MicroRNAs located in the Hox gene clusters are implicated in huntington's disease pathogenesis.

Transcriptional dysregulation has long been recognized as central to the pathogenesis of Huntington's disease (HD). MicroRNAs (miRNAs) represent a major system of post-transcriptional regulation, by either preventing translational initiation or by targeting transcripts for storage or for degradation. Using next-generation miRNA sequencing in prefrontal cortex (Brodmann Area 9) of twelve HD and nine controls, we identified five miRNAs (miR-10b-5p, miR-196a-5p, miR-196b-5p, miR-615-3p and miR-1247-5p) up-regulated in HD at genome-wide significance (FDR q-value<0.05). Three of these, miR-196a-5p, miR-196b-5p and miR-615-3p, were expressed at near zero levels in control brains. Expression was verified for all five miRNAs using reverse transcription quantitative PCR and all but miR-1247-5p were replicated in an independent sample (8HD/8C). Ectopic miR-10b-5p expression in PC12 HTT-Q73 cells increased survival by MTT assay and cell viability staining suggesting increased expression may be a protective response. All of the miRNAs but miR-1247-5p are located in intergenic regions of Hox clusters. Total mRNA sequencing in the same samples identified fifteen of 55 genes within the Hox cluster gene regions as differentially expressed in HD, and the Hox genes immediately adjacent to the four Hox cluster miRNAs as up-regulated. Pathway analysis of mRNA targets of these miRNAs implicated functions for neuronal differentiation, neurite outgrowth, cell death and survival. In regression models among the HD brains, huntingtin CAG repeat size, onset age and age at death were independently found to be inversely related to miR-10b-5p levels. CAG repeat size and onset age were independently inversely related to miR-196a-5p, onset age was inversely related to miR-196b-5p and age at death was inversely related to miR-615-3p expression. These results suggest these Hox-related miRNAs may be involved in neuroprotective response in HD. Recently, miRNAs have shown promise as biomarkers for human diseases and given their relationship to disease expression, these miRNAs are biomarker candidates in HD.

Novel microRNA discovery using small RNA sequencing in post-mortem human brain.

BACKGROUND: MicroRNAs (miRNAs) are short, non-coding RNAs that regulate gene expression mainly through translational repression of target mRNA molecules. More than 2700 human miRNAs have been identified and some are known to be associated with disease phenotypes and to display tissue-specific patterns of expression. METHODS: We used high-throughput small RNA sequencing to discover novel miRNAs in 93 human post-mortem prefrontal cortex samples from individuals with Huntington's disease (n = 28) or Parkinson's disease (n = 29) and controls without neurological impairment (n = 36). A custom miRNA identification analysis pipeline was built, which utilizes miRDeep* miRNA identification and result filtering based on false positive rate estimates. RESULTS: Ninety-nine novel miRNA candidates with a false positive rate of less than 5 % were identified. Thirty-four of the candidate miRNAs show sequence similarity with known mature miRNA sequences and may be novel members of known miRNA families, while the remaining 65 may constitute previously undiscovered families of miRNAs. Nineteen of the 99 candidate miRNAs were replicated using independent, publicly-available human brain RNA-sequencing samples, and seven were experimentally validated using qPCR. CONCLUSIONS: We have used small RNA sequencing to identify 99 putative novel miRNAs that are present in human brain samples.

Gene expression profiles in Parkinson disease prefrontal cortex implicate FOXO1 and genes under its transcriptional regulation.

Parkinson disease (PD) is a complex neurodegenerative disorder with largely unknown genetic mechanisms. While the degeneration of dopaminergic neurons in PD mainly takes place in the substantia nigra pars compacta (SN) region, other brain areas, including the prefrontal cortex, develop Lewy bodies, the neuropathological hallmark of PD. We generated and analyzed expression data from the prefrontal cortex Brodmann Area 9 (BA9) of 27 PD and 26 control samples using the 44K One-Color Agilent 60-mer Whole Human Genome Microarray. All samples were male, without significant Alzheimer disease pathology and with extensive pathological annotation available. 507 of the 39,122 analyzed expression probes were different between PD and control samples at false discovery rate (FDR) of 5%. One of the genes with significantly increased expression in PD was the forkhead box O1 (FOXO1) transcription factor. Notably, genes carrying the FoxO1 binding site were significantly enriched in the FDR-significant group of genes (177 genes covered by 189 probes), suggesting a role for FoxO1 upstream of the observed expression changes. Single-nucleotide polymorphisms (SNPs) selected from a recent meta-analysis of PD genome-wide association studies (GWAS) were successfully genotyped in 50 out of the 53 microarray brains, allowing a targeted expression-SNP (eSNP) analysis for 52 SNPs associated with PD affection at genome-wide significance and the 189 probes from FoxO1 regulated genes. A significant association was observed between a SNP in the cyclin G associated kinase (GAK) gene and a probe in the spermine oxidase (SMOX) gene. Further examination of the FOXO1 region in a meta-analysis of six available GWAS showed two SNPs significantly associated with age at onset of PD. These results implicate FOXO1 as a PD-relevant gene and warrant further functional analyses of its transcriptional regulatory mechanisms.

Real-world evidence study finds no new-onset diabetes or drug-related hyperglycemia in Pompe disease patients treated with avalglucosidase alfa.

Avalglucosidase alfa therapy for Pompe disease is diluted in dextrose 5% solution in water (D5W) for infusion, which raises questions about the potential for hyperglycemia or worsening diabetes. Using United States insurance claims data, we assessed the impact of biweekly infusions on hyperglycemia, new-onset diabetes mellitus, insulin resistance, and prediabetes in patients with Pompe disease. After starting avalglucosidase alfa treatment, 1 of 26 patients had one claim for hyperglycemia, which was attributed to acute pancreatitis.

Cyclin-G-associated kinase modifies α-synuclein expression levels and toxicity in Parkinson's disease: results from the GenePD Study.

Although family history is a well-established risk factor for Parkinson's disease (PD), fewer than 5% of PD cases can be attributed to known genetic mutations. The etiology for the remainder of PD cases is unclear; however, neuronal accumulation of the protein α-synuclein is common to nearly all patients, implicating pathways that influence α-synuclein in PD pathogenesis. We report a genome-wide significant association (P = 3.97 × 10(-8)) between a polymorphism, rs1564282, in the cyclin-G-associated kinase (GAK) gene and increased PD risk, with a meta-analysis odds ratio of 1.48. This association result is based on the meta-analysis of three publicly available PD case-control genome-wide association study and genotyping from a new, independent Italian cohort. Microarray expression analysis of post-mortem frontal cortex from PD and control brains demonstrates a significant association between rs1564282 and higher α-synuclein expression, a known cause of early onset PD. Functional knockdown of GAK in cell culture causes a significant increase in toxicity when α-synuclein is over-expressed. Furthermore, knockdown of GAK in rat primary neurons expressing the A53T mutation of α-synuclein, a well-established model for PD, decreases cell viability. These observations provide evidence that GAK is associated with PD risk and suggest that GAK and α-synuclein interact in a pathway involved in PD pathogenesis. The GAK protein, a serine/threonine kinase, belongs to a family of proteins commonly targeted for drug development. This, combined with GAK's observed relationship to the levels of α-synuclein expression and toxicity, suggests that the protein is an attractive therapeutic target for the treatment of PD.

Development of a rare disease algorithm to identify persons at risk of Gaucher disease using electronic health records in the United States.

BACKGROUND: Early diagnosis of Gaucher disease (GD) allows for disease-specific treatment before significant symptoms arise, preventing/delaying onset of complications. Yet, many endure years-long diagnostic odysseys. We report the development of a machine learning algorithm to identify patients with GD from electronic health records. METHODS: We utilized Optum's de-identified Integrated Claims-Clinical dataset (2007-2019) for feature engineering and algorithm training/testing, based on clinical characteristics of GD. Two algorithms were selected: one based on age of feature occurrence (age-based), and one based on occurrence of features (prevalence-based). Performance was compared with an adaptation of the available clinical diagnostic algorithm for identifying patients with diagnosed GD. Undiagnosed patients highly-ranked by the algorithms were compared with diagnosed GD patients. RESULTS: Splenomegaly was the most important predictor for diagnosed GD with both algorithms, followed by geographical location (northeast USA), thrombocytopenia, osteonecrosis, bone density disorders, and bone pain. Overall, 1204 and 2862 patients, respectively, would need to be assessed with the age- and prevalence-based algorithms, compared with 20,743 with the clinical diagnostic algorithm, to identify 28 patients with diagnosed GD in the integrated dataset. Undiagnosed patients highly-ranked by the algorithms had similar clinical manifestations as diagnosed GD patients. CONCLUSIONS: The age-based algorithm identified younger patients, while the prevalence-based identified patients with advanced clinical manifestations. Their combined use better captures GD heterogeneity. The two algorithms were about 10-20-fold more efficient at identifying GD patients than the clinical diagnostic algorithm. Application of these algorithms could shorten diagnostic delay by identifying undiagnosed GD patients.

Genomewide association study for onset age in Parkinson disease.

BACKGROUND: Age at onset in Parkinson disease (PD) is a highly heritable quantitative trait for which a significant genetic influence is supported by multiple segregation analyses. Because genes associated with onset age may represent invaluable therapeutic targets to delay the disease, we sought to identify such genetic modifiers using a genomewide association study in familial PD. There have been previous genomewide association studies (GWAS) to identify genes influencing PD susceptibility, but this is the first to identify genes contributing to the variation in onset age. METHODS: Initial analyses were performed using genotypes generated with the Illumina HumanCNV370Duo array in a sample of 857 unrelated, familial PD cases. Subsequently, a meta-analysis of imputed SNPs was performed combining the familial PD data with that from a previous GWAS of 440 idiopathic PD cases. The SNPs from the meta-analysis with the lowest p-values and consistency in the direction of effect for onset age were then genotyped in a replication sample of 747 idiopathic PD cases from the Parkinson Institute Biobank of Milan, Italy. RESULTS: Meta-analysis across the three studies detected consistent association (p < 1 x 10(-5)) with five SNPs, none of which reached genomewide significance. On chromosome 11, the SNP with the lowest p-value (rs10767971; p = 5.4 x 10(-7)) lies between the genes QSER1 and PRRG4. Near the PARK3 linkage region on chromosome 2p13, association was observed with a SNP (rs7577851; p = 8.7 x 10(-6)) which lies in an intron of the AAK1 gene. This gene is closely related to GAK, identified as a possible PD susceptibility gene in the GWAS of the familial PD cases. CONCLUSION: Taken together, these results suggest an influence of genes involved in endocytosis and lysosomal sorting in PD pathogenesis.

Integrating Clinical Knowledge and Real-World Evidence for Type 2 Diabetes Treatment.

Clinical decision support system (CDSS) plays a significant role nowadays and it assists physicians in making decisions for treatment. Generally based on clinical guideline, the principles of the recommendation are provided and may suggest several candidate medications for similar patient group with certain clinical conditions. However, it is challenging to prioritize these candidates and even refine the guideline to a finer level for patient-specific recommendation. Here we propose a method and system to integrate the clinical knowledge and real-world evidence (RWE) for type 2 diabetes treatment, to enable both standardized and personalized medication recommendation. The RWE is generated by medication effectiveness analysis and subgroup analysis. The knowledge model has been verified by clinical experts from the advanced hospitals. The data verification results show that the medications that are consistent with the method recommendation can lead to better clinical outcome in terms of glycemic control, compared to those inconsistent.

Application of Machine Learning Methods to Predict Non-Alcoholic Steatohepatitis (NASH) in Non-Alcoholic Fatty Liver (NAFL) Patients.

Non-alcoholic fatty liver disease (NAFLD) is the leading cause of chronic liver disease worldwide. NAFLD patients have excessive liver fat (steatosis), without other liver diseases and without excessive alcohol consumption. NAFLD consists of a spectrum of conditions: benign steatosis or non-alcoholic fatty liver (NAFL), steatosis accompanied by inflammation and fibrosis or nonalcoholic steatohepatitis (NASH), and cirrhosis. Given a lack of clinical biomarkers and its asymptomatic nature, NASH is under-diagnosed. We use electronic health records from the Optum Analytics to (1) identify patients diagnosed with benign steatosis and NASH, and (2) train machine learning classifiers for NASH and healthy (non-NASH) populations to (3) predict NASH disease status on patients diagnosed with NAFL. Summarized temporal lab data for alanine aminotransferase, aspartate aminotransferase, and platelet counts, with basic demographic information and type 2 diabetes status were included in the models.

Body Composition Profiling in the UK Biobank Imaging Study.

OBJECTIVE: This study aimed to investigate the value of imaging-based multivariable body composition profiling by describing its association with coronary heart disease (CHD), type 2 diabetes (T2D), and metabolic health on individual and population levels. METHODS: The first 6,021 participants scanned by UK Biobank were included. Body composition profiles (BCPs) were calculated, including abdominal subcutaneous adipose tissue, visceral adipose tissue (VAT), thigh muscle volume, liver fat, and muscle fat infiltration (MFI), determined using magnetic resonance imaging. Associations between BCP and metabolic status were investigated using matching procedures and multivariable statistical modeling. RESULTS: Matched control analysis showed that higher VAT and MFI were associated with CHD and T2D (P < 0.001). Higher liver fat was associated with T2D (P < 0.001) and lower liver fat with CHD (P < 0.05), matching on VAT. Multivariable modeling showed that lower VAT and MFI were associated with metabolic health (P < 0.001), and liver fat was nonsignificant. Associations remained significant adjusting for sex, age, BMI, alcohol, smoking, and physical activity. CONCLUSIONS: Body composition profiling enabled an intuitive visualization of body composition and showed the complexity of associations between fat distribution and metabolic status, stressing the importance of a multivariable approach. Different diseases were linked to different BCPs, which could not be described by a single fat compartment alone.

Integrative analyses of proteomics and RNA transcriptomics implicate mitochondrial processes, protein folding pathways and GWAS loci in Parkinson disease.

BACKGROUND: Parkinson disease (PD) is a neurodegenerative disease characterized by the accumulation of alpha-synuclein (SNCA) and other proteins in aggregates termed "Lewy Bodies" within neurons. PD has both genetic and environmental risk factors, and while processes leading to aberrant protein aggregation are unknown, past work points to abnormal levels of SNCA and other proteins. Although several genome-wide studies have been performed for PD, these have focused on DNA sequence variants by genome-wide association studies (GWAS) and on RNA levels (microarray transcriptomics), while genome-wide proteomics analysis has been lacking. METHODS: This study employed two state-of-the-art technologies, three-stage Mass Spectrometry Tandem Mass Tag Proteomics (12 PD, 12 controls) and RNA-sequencing transcriptomics (29 PD, 44 controls), evaluated in the context of PD GWAS implicated loci and microarray transcriptomics (19 PD, 24 controls). The technologies applied for this study were performed in a set of overlapping prefrontal cortex (Brodmann area 9) samples obtained from PD patients and sex and age similar neurologically healthy controls. RESULTS: After appropriate filters, proteomics robustly identified 3558 unique proteins, with 283 of these (7.9 %) significantly different between PD and controls (q-value < 0.05). RNA-sequencing identified 17,580 protein-coding genes, with 1095 of these (6.2 %) significantly different (FDR p-value < 0.05); only 166 of the FDR significant protein-coding genes (0.94 %) were present among the 3558 proteins characterized. Of these 166, eight genes (4.8 %) were significant in both studies, with the same direction of effect. Functional enrichment analysis of the proteomics results strongly supports mitochondrial-related pathways, while comparable analysis of the RNA-sequencing results implicates protein folding pathways and metallothioneins. Ten of the implicated genes or proteins co-localized to GWAS loci. Evidence implicating SNCA was stronger in proteomics than in RNA-sequencing analyses. CONCLUSIONS: We report the largest analysis of proteomics in PD to date, and the first to combine this technology with RNA-sequencing to investigate GWAS implicated loci. Notably, differentially expressed protein-coding genes were more likely to not be characterized in the proteomics analysis, which lessens the ability to compare across platforms. Combining multiple genome-wide platforms offers novel insights into the pathological processes responsible for this disease by identifying pathways implicated across methodologies.

The 4p16.3 Parkinson Disease Risk Locus Is Associated with GAK Expression and Genes Involved with the Synaptic Vesicle Membrane.

Genome-wide association studies (GWAS) have identified the GAK/DGKQ/IDUA region on 4p16.3 among the top three risk loci for Parkinson's disease (PD), but the specific gene and risk mechanism are unclear. Here, we report transcripts containing the 3' clathrin-binding domain of GAK identified by RNA deep-sequencing in post-mortem human brain tissue as having increased expression in PD. Furthermore, carriers of 4p16.3 PD GWAS risk SNPs show decreased expression of one of these transcripts, GAK25 (Gencode Transcript 009), which correlates with the expression of genes functioning in the synaptic vesicle membrane. Together, these findings provide strong evidence for GAK clathrin-binding- and J-domain transcripts' influence on PD pathogenicity, and for a role for GAK in regulating synaptic function in PD.

Comparative multi-goal tradeoffs in systems engineering of microbial metabolism.

BACKGROUND: Metabolic engineering design methodology has evolved from using pathway-centric, random and empirical-based methods to using systems-wide, rational and integrated computational and experimental approaches. Persistent during these advances has been the desire to develop design strategies that address multiple simultaneous engineering goals, such as maximizing productivity, while minimizing raw material costs. RESULTS: Here, we use constraint-based modeling to systematically design multiple combinations of medium compositions and gene-deletion strains for three microorganisms (Escherichia coli, Saccharomyces cerevisiae, and Shewanella oneidensis) and six industrially important byproducts (acetate, D-lactate, hydrogen, ethanol, formate, and succinate). We evaluated over 435 million simulated conditions and 36 engineering metabolic traits, including product rates, costs, yields and purity. CONCLUSIONS: The resulting metabolic phenotypes can be classified into dominant clusters (meta-phenotypes) for each organism. These meta-phenotypes illustrate global phenotypic variation and sensitivities, trade-offs associated with multiple engineering goals, and fundamental differences in organism-specific capabilities. Given the increasing number of sequenced genomes and corresponding stoichiometric models, we envisage that the proposed strategy could be extended to address a growing range of biological questions and engineering applications.

Evaluation of Parkinson disease risk variants as expression-QTLs.

The recent Parkinson Disease GWAS Consortium meta-analysis and replication study reports association at several previously confirmed risk loci SNCA, MAPT, GAK/DGKQ, and HLA and identified a novel risk locus at RIT2. To further explore functional consequences of these associations, we investigated modification of gene expression in prefrontal cortex brain samples of pathologically confirmed PD cases (N = 26) and controls (N = 24) by 67 associated SNPs in these 5 loci. Association between the eSNPs and expression was evaluated using a 2-degrees of freedom test of both association and difference in association between cases and controls, adjusted for relevant covariates. SNPs at each of the 5 loci were tested for cis-acting effects on all probes within 250 kb of each locus. Trans-effects of the SNPs on the 39,122 probes passing all QC on the microarray were also examined. From the analysis of cis-acting SNP effects, several SNPs in the MAPT region show significant association to multiple nearby probes, including two strongly correlated probes targeting the gene LOC644246 and the duplicated genes LRRC37A and LRRC37A2, and a third uncorrelated probe targeting the gene DCAKD. Significant cis-associations were also observed between SNPs and two probes targeting genes in the HLA region on chromosome 6. Expanding the association study to examine trans effects revealed an additional 23 SNP-probe associations reaching statistical significance (p<2.8 × 10(-8)) including SNPs from the SNCA, MAPT and RIT2 regions. These findings provide additional context for the interpretation of PD associated SNPs identified in recent GWAS as well as potential insight into the mechanisms underlying the observed SNP associations.

Postmortem Interval Influences α-Synuclein Expression in Parkinson Disease Brain.

Duplications and triplications of the α-synuclein (SNCA) gene increase risk for PD, suggesting increased expression levels of the gene to be associated with increased PD risk. However, past SNCA expression studies in brain tissue report inconsistent results. We examined expression of the full-length SNCA transcript (140 amino acid protein isoform), as well as total SNCA mRNA levels in 165 frontal cortex samples (101 PD, 64 control) using quantitative real-time polymerase chain reaction. Additionally, we evaluated the relationship of eight SNPs in both 5' and 3' regions of SNCA with the gene expression levels. The association between postmortem interval (PMI) and SNCA expression was different for PD and control samples: SNCA expression decreased with increasing PMI in cases, while staying relatively constant in controls. For short PMI, SNCA expression was increased in PD relative to control samples, whereas for long PMI, SNCA expression in PD was decreased relative to control samples.

Copy number variation in familial Parkinson disease.

Copy number variants (CNVs) are known to cause Mendelian forms of Parkinson disease (PD), most notably in SNCA and PARK2. PARK2 has a recessive mode of inheritance; however, recent evidence demonstrates that a single CNV in PARK2 (but not a single missense mutation) may increase risk for PD. We recently performed a genome-wide association study for PD that excluded individuals known to have either a LRRK2 mutation or two PARK2 mutations. Data from the Illumina370Duo arrays were re-clustered using only white individuals with high quality intensity data, and CNV calls were made using two algorithms, PennCNV and QuantiSNP. After quality assessment, the final sample included 816 cases and 856 controls. Results varied between the two CNV calling algorithms for many regions, including the PARK2 locus (genome-wide p = 0.04 for PennCNV and p = 0.13 for QuantiSNP). However, there was consistent evidence with both algorithms for two novel genes, USP32 and DOCK5 (empirical, genome-wide p-values<0.001). PARK2 CNVs tended to be larger, and all instances that were molecularly tested were validated. In contrast, the CNVs in both novel loci were smaller and failed to replicate using real-time PCR, MLPA, and gel electrophoresis. The DOCK5 variation is more akin to a VNTR than a typical CNV and the association is likely caused by artifact due to DNA source. DNA for all the cases was derived from whole blood, while the DNA for all controls was derived from lymphoblast cell lines. The USP32 locus contains many SNPs with low minor allele frequency leading to a loss of heterozygosity that may have been spuriously interpreted by the CNV calling algorithms as support for a deletion. Thus, only the CNVs within the PARK2 locus could be molecularly validated and associated with PD susceptibility.

Decreased glutamic acid decarboxylase mRNA expression in prefrontal cortex in Parkinson's disease.

Parkinson's disease (PD) patients typically suffer from motor disorders but mild to severe cognitive deficits can also be present. Neuropathology of PD primarily involves loss of dopaminergic neurons in the substantia nigra, pars compacta, although more widespread pathology from the brainstem to the cerebral cortex occurs at different stages of the disease. Cognitive deficits in PD are thought to involve the cerebral cortex, and imaging studies have identified the dorsolateral prefrontal cortex (DLPFC) as a possible site for some of the symptoms. GABAergic neurons in the cerebral cortex play a key role in the modulation of pyramidal neurons and alterations in muscimol binding to GABA(A) receptors have been reported in Brodmann area 9 (BA9) of the prefrontal cortex in PD patients (Nishino et al., 1988). In order to further assess the likelihood that GABAergic activity is altered in the prefrontal cortex in PD, gene expression of the 67 kilodalton isoform of the GABA-synthesizing enzyme, glutamic acid decarboxylase (GAD67 encoded by the GAD1 gene), was examined in BA9 of post-mortem brains from 19 patients and 20 controls using isotopic in situ hybridization histochemistry. GAD67 mRNA labeling was examined and quantified on X-ray films and emulsion radioautographs. We show that GAD67 mRNA labeling is significantly lower in PD compared to control cases. Analysis of emulsion radioautographs indicates that GAD67 mRNA labeling is decreased in individual neurons and is not paralleled by a decrease in the number of GAD67 mRNA-labeled neurons. Analysis of expression data from a microarray study performed in 29 control and 33 PD samples from BA9 confirms that GAD67 expression is decreased in PD. Another finding from the microarray study is a negative relationship between GAD67 mRNA expression and age at death. Altogether, the results support the possibility that GABAergic neurotransmission is impaired in the DLPFC in PD, an effect that may be involved in some of the behavioral deficits associated with the disease.

Fosmids of novel marine Planctomycetes from the Namibian and Oregon coast upwelling systems and their cross-comparison with planctomycete genomes.

Planctomycetes are widely distributed in marine environments, where they supposedly play a role in carbon recycling. To deepen our understanding about the ecology of this sparsely studied phylum six planctomycete fosmids from two marine upwelling systems were investigated and compared with all available planctomycete genomic sequences including the as yet unpublished near-complete genomes of Blastopirellula marina DSM 3645(T) and Planctomyces maris DSM 8797(T). High numbers of sulfatase genes (41-109) were found on all marine planctomycete genomes and on two fosmids (2). Furthermore, C1 metabolism genes otherwise only known from methanogenic Archaea and methylotrophic Proteobacteria were found on two fosmids and all planctomycete genomes, except for 'Candidatus Kuenenia stuttgartiensis'. Codon usage analysis indicated high expression levels for some of these genes. In addition, novel large families of planctomycete-specific paralogs with as yet unknown functions were identified, which are notably absent from the genome of 'Candidatus Kuenenia stuttgartiensis'. The high numbers of sulfatases in marine planctomycetes characterizes them as specialists for the initial breakdown of sulfatated heteropolysaccharides and indicate their importance for recycling carbon from these compounds. The almost ubiquitous presence of C1 metabolism genes among Planctomycetes together with codon usage analysis and information from the genomes suggest a general importance of these genes for Planctomycetes other than formaldehyde detoxification. The notable absence of these genes in Candidatus K. stuttgartiensis plus the surprising lack of almost any planctomycete-specific gene within this organism reveals an unexpected distinctiveness of anammox bacteria from all other Planctomycetes.