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Acidobacteria are among the most abundant bacterial phyla found in terrestrial ecosystems, but relatively little is known about their diversity, distribution and most critically, their function. Understanding the functional activities encoded in their genomes will provide insights into their ecological roles. Here we describe the genomes of three novel cold-adapted strains of subdivision 1 Acidobacteria. The genomes consist of a circular chromosome of 6.2 Mbp for Granulicella mallensis MP5ACTX8, 4.3 Mbp for Granulicella tundricola MP5ACTX9, and 5.0 Mbp for Terriglobus saanensis SP1PR4. In addition, G. tundricola has five mega plasmids for a total genome size of 5.5 Mbp. The three genomes showed an abundance of genes assigned to metabolism and transport of carbohydrates. In comparison to three mesophilic Acidobacteria, namely Acidobacterium capsulatum ATCC 51196, ‘Candidatus Koribacter versatilis’ Ellin345, and ‘Candidatus Solibacter usitatus’ Ellin6076, the genomes of the three tundra soil strains contained an abundance of conserved genes/gene clusters encoding for modules of the carbohydrate-active enzyme (CAZyme) family. Furthermore, a large number of glycoside hydrolases and glycosyl transferases were prevalent. We infer that gene content and biochemical mechanisms encoded in the genomes of three Arctic tundra soil Acidobacteria strains are shaped to allow for breakdown, utilization, and biosynthesis of diverse structural and storage polysaccharides and resilience to fluctuating temperatures and nutrient-deficient conditions in Arctic tundra soils.

Technological advances hold the promise of rapidly catalyzing the discovery of pathogenic variants for genetic disease. However, this possibility is tempered by limitations in interpreting the functional consequences of genetic variation at candidate loci. Here, we present a systematic approach, grounded on physiologically relevant assays, to evaluate the mutational content (125 alleles) of the 14 genes associated with Bardet–Biedl syndrome (BBS). A combination of in vivo assays with subsequent in vitro validation suggests that a significant fraction of BBS-associated mutations have a dominant-negative mode of action. Moreover, we find that a subset of common alleles, previously considered to be benign, are, in fact, detrimental to protein function and can interact with strong rare alleles to modulate disease presentation. These data represent a comprehensive evaluation of genetic load in a multilocus disease. Importantly, superimposition of these results to human genetics data suggests a previously underappreciated complexity in disease architecture that might be shared among diverse clinical phenotypes.

Successful establishment and persistence of adenovirus (Ad) infections are facilitated by immunosubversive functions encoded in the early transcription unit 3 (E3). The E3/19K protein has a dual role, preventing cell surface transport of MHC class I/HLA class I (MHC-I/HLA-I) Ags and the MHC-I–like molecules (MHC-I chain-related chain A and B [MICA/B]), thereby inhibiting both recognition by CD8 T cells and NK cells. Although some crucial functional elements in E3/19K have been identified, a systematic analysis of the functional importance of individual amino acids is missing. We now have substituted alanine for each of 21 aas in the luminal domain of Ad2 E3/19K conserved among Ads and investigated the effects on HLA-I downregulation by coimmunoprecipitation, pulse-chase analysis, and/or flow cytometry. Potential structural alterations were monitored using conformation-dependent E3/19K-specific mAbs. The results revealed that only a small number of mutations abrogated HLA-I complex formation (e.g., substitutions W52, M87, and W96). Mutants M87 and W96 were particularly interesting as they exhibited only minimal structural changes suggesting that these amino acids make direct contacts with HLA-I. The considerable number of substitutions with little functional defects implied that E3/19K may have additional cellular target molecules. Indeed, when assessing MICA/B cell-surface expression we found that mutation of T14 and M82 selectively compromised MICA/B downregulation with essentially no effect on HLA-I modulation. In general, downregulation of HLA-I was more severely affected than that of MICA/B; for example, substitutions W52, M87, and W96 essentially abrogated HLA-I modulation while largely retaining the ability to sequester MICA/B. Thus, distinct conserved amino acids seem preferentially important for a particular functional activity of E3/19K.

The rapidly increasing quantity of protein sequence data continues to widen the gap between available sequences and annotations. Comparative modeling suggests some aspects of the 3D structures of approximately half of all known proteins; homology- and network-based inferences annotate some aspect of function for a similar fraction of the proteome. For most known protein sequences, however, there is detailed knowledge about neither their function nor their structure. Comprehensive efforts towards the expert curation of sequence annotations have failed to meet the demand of the rapidly increasing number of available sequences. Only the automated prediction of protein function in the absence of homology can close the gap between available sequences and annotations in the foreseeable future. This review focuses on two novel methods for automated annotation, and briefly presents an outlook on how modern web software may revolutionize the field of protein sequence annotation. First, predictions of protein binding sites and functional hotspots, and the evolution of these into the most successful type of prediction of protein function from sequence will be discussed. Second, a new tool, comprehensive in silico mutagenesis, which contributes important novel predictions of function and at the same time prepares for the onset of the next sequencing revolution, will be described. While these two new sub-fields of protein prediction represent the breakthroughs that have been achieved methodologically, it will then be argued that a different development might further change the way biomedical researchers benefit from annotations: modern web software can connect the worldwide web in any browser with the 'Deep Web' (ie, proprietary data resources). The availability of this direct connection, and the resulting access to a wealth of data, may impact drug discovery and development more than any existing method that contributes to protein annotation.

Functionally significant heterozygous mutations in the Melanocortin-4 receptor (MC4R) have been implicated in 2.5% of early onset obesity cases in European cohorts. The role of mutations in this gene in severely obese adults, particularly in smaller North American patient cohorts, has been less convincing. More recently, it has been proposed that mutations in a phylogenetically and physiologically related receptor, the Melanocortin-3 receptor (MC3R), could also be a cause of severe human obesity. The objectives of this study were to determine if mutations impairing the function of MC4R or MC3R were associated with severe obesity in North American adults. We studied MC4R and MC3R mutations detected in a total of 1821 adults (889 severely obese and 932 lean controls) from two cohorts. We systematically and comparatively evaluated the functional consequences of all mutations found in both MC4R and MC3R. The total prevalence of rare MC4R variants in severely obese North American adults was 2.25% (CI(95%): 1.44-3.47) compared with 0.64% (CI(95%): 0.26-1.43) in lean controls (P0.005). After classification of functional consequence, the prevalence of MC4R mutations with functional alterations was significantly greater when compared with controls (P0.005). In contrast, the prevalence of rare MC3R variants was not significantly increased in severely obese adults [0.67% (CI(95%): 0.27-1.50) versus 0.32% (CI(95%): 0.06-0.99)] (P = 0.332). Our results confirm that mutations in MC4R are a significant cause of severe obesity, extending this finding to North American adults. However, our data suggest that MC3R mutations are not associated with severe obesity in this population.

The decreasing cost of high-throughput sequencing is still rapidly increasing the number of known genetic variants [1,2]. For example, the size of the dbSNP database [3] has nearly doubled over the past years to 110 million human single nucleotide polymorphisms and short genomic variants. Unfortunately the characterization, annotation, and interpretation of these variants are still lagging. In particular, the interpretation of genetic variants and their implication in disease is one of the major challenges in personalized medicine [4-6]. The 4th edition of the Variant Interpretation Special Interest Group (VarI-SIG, formerly SNP-SIG) meeting [7-9] was held on July 12 at the ISMB 2014 in Boston (MA). The central meeting themes were "Annotation and prediction of structural/functional impacts of coding variants" and "Genetic variants as effectors of change: disease and evolution". The VarI-SIG is organized as a venue for the development of a research network of scientists, necessary for facilitating the exchange of ideas and establishing new collaborations. This year's meeting attracted over 100 participants, with seven research talks and five presentations from the leading scientists of the field.

Rat strains differ dramatically in their susceptibility to mammary carcinogenesis. On the assumption that susceptibility genes are conserved across mammalian species and hence inform human carcinogenesis, numerous investigators have used genetic linkage studies in rats to identify genes responsible for differential susceptibility to carcinogenesis. Using a genetic backcross between the resistant Copenhagen (Cop) and susceptible Fischer 344 (F344) strains, we mapped a novel mammary carcinoma susceptibility (Mcs30) locus to the centromeric region on chromosome 12 (LOD score of ∼8.6 at the D12Rat59 marker). The Mcs30 locus comprises approximately 12 Mbp on the long arm of rat RNO12 whose synteny is conserved on human chromosome 13q12 to 13q13. After analyzing numerous genes comprising this locus, we identified Fry, the rat ortholog of the furry gene of Drosophila melanogaster, as a candidate Mcs gene. We cloned and determined the complete nucleotide sequence of the 13 kbp Fry mRNA. Sequence analysis indicated that the Fry gene was highly conserved across evolution, with 90% similarity of the predicted amino acid sequence among eutherian mammals. Comparison of the Fry sequence in the Cop and F344 strains identified two non-synonymous single nucleotide polymorphisms (SNPs), one of which creates a putative, de novo phosphorylation site. Further analysis showed that the expression of the Fry gene is reduced in a majority of rat mammary tumors. Our results also suggested that FRY activity was reduced in human breast carcinoma cell lines as a result of reduced levels or mutation. This study is the first to identify the Fry gene as a candidate Mcs gene. Our data suggest that the SNPs within the Fry gene contribute to the genetic susceptibility of the F344 rat strain to mammary carcinogenesis. These results provide the foundation for analyzing the role of the human FRY gene in cancer susceptibility and progression.

Transforming Growth Factor-β (TGF-β) inhibits cell growth and drives of epithelial-to-mesenchymal transition, invasion and migration. Somatic mutations in the TGFBR1 and -2 genes have been found in cancer (n=9), while germline mutations are the proximal cause of the Loeys-Dietz aortic aneurysm syndrome (LDS)(n=42). Each of the 40 missense mutations associated with disease affect the protein kinase domain. The majority of mutations affect buried residues, where biochemically non-conservative substitutions may disrupt protein folding. This is in sharp contrast with disease-associated mutations in TGFBR2, all of which affect solvent accessible residues and likely alter protein-protein interactions. We performed an in silico analysis of TGFBR1 mutants using SNAP (Screening for Non-Acceptable Polymorphisms), a sequence-based method to predict the functional effects of single amino acid substitutions. First, the means of SNAP scores for all possible non-conservative substitutions at each residue were used to predict functionally important domains of the wild type protein. All of the disease-associated TGFBR1 mutants was located within such domain. Secondly, 35 (92%) of the 38 disease-associated TGFBR1 missense mutations were correctly classified as non-neutral. In order to validate these predictions experimentally, we have constructed a library of HA-tagged mutants in a pcDNA3 backbone. Thus far, six of these (T200I, K223E, A230T, K232E, N267H, and S387Y) have been characterized in detail. The known kinase inactive K232R mutant was included as negative control. To determine the effect of these mutations on TGF-β-regulated gene transcription, we carried out transient transfection assays using the Smad-responsive SBE4-Luc reporter in both wild type (Mv1Lu) and TGFBR1- deficient (R1B) mink lung epithelial cells. In R1B cells, the T200I, K232E, and K232R mutants were inactive, while A230T, K223E, N267H, and S387Y restored TGF-β-induced SBE4-Luc responses. Interestingly, the K223E mutation constitutively activates SBE4-Luc signaling in the absence of TGF-β, while the response of the S387Y mutant to TGF-β is attenuated. As expected, T200I, K232E and K232R were unable to phosphorylate Smads 2 and -3, while S387Y, K223E, and A230T had retained their kinase activity. Interestingly, the ability of the N267H mutant to activate pSmad2 is attenuated compared to wild type TGFBR1. Additionally, A230T is able to induce pSmad2 in the presence of the kinase inhibitor, SD-093, indicating that this mutant kinase is constitutively active. In conclusion, disease-associated TGFBR1 gene mutants display considerable phenotypic diversity, ranging from complete loss of function to constitutive activation. These findings have important implications for the selection of patients with cancer or LDS for treatment with TGFBR1 selective kinase inhibitors. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 2118. doi:10.1158/1538-7445.AM2011-2118

The discrimination between functionally neutral amino acid substitutions and non-neutral mutations, affecting protein function, is very important for our understanding of diseases. The rapidly growing amounts of experimental data enable the development of computational tools to facilitate the annotation of these substitutions. Here, we describe a Random Forests-based classifier, named Mutation Detector (MuD) that utilizes structural and sequence-derived features to assess the impact of a given substitution on the protein function. In its automatic mode, MuD is comparable to alternative tools in performance. However, the uniqueness of MuD is that user-reported protein-specific structural and functional information can be added at run-time, thereby enhancing the prediction accuracy further. The MuD server, available at http://mud.tau.ac.il, assigns a reliability score to every prediction, thus offering a useful tool for the prioritization of substitutions in proteins with an available 3D structure.

In 404 Lepob/ob F2 progeny of a C57BL/6J (B6) x DBA/2J (DBA) intercross, we mapped a DBA-related quantitative trait locus (QTL) to distal Chr1 at 169.6 Mb, centered about D1Mit110, for diabetes-related phenotypes that included blood glucose, HbA1c, and pancreatic islet histology. The interval was refined to 1.8 Mb in a series of B6.DBA congenic/subcongenic lines also segregating for Lepob. The phenotypes of B6.DBA congenic mice include reduced β-cell replication rates accompanied by reduced β-cell mass, reduced insulin/glucose ratio in blood, reduced glucose tolerance, and persistent mild hypoinsulinemic hyperglycemia. Nucleotide sequence and expression analysis of 14 genes in this interval identified a predicted gene that we have designated “Lisch-like” (Ll) as the most likely candidate. The gene spans 62.7 kb on Chr1qH2.3, encoding a 10-exon, 646–amino acid polypeptide, homologous to Lsr on Chr7qB1 and to Ildr1 on Chr16qB3. The largest isoform of Ll is predicted to be a transmembrane molecule with an immunoglobulin-like extracellular domain and a serine/threonine-rich intracellular domain that contains a 14-3-3 binding domain. Morpholino knockdown of the zebrafish paralog of Ll resulted in a generalized delay in endodermal development in the gut region and dispersion of insulin-positive cells. Mice segregating for an ENU-induced null allele of Ll have phenotypes comparable to the B.D congenic lines. The human ortholog, C1orf32, is in the middle of a 30-Mb region of Chr1q23-25 that has been repeatedly associated with type 2 diabetes., Author Summary Type 2 diabetes (T2D) accounts for over 90% of instances of diabetes and is a leading cause of medical morbidity and mortality. Twin studies indicate a strong polygenic contribution to susceptibility within the context of obesity. Although approximately ten genes making important contributions to individual risk have been identified, it is clear that others remain to be identified. In this study, we intercrossed obese, diabetes-resistant and diabetes-prone mouse strains to implicate a genetic interval on mouse Chr1 associated with reduced β-cell numbers and elevated blood glucose. We narrowed the region using molecular genetics and computational approaches to identify a novel gene we designated “Lisch-like” (Ll). The orthologous human genetic interval has been repeatedly implicated in T2D. Mice with an induced mutation that reduces Ll expression are impaired in both β-cell development and glucose metabolism, and reduced expression of the homologous gene in zebrafish disrupts islet development. Ll is expressed in organs implicated in the pathophysiology of T2D (hypothalamus, islets, liver, and skeletal muscle) and is predicted to encode a transmembrane protein that could mediate cholesterol transport and/or convey signals related to cell division. Either mechanism could mediate effects on β-cell mass that would predispose to T2D.

The growing availability of high-throughput sequencing continues to increase the number of identified genetic variants [1, 2]. For example, the size of the dbSNP database [3] has grown exponentially over the past years to ~150 million human single nucleotide polymorphisms and short genomic variants. Unfortunately, the characterization, annotation, and interpretation of these variants are still lagging. In particular, their implication in disease is one of the major challenges in personalized medicine [4–8]. The 5th edition of the Variant Interpretation Special Interest Group (VarI-SIG, formerly SNP-SIG) meeting [9–12] was held on July 11th, 2015 at the joint ISMB/ECCB meeting in Dublin, Ireland. The central VarI-SIG themes were “Annotation and prediction of structural/functional impacts of coding variants” and “Genetic variants as effectors of change: disease and evolution”. Our meeting is organized as a venue for the development of a research network of scientists, necessary for facilitating the exchange of ideas and establishing new collaborations. This year’s meeting attracted over 60 participants, with eight research talks and five presentations from the leading scientists in the field.

Background Mutations resulting in the disruption of protein function are the underlying causes of many genetic diseases. Some mutations affect the number of expressed proteins while others alter the activity on a per-molecule basis. Single amino acid substitutions as caused by non-synonymous Single Nucleotide Polymorphisms (nsSNPs) often disrupt function by altering protein structure and/or stability, but can also wreak havoc by directly impacting functional binding sites. Given the experimental three-dimensional (3D) structure of a protein, we can try to differentiate between the "effect on structure/stability" and the "effect on binding". However, experimental 3D structures are available for only 1% of all known proteins; the magnitude of stability change caused by a given mutation is more widely available. Results Here, we analyze to which extent the functional effect of a mutation can be predicted from the effect on protein stability. We find that simple sequence-based methods succeed in predicting functional effects of nsSNPs. In fact, such methods consistently outperform approaches that predict functional change through the application of binary thresholds to stability change. We also observed that if stability is affected, functional change is easier to predict than when stability is not affected. Conclusion Our results confirmed that stability change is somehow related to function change. However, we also show that the knowledge of stability changes in no way suffices to predict functional changes and that many function changing mutations have no effect on stability.

Background Non-synonymous single nucleotide polymorphisms (nsSNPs) alter the protein sequence and can cause disease. The impact has been described by reliable experiments for relatively few mutations. Here, we study predictions for functional impact of disease-annotated mutations from OMIM, PMD and Swiss-Prot and of variants not linked to disease. Results Most disease-causing mutations were predicted to impact protein function. More surprisingly, the raw predictions scores for disease-causing mutations were higher than the scores for the function-altering data set originally used for developing the prediction method (here SNAP). We might expect that diseases are caused by change-of-function mutations. However, it is surprising how well prediction methods developed for different purposes identify this link. Conversely, our predictions suggest that the set of nsSNPs not currently linked to diseases contains very few strong disease associations to be discovered. Conclusions Firstly, annotations of disease-causing nsSNPs are on average so reliable that they can be used as proxies for functional impact. Secondly, disease-causing nsSNPs can be identified very well by methods that predict the impact of mutations on protein function. This implies that the existing prediction methods provide a very good means of choosing a set of suspect SNPs relevant for disease.