Predicting gene expression from sequence: a reexamination

Although much of the information regarding genes'' expressions is encoded in the genome, deciphering such information has been very challenging. We reexamined Beer and Tavazoie''s (BT) approach to predict mRNA expression patterns of 2,587 genes in Saccharomyces cerevisiae from the information in their respective promoter sequences. Instead of fitting complex Bayesian network models, we trained naïve Bayes classifiers using only the sequence-motif matching scores provided by BT. Our simple models correctly predict expression patterns for 79% of the genes, based on the same criterion and the same cross-validation (CV) procedure as BT, which compares favorably to the 73% accuracy of BT. The fact that our approach did not use position and orientation information of the predicted binding sites but achieved a higher prediction accuracy, motivated us to investigate a few biological predictions made by BT. We found that some of their predictions, especially those related to motif orientations and positions, are at best circumstantial. For example, the combinatorial rules suggested by BT for the PAC and RRPE motifs are not unique to the cluster of genes from which the predictive model was inferred, and there are simpler rules that are statistically more significant than BT''s ones. We also show that CV procedure used by BT to estimate their method''s prediction accuracy is inappropriate and may have overestimated the prediction accuracy by about 10%.     

High-Throughput Sequencing Identifies Novel and Conserved Cucumber (Cucumis sativus L.) microRNAs in Response to Cucumber Green Mottle Mosaic Virus Infection

Seedlings of Cucumis sativus L. (cv. ''Zhongnong 16'') were artificially inoculated with Cucumber green mottle mosaic virus (CGMMV) at the three-true-leaf stage. Leaf and flower samples were collected at different time points post-inoculation (10, 30 and 50 d), and processed by high throughput sequencing analysis to identify candidate miRNA sequences. Bioinformatic analysis using screening criteria, and secondary structure prediction, indicated that 8 novel and 23 known miRNAs (including 15 miRNAs described for the first time in vivo) were produced by cucumber plants in response to CGMMV infection. Moreover, gene expression profiles (p-value <0.01) validated the expression of 3 of the novel miRNAs and 3 of the putative candidate miRNAs and identified a further 82 conserved miRNAs in CGMMV-infected cucumbers. Gene ontology (GO) analysis revealed that the predicted target genes of these 88 miRNAs, which were screened using the psRNATarget and miRanda algorithms, were involved in three functional categories: 2265 in molecular function, 1362 as cellular components and 276 in biological process. The subsequent Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the predicted target genes were frequently involved in metabolic processes (166 pathways) and genetic information processes (40 pathways) and to a lesser degree the biosynthesis of secondary metabolites (12 pathways). These results could provide useful clues to help elucidate host-pathogen interactions in CGMMV and cucumber, as well as for the screening of resistance genes.     

Systems Level Analysis of Systemic Sclerosis Shows a Network of Immune and Profibrotic Pathways Connected with Genetic Polymorphisms

Systemic sclerosis (SSc) is a rare systemic autoimmune disease characterized by skin and organ fibrosis. The pathogenesis of SSc and its progression are poorly understood. The SSc intrinsic gene expression subsets (inflammatory, fibroproliferative, normal-like, and limited) are observed in multiple clinical cohorts of patients with SSc. Analysis of longitudinal skin biopsies suggests that a patient''s subset assignment is stable over 6–12 months. Genetically, SSc is multi-factorial with many genetic risk loci for SSc generally and for specific clinical manifestations. Here we identify the genes consistently associated with the intrinsic subsets across three independent cohorts, show the relationship between these genes using a gene-gene interaction network, and place the genetic risk loci in the context of the intrinsic subsets. To identify gene expression modules common to three independent datasets from three different clinical centers, we developed a consensus clustering procedure based on mutual information of partitions, an information theory concept, and performed a meta-analysis of these genome-wide gene expression datasets. We created a gene-gene interaction network of the conserved molecular features across the intrinsic subsets and analyzed their connections with SSc-associated genetic polymorphisms. The network is composed of distinct, but interconnected, components related to interferon activation, M2 macrophages, adaptive immunity, extracellular matrix remodeling, and cell proliferation. The network shows extensive connections between the inflammatory- and fibroproliferative-specific genes. The network also shows connections between these subset-specific genes and 30 SSc-associated polymorphic genes including STAT4, BLK, IRF7, NOTCH4, PLAUR, CSK, IRAK1, and several human leukocyte antigen (HLA) genes. Our analyses suggest that the gene expression changes underlying the SSc subsets may be long-lived, but mechanistically interconnected and related to a patients underlying genetic risk.     

Why Does the Giant Panda Eat Bamboo? A Comparative Analysis of Appetite-Reward-Related Genes among Mammals

Background

The giant panda has an interesting bamboo diet unlike the other species in the order of Carnivora. The umami taste receptor gene T1R1 has been identified as a pseudogene during its genome sequencing project and confirmed using a different giant panda sample. The estimated mutation time for this gene is about 4.2 Myr. Such mutation coincided with the giant panda''s dietary change and also reinforced its herbivorous life style. However, as this gene is preserved in herbivores such as cow and horse, we need to look for other reasons behind the giant panda''s diet switch.

Methodology/Principal Findings

Since taste is part of the reward properties of food related to its energy and nutrition contents, we did a systematic analysis on those genes involved in the appetite-reward system for the giant panda. We extracted the giant panda sequence information for those genes and compared with the human sequence first and then with seven other species including chimpanzee, mouse, rat, dog, cat, horse, and cow. Orthologs in panda were further analyzed based on the coding region, Kozak consensus sequence, and potential microRNA binding of those genes.

Conclusions/Significance

Our results revealed an interesting dopamine metabolic involvement in the panda''s food choice. This finding suggests a new direction for molecular evolution studies behind the panda''s dietary switch.     

Genetic signature of human longevity in PKC and NF‐κB signaling

Gene variants associated with longevity are also associated with protection against cognitive decline, dementia and Alzheimer''s disease, suggesting that common physiologic pathways act at the interface of longevity and cognitive function. To test the hypothesis that variants in genes implicated in cognitive function may promote exceptional longevity, we performed a comprehensive 3‐stage study to identify functional longevity‐associated variants in ~700 candidate genes in up to 450 centenarians and 500 controls by target capture sequencing analysis. We found an enrichment of longevity‐associated genes in the nPKC and NF‐κB signaling pathways by gene‐based association analyses. Functional analysis of the top three gene variants (NFKBIA, CLU, PRKCH) suggests that non‐coding variants modulate the expression of cognate genes, thereby reducing signaling through the nPKC and NF‐κB. This matches genetic studies in multiple model organisms, suggesting that the evolutionary conservation of reduced PKC and NF‐κB signaling pathways in exceptional longevity may include humans.     

Fine Mapping and Characterization of Candidate Genes that Control Resistance to Cercospora sojina K. Hara in Two Soybean Germplasm Accessions

Frogeye leaf spot (FLS), caused by the fungus Cercospora sojina K. Hara, may cause a significant yield loss to soybean growers in regions with a warm and humid climate. Two soybean accessions, PI 594891 and PI 594774, were identified to carry a high level of resistance similar to that conditioned by the Rcs3 gene in ''Davis''. Previously, we reported that the resistance to FLS in these two plant introductions (PIs) was controlled by a novel gene (s) on chromosome 13 that is different from Rcs3. To fine-map the novel FLS resistance gene(s) in these two PIs, F_{2: 3} seeds from the crosses between PI 594891 and PI 594774, and the FLS susceptible genotype ''Blackhawk'' were genotyped with SNP markers that were designed based on the SoySNP50k iSelect BeadChip data to identify recombinant events and locate candidate genes. Analysis of lines possessing key recombination events helped narrow down the FLS-resistance genomic region in PI 594891 from 3.3 Mb to a 72.6 kb region with five annotated genes. The resistance gene in PI 594774 was fine-mapped into a 540 kb region that encompasses the 72.6 kb region found in PI 594891. Sequencing five candidate genes in PI 594891 identified three genes that have several mutations in the promoter, intron, 5'', and 3'' UTR regions. qPCR analysis showed a difference in expression levels of these genes in both lines compared to Blackhawk in the presence of C. sojina. Based on phenotype, genotype and haplotype analysis results, these two soybean accessions might carry different resistance alleles of the same gene or two different gene(s). The identified SNPs were used to develop Kompetitive Allele Specific PCR (KASP) assays to detect the resistance alleles on chromosome 13 from the two PIs for marker-assisted selection.     

Genomic imprinting and the units of adaptation

Two guiding principles identify which biological entities are able to evolve adaptations. Williams'' principle holds that, in order for an entity to evolve adaptations, there must be selection between such entities. Maynard Smith''s principle holds that, in order for an entity to evolve adaptations, selection within such entities must be absent or negligible. However, although the kinship theory of genomic imprinting suggests that parent-of-origin-specific gene expression evolves as a consequence of natural selection acting between—rather than within—individuals, it evades adaptive interpretation at the individual level and is instead viewed as an outcome of an intragenomic conflict of interest between an individual''s genes. Here, I formalize the idea that natural selection drives intragenomic conflicts of interest between genes originating from different parents. Specifically, I establish mathematical links between the dynamics of natural selection and the idea of the gene as an intentional, inclusive-fitness-maximizing agent, and I clarify the role that information about parent of origin plays in mediating conflicts of interest between genes residing in the same genome. These results highlight that the suppression of divisive information may be as important as the suppression of lower levels of selection in maintaining the integrity of units of adaptation.     

Genome-Wide Analysis Using Exon Arrays Demonstrates an Important Role for Expression of Extra-Cellular Matrix,Fibrotic Control and Tissue Remodelling Genes in Dupuytren's Disease

Dupuytren''s disease (DD) is a classic example of pathological fibrosis which results in a debilitating disorder affecting a large sector of the human population. It is characterized by excessive local proliferation of fibroblasts and over-production of collagen and other components of extracellular matrix (ECM) in the palmar fascia. The fibrosis progressively results in contracture of elements between the palmar fascia and skin causing flexion deformity or clawing of the fingers and a severe reduction in hand function. While much is known about the pathogenesis and surgical treatment of DD, little is known about the factors that cause its onset and progression, despite many years of research. Gene expression patterns in DD patients now offers the potential to identify genes that direct the pathogenesis of DD. In this study we used primary cultures of fibroblasts derived from excisional biopsies of fibrotic tissue from DD patients to compare the gene expression profiles on a genome-wide basis with normal control fibroblasts. Our investigations have identified genes that may be involved with DD pathogenesis including some which are directly relevant to fibrosis. In particular, these include significantly reduced expression levels of three matrix metallopeptidases (MMP1, MMP3, MMP16), follistatin, and STAT1, and significantly increased expression levels of fibroblast growth factors (FGF9, FGF11), a number of collagen genes and other ECM genes in DD patient samples. Many of these gene products are known to be involved in fibrosis, tumour formation and in the normal processes of tissue remodelling. In addition, alternative splicing was identified in some DD associated genes. These highly sensitive genomic investigations provide new insight into the molecular mechanisms that may underpin the development and progression of DD.     

Deeplasmid: deep learning accurately separates plasmids from bacterial chromosomes

Plasmids are mobile genetic elements that play a key role in microbial ecology and evolution by mediating horizontal transfer of important genes, such as antimicrobial resistance genes. Many microbial genomes have been sequenced by short read sequencers and have resulted in a mix of contigs that derive from plasmids or chromosomes. New tools that accurately identify plasmids are needed to elucidate new plasmid-borne genes of high biological importance. We have developed Deeplasmid, a deep learning tool for distinguishing plasmids from bacterial chromosomes based on the DNA sequence and its encoded biological data. It requires as input only assembled sequences generated by any sequencing platform and assembly algorithm and its runtime scales linearly with the number of assembled sequences. Deeplasmid achieves an AUC–ROC of over 89%, and it was more accurate than five other plasmid classification methods. Finally, as a proof of concept, we used Deeplasmid to predict new plasmids in the fish pathogen Yersinia ruckeri ATCC 29473 that has no annotated plasmids. Deeplasmid predicted with high reliability that a long assembled contig is part of a plasmid. Using long read sequencing we indeed validated the existence of a 102 kb long plasmid, demonstrating Deeplasmid''s ability to detect novel plasmids.