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Hepatocyte apoptosis in nonalcoholic steatohepatitis (NASH) can lead to fibrosis and cirrhosis, which permanently damage the liver. Understanding the regulation of hepatocyte apoptosis is therefore important to identify therapeutic targets that may prevent the progression of NASH to fibrosis. Recently, increasing evidence has shown that long noncoding (lnc) RNAs are involved in various biological processes and that their dysregulation underlies a number of complex human diseases. By performing gene expression profiling of 4,383 lncRNAs in 82 liver samples from individuals with NASH (n = 48), simple steatosis but no NASH (n = 11), and healthy controls (n = 23), we discovered a liver-specific lncRNA (RP11-484N16.1) on chromosome 18 that showed significantly elevated expression in the liver tissue of NASH patients. This lncRNA, which we named lnc18q22.2 based on its chromosomal location, correlated with NASH grade (r = 0.51, P = 8.11 × 10-7 ), lobular inflammation (r = 0.49, P = 2.35 × 10-6 ), and nonalcoholic fatty liver disease activity score (r = 0.48, P = 4.69 × 10-6 ). The association of lnc18q22.2 to liver steatosis and steatohepatitis was replicated in 44 independent liver biopsies (r = 0.47, P = 0.0013). We provided a genetic structure of lnc18q22.2 showing an extended exon 2 in liver. Knockdown of lnc18q22.2 in four different hepatocyte cell lines resulted in severe phenotypes ranging from reduced cell growth to lethality. This observation was consistent with pathway analyses of genes coexpressed with lnc18q22.2 in human liver or affected by lnc18q22.2 knockdown. Conclusion We identified an lncRNA that can play an important regulatory role in liver function and provide new insights into the regulation of hepatocyte viability in NASH. (Hepatology 2017;66:794-808).

Dyslipidemia is strongly associated with raised plasma glucose levels and insulin resistance (IR), and genome-wide association studies have identified 95 loci that explain a substantial proportion of the variance in blood lipids. However, the loci’s effects on glucose-related traits are largely unknown. We have studied these lipid loci and tested their association collectively and individually with fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), and IR in two independent cohorts: 10,995 subjects from LifeLines Cohort Study and 2,438 subjects from Prevention of Renal and Vascular Endstage Disease (PREVEND) study. In contrast to the positive relationship between dyslipidemia and glucose traits, the genetic predisposition to dyslipidemia showed a pleiotropic lowering effect on glucose traits. Specifically, the genetic risk score related to higher triglyceride level was correlated with lower levels of FPG (P = 9.6 × 10−10 and P = 0.03 in LifeLines and PREVEND, respectively), HbA1c (P = 4.2 × 10−7 in LifeLines), and HOMA of estimated IR (P = 6.2 × 10−4 in PREVEND), after adjusting for blood lipid levels. At the single nucleotide polymorphism level, 15 lipid loci showed a pleiotropic association with glucose traits (P < 0.01), of which eight (CETP, MLXIPL, PLTP, GCKR, APOB, APOE-C1-C2, CYP7A1, and TIMD4) had opposite allelic directions of effect on dyslipidemia and glucose levels. Our findings suggest a complex genetic regulation and metabolic interplay between lipids and glucose.

Summary Lactococcus lactis produces lactic acid and is widely used in the manufacturing of various fermented dairy products. However, the species is also frequently isolated from non-dairy niches, such as fermented plant material. Recently, these non-dairy strains have gained increasing interest, as they have been described to possess flavour-forming activities that are rarely found in dairy isolates and have diverse metabolic properties. We performed an extensive whole-genome diversity analysis on 39 L. lactis strains, isolated from dairy and plant sources. Comparative genome hybridization analysis with multistrain microarrays was used to assess presence or absence of genes and gene clusters in these strains, relative to all L. lactis sequences in public databases, whereby chromosomal and plasmid-encoded genes were computationally analysed separately. Nearly 3900 chromosomal orthologous groups (chrOGs) were defined on basis of four sequenced chromosomes of L. lactis strains (IL1403, KF147, SK11, MG1363). Of these, 1268 chrOGs are present in at least 35 strains and represent the presently known core genome of L. lactis, and 72 chrOGs appear to be unique for L. lactis. Nearly 600 and 400 chrOGs were found to be specific for either the subspecies lactis or subspecies cremoris respectively. Strain variability was found in presence or absence of gene clusters related to growth on plant substrates, such as genes involved in the consumption of arabinose, xylan, a-galactosides and galacturonate. Further nichespecific differences were found in gene clusters for exopolysaccharides biosynthesis, stress response (iron transport, osmotolerance) and bacterial defence mechanisms (nisin biosynthesis). Strain variability of functions encoded on known plasmids included proteolysis, lactose fermentation, citrate uptake, metal ion resistance and exopolysaccharides biosynthesis. The present study supports the view of L. lactis as a species with a very flexible genome.

Background RNA-sequencing (RNA-seq) is a powerful technique for the identification of genetic variants that affect gene-expression levels, either through expression quantitative trait locus (eQTL) mapping or through allele-specific expression (ASE) analysis. Given increasing numbers of RNA-seq samples in the public domain, we here studied to what extent eQTLs and ASE effects can be identified when using public RNA-seq data while deriving the genotypes from the RNA-sequencing reads themselves. Methods We downloaded the raw reads for all available human RNA-seq datasets. Using these reads we performed gene expression quantification. All samples were jointly normalized and subjected to a strict quality control. We also derived genotypes using the RNA-seq reads and used imputation to infer non-coding variants. This allowed us to perform eQTL mapping and ASE analyses jointly on all samples that passed quality control. Our results were validated using samples for which DNA-seq genotypes were available. Results 4,978 public human RNA-seq runs, representing many different tissues and cell-types, passed quality control. Even though these data originated from many different laboratories, samples reflecting the same cell type clustered together, suggesting that technical biases due to different sequencing protocols are limited. In a joint analysis on the 1,262 samples with high quality genotypes, we identified cis-eQTLs effects for 8,034 unique genes (at a false discovery rate ≤0.05). eQTL mapping on individual tissues revealed that a limited number of samples already suffice to identify tissue-specific eQTLs for known disease-associated genetic variants. Additionally, we observed strong ASE effects for 34 rare pathogenic variants, corroborating previously observed effects on the corresponding protein levels. Conclusions By deriving and imputing genotypes from RNA-seq data, it is possible to identify both eQTLs and ASE effects. Given the exponential growth of the number of publicly available RNA-seq samples, we expect this approach will become especially relevant for studying the effects of tissue-specific and rare pathogenic genetic variants to aid clinical interpretation of exome and genome sequencing. Electronic supplementary material The online version of this article (doi:10.1186/s13073-015-0152-4) contains supplementary material, which is available to authorized users.

Cellular differentiation is regulated on the level of gene expression and it is known that dysregulation of gene expression can lead to deficiencies in differentiation that contribute to a variety of diseases, particularly of the immune system. Until recently, it was thought that the dysregulation was governed by changes in the binding or activity of a class of proteins called transcription factors. However, the discovery of micro-RNAs and recent descriptions of long noncoding RNAs have given enormous momentum to a whole new field of biology: the regulatory RNAs. In this review, we describe these two classes of regulatory RNAs and summarize what is known about how they regulate aspects of the adaptive and innate immune systems. Finally, we describe what is known about the involvement of micro-RNAs and long noncoding RNAs in three different autoimmune diseases (celiac disease, inflammatory bowel disease, and multiple sclerosis).

Most common diseases are complex, involving multiple genetic and environmental factors and their interactions. In the past decade, genome-wide association studies (GWAS) have successfully identified thousands of genetic variants underlying susceptibility to complex diseases. However, the results from these studies often do not provide evidence on how the variants affect downstream pathways and lead to the disease. Therefore, in the post-GWAS era the greatest challenge lies in combining GWAS findings with additional molecular data to functionally characterize the associations. The advances in various ~omics techniques have made it possible to investigate the effect of risk variants on intermediate molecular levels, such as gene expression, methylation, protein abundance or metabolite levels. As disease aetiology is complex, no single molecular analysis is expected to fully unravel the disease mechanism. Multiple molecular levels can interact and also show plasticity in different physiological conditions, cell types and disease stages. There is therefore a great need for new integrative approaches that can combine data from different molecular levels and can help construct the causal inference from genotype to phenotype. Systems genetics is such an approach; it is used to study genetic effects within the larger scope of systems biology by integrating genotype information with various ~omics datasets as well as with environmental and physiological variables. In this review, we describe this approach and discuss how it can help us unravel the molecular mechanisms through which genetic variation causes disease. This article is part of a Special Issue entitled: From Genome to Function.

Summary Lactococcus lactis produces lactic acid and is widely used in the manufacturing of various fermented dairy products. However, the species is also frequently isolated from non‐dairy niches, such as fermented plant material. Recently, these non‐dairy strains have gained increasing interest, as they have been described to possess flavour‐forming activities that are rarely found in dairy isolates and have diverse metabolic properties. We performed an extensive whole‐genome diversity analysis on 39 L. lactis strains, isolated from dairy and plant sources. Comparative genome hybridization analysis with multi‐strain microarrays was used to assess presence or absence of genes and gene clusters in these strains, relative to all L. lactis sequences in public databases, whereby chromosomal and plasmid‐encoded genes were computationally analysed separately. Nearly 3900 chromosomal orthologous groups (chrOGs) were defined on basis of four sequenced chromosomes of L. lactis strains (IL1403, KF147, SK11, MG1363). Of these, 1268 chrOGs are present in at least 35 strains and represent the presently known core genome of L. lactis, and 72 chrOGs appear to be unique for L. lactis. Nearly 600 and 400 chrOGs were found to be specific for either the subspecies lactis or subspecies cremoris respectively. Strain variability was found in presence or absence of gene clusters related to growth on plant substrates, such as genes involved in the consumption of arabinose, xylan, α‐galactosides and galacturonate. Further niche‐specific differences were found in gene clusters for exopolysaccharides biosynthesis, stress response (iron transport, osmotolerance) and bacterial defence mechanisms (nisin biosynthesis). Strain variability of functions encoded on known plasmids included proteolysis, lactose fermentation, citrate uptake, metal ion resistance and exopolysaccharides biosynthesis. The present study supports the view of L. lactis as a species with a very flexible genome.

BackgroundReversible phosphorylation catalysed by kinases is probably the most important regulatory mechanism in eukaryotes.Methodology/principal findingsWe studied the in vitro phosphorylation of peptide arrays exhibiting the majority of PhosphoBase-deposited protein sequences, by factors in cell lysates from representatives of various branches of the eukaryotic species. We derived a set of substrates from the PhosphoBase whose phosphorylation by cellular extracts is common to the divergent members of different kingdoms and thus may be considered a minimal eukaryotic phosphoproteome. The protein kinases (or kinome) responsible for phosphorylation of these substrates are involved in a variety of processes such as transcription, translation, and cytoskeletal reorganisation.Conclusions/significanceThese results indicate that the divergence in eukaryotic kinases is not reflected at the level of substrate phosphorylation, revealing the presence of a limited common substrate space for kinases in eukaryotes and suggests the presence of a set of kinase substrates and regulatory mechanisms in an ancestral eukaryote that has since remained constant in eukaryotic life.

Background Lactococcus lactis is used in dairy food fermentation and for the efficient production of industrially relevant enzymes. The genome content and different phenotypes have been determined for multiple L. lactis strains in order to understand intra-species genotype and phenotype diversity and annotate gene functions. In this study, we identified relations between gene presence and a collection of 207 phenotypes across 38 L. lactis strains of dairy and plant origin. Gene occurrence and phenotype data were used in an iterative gene selection procedure, based on the Random Forest algorithm, to identify genotype-phenotype relations. Results A total of 1388 gene-phenotype relations were found, of which some confirmed known gene-phenotype relations, such as the importance of arabinose utilization genes only for strains of plant origin. We also identified a gene cluster related to growth on melibiose, a plant disaccharide; this cluster is present only in melibiose-positive strains and can be used as a genetic marker in trait improvement. Additionally, several novel gene-phenotype relations were uncovered, for instance, genes related to arsenite resistance or arginine metabolism. Conclusions Our results indicate that genotype-phenotype matching by integrating large data sets provides the possibility to identify gene-phenotype relations, possibly improve gene function annotation and identified relations can be used for screening bacterial culture collections for desired phenotypes. In addition to all gene-phenotype relations, we also provide coherent phenotype data for 38 Lactococcus strains assessed in 207 different phenotyping experiments, which to our knowledge is the largest to date for the Lactococcus lactis species.

Most common diseases are complex, involving multiple genetic and environmental factors and their interactions. In the past decade, genome-wide association studies (GWAS) have successfully identified thousands of genetic variants underlying susceptibility to complex diseases. However, the results from these studies often do not provide evidence on how the variants affect downstream pathways and lead to the disease. Therefore, in the post-GWAS era the greatest challenge lies in combining GWAS findings with additional molecular data to functionally characterize the associations. The advances in various similar to omics techniques have made it possible to investigate the effect of risk variants on intermediate molecular levels, such as gene expression, methylation, protein abundance or metabolite levels. As disease aetiology is complex, no single molecular analysis is expected to fully unravel the disease mechanism. Multiple molecular levels can interact and also show plasticity in different physiological conditions, cell types and disease stages. There is therefore a great need for new integrative approaches that can combine data from different molecular levels and can help construct the causal inference from genotype to phenotype. Systems genetics is such an approach; it is used to study genetic effects within the larger scope of systems biology by integrating genotype information with various similar to omics datasets as well as with environmental and physiological variables. In this review, we describe this approach and discuss how it can help us unravel the molecular mechanisms through which genetic variation causes disease. This article is part of a Special Issue entitled: From Genome to Function. (C) 2014 Elsevier B.V. All rights reserved.