The long-term genetic stability and individual specificity of the human gut microbiome.

By following up the gut microbiome, 51 human phenotypes and plasma levels of 1,183 metabolites in 338 individuals after 4 years, we characterize microbial stability and variation in relation to host physiology. Using these individual-specific and temporally stable microbial profiles, including bacterial SNPs and structural variations, we develop a microbial fingerprinting method that shows up to 85% accuracy in classifying metagenomic samples taken 4 years apart. Application of our fingerprinting method to the independent HMP cohort results in 95% accuracy for samples taken 1 year apart. We further observe temporal changes in the abundance of multiple bacterial species, metabolic pathways, and structural variation, as well as strain replacement. We report 190 longitudinal microbial associations with host phenotypes and 519 associations with plasma metabolites. These associations are enriched for cardiometabolic traits, vitamin B, and uremic toxins. Finally, mediation analysis suggests that the gut microbiome may influence cardiometabolic health through its metabolites.

Phage-display immunoprecipitation sequencing of the antibody epitope repertoire in inflammatory bowel disease reveals distinct antibody signatures.

Inflammatory bowel diseases (IBDs), e.g., Crohn's disease (CD) and ulcerative colitis (UC), are chronic immune-mediated inflammatory diseases. A comprehensive overview of an IBD-specific antibody epitope repertoire is, however, lacking. Using high-throughput phage-display immunoprecipitation sequencing (PhIP-Seq), we identified antibodies against 344,000 antimicrobial, immune, and food antigens in 497 individuals with IBD compared with 1,326 controls. IBD was characterized by 373 differentially abundant antibody responses (202 overrepresented and 171 underrepresented), with 17% shared by both IBDs, 55% unique to CD, and 28% unique to UC. Antibody reactivities against bacterial flagellins dominated in CD and were associated with ileal involvement, fibrostenotic disease, and anti-Saccharomyces cerevisiae antibody positivity, but not with fecal microbiome composition. Antibody epitope repertoires accurately discriminated CD from controls (area under the curve [AUC] = 0.89), and similar discrimination was achieved when using only ten antibodies (AUC = 0.87). Individuals with IBD thus show a distinct antibody repertoire against selected peptides, allowing clinical stratification and discovery of immunological targets.

Host genetic regulation of human gut microbial structural variation.

Although the impact of host genetics on gut microbial diversity and the abundance of specific taxa is well established1-6, little is known about how host genetics regulates the genetic diversity of gut microorganisms. Here we conducted a meta-analysis of associations between human genetic variation and gut microbial structural variation in 9,015 individuals from four Dutch cohorts. Strikingly, the presence rate of a structural variation segment in Faecalibacterium prausnitzii that harbours an N-acetylgalactosamine (GalNAc) utilization gene cluster is higher in individuals who secrete the type A oligosaccharide antigen terminating in GalNAc, a feature that is jointly determined by human ABO and FUT2 genotypes, and we could replicate this association in a Tanzanian cohort. In vitro experiments demonstrated that GalNAc can be used as the sole carbohydrate source for F. prausnitzii strains that carry the GalNAc-metabolizing pathway. Further in silico and in vitro studies demonstrated that other ABO-associated species can also utilize GalNAc, particularly Collinsella aerofaciens. The GalNAc utilization genes are also associated with the host's cardiometabolic health, particularly in individuals with mucosal A-antigen. Together, the findings of our study demonstrate that genetic associations across the human genome and bacterial metagenome can provide functional insights into the reciprocal host-microbiome relationship.

Multiple indicators of gut dysbiosis predict all-cause and cause-specific mortality in solid organ transplant recipients.

OBJECTIVE: Gut microbiome composition is associated with multiple diseases, but relatively little is known about its relationship with long-term outcome measures. While gut dysbiosis has been linked to mortality risk in the general population, the relationship with overall survival in specific diseases has not been extensively studied. In the current study, we present results from an in-depth analysis of the relationship between gut dysbiosis and all-cause and cause-specific mortality in the setting of solid organ transplant recipients (SOTR). DESIGN: We analysed 1337 metagenomes derived from faecal samples of 766 kidney, 334 liver, 170 lung and 67 heart transplant recipients part of the TransplantLines Biobank and Cohort-a prospective cohort study including extensive phenotype data with 6.5 years of follow-up. To analyze gut dysbiosis, we included an additional 8208 metagenomes from the general population of the same geographical area (northern Netherlands). Multivariable Cox regression and a machine learning algorithm were used to analyse the association between multiple indicators of gut dysbiosis, including individual species abundances, and all-cause and cause-specific mortality. RESULTS: We identified two patterns representing overall microbiome community variation that were associated with both all-cause and cause-specific mortality. The gut microbiome distance between each transplantation recipient to the average of the general population was associated with all-cause mortality and death from infection, malignancy and cardiovascular disease. A multivariable Cox regression on individual species abundances identified 23 bacterial species that were associated with all-cause mortality, and by applying a machine learning algorithm, we identified a balance (a type of log-ratio) consisting of 19 out of the 23 species that were associated with all-cause mortality. CONCLUSION: Gut dysbiosis is consistently associated with mortality in SOTR. Our results support the observations that gut dysbiosis is associated with long-term survival. Since our data do not allow us to infer causality, more preclinical research is needed to understand mechanisms before we can determine whether gut microbiome-directed therapies may be designed to improve long-term outcomes.

Faecal metabolome and its determinants in inflammatory bowel disease.

OBJECTIVE: Inflammatory bowel disease (IBD) is a multifactorial immune-mediated inflammatory disease of the intestine, comprising Crohn's disease and ulcerative colitis. By characterising metabolites in faeces, combined with faecal metagenomics, host genetics and clinical characteristics, we aimed to unravel metabolic alterations in IBD. DESIGN: We measured 1684 different faecal metabolites and 8 short-chain and branched-chain fatty acids in stool samples of 424 patients with IBD and 255 non-IBD controls. Regression analyses were used to compare concentrations of metabolites between cases and controls and determine the relationship between metabolites and each participant's lifestyle, clinical characteristics and gut microbiota composition. Moreover, genome-wide association analysis was conducted on faecal metabolite levels. RESULTS: We identified over 300 molecules that were differentially abundant in the faeces of patients with IBD. The ratio between a sphingolipid and L-urobilin could discriminate between IBD and non-IBD samples (AUC=0.85). We found changes in the bile acid pool in patients with dysbiotic microbial communities and a strong association between faecal metabolome and gut microbiota. For example, the abundance of Ruminococcus gnavus was positively associated with tryptamine levels. In addition, we found 158 associations between metabolites and dietary patterns, and polymorphisms near NAT2 strongly associated with coffee metabolism. CONCLUSION: In this large-scale analysis, we identified alterations in the metabolome of patients with IBD that are independent of commonly overlooked confounders such as diet and surgical history. Considering the influence of the microbiome on faecal metabolites, our results pave the way for future interventions targeting intestinal inflammation.

Gut Microbiome Composition in Dystonia Patients.

Dystonia is a movement disorder in which patients have involuntary abnormal movements or postures. Non-motor symptoms, such as psychiatric symptoms, sleep problems and fatigue, are common. We hypothesise that the gut microbiome might play a role in the pathophysiology of the (non-)motor symptoms in dystonia via the gut-brain axis. This exploratory study investigates the composition of the gut microbiome in dystonia patients compared to healthy controls. Furthermore, the abundance of neuro-active metabolic pathways, which might be implicated in the (non-)motor symptoms, was investigated. We performed both metagenomic and 16S rRNA sequencing on the stool samples of three subtypes of dystonia (27 cervical dystonia, 20 dopa-responsive dystonia and 24 myoclonus-dystonia patients) and 25 controls. While microbiome alpha and beta diversity was not different between dystonia patients and controls, dystonia patients had higher abundances of Ruminococcus torques and Dorea formicigenerans, and a lower abundance of Butyrivibrio crossotus compared to controls. For those with dystonia, non-motor symptoms and the levels of neurotransmitters in plasma explained the variance in the gut microbiome composition. Several neuro-active metabolic pathways, especially tryptophan degradation, were less abundant in the dystonia patients compared to controls. This suggest that the gut-brain axis might be involved in the pathophysiology of dystonia. Further studies are necessary to confirm our preliminary findings.

Healthy Cotwins Share Gut Microbiome Signatures With Their Inflammatory Bowel Disease Twins and Unrelated Patients.

BACKGROUND & AIMS: It is currently unclear whether reported changes in the gut microbiome are cause or consequence of inflammatory bowel disease (IBD). Therefore, we studied the gut microbiome of IBD-discordant and -concordant twin pairs, which offers the unique opportunity to assess individuals at increased risk of developing IBD, namely healthy cotwins from IBD-discordant twin pairs. METHODS: Fecal samples were obtained from 99 twins (belonging to 51 twin pairs), 495 healthy age-, sex-, and body mass index-matched controls, and 99 unrelated patients with IBD. Whole-genome metagenomic shotgun sequencing was performed. Taxonomic and functional (pathways) composition was compared among healthy cotwins, IBD-twins, unrelated patients with IBD, and healthy controls with multivariable (ie, adjusted for potential confounding) generalized linear models. RESULTS: No significant differences were observed in the relative abundance of species and pathways between healthy cotwins and their IBD-twins (false discovery rate <0.10). Compared with healthy controls, 13, 19, and 18 species, and 78, 105, and 153 pathways were found to be differentially abundant in healthy cotwins, IBD-twins, and unrelated patients with IBD, respectively (false discovery rate <0.10). Of these, 8 (42.1%) of 19 and 1 (5.6%) of 18 species, and 37 (35.2%) of 105 and 30 (19.6%) of 153 pathways overlapped between healthy cotwins and IBD-twins, and healthy cotwins and unrelated patients with IBD, respectively. Many of the shared species and pathways have previously been associated with IBD. The shared pathways include potentially inflammation-related pathways, for example, an increase in propionate degradation and L-arginine degradation pathways. CONCLUSIONS: The gut microbiome of healthy cotwins from IBD-discordant twin pairs displays IBD-like signatures. These IBD-like microbiome signatures might precede the onset of IBD. However, longitudinal follow-up studies are needed to infer a causal relationship.

Long-term dietary patterns are associated with pro-inflammatory and anti-inflammatory features of the gut microbiome.

OBJECTIVE: The microbiome directly affects the balance of pro-inflammatory and anti-inflammatory responses in the gut. As microbes thrive on dietary substrates, the question arises whether we can nourish an anti-inflammatory gut ecosystem. We aim to unravel interactions between diet, gut microbiota and their functional ability to induce intestinal inflammation. DESIGN: We investigated the relation between 173 dietary factors and the microbiome of 1425 individuals spanning four cohorts: Crohn's disease, ulcerative colitis, irritable bowel syndrome and the general population. Shotgun metagenomic sequencing was performed to profile gut microbial composition and function. Dietary intake was assessed through food frequency questionnaires. We performed unsupervised clustering to identify dietary patterns and microbial clusters. Associations between diet and microbial features were explored per cohort, followed by a meta-analysis and heterogeneity estimation. RESULTS: We identified 38 associations between dietary patterns and microbial clusters. Moreover, 61 individual foods and nutrients were associated with 61 species and 249 metabolic pathways in the meta-analysis across healthy individuals and patients with IBS, Crohn's disease and UC (false discovery rate<0.05). Processed foods and animal-derived foods were consistently associated with higher abundances of Firmicutes, Ruminococcus species of the Blautia genus and endotoxin synthesis pathways. The opposite was found for plant foods and fish, which were positively associated with short-chain fatty acid-producing commensals and pathways of nutrient metabolism. CONCLUSION: We identified dietary patterns that consistently correlate with groups of bacteria with shared functional roles in both, health and disease. Moreover, specific foods and nutrients were associated with species known to infer mucosal protection and anti-inflammatory effects. We propose microbial mechanisms through which the diet affects inflammatory responses in the gut as a rationale for future intervention studies.

Whole exome sequencing analyses reveal gene-microbiota interactions in the context of IBD.

OBJECTIVE: Both the gut microbiome and host genetics are known to play significant roles in the pathogenesis of IBD. However, the interaction between these two factors and its implications in the aetiology of IBD remain underexplored. Here, we report on the influence of host genetics on the gut microbiome in IBD. DESIGN: To evaluate the impact of host genetics on the gut microbiota of patients with IBD, we combined whole exome sequencing of the host genome and whole genome shotgun sequencing of 1464 faecal samples from 525 patients with IBD and 939 population-based controls. We followed a four-step analysis: (1) exome-wide microbial quantitative trait loci (mbQTL) analyses, (2) a targeted approach focusing on IBD-associated genomic regions and protein truncating variants (PTVs, minor allele frequency (MAF) >5%), (3) gene-based burden tests on PTVs with MAF <5% and exome copy number variations (CNVs) with site frequency <1%, (4) joint analysis of both cohorts to identify the interactions between disease and host genetics. RESULTS: We identified 12 mbQTLs, including variants in the IBD-associated genes IL17REL, MYRF, SEC16A and WDR78. For example, the decrease of the pathway acetyl-coenzyme A biosynthesis, which is involved in short chain fatty acids production, was associated with variants in the gene MYRF (false discovery rate <0.05). Changes in functional pathways involved in the metabolic potential were also observed in participants carrying rare PTVs or CNVs in CYP2D6, GPR151 and CD160 genes. These genes are known for their function in the immune system. Moreover, interaction analyses confirmed previously known IBD disease-specific mbQTLs in TNFSF15. CONCLUSION: This study highlights that both common and rare genetic variants affecting the immune system are key factors in shaping the gut microbiota in the context of IBD and pinpoints towards potential mechanisms for disease treatment.

Donor genetic variants as risk factors for thrombosis after liver transplantation: A genome-wide association study.

Thrombosis after liver transplantation substantially impairs graft- and patient survival. Inevitably, heritable disorders of coagulation originating in the donor liver are transmitted by transplantation. We hypothesized that genetic variants in donor thrombophilia genes are associated with increased risk of posttransplant thrombosis. We genotyped 775 donors for adult recipients and 310 donors for pediatric recipients transplanted between 1993 and 2018. We determined the association between known donor thrombophilia gene variants and recipient posttransplant thrombosis. In addition, we performed a genome-wide association study (GWAS) and meta-analyzed 1085 liver transplantations. In our donor cohort, known thrombosis risk loci were not associated with posttransplant thrombosis, suggesting that it is unnecessary to exclude liver donors based on thrombosis-susceptible polymorphisms. By performing a meta-GWAS from children and adults, we identified 280 variants in 55 loci at suggestive genetic significance threshold. Downstream prioritization strategies identified biologically plausible candidate genes, among which were AK4 (rs11208611-T, p = 4.22 × 10-05 ) which encodes a protein that regulates cellular ATP levels and concurrent activation of AMPK and mTOR, and RGS5 (rs10917696-C, p = 2.62 × 10-05 ) which is involved in vascular development. We provide evidence that common genetic variants in the donor, but not previously known thrombophilia-related variants, are associated with increased risk of thrombosis after liver transplantation.

Multi-omics examination of Q fever fatigue syndrome identifies similarities with chronic fatigue syndrome.

BACKGROUND: Q fever fatigue syndrome (QFS) is characterised by a state of prolonged fatigue that is seen in 20% of acute Q fever infections and has major health-related consequences. The molecular mechanisms underlying QFS are largely unclear. In order to better understand its pathogenesis, we applied a multi-omics approach to study the patterns of the gut microbiome, blood metabolome, and inflammatory proteome of QFS patients, and compared these with those of chronic fatigue syndrome (CFS) patients and healthy controls (HC). METHODS: The study population consisted of 31 QFS patients, 50 CFS patients, and 72 HC. All subjects were matched for age, gender, and general geographical region (South-East part of the Netherlands). The gut microbiome composition was assessed by Metagenomic sequencing using the Illumina HiSeq platform. A total of 92 circulating inflammatory markers were measured using Proximity Extension Essay and 1607 metabolic features were assessed with a high-throughput non-targeted metabolomics approach. RESULTS: Inflammatory markers, including 4E-BP1 (P = 9.60-16 and 1.41-7) and MMP-1 (P = 7.09-9 and 3.51-9), are significantly more expressed in both QFS and CFS patients compared to HC. Blood metabolite profiles show significant differences when comparing QFS (319 metabolites) and CFS (441 metabolites) patients to HC, and are significantly enriched in pathways like sphingolipid (P = 0.0256 and 0.0033) metabolism. When comparing QFS to CFS patients, almost no significant differences in metabolome were found. Comparison of microbiome taxonomy of QFS and CFS patients with that of HC, shows both in- and decreases in abundancies in Bacteroidetes (with emphasis on Bacteroides and Alistiples spp.), and Firmicutes and Actinobacteria (with emphasis on Ruminococcus and Bifidobacterium spp.). When we compare QFS patients to CFS patients, there is a striking resemblance and hardly any significant differences in microbiome taxonomy are found. CONCLUSIONS: We show that QFS and CFS patients are similar across three different omics layers and 4E-BP1 and MMP-1 have the potential to distinguish QFS and CFS patients from HC.

Solid-Phase Synthesis and In-Silico Analysis of Iron-Binding Catecholato Chelators.

Siderophores are iron-complexing compounds synthesized by bacteria and fungi. They are low molecular weight compounds (500-1500 Daltons) possessing high affinity for iron(III). Since 1970 a large number of siderophores have been characterized, the majority using hydroxamate or catecholate as functional groups. The biosynthesis of siderophores is typically regulated by the iron levels of the environment where the organism is located. Because of their exclusive affinity and specificity for iron(III), natural siderophores and their synthetic derivatives have been exploited in the treatment of human iron-overload diseases, as both diagnostic and therapeutic agents. Here, solid-phase approach for the preparation of hexadentate, peptide-based tricatecholato containing peptides is described. The versatility of the synthetic method allows for the design of a common scaffolding structure whereby diverse ligands can be conjugated. With so many possibilities, a computational approach has been developed which will facilitate the identification of those peptides which are capable of providing a high affinity iron(III) binding site. This study reports an integrated computational/synthetic approach towards a rational development of peptide-based siderophores.

Bioprospecting for Genes Encoding Hydrocarbon-Degrading Enzymes from Metagenomic Samples Isolated from Northern Adriatic Sea Sediments.

Three metagenomic libraries were constructed using surface sediment samples from the northern Adriatic Sea. Two of the samples were taken from a highly polluted and an unpolluted site respectively. The third sample from a polluted site had been enriched using crude oil. The results of the metagenome analyses were incorporated in the REDPET relational database (http://redpet.bioinfo.pbf.hr/REDPET), which was generated using the previously developed MEGGASENSE platform. The database includes taxonomic data to allow the assessment of the biodiversity of metagenomic libraries and a general functional analysis of genes using hidden Markov model (HMM) profiles based on the KEGG database. A set of 22 specialised HMM profiles was developed to detect putative genes for hydrocarbon-degrading enzymes. Use of these profiles showed that the metagenomic library generated after selection on crude oil had enriched genes for aerobic n-alkane degradation. The use of this system for bioprospecting was exemplified using potential alkB and almA genes from this library.

MEGGASENSE - The Metagenome/Genome Annotated Sequence Natural Language Search Engine: A Platform for the Construction of Sequence Data Warehouses.

The MEGGASENSE platform constructs relational databases of DNA or protein sequences. The default functional analysis uses 14 106 hidden Markov model (HMM) profiles based on sequences in the KEGG database. The Solr search engine allows sophisticated queries and a BLAST search function is also incorporated. These standard capabilities were used to generate the SCATT database from the predicted proteome of Streptomyces cattleya. The implementation of a specialised metagenome database (AMYLOMICS) for bioprospecting of carbohydrate-modifying enzymes is described. In addition to standard assembly of reads, a novel 'functional' assembly was developed, in which screening of reads with the HMM profiles occurs before the assembly. The AMYLOMICS database incorporates additional HMM profiles for carbohydrate-modifying enzymes and it is illustrated how the combination of HMM and BLAST analyses helps identify interesting genes. A variety of different proteome and metagenome databases have been generated by MEGGASENSE.

Gene duplications are extensive and contribute significantly to the toxic proteome of nematocysts isolated from Acropora digitifera (Cnidaria: Anthozoa: Scleractinia).

BACKGROUND: Gene duplication followed by adaptive selection is a well-accepted process leading to toxin diversification in venoms. However, emergent genomic, transcriptomic and proteomic evidence now challenges this role to be at best equivocal to other processess . Cnidaria are arguably the most ancient phylum of the extant metazoa that are venomous and such provide a definitive ancestral anchor to examine the evolution of this trait. METHODS: Here we compare predicted toxins from the translated genome of the coral Acropora digitifera to putative toxins revealed by proteomic analysis of soluble proteins discharged from nematocysts, to determine the extent to which gene duplications contribute to venom innovation in this reef-building coral species. A new bioinformatics tool called HHCompare was developed to detect potential gene duplications in the genomic data, which is made freely available ( https://github.com/rgacesa/HHCompare ). RESULTS: A total of 55 potential toxin encoding genes could be predicted from the A. digitifera genome, of which 36 (65 %) had likely arisen by gene duplication as evinced using the HHCompare tool and verified using two standard phylogeny methods. Surprisingly, only 22 % (12/55) of the potential toxin repertoire could be detected following rigorous proteomic analysis, for which only half (6/12) of the toxin proteome could be accounted for as peptides encoded by the gene duplicates. Biological activities of these toxins are dominatedby putative phospholipases and toxic peptidases. CONCLUSIONS: Gene expansions in A. digitifera venom are the most extensive yet described in any venomous animal, and gene duplication plays a significant role leading to toxin diversification in this coral species. Since such low numbers of toxins were detected in the proteome, it is unlikely that the venom is evolving rapidly by prey-driven positive natural selection. Rather we contend that the venom has a defensive role deterring predation or harm from interspecific competition and overgrowth by fouling organisms. Factors influencing translation of toxin encoding genes perhaps warrants more profound experimental consideration.

O-Methyltransferase is shared between the pentose phosphate and shikimate pathways and is essential for mycosporine-like amino acid biosynthesis in Anabaena variabilis ATCC 29413.

The parent core structure of mycosporine-like amino acids (MAAs) is 4-deoxygadusol, which, in cyanobacteria, is derived from conversion of the pentose phosphate pathway intermediate sedoheptulose 7-phosphate by the enzymes 2-epi-5-epivaliolone synthase (EVS) and O-methyltransferase (OMT). Yet, deletion of the EVS gene from Anabaena variabilis ATCC 29413 was shown to have little effect on MAA production, thus suggesting that its biosynthesis is not exclusive to the pentose phosphate pathway. Herein, we report how, using pathway-specific inhibitors, we demonstrated unequivocally that MAA biosynthesis occurs also via the shikimate pathway. In addition, complete in-frame gene deletion of the OMT gene from A. variabilis ATCC 29413 reveals that, although biochemically distinct, the pentose phosphate and shikimate pathways are inextricably linked to MAA biosynthesis in this cyanobacterium. Furthermore, proteomic data reveal that the shikimate pathway is the predominate route for UV-induced MAA biosynthesis.

Predicting substrate specificity of adenylation domains of nonribosomal peptide synthetases and other protein properties by latent semantic indexing.

Successful genome mining is dependent on accurate prediction of protein function from sequence. This often involves dividing protein families into functional subtypes (e.g., with different substrates). In many cases, there are only a small number of known functional subtypes, but in the case of the adenylation domains of nonribosomal peptide synthetases (NRPS), there are >500 known substrates. Latent semantic indexing (LSI) was originally developed for text processing but has also been used to assign proteins to families. Proteins are treated as ''documents'' and it is necessary to encode properties of the amino acid sequence as ''terms'' in order to construct a term-document matrix, which counts the terms in each document. This matrix is then processed to produce a document-concept matrix, where each protein is represented as a row vector. A standard measure of the closeness of vectors to each other (cosines of the angle between them) provides a measure of protein similarity. Previous work encoded proteins as oligopeptide terms, i.e. counted oligopeptides, but used no information regarding location of oligopeptides in the proteins. A novel tokenization method was developed to analyze information from multiple alignments. LSI successfully distinguished between two functional subtypes in five well-characterized families. Visualization of different ''concept'' dimensions allows exploration of the structure of protein families. LSI was also used to predict the amino acid substrate of adenylation domains of NRPS. Better results were obtained when selected residues from multiple alignments were used rather than the total sequence of the adenylation domains. Using ten residues from the substrate binding pocket performed better than using 34 residues within 8 Å of the active site. Prediction efficiency was somewhat better than that of the best published method using a support vector machine.

Evolutionary concepts in natural products discovery: what actinomycetes have taught us.

Actinomycetes are a very important source of natural products for the pharmaceutical industry and other applications. Most of the strains belong to Streptomyces or related genera, partly because they are particularly amenable to growth in the laboratory and industrial fermenters. It is unlikely that chemical synthesis can fulfil the needs of the pharmaceutical industry for novel compounds so there is a continuing need to find novel natural products. An evolutionary perspective can help this process in several ways. Genome mining attempts to identify secondary metabolite biosynthetic clusters in DNA sequences, which are likely to produce interesting chemical entities. There are often technical problems in assembling the DNA sequences of large modular clusters in genome and metagenome projects, which can be overcome partially using information about the evolution of the domain sequences. Understanding the evolutionary mechanisms of modular clusters should allow simulation of evolutionary pathways in the laboratory to generate novel compounds.

Potential biomarkers for multiple sclerosis stage from targeted proteomics and microRNA sequencing.

Multiple sclerosis is a chronic demyelinating disease of the central nervous system. There is a need for new circulating biomarkers for multiple sclerosis, in particular, markers that differentiate multiple sclerosis subtypes (relapsing-remitting, secondary progressive and primary progressive multiple sclerosis), as this can help in making treatment decisions. In this study, we explore two classes of potential multiple sclerosis biomarkers-proteins and microRNAs-circulating in the cerebrospinal fluid and serum. Targeted medium-throughput proteomics (92 proteins) and microRNA sequencing were performed on serum samples collected in a cross-sectional case-control cohort (cohort I, controls n = 30, multiple sclerosis n = 75) and a prospective multiple sclerosis cohort (cohort II, n = 93). For cohort I, we also made these measurements in paired cerebrospinal fluid samples. In the cohort I cerebrospinal fluid, we observed differences between multiple sclerosis and controls for 13 proteins, including some previously described to be markers for multiple sclerosis [e.g. CD27, C-X-C motif chemokine 13 (CXCL13) and interleukin-7 (IL7)]. No microRNAs were significantly differentially expressed between multiple sclerosis and controls in the cerebrospinal fluid. In serum, 10 proteins, including angiopoietin-1 receptor (TIE2), and 16 microRNAs were significantly different between relapsing-remitting multiple sclerosis and secondary progressive multiple sclerosis after performing a meta-analysis combining both cohorts. In the prospective part of the study, participants with relapsing-remitting multiple sclerosis were followed for around 3 years, during which time 12 participants converted to secondary progressive multiple sclerosis. In these longitudinally collected serum samples, we observed a peak in granzyme B, A and H proteins around the time of conversion. Single-sample enrichment analysis of serum microRNA profiles revealed that the peak in granzyme B levels around conversion coincides with enrichment for microRNAs that are enriched in CD4+, CD8+ and natural killer cells (e.g. miRNA-150). We identified several proteins and microRNAs in serum that represent potential biomarkers for relapsing-remitting and secondary progressive multiple sclerosis. Conversion to secondary progressive disease is marked by a peak in granzyme B levels and enrichment for immune-related microRNAs. This indicates that specific immune cell-driven processes may contribute to the conversion of relapsing-remitting multiple sclerosis to secondary progressive multiple sclerosis.