Widespread Expansion of Protein Interaction Capabilities by Alternative Splicing.

While alternative splicing is known to diversify the functional characteristics of some genes, the extent to which protein isoforms globally contribute to functional complexity on a proteomic scale remains unknown. To address this systematically, we cloned full-length open reading frames of alternatively spliced transcripts for a large number of human genes and used protein-protein interaction profiling to functionally compare hundreds of protein isoform pairs. The majority of isoform pairs share less than 50% of their interactions. In the global context of interactome network maps, alternative isoforms tend to behave like distinct proteins rather than minor variants of each other. Interaction partners specific to alternative isoforms tend to be expressed in a highly tissue-specific manner and belong to distinct functional modules. Our strategy, applicable to other functional characteristics, reveals a widespread expansion of protein interaction capabilities through alternative splicing and suggests that many alternative "isoforms" are functionally divergent (i.e., "functional alloforms").

A proteome-scale map of the human interactome network.

Just as reference genome sequences revolutionized human genetics, reference maps of interactome networks will be critical to fully understand genotype-phenotype relationships. Here, we describe a systematic map of ?14,000 high-quality human binary protein-protein interactions. At equal quality, this map is ?30% larger than what is available from small-scale studies published in the literature in the last few decades. While currently available information is highly biased and only covers a relatively small portion of the proteome, our systematic map appears strikingly more homogeneous, revealing a "broader" human interactome network than currently appreciated. The map also uncovers significant interconnectivity between known and candidate cancer gene products, providing unbiased evidence for an expanded functional cancer landscape, while demonstrating how high-quality interactome models will help "connect the dots" of the genomic revolution.

Whole-genome sequencing in autism identifies hot spots for de novo germline mutation.

De novo mutation plays an important role in autism spectrum disorders (ASDs). Notably, pathogenic copy number variants (CNVs) are characterized by high mutation rates. We hypothesize that hypermutability is a property of ASD genes and may also include nucleotide-substitution hot spots. We investigated global patterns of germline mutation by whole-genome sequencing of monozygotic twins concordant for ASD and their parents. Mutation rates varied widely throughout the genome (by 100-fold) and could be explained by intrinsic characteristics of DNA sequence and chromatin structure. Dense clusters of mutations within individual genomes were attributable to compound mutation or gene conversion. Hypermutability was a characteristic of genes involved in ASD and other diseases. In addition, genes impacted by mutations in this study were associated with ASD in independent exome-sequencing data sets. Our findings suggest that regional hypermutation is a significant factor shaping patterns of genetic variation and disease risk in humans.

Metagenomic insights into nitrogen cycling functional gene responses to nitrogen fixation and transfer in maize-peanut intercropping.

The fixation and transfer of biological nitrogen from peanuts to maize in maize-peanut intercropping systems play a pivotal role in maintaining the soil nutrient balance. However, the mechanisms through which root interactions regulate biological nitrogen fixation and transfer remain unclear. This study employed a 15N isotope labelling method to quantify nitrogen fixation and transfer from peanuts to maize, concurrently elucidating key microorganisms and genera in the nitrogen cycle through metagenomic sequencing. The results revealed that biological nitrogen fixation in peanut was 50 mg and transfer to maize was 230 mg when the roots interacted. Moreover, root interactions significantly increased nitrogen content and the activities of protease, dehydrogenase (DHO) and nitrate reductase in the rhizosphere soil. Metagenomic analyses and structural equation modelling indicated that nrfC and nirA genes played important roles in regulating nitrogen fixation and transfer. Bradyrhizobium was affected by soil nitrogen content and DHO, indirectly influencing the efficiency of nitrogen fixation and transfer. Overall, our study identified key bacterial genera and genes associated with nitrogen fixation and transfer, thus advancing our understanding of interspecific interactions and highlighting the pivotal role of soil microorganisms and functional genes in maintaining soil ecosystem stability from a molecular ecological perspective.

Integrative multi-omics analysis reveals the crucial biological pathways involved in the adaptive response to NaCl stress in peanut seedlings.

Plant growth is restricted by salt stress, which is a significant abiotic factor, particularly during the seedling stage. The aim of this study was to investigate the mechanisms underlying peanut adaptation to salt stress by transcriptomic and metabolomic analysis during the seedling stage. In this study, phenotypic variations of FH23 and NH5, two peanut varieties with contrasting tolerance to salt, changed obviously, with the strongest differences observed at 24 h. FH23 leaves wilted and the membrane system was seriously damaged. A total of 1470 metabolites were identified, with flavonoids being the most common (21.22%). Multi-omics analyses demonstrated that flavonoid biosynthesis (ko00941), isoflavones biosynthesis (ko00943), and plant hormone signal transduction (ko04075) were key metabolic pathways. The comparison of metabolites in isoflavone biosynthesis pathways of peanut varieties with different salt tolerant levels demonstrated that the accumulation of naringenin and formononetin may be the key metabolite leading to their different tolerance. Using our transcriptomic data, we identified three possible reasons for the difference in salt tolerance between the two varieties: (1) differential expression of LOC112715558 (HIDH) and LOC112709716 (HCT), (2) differential expression of LOC112719763 (PYR/PYL) and LOC112764051 (ABF) in the abscisic acid (ABA) signal transduction pathway, then (3) differential expression of genes encoding JAZ proteins (LOC112696383 and LOC112790545). Key metabolites and candidate genes related to improving the salt tolerance in peanuts were screened to promote the study of the responses of peanuts to NaCl stress and guide their genetic improvement.

The determination of peanut (Arachis hypogaea L.) pod-sizes during the rapid-growth stage by phytohormones.

BACKGROUND: Pod size is an important yield target trait for peanut breeding. However, the molecular mechanism underlying the determination of peanut pod size still remains unclear. RESULTS: In this study, two peanut varieties with contrasting pod sizes were used for comparison of differences on the transcriptomic and endogenous hormonal levels. Developing peanut pods were sampled at 10, 15, 20, 25 and 30 days after pegging (DAP). Our results showed that the process of peanut pod-expansion could be divided into three stages: the gradual-growth stage, the rapid-growth stage and the slow-growth stage. Cytological analysis confirmed that the faster increase of cell-number during the rapid-growth stage was the main reason for the formation of larger pod size in Lps. Transcriptomic analyses showed that the expression of key genes related to the auxin, the cytokinin (CK) and the gibberellin (GA) were mostly up-regulated during the rapid-growth stage. Meanwhile, the cell division-related differentially expressed genes (DEGs) were mostly up-regulated at 10DAP which was consistent with the cytological-observation. Additionally, the absolute quantification of phytohormones were carried out by liquid-chromatography coupled with the tandem-mass-spectrometry (LC-MS/MS), and results supported the findings from comparative transcriptomic studies. CONCLUSIONS: It was speculated that the differential expression levels of TAA1 and ARF (auxin-related), IPT and B-ARR (CK-related), KAO, GA20ox and GA3ox (GA-related), and certain cell division-related genes (gene-LOC112747313 and gene-LOC112754661) were important participating factors of the determination-mechanism of peanut pod sizes. These results were informative for the elucidation of the underlying regulatory network in peanut pod-growth and would facilitate further identification of valuable target genes.

Comparative Multi-Omics Analysis Reveals Lignin Accumulation Affects Peanut Pod Size.

Pod size is one of the important factors affecting peanut yield. However, the metabolites relating to pod size and their biosynthesis regulatory mechanisms are still unclear. In the present study, two peanut varieties (Tif and Lps) with contrasting pod sizes were used for a comparative metabolome and transcriptome analysis. Developing peanut pods were sampled at 10, 20 and 30 days after pegging (DAP). A total of 720 metabolites were detected, most of which were lipids (20.3%), followed by phenolic acids (17.8%). There were 43, 64 and 99 metabolites identified as differentially accumulated metabolites (DAMs) at 10, 20 and 30 DAP, respectively, and flavonoids were the major DAMs between Tif and Lps at all three growth stages. Multi-omics analysis revealed that DAMs and DEGs (differentially expressed genes) were significantly enriched in the phenylpropanoid biosynthesis (ko00940) pathway, the main pathway of lignin biosynthesis, in each comparison group. The comparisons of the metabolites in the phenylpropanoid biosynthesis pathway accumulating in Tif and Lps at different growth stages revealed that the accumulation of p-coumaryl alcohol (H-monolignol) in Tif was significantly greater than that in Lps at 30 DAP. The differential expression of gene-LOC112771695, which is highly correlated with p-coumaryl alcohol and involved in the biosynthesis of monolignols, between Tif and Lps might explain the differential accumulation of p-coumaryl alcohol. The content of H-lignin in genetically diverse peanut varieties demonstrated that H-lignin content affected peanut pod size. Our findings would provide insights into the metabolic factors influencing peanut pod size and guidance for the genetic improvement of the peanut.

CancerDetector: ultrasensitive and non-invasive cancer detection at the resolution of individual reads using cell-free DNA methylation sequencing data.

The detection of tumor-derived cell-free DNA in plasma is one of the most promising directions in cancer diagnosis. The major challenge in such an approach is how to identify the tiny amount of tumor DNAs out of total cell-free DNAs in blood. Here we propose an ultrasensitive cancer detection method, termed 'CancerDetector', using the DNA methylation profiles of cell-free DNAs. The key of our method is to probabilistically model the joint methylation states of multiple adjacent CpG sites on an individual sequencing read, in order to exploit the pervasive nature of DNA methylation for signal amplification. Therefore, CancerDetector can sensitively identify a trace amount of tumor cfDNAs in plasma, at the level of individual reads. We evaluated CancerDetector on the simulated data, and showed a high concordance of the predicted and true tumor fraction. Testing CancerDetector on real plasma data demonstrated its high sensitivity and specificity in detecting tumor cfDNAs. In addition, the predicted tumor fraction showed great consistency with tumor size and survival outcome. Note that all of those testing were performed on sequencing data at low to medium coverage (1× to 10×). Therefore, CancerDetector holds the great potential to detect cancer early and cost-effectively.

Pushing the annotation of cellular activities to a higher resolution: Predicting functions at the isoform level.

In past decades, the experimental determination of protein functions was expensive and time-consuming, so numerous computational methods were developed to speed up and guide the process. However, most of these methods predict protein functions at the gene level and do not consider the fact that protein isoforms (translated from alternatively spliced transcripts), not genes, are the actual function carriers. Now, high-throughput RNA-seq technology is providing unprecedented opportunities to unravel protein functions at the isoform level. In this article, we review recent progress in the high-resolution functional annotations of protein isoforms, focusing on two methods developed by the authors. Both methods can integrate multiple RNA-seq datasets for comprehensively characterizing functions of protein isoforms.

Mapping copy number variation by population-scale genome sequencing.

Genomic structural variants (SVs) are abundant in humans, differing from other forms of variation in extent, origin and functional impact. Despite progress in SV characterization, the nucleotide resolution architecture of most SVs remains unknown. We constructed a map of unbalanced SVs (that is, copy number variants) based on whole genome DNA sequencing data from 185 human genomes, integrating evidence from complementary SV discovery approaches with extensive experimental validations. Our map encompassed 22,025 deletions and 6,000 additional SVs, including insertions and tandem duplications. Most SVs (53%) were mapped to nucleotide resolution, which facilitated analysing their origin and functional impact. We examined numerous whole and partial gene deletions with a genotyping approach and observed a depletion of gene disruptions amongst high frequency deletions. Furthermore, we observed differences in the size spectra of SVs originating from distinct formation mechanisms, and constructed a map of SV hotspots formed by common mechanisms. Our analytical framework and SV map serves as a resource for sequencing-based association studies.

High-resolution functional annotation of human transcriptome: predicting isoform functions by a novel multiple instance-based label propagation method.

Alternative transcript processing is an important mechanism for generating functional diversity in genes. However, little is known about the precise functions of individual isoforms. In fact, proteins (translated from transcript isoforms), not genes, are the function carriers. By integrating multiple human RNA-seq data sets, we carried out the first systematic prediction of isoform functions, enabling high-resolution functional annotation of human transcriptome. Unlike gene function prediction, isoform function prediction faces a unique challenge: the lack of the training data--all known functional annotations are at the gene level. To address this challenge, we modelled the gene-isoform relationships as multiple instance data and developed a novel label propagation method to predict functions. Our method achieved an average area under the receiver operating characteristic curve of 0.67 and assigned functions to 15 572 isoforms. Interestingly, we observed that different functions have different sensitivities to alternative isoform processing, and that the function diversity of isoforms from the same gene is positively correlated with their tissue expression diversity. Finally, we surveyed the literature to validate our predictions for a number of apoptotic genes. Strikingly, for the famous 'TP53' gene, we not only accurately identified the apoptosis regulation function of its five isoforms, but also correctly predicted the precise direction of the regulation.

Integrative analysis of many RNA-seq datasets to study alternative splicing.

Alternative splicing is an important gene regulatory mechanism that dramatically increases the complexity of the proteome. However, how alternative splicing is regulated and how transcription and splicing are coordinated are still poorly understood, and functions of transcript isoforms have been studied only in a few limited cases. Nowadays, RNA-seq technology provides an exceptional opportunity to study alternative splicing on genome-wide scales and in an unbiased manner. With the rapid accumulation of data in public repositories, new challenges arise from the urgent need to effectively integrate many different RNA-seq datasets for study alterative splicing. This paper discusses a set of advanced computational methods that can integrate and analyze many RNA-seq datasets to systematically identify splicing modules, unravel the coupling of transcription and splicing, and predict the functions of splicing isoforms on a genome-wide scale.

Large-scale prediction of long non-coding RNA functions in a coding-non-coding gene co-expression network.

Although accumulating evidence has provided insight into the various functions of long-non-coding RNAs (lncRNAs), the exact functions of the majority of such transcripts are still unknown. Here, we report the first computational annotation of lncRNA functions based on public microarray expression profiles. A coding-non-coding gene co-expression (CNC) network was constructed from re-annotated Affymetrix Mouse Genome Array data. Probable functions for altogether 340 lncRNAs were predicted based on topological or other network characteristics, such as module sharing, association with network hubs and combinations of co-expression and genomic adjacency. The functions annotated to the lncRNAs mainly involve organ or tissue development (e.g. neuron, eye and muscle development), cellular transport (e.g. neuronal transport and sodium ion, acid or lipid transport) or metabolic processes (e.g. involving macromolecules, phosphocreatine and tyrosine).

Genome-wide analysis reveals regulatory mechanisms and expression patterns of TGA genes in peanut under abiotic stress and hormone treatments.

Introduction: The TGA transcription factors, plays a crucial role in regulating gene expression. In cultivated peanut (Arachis hypogaea), which faces abiotic stress challenges, understanding the role of TGAs is important. Methods: In this study, we conducted a comprehensive in analysis of the TGA gene family in peanut to elucidate their regulatory mechanisms and expression patterns under abiotic stress and hormone treatments. Furthermore, functional studies on the representative AhTGA gene in peanut cultivars were conducted using transgenic Arabidopsis and soybean hair roots. Results: The genome-wide analysis revealed that a total of 20 AhTGA genes were identified and classified into five subfamilies. Collinearity analysis revealed that AhTGA genes lack tandem duplication, and their amplification in the cultivated peanut genome primarily relies on the whole-genome duplication of the diploid wild peanut to form tetraploid cultivated peanut, as well as segment duplication between the A and B subgenomes. Promoter and Protein-protein interaction analysis identified a wide range of cis-acting elements and potential interacting proteins associated with growth and development, hormones, and stress responses. Expression patterns of AhTGA genes in different tissues, under abiotic stress conditions for low temperature and drought, and in response to hormonal stimuli revealed that seven AhTGA genes from groups I (AhTGA04, AhTGA14 and AhTGA20) and II (AhTGA07, AhTGA11, AhTGA16 and AhTGA18) are involved in the response to abiotic stress and hormonal stimuli. The hormone treatment results indicate that these AhTGA genes primarily respond to the regulation of jasmonic acid and salicylic acid. Overexpressing AhTGA11 in Arabidopsis enhances resistance to cold and drought stress by increasing antioxidant activities and altering endogenous hormone levels, particularly ABA, SA and JA. Discussion: The AhTGA genes plays a crucial role in hormone regulation and stress response during peanut growth and development. The findings provide insights into peanut's abiotic stress tolerance mechanisms and pave the way for future functional studies.

Genome-wide identification of TPS and TPP genes in cultivated peanut (Arachis hypogaea) and functional characterization of AhTPS9 in response to cold stress.

Introduction: Trehalose is vital for plant metabolism, growth, and stress resilience, relying on Trehalose-6-phosphate synthase (TPS) and Trehalose-6-phosphate phosphatase (TPP) genes. Research on these genes in cultivated peanuts (Arachis hypogaea) is limited. Methods: This study employed bioinformatics to identify and analyze AhTPS and AhTPP genes in cultivated peanuts, with subsequent experimental validation of AhTPS9's role in cold tolerance. Results: In the cultivated peanut genome, a total of 16 AhTPS and 17 AhTPP genes were identified. AhTPS and AhTPP genes were observed in phylogenetic analysis, closely related to wild diploid peanuts, respectively. The evolutionary patterns of AhTPS and AhTPP genes were predominantly characterized by gene segmental duplication events and robust purifying selection. A variety of hormone-responsive and stress-related cis-elements were unveiled in our analysis of cis-regulatory elements. Distinct expression patterns of AhTPS and AhTPP genes across different peanut tissues, developmental stages, and treatments were revealed, suggesting potential roles in growth, development, and stress responses. Under low-temperature stress, qPCR results showcased upregulation in AhTPS genes (AhTPS2-5, AhTPS9-12, AhTPS14, AhTPS15) and AhTPP genes (AhTPP1, AhTPP6, AhTPP11, AhTPP13). Furthermore, AhTPS9, exhibiting the most significant expression difference under cold stress, was obviously induced by cold stress in cultivated peanut, and AhTPS9-overexpression improved the cold tolerance of Arabidopsis by protect the photosynthetic system of plants, and regulates sugar-related metabolites and genes. Discussion: This comprehensive study lays the groundwork for understanding the roles of AhTPS and AhTPP gene families in trehalose regulation within cultivated peanuts and provides valuable insights into the mechanisms related to cold stress tolerance.

Transcriptome-based analysis of key pathways relating to yield formation stage of foxtail millet under different drought stress conditions.

Although foxtail millet, as small Panicoid crop, is of drought resilient, drought stress has a significant effect on panicle of foxtail millet at the yield formation stage. In this study, the changes of panicle morphology, photosynthesis, antioxidant protective enzyme system, reactive oxygen species (ROS) system, and osmotic regulatory substance and RNA-seq of functional leaves under light drought stress (LD), heavy drought stress (HD), light drought control (LDCK) and heavy drought control (HDCK) were studied to get a snap-shot of specific panicle morphological changes, physiological responses and related molecular mechanisms. The results showed that the length and weight of panicle had decreased, but with increased empty abortive rate, and then yield dropped off 14.9% and 36.9%, respectively. The photosynthesis of millet was significantly decreased, like net photosynthesis rate, stomatal conductance and transpiration rate, especially under HD treatment with reluctant recovery from rehydration. Under LD and HD treatment, the peroxidase (POD) was increased by 34% and 14% and the same as H2O2 by 34.7% and 17.2% compared with LDCK and HDCK. The ability to produce and inhibit O2- free radicals under LD treatment was higher than HD. The content of soluble sugar was higher under LD treatment but the proline was higher under HD treatment. Through RNA-seq analysis, there were 2,393 and 3,078 different genes expressed under LD and HD treatment. According to the correlation analysis between weighted gene coexpression network analysis (WGCNA) and physiological traits, the co-expression network of several modules with high correlation was constructed, and some hub genes of millet in response to drought stress were found. The expression changes relating to carbon fixation, sucrose and starch synthesis, lignin synthesis, gibberellin synthesis, and proline synthesis of millet were specifically analyzed. These findings provide a full perspective on how drought affects the yield formation of foxtail millet by constructing one work model thereby providing theoretical foundation for hub genes exploration and drought resistance breeding of foxtail millet.

miRecords: an integrated resource for microRNA-target interactions.

MicroRNAs (miRNAs) are an important class of small noncoding RNAs capable of regulating other genes' expression. Much progress has been made in computational target prediction of miRNAs in recent years. More than 10 miRNA target prediction programs have been established, yet, the prediction of animal miRNA targets remains a challenging task. We have developed miRecords, an integrated resource for animal miRNA-target interactions. The Validated Targets component of this resource hosts a large, high-quality manually curated database of experimentally validated miRNA-target interactions with systematic documentation of experimental support for each interaction. The current release of this database includes 1135 records of validated miRNA-target interactions between 301 miRNAs and 902 target genes in seven animal species. The Predicted Targets component of miRecords stores predicted miRNA targets produced by 11 established miRNA target prediction programs. miRecords is expected to serve as a useful resource not only for experimental miRNA researchers, but also for informatics scientists developing the next-generation miRNA target prediction programs. The miRecords is available at http://miRecords.umn.edu/miRecords.

Meta-prediction of phosphorylation sites with weighted voting and restricted grid search parameter selection.

Meta-predictors make predictions by organizing and processing the predictions produced by several other predictors in a defined problem domain. A proficient meta-predictor not only offers better predicting performance than the individual predictors from which it is constructed, but it also relieves experimentally researchers from making difficult judgments when faced with conflicting results made by multiple prediction programs. As increasing numbers of predicting programs are being developed in a large number of fields of life sciences, there is an urgent need for effective meta-prediction strategies to be investigated. We compiled four unbiased phosphorylation site datasets, each for one of the four major serine/threonine (S/T) protein kinase families-CDK, CK2, PKA and PKC. Using these datasets, we examined several meta-predicting strategies with 15 phosphorylation site predictors from six predicting programs: GPS, KinasePhos, NetPhosK, PPSP, PredPhospho and Scansite. Meta-predictors constructed with a generalized weighted voting meta-predicting strategy with parameters determined by restricted grid search possess the best performance, exceeding that of all individual predictors in predicting phosphorylation sites of all four kinase families. Our results demonstrate a useful decision-making tool for analysing the predictions of the various S/T phosphorylation site predictors. An implementation of these meta-predictors is available on the web at: http://MetaPred.umn.edu/MetaPredPS/.

siDRM: an effective and generally applicable online siRNA design tool.

Small interfering RNAs (siRNAs) have become an indispensable tool for the investigation of gene functions. Most existing siRNA design tools were trained on datasets assembled from confined origins, incompatible with the diverse siRNA laboratory practice to which these tools will ultimately be applied. We have performed an updated analysis using the disjunctive rule merging (DRM) approach on a large and diverse dataset compiled from siRecords, and implemented the resulting rule sets in siDRM, a new online siRNA design tool. siDRM also implements a few high-sensitivity rule sets and fast rule sets, links to siRecords, and uses several filters to check unwanted detrimental effects, including innate immune responses, cell toxic effects and off-target activities in selecting siRNAs. A performance comparison using an independent dataset indicated that siDRM outperforms 19 existing siRNA design tools in identifying effective siRNAs.

Meta-prediction of protein subcellular localization with reduced voting.

Meta-prediction seeks to harness the combined strengths of multiple predicting programs with the hope of achieving predicting performance surpassing that of all existing predictors in a defined problem domain. We investigated meta-prediction for the four-compartment eukaryotic subcellular localization problem. We compiled an unbiased subcellular localization dataset of 1693 nuclear, cytoplasmic, mitochondrial and extracellular animal proteins from Swiss-Prot 50.2. Using this dataset, we assessed the predicting performance of 12 predictors from eight independent subcellular localization predicting programs: ELSPred, LOCtree, PLOC, Proteome Analyst, PSORT, PSORT II, SubLoc and WoLF PSORT. Gorodkin correlation coefficient (GCC) was one of the performance measures. Proteome Analyst is the best individual subcellular localization predictor tested in this four-compartment prediction problem, with GCC = 0.811. A reduced voting strategy eliminating six of the 12 predictors yields a meta-predictor (RAW-RAG-6) with GCC = 0.856, substantially better than all tested individual subcellular localization predictors (P = 8.2 x 10(-6), Fisher's Z-transformation test). The improvement in performance persists when the meta-predictor is tested with data not used in its development. This and similar voting strategies, when properly applied, are expected to produce meta-predictors with outstanding performance in other life sciences problem domains.