressed throughout endosperm development. Category IV has 9 genes and they showed a high expression at 14 and 28 DAF, but a decreased expression from 42 to 56 DAF. Category V consists of 2 genes and both genes showed a medium expression at 14 DAF, the lowest expression at 28 or 42 DAF, and the highest expression at 56 DAF. In addition, genes encoding enzymes or proteins with similar function were differentially expressed during endosperm development. CONCLUSION: The formation of oil bodies in jatropha endosperm is developmentally regulated. The expression of the majority of fatty acid and lipid biosynthetic genes is highly consistent with the development of oil bodies and endosperm in jatropha seeds, while the genes encoding enzymes with similar function may be differentially expressed during endosperm development. These results not only provide the initial information on spatial and temporal expression of fatty acid and lipid biosynthetic genes in jatropha developing endosperm, but are also valuable to identify the rate-limiting genes for storage lipid biosynthesis and accumulation during seed development

Enhanced Runoff Modeling by Incorporating Information from the GR4J Hydrological Model and Multiple Remotely Sensed Precipitation Datasets

Reliable runoff modeling is essential for water resource allocation and management. However, a key uncertainty source is that the true precipitation field is difficult to measure, making reliable runoff modeling still challenging. To account for this uncertainty, this study developed a two-step approach combining ensemble average and cumulative distribution correction (i.e., EC) to incorporate information from the GR4J (modèle du Génie Rural à 4 paramètres Journalier) hydrological model and multiple remotely sensed precipitation datasets. In the EC approach, firstly, the ensemble average is applied to construct transitional fluxes using the reproduced runoff information, which is yielded by applying various remotely sensed precipitation datasets to drive the GR4J model. Subsequently, the cumulative distribution correction is applied to enhance the transitional fluxes to model runoff. In our experiments, the effectiveness of the EC approach was investigated by runoff modeling to incorporate information from the GR4J model and six precipitation datasets in the Pingtang Watershed (PW; Southwest China), and the single precipitation dataset-based approaches and the ensemble average were used as benchmarks. The results show that the EC method performed better than the benchmarks and had a satisfactory performance with Nash–Sutcliffe values of 0.68 during calibration and validation. Meanwhile, the EC method exhibited a more stable performance than the ensemble averaging method under different incorporation scenarios. However, the single precipitation dataset-based approaches tended to underestimate runoff (regression coefficients < 1), and there were similar errors between the calibration and validation stages. To further illustrate the effectiveness of the EC model, five watersheds (including the PW) of different hydrometeorological features were used to test the EC model and its benchmarks. The results show that both the EC model and the ensemble averaging had good transferability, but the EC model had better performance across all the test watersheds. Conversely, the single precipitation dataset-based approaches exhibited significant regional variations and, therefore, had low transferability. The current study concludes that the EC approach can be a robust alternative to model runoff and highlights the value of the incorporation of multiple precipitation datasets in runoff modeling

Adjoint Tomography of Ambient Noise Data and Teleseismic P Waves: Methodology and Applications to Central California

Adjoint tomography has been recently applied to ambient seismic noise and teleseismic P waves separately to unveil fine-scale lithospheric structures beyond the resolving ability of traditional ray-based traveltime tomography. In this study, we propose an inversion scheme that alternates between frequency-dependent traveltime inversions of ambient noise surface waves and waveform inversions of teleseismic P waves to take advantage of their complementary sensitivities to the Earth's structure. We apply our method to ambient noise empirical Green's functions from 60 virtual sources, direct P and scattered waves from 11 teleseismic events recorded by a dense linear array (∼7 km station spacing) and other regional stations (∼40 km average station spacing) in central California. To evaluate the performance of the method, we compare tomographic results from ambient noise adjoint tomography, full-waveform inversion of teleseismic P waves, and the alternating inversion of the two data sets. Both applications to practical field data sets and synthetic checkerboard tests demonstrate the advantage of the alternating inversion over individual inversions as it combines the complementary sensitivities of the two independent data sets toward a more unified model. The three dimensional model from our alternating inversion not only shows major features of velocity anomalies and discontinuities in agreement with previous studies, but also reveals small-scale heterogeneities which provide new constraints on the geometry of the Isabella Anomaly and mantle dynamic processes in central California. The proposed alternating inversion scheme can be applied to other regions with similar array deployments for high-resolution lithospheric imaging.K. Wang (after January 2020) and Y. Yang are supported by the Australian Research Council Discovery Grants DP190102940. K. Wang (before January 2020) and Q. Liu are supported by the NSERC Discovery Grant 487237. This is contribution 1664 from the ARC Center of Excellence for Core to Crust Fluid Systems and 1465 in the GEMOC Key Center

Fire dynamics and driving mechanisms on the Eastern Coast of China since the Late Pleistocene: evidence from charcoal records on Shengshan Island

Fires play a significant role in ecosystems, exerting a profound influence on climate, vegetation, and geochemical cycles, while being reciprocally affected by these factors. The reconstruction of past fire events serves as a valuable window into understanding environmental changes over time. To investigate the history of ancient fires on the Eastern Coast of China, we conducted the first charcoal analysis on a loess profile of Shengshan Island (East China Sea). Along with other biological and geochemical proxies, we successfully reconstructed the ancient fire dynamics and elucidated their driving mechanisms in this region since the Late Pleistocene. Our initial findings revealed a peak in charcoal concentration during the 60-50 ka period, but after calibrating for sedimentation rate, the concentration significantly decreased. Fire activities remained weak during 50-30 ka, likely due to the scarcity of combustible materials. Between 30-12 ka, fires were frequent in the early period, while gradually diminishing during the later stage. Dry climate and dense vegetation likely attributed to frequent fires in early period, while some extreme events (e.g., sudden change in temperature) may have decreased the fire frequency in later period. The Holocene (began ~12 ka) evidenced the most frequent fire events as a high charcoal concentration was recorded, likely caused by human activities. After comparing our findings with other paleoecological records from surrounding areas, we confirmed the accuracy of our reconstruction of ancient fires. This reconstruction captures not only local shifts but also broader regional changes. Overall, our study highlights the importance of calibrating sedimentation rate in charcoal profiles, while also contributing to an enhanced understanding of environmental changes along the Eastern Coast of China since the Late Pleistocene

A First Generation Microsatellite- and SNP-Based Linkage Map of Jatropha

Jatropha curcas is a potential plant species for biodiesel production. However, its seed yield is too low for profitable production of biodiesel. To improve the productivity, genetic improvement through breeding is essential. A linkage map is an important component in molecular breeding. We established a first-generation linkage map using a mapping panel containing two backcross populations with 93 progeny. We mapped 506 markers (216 microsatellites and 290 SNPs from ESTs) onto 11 linkage groups. The total length of the map was 1440.9 cM with an average marker space of 2.8 cM. Blasting of 222 Jatropha ESTs containing polymorphic SSR or SNP markers against EST-databases revealed that 91.0%, 86.5% and 79.2% of Jatropha ESTs were homologous to counterparts in castor bean, poplar and Arabidopsis respectively. Mapping 192 orthologous markers to the assembled whole genome sequence of Arabidopsis thaliana identified 38 syntenic blocks and revealed that small linkage blocks were well conserved, but often shuffled. The first generation linkage map and the data of comparative mapping could lay a solid foundation for QTL mapping of agronomic traits, marker-assisted breeding and cloning genes responsible for phenotypic variation

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

Background The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. Conclusion We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.Peer reviewe

The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

BackgroundThe Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function.ResultsHere, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory.ConclusionWe conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens.</p

How does ecological civilization construction affect carbon emission intensity? Evidence from Chinese provinces’ panel data

Ecological civilization construction is a new concept and trend in the era of China's high-quality development. It requires the collaborative propulsion of an ecological economic civilization, ecological social civilization, and ecological environment civilization. Reducing carbon emission intensity is an important issue facing the Chinese government in the backdrop of global warming. Thus, studying the influence of ecological civilization construction on carbon emission intensity from different perspectives has important theoretical and practical significance. In this study, the influences of the three subsystems of an ecological civilization on carbon emission intensity are empirically analyzed using Chinese provincial panel data from 2004 to 2016 and a spatial Durbin model based on the STIRPAT model. First, the Moran's I of carbon emission intensity in Chinese provinces was between 0.425 and 0.473. This indicates positive spatial correlation and illustrates that the carbon emission intensity of China's provinces can influence each other. The reasons behind this correlation include close ties between neighboring provinces and similarities in natural, economic, and social characteristics. Second, the correlation coefficients of ecological economic civilization, ecological social civilization, and ecological environment civilization to carbon emission intensity are −4.743139, 2.865884, and −0.3246447, respectively. This illustrates that an ecological economic civilization and ecological environment civilization can reduce carbon emission intensity, while an ecological social civilization can increase it. To reduce total carbon emission intensity, the three subsystems of ecological civilization should have a negative relationship with carbon emission intensity, so the effect of ecological social civilization on carbon emission intensity should be changed. Third, the spatial spillover effect of ecological social civilization did not pass the significance test. The correlation coefficients of spatial spillover effect to ecological economic civilization and ecological environment civilization are 2.046531 and −3.238323, respectively. Improving the ecological economic civilization can increase the carbon emission intensity of periphery provinces, while improving the ecological environment civilization can reduce it. Thus, it is necessary to enhance cooperation between periphery provinces and establish a trans-provincial cooperation mechanism for reducing carbon emissions