ALBERT

Description: Motivation Accurate automatic annotation of protein function relies on both innovative models and robust data sets. Due to their importance in biological processes, the identification of DNA-binding proteins directly from protein sequence has been the focus of many studies. However, the data sets used to train and evaluate these methods have suffered from substantial flaws. We describe some of the weaknesses of the data sets used in previous DNA-binding protein literature and provide several new data sets addressing these problems. We suggest new evaluative benchmark tasks that more realistically assess real-world performance for protein annotation models. We propose a simple new model for the prediction of DNA-binding proteins and compare its performance on the improved data sets to two previously published models. Additionally, we provide extensive tests showing how the best models predict across taxonomies. Results Our new gradient boosting model, which uses features derived from a published protein language model, outperforms the earlier models. Perhaps surprisingly, so does a baseline nearest neighbor model using BLAST percent identity. We evaluate the sensitivity of these models to perturbations of DNA-binding regions and control regions of protein sequences. The successful data-driven models learn to focus on DNA-binding regions. When predicting across taxonomies, the best models are highly accurate across species in the same kingdom and can provide some information when predicting across kingdoms. Code and Data Availability The data and results for this paper can be found at https://doi.org/10.5281/zenodo.5153906. The code for this paper can be found at https://doi.org/10.5281/zenodo.5153683. The code, data and results can also be found at https://github.com/AZaitzeff/tools_for_dna_binding_proteins.

Description: Motivation The investigation of quantitative trait loci (QTL) is an essential component in our understanding of how organisms vary phenotypically. However, many important crop species are polyploid (carrying more than two copies of each chromosome), requiring specialized tools for such analyses. Moreover, deciphering meiotic processes at higher ploidy levels is not straightforward, but is necessary to understand the reproductive dynamics of these species, or uncover potential barriers to their genetic improvement. Results Here, we present polyqtlR, a novel software tool to facilitate such analyses in (auto)polyploid crops. It performs QTL interval mapping in F1 populations of outcrossing polyploids of any ploidy level using identity-by-descent probabilities. The allelic composition of discovered QTL can be explored, enabling favourable alleles to be identified and tracked in the population. Visualization tools within the package facilitate this process, and options to include genetic co-factors and experimental factors are included. Detailed information on polyploid meiosis including prediction of multivalent pairing structures, detection of preferential chromosomal pairing and location of double reduction events can be performed. Availabilityand implementation polyqtlR is freely available from the Comprehensive R Archive Network (CRAN) at http://cran.r-project.org/package=polyqtlR. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Glioblastoma (GB) is the most aggressive and common form of primary brain tumor characterized by fast proliferation, high invasion and resistance to current standard treatment. The average survival rate post-diagnosis is 14.6 months, despite the aggressive standard post-surgery radiotherapy concomitant with chemotherapy with temozolomide (TMZ). Currently, efforts are being endowed to develop new and more efficient therapeutic approaches capable to overcome chemoresistance, inhibit tumor progression and improve overall patient survival rate. Abnormal microRNA (miRNA) expression has been correlated with chemoresistance, proliferation and resistance to apoptosis, which result from their master regulatory role of gene expression. Altered cell metabolism, favoring glycolysis, was identified as an emerging cancer hallmark and has been described in GB, thus offering a new target for innovative GB therapies. In this work, we hypothesized that a gene therapy-based strategy consisting of the overexpression of a miRNA downregulated in GB and predicted to target crucial metabolic enzymes might promote a shift of GB cell metabolism, decreasing the glycolytic dependence of tumor cells and contributing to their sensitization to chemotherapy with TMZ. The increase of miR-200c levels in DBTRG cells resulted in downregulation of messenger RNA of enzymes involved in bioenergetics pathways and impaired cell metabolism and mobility. In addition, miR-200c overexpression prior to DBTRG cell exposure to TMZ resulted in cell cycle arrest. Overall, our results show that miR-200c overexpression could offer a way to overcome chemoresistance developed by GB cells in response to current standard chemotherapy, providing an improvement to current GB standard treatment, with benefit for patient outcome.

Description: Motivation The investigation of the structure of biological systems at the molecular level gives insights about their functions and dynamics. Shape and surface of biomolecules are fundamental to molecular recognition events. Characterizing their geometry can lead to more adequate predictions of their interactions. In the present work, we assess the performance of reference shape retrieval methods from the computer vision community on protein shapes. Results Shape retrieval methods are efficient in identifying orthologous proteins and tracking large conformational changes. This work illustrates the interest for the protein surface shape as a higher-level representation of the protein structure that (i) abstracts the underlying protein sequence, structure or fold, (ii) allows the use of shape retrieval methods to screen large databases of protein structures to identify surficial homologs and possible interacting partners and (iii) opens an extension of the protein structure–function paradigm toward a protein structure-surface(s)-function paradigm. Availabilityand implementation All data are available online at http://datasetmachat.drugdesign.fr. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Contactin 4 (CNTN4) is a crucial synaptic adhesion protein that belongs to the contactin superfamily. Evidence from both human genetics and mouse models suggests that synapse formation and structural deficits strongly correlate with neurodevelopmental disorders, including autism. In addition, several lines of evidence suggest that CNTN4 is associated with the risk of autism. However, the biological functions of CNTN4 in neural development and disease pathogenesis are poorly understood. In this study, we investigated whether and how CNTN4 is autonomously involved in the development of dendrites and dendritic spines in cortical neurons. Disruption of Cntn4 decreased the number of excitatory synapses, which led to a reduction in neural activity. Truncated proteins lacking the signal peptide, FnIII domains, or GPI domain lacked the ability to regulate dendritic spine formation, indicating that CNTN4 regulates dendritic spine density through a mechanism dependent on FnIII domains. Importantly, we revealed that autism-related variants lacked the ability to regulate spine density and neural activity. In conclusion, our study suggests that CNTN4 is essential for promoting dendrite growth and dendritic spine formation and that disruptive variants of CNTN4 interfere with abnormal synapse formation and may increase the risk of autism.

Description: Numerous genome-wide association studies (GWASs) have been conducted for the identification of genetic variants involved with human height. The vast majority of these studies, however, have been conducted in populations of European ancestry. Here, we report the first GWAS of adult height in the Taiwan Biobank using a discovery sample of 14 571 individuals and an independent replication sample of 20 506 individuals. From our analysis, we generalize to the Taiwanese population genome-wide significant associations with height and 18 previously identified genes in European and non-Taiwanese East Asian populations. We also identify and replicate, at the genome-wide significance level, associated variants for height in four novel genes at two loci that have not previously been reported: RASA2 on chromosome 3 and NABP2, RNF41 and SLC39A5 at 12q13.3 on chromosome 12. RASA2 and RNF41 are strong candidates for having a role in height with copy number and loss of function variants in RASA2 previously found to be associated with short stature disorders, and decreased expression of the RNF41 gene resulting in insulin resistance in skeletal muscle. The results from our analysis of the Taiwan Biobank underscore the potential for the identification of novel genetic discoveries in underrepresented worldwide populations, even for traits, such as height, that have been extensively investigated in large-scale studies of European ancestry populations.

Description: Motivation The mathematically optimal solution in computational protein folding simulations does not always correspond to the native structure, due to the imperfection of the energy force fields. There is therefore a need to search for more diverse suboptimal solutions in order to identify the states close to the native. We propose a novel multimodal optimization protocol to improve the conformation sampling efficiency and modeling accuracy of de novo protein structure folding simulations. Results A distance-assisted multimodal optimization sampling algorithm, MMpred, is proposed for de novo protein structure prediction. The protocol consists of three stages: The first is a modal exploration stage, in which a structural similarity evaluation model DMscore is designed to control the diversity of conformations, generating a population of diverse structures in different low-energy basins. The second is a modal maintaining stage, where an adaptive clustering algorithm MNDcluster is proposed to divide the populations and merge the modal by adjusting the annealing temperature to locate the promising basins. In the last stage of modal exploitation, a greedy search strategy is used to accelerate the convergence of the modal. Distance constraint information is used to construct the conformation scoring model to guide sampling. MMpred is tested on a large set of 320 non-redundant proteins, where MMpred obtains models with TM-score≥0.5 on 291 cases, which is 28% higher than that of Rosetta guided with the same set of distance constraints. In addition, on 320 benchmark proteins, the enhanced version of MMpred (E-MMpred) has 167 targets better than trRosetta when the best of five models are evaluated. The average TM-score of the best model of E-MMpred is 0.732, which is comparable to trRosetta (0.730). Availability and implementation The source code and executable are freely available at https://github.com/iobio-zjut/MMpred. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Asthenoteratospermia is a common cause of male infertility. Recent studies have revealed that CFAP65 mutations lead to severe asthenoteratospermia due to acrosome hypoplasia and flagellum malformations. However, the molecular mechanism underlying CFAP65-associated sperm malformation is largely unclear. Here, we initially examined the role of CFAP65 during spermiogenesis using Cfap65 knockout (Cfap65−/−) mice. The results showed that Cfap65−/− male mice exhibited severe asthenoteratospermia characterized by morphologically defective sperm heads and flagella. In Cfap65−/− mouse testes, hyper-constricted sperm heads were apparent in step 9 spermatids accompanied by abnormal manchette development, and acrosome biogenesis was abnormal in the maturation phase. Moreover, subsequent flagellar elongation was also severely affected and characterized by disrupted assembly of the mitochondrial sheath (MS) in Cfap65−/− male mice. Furthermore, the proteomic analysis revealed that the proteostatic system during acrosome formation, manchette organization and MS assembly was disrupted when CFAP65 was lost. Importantly, endogenous immunoprecipitation and immunostaining experiments revealed that CFAP65 may form a cytoplasmic protein network comprising MNS1, RSPH1, TPPP2, ZPBP1 and SPACA1. Overall, these findings provide insights into the complex molecular mechanisms of spermiogenesis by uncovering the essential roles of CFAP65 during sperm head shaping, acrosome biogenesis and MS assembly.

Description: Mitochondrial DNA (mtDNA) disorders are recognized as one of the most common causes of inherited metabolic disorders. The mitochondrial genome occurs in multiple copies resulting in both homoplasmic and heteroplasmic pathogenic mtDNA variants. A biochemical defect arises when the pathogenic variant level reaches a threshold, which differs between variants. Moreover, variants can segregate, clonally expand, or be lost from cellular populations resulting in a dynamic and tissue-specific mosaic pattern of oxidative deficiency. MtDNA is maternally inherited but transmission patterns of heteroplasmic pathogenic variants are complex. During oogenesis, a mitochondrial bottleneck results in offspring with widely differing variant levels to their mother, whilst highly deleterious variants, such as deletions, are not transmitted. Complemented by a complex interplay between mitochondrial and nuclear genomes, these peculiar genetics produce marked phenotypic variation, posing challenges to the diagnosis and clinical management of patients. Novel therapeutic compounds and several genetic therapies are currently under investigation, but proven disease-modifying therapies remain elusive. Women who carry pathogenic mtDNA variants require bespoke genetic counselling to determine their reproductive options. Recent advances in in vitro fertilization techniques, have greatly improved reproductive choices, but are not without their challenges. Since the first pathogenic mtDNA variants were identified over 30 years ago, there has been remarkable progress in our understanding of these diseases. However, many questions remain unanswered and future studies are required to investigate the mechanisms of disease progression and to identify new disease-specific therapeutic targets.

Description: Chagas disease is an infection caused by the parasite Trypanosoma cruzi, endemic in Latino America. Leveraging the three-way admixture between Native American (AMR), European (EUR) and African (AFR) populations in Latin Americans, we aimed to better understand the genetic basis of Chagas disease by performing an admixture mapping study in a Colombian population. A two-stage study was conducted, and subjects were classified as seropositive and seronegative for T. cruzi. In stage 1, global and local ancestries were estimated using reference data from the 1000 Genomes Project (1KGP), and local ancestry associations were performed by logistic regression models. The AMR ancestry showed a protective association with Chagas disease within the major histocompatibility complex region [Odds ratio (OR) = 0.74, 95% confidence interval (CI) = 0.66–0.83, lowest P-value = 4.53 × 10−8]. The fine mapping assessment on imputed genotypes combining data from stage 1 and 2 from an independent Colombian cohort, revealed nominally associated variants in high linkage disequilibrium with the top signal (rs2032134, OR = 0.93, 95% CI = 0.90–0.97, P-value = 3.54 × 10−4) in the previously associated locus. To assess ancestry-specific adaptive signals, a selective sweep scan in an AMR reference population from 1KGP together with an in silico functional analysis highlighted the Tripartite Motif family and the human leukocyte antigen genes, with crucial role in the immune response against pathogens. Furthermore, these analyses emphasized the macrophages, neutrophils and eosinophils, as key players in the defense against T. cruzi. This first admixture mapping study in Chagas disease provided novel insights underlying the host immune response in the pathogenesis of this neglected disease.

Description: Motivation Co-evolution analysis can be used to accurately predict residue–residue contacts from multiple sequence alignments. The introduction of machine-learning techniques has enabled substantial improvements in precision and a shift from predicting binary contacts to predict distances between pairs of residues. These developments have significantly improved the accuracy of de novo prediction of static protein structures. With AlphaFold2 lifting the accuracy of some predicted protein models close to experimental levels, structure prediction research will move on to other challenges. One of those areas is the prediction of more than one conformation of a protein. Here, we examine the potential of residue–residue distance predictions to be informative of protein flexibility rather than simply static structure. Results We used DMPfold to predict distance distributions for every residue pair in a set of proteins that showed both rigid and flexible behaviour. Residue pairs that were in contact in at least one reference structure were classified as rigid, flexible or neither. The predicted distance distribution of each residue pair was analysed for local maxima of probability indicating the most likely distance or distances between a pair of residues. We found that rigid residue pairs tended to have only a single local maximum in their predicted distance distributions while flexible residue pairs more often had multiple local maxima. These results suggest that the shape of predicted distance distributions contains information on the rigidity or flexibility of a protein and its constituent residues. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Antimicrobial peptides harboring S- and or O-linked glycans are known as glycocins. Glycocins were first discovered and best characterized in Firmicutes. S-glycosylation is an enzymatic process catalyzed by S-glycosyltransferases of the GT2 family. Using a heterologous expression system, here we describe an inverting S/O-HexNAc-transferase (SvGT), encoded by ORF AQF52_3101 of S. venezuelae ATCC 15439, along with its acceptor substrate (SvC), encoded by ORF AQF52_3099. Using in vitro and in vivo assays, we define the distinct donor specificity, acceptor specificity, regioselectivity, chemoselectivity, and Y(G/A/K/Q/E ≠ ΔG)(C/S/T ≠ Y/N)(G/A ≠ P/Q)G as the minimum acceptor sequon of SvGT. Although UDP-GlcNAc served as the donor in the cellular milieu, SvGT could also utilize UDP-Glc and UDP-GalNAc as donors in vitro. Using mass spectrometry and western blotting, we provide evidence that an anti-O-GlcNAc antibody (CTD110.6) cross-reacts with S-GlcNAc and may be used to detect S-GlcNAcylated glycoconjugates directly. With an understanding of enzyme specificities, we finally employed SvGT to generate two proof-of-concept neoglycocins against L. monocytogenes. In conclusion, this study provides the first experimental evidence for S-glycosylation in Actinobacteria and the application of its S/O-HexNAc-transferase in glycocin engineering.

Description: Truncated O-GalNAc glycosylation is an important feature of pancreatic ductal adenocarcinomas (PDAC) and expression of truncated O-GalNAc glycans is strongly associated with decreased survival and poor prognosis. It has been proven, that aberrant O-GalNAc glycosylation influence PDAC signaling to promote oncogenic properties, but elucidation of the influence of truncated O-GalNAc glycosylation on different signaling molecules has just been started. We herein elucidated the impact of aberrant O-GalNAc glycosylation on two important PDAC signaling pathways, namely AKT/mTOR and RAS/MAPK. In PDAC cells expressing truncated O-GalNAc glycans, we identified differentially expressed proteins associated with AKT/mTOR and RAS/MAPK pathways using quantitative proteomics. Since AKT, a key-signaling molecule in PDAC, was among the identified proteins, we analyzed AKT and found a strikingly enhanced S473 phosphorylation and identified a previously unknown O-GalNAc-modification. Consecutive analysis of COSMC knockdowns in PDAC revealed strong effects on AKT upstream and downstream effector molecules. Interestingly, truncated O-GalNAc glycans could facilitate an mTORC1 inhibitor resistance using AZD8055. In addition, as AKT/mTOR pathway has extensive cross talks with RAS/MAPK pathway we analyzed the pathways and found it negatively regulated. Finally, we found that the expression of epithelial-mesenchymal-transition markers, key features of aggressive PDACs cells, are enhanced and truncated O-GalNAc glycans enhance pancreatic cancer cell growth in a xenograft mouse model. Our study demonstrates that truncated O-GalNAc glycans have a strong impact on AKT/mTOR and RAS/MAPK signaling pathways, are modulated by EGF or IGF-1 signaling and should be considered for targeted therapy of these pathways in PDAC.

Description: Congenital disorders of glycosylation (CDG) include 150 disorders constituting in genetically and clinically heterogeneous diseases, showing significant glycoprotein hypoglycosylation that leads to pathological consequences on multiple organs and systems which underlying mechanisms are not yet understood. A few cellular and animal models have been used to study specific CDG characteristics although they have given limited information due to the few CDG mutations tested and the still missing comprehensive molecular and cellular basic research. Here we provide specific gene expression profiles, based on RNA microarray analysis, together with some biochemical and cellular characteristics of a total of 9 control EBV-transformed lymphoblastoid B cell lines (B-LCL) and 13 CDG B-LCL from patients carrying severe mutations in the PMM2 gene, strong serum protein hypoglycosylation and neurological symptoms. Significantly dysregulated genes in PMM2-CDG cells included those regulating stress responses, transcription factors, glycosylation, motility, cell junction and, importantly, those related to development and neuronal differentiation and synapse such as CA2 and ADAM23. PMM2-CDG associated biological consequences involved the unfolded protein response, RNA metabolism and the endoplasmic reticulum, Golgi apparatus and mitochondria components. Changes in transcriptional and CA2 protein levels are consistent with CDG physiopathology. These results demonstrate the global transcriptional impact in phosphomannomutase 2 deficient cells, reveal CA2 as a potential cellular biomarker and confirm B-LCL as an advantageous model for CDG studies.

Description: Motivation The well-known fact that protein structures are more conserved than their sequences forms the basis of several areas of computational structural biology. Methods based on the structure analysis provide more complete information on residue conservation in evolutionary processes. This is crucial for the determination of evolutionary relationships between proteins and for the identification of recurrent structural patterns present in biomolecules involved in similar functions. However, algorithmic structural alignment is much more difficult than multiple sequence alignment. This study is devoted to the development and applications of DAMA—a novel effective environment capable to compute and analyze multiple structure alignments. Results DAMA is based on local structural similarities, using local 3D structure descriptors and thus accounts for nearest-neighbor molecular environments of aligned residues. It is constrained neither by protein topology nor by its global structure. DAMA is an extension of our previous study (DEDAL) which demonstrated the applicability of local descriptors to pairwise alignment problems. Since the multiple alignment problem is NP-complete, an effective heuristic approach has been developed without imposing any artificial constraints. The alignment algorithm searches for the largest, consistent ensemble of similar descriptors. The new method is capable to capture most of the biologically significant similarities present in canonical test sets and is discriminatory enough to prevent the emergence of larger, but meaningless, solutions. Tests performed on the test sets, including protein kinases, demonstrate DAMA’s capability of identifying equivalent residues, which should be very useful in discovering the biological nature of proteins similarity. Performance profiles show the advantage of DAMA over other methods, in particular when using a strict similarity measure QC, which is the ratio of correctly aligned columns, and when applying the methods to more difficult cases. Availability and implementation DAMA is available online at http://dworkowa.imdik.pan.pl/EP/DAMA. Linux binaries of the software are available upon request. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Sperm fibrous sheath is closely related to sperm maturation, capacitation and motility, and A-kinase anchor protein 4 (AKAP4) is the most abundant protein in sperm fibrous sheath. Previous studies found incomplete sperm fibrous sheaths and abnormal flagella in Akap4 knockout (KO) mice. Meanwhile, it was reported that the partial deletion in AKAP4 is highly relevant to the dysplasia of the fibrous sheath in an infertile man, and so far, there is no report about male infertility caused by hemizygous AKAP4 variant. Furthermore, the specific mechanisms of how the variant is relevant to the phenotype remain elusive. In this study, we investigated three multiple morphological abnormalities of the sperm flagella (MMAF)-affected men from three independent families (including one consanguine family) carried hemizygous c.C1285T variant in AKAP4. The patients carried thisvariant showed dysplastic sperm fibrous sheath and the protein expression of AKAP4 was decreased in flagella which was further confirmed in HEK-293 T cells in vitro. In addition, the co-localization and interaction between AKAP4 and glutamine-rich protein 2 (QRICH2) on the molecular level were identified by immunofluorescence and Co-immunoprecipitation (CO-IP). The hemizygous c.1285C 〉 T variant in AKAP4 induced decreased protein expression of QRICH2 in spermatozoa. These results suggested that the normal expression of AKAP4 is required for maintaining the expression of QRICH2 and the decreased protein expression of AKAP4 and QRICH2，as well as the interaction between them induced by the hemizygous variant of AKAP4 caused dysplastic fibrous sheath, which eventually led to reduced sperm motility and male infertility.

Description: Sulfomucins are in some body-locations and species a normal occurrence, whereas in other situations a sign of pathology. Sulfomucin content on histological sections and isolated material is frequently analyzed with Alcian blue staining at pH 1.0. However, since the stain detects the charge, a high density of other charged molecules, such as sialic acids has potential to impede specificity. Here, we compared the outcome from four staining protocols with the level of sulfation determined by liquid chromatography–tandem mass spectrometry analysis (MS) on samples from various tissues with variable sulfation and sialylation levels. We found that a protocol we designed, including rinsing with MetOH and 0.5 M NaCl buffer at pH 1.0 eliminates false positive staining of tissues outperforming commonly recommended solutions. In tissues with low to moderately sulfated mucins (e.g. human stomach and salmonid epithelia) this method enables accurate relative quantification (e.g. sulfate scoring comparisons between healthy and diseased tissues) whereas the range of the method is not suitable for comparisons between tissues with high sulfomucin content (e.g. pig stomach and colon).

Description: Nephronophthisis (NPH) is the most prevalent monogenetic disorder leading to end-stage renal failure (ESRD) in childhood. Mutations in Nphp1, encoding a cilia-localized protein, account for the majority of NPH cases. Despite its identification many years ago, Nphp1 deletions targeting exon 4 or exon 20 have not reproduced the histological features of human NPH in murine models. In this study, we deleted exon 2–20 of Nphp1 by CRISPR/Cas9 gene editing to create a near-total knockout (KO) mouse model (Nphp1del2–20/del2–20). Nphp1del2–20/del2–20 mice faithfully reproduced the renal and extrarenal phenotypes associated with human NPH, including renal cyst development, tubular basement membrane thickening, retinal degeneration and abnormal spermatogenesis. Importantly, Nphp1 re-expression using an adenoviral-associated-virus-9 (AAV9) vector could partially rescue both renal and retinal phenotypes in Nphp1del2–20/del2–20 mice. Our results reported the first relevant Nphp1 mouse model with renal phenotypes for human disease. It will be a valuable model for future studies of Nphp1 function and to develop novel treatments for this common childhood disease.

Description: Tick-borne illnesses pose a serious concern to human and veterinary health and their prevalence is on the rise. The interactions between ticks and the pathogens they carry are largely undefined. However, the genus Anaplasma, a group of tick-borne bacteria, has been instrumental in uncovering novel paradigms in tick biology. The emergence of sophisticated technologies and the convergence of entomology with microbiology, immunology, metabolism, and systems biology has brought tick-Anaplasma interactions to the forefront of vector biology with broader implications for the infectious disease community. Here, we discuss the use of Anaplasma as an instrument for the elucidation of novel principles in arthropod-microbe interactions. We offer an outlook of the primary areas of study, outstanding questions, and future research directions.

Description: Motivation Assigning new sequences to known protein families and subfamilies is a prerequisite for many functional, comparative and evolutionary genomics analyses. Such assignment is commonly achieved by looking for the closest sequence in a reference database, using a method such as BLAST. However, ignoring the gene phylogeny can be misleading because a query sequence does not necessarily belong to the same subfamily as its closest sequence. For example, a hemoglobin which branched out prior to the hemoglobin alpha/beta duplication could be closest to a hemoglobin alpha or beta sequence, whereas it is neither. To overcome this problem, phylogeny-driven tools have emerged but rely on gene trees, whose inference is computationally expensive. Results Here, we first show that in multiple animal and plant datasets, 18 to 62% of assignments by closest sequence are misassigned, typically to an over-specific subfamily. Then, we introduce OMAmer, a novel alignment-free protein subfamily assignment method, which limits over-specific subfamily assignments and is suited to phylogenomic databases with thousands of genomes. OMAmer is based on an innovative method using evolutionarily-informed k-mers for alignment-free mapping to ancestral protein subfamilies. Whilst able to reject non-homologous family-level assignments, we show that OMAmer provides better and quicker subfamily-level assignments than approaches relying on the closest sequence, whether inferred exactly by Smith-Waterman or by the fast heuristic DIAMOND. Availability OMAmer is available from the Python Package Index (as omamer), with the source code and a precomputed database available at https://github.com/DessimozLab/omamer. Supplementary information Supplementary data are available at Bioinformatics online.

Description: The ten-eleven translocation (Tet) family of dioxygenases convert 5-methylcytosine to 5-hydroxymethylcytosine (5hmC). Previous studies have shown that 5hmC-mediated epigenetic modifications play essential roles in diverse biological processes and diseases. Here we show that Tet proteins and 5hmC display dynamic features during postnatal cardiac development and that Tet2 is the predominant dioxygenase present in heart. Tet2 knockout (KO) results in abnormal cardiac function, progressive cardiac hypertrophy and fibrosis. Mechanistically, Tet2 deficiency leads to reduced hydroxymethylation in the cardiac genome and alters the cardiac transcriptome. Mechanistically, Tet2 loss leads to a decrease of Hspa1b expression, a regulator of the Erk signaling pathway, which leads to over-activation of Erk signaling. Acute Hspa1b knock down increased the phosphorylation of Erk and induced hypertrophy of cardiomyocytes, which could be blocked by Erk signaling inhibitor. Consistently, ectopic expression of Hspal1b was able to rescue the deficits of cardiomyocytes induced by Tet2 depletion. Taken together, our study’s results reveal the important roles of Tet2-mediated DNA hydroxymethylation in cardiac development and function.

Description: P2RX2 encodes the P2X2 receptor, which is an adenosine triphosphate (ATP) gated (purinoreceptor) ion channel. P2RX2 c. 178G 〉 T (p.V60L) mutation was previously identified in two unrelated Chinese families, as the cause of human DFNA41, a form of dominant, early-onset and progressive sensorineural hearing loss. We generated and characterized a knock-in mouse model based on human p.V60L mutation that recapitulate the human phenotype. Heterozygous KI mice started to exhibit hearing loss at 21-day-old and progressed to deafness by 6-month-old. Vestibular dysfunction was also observed in mutant mice. Abnormal morphology of the inner hair cells and ribbon synapses was progressively observed in KI animals suggesting that P2rx2 plays a role in the membrane spatial location of the ribbon synapses. These results suggest that P2rx2 is essential for acoustic information transfer, which can be the molecular mechanism related to hearing loss.

Description: Summary VCF files with results of sequencing projects take a lot of space. We propose the VCFShark, which is able to compress VCF files up to an order of magnitude better than the de facto standards (gzipped VCF and BCF). The advantage over competitors is the greatest when compressing VCF files containing large amounts of genotype data. The processing speeds up to 100 MB/s and main memory requirements lower than 30 GB allow to use our tool at typical workstations even for large datasets. Availability and Implementation https://github.com/refresh-bio/vcfshark Supplementary information Supplementary data are available at publisher’s Web site.

Description: Age-related macular degeneration (AMD) is a complex neurodegenerative eye disease with behavioral and genetic etiology, and is the leading cause of irreversible vision loss among elderly Caucasians. Functionally significant genetic variants in the alternative pathway of complement have been strongly linked to disease. More recently, a rare variant in the terminal pathway of complement has been associated with increased risk, Complement component 9 (C9) P167S. To assess the functional consequence of this variant, C9 levels were measured in two independent cohorts of AMD patients. In both cohorts it was demonstrated that the P167S variant was associated with low C9 plasma levels. Further analysis showed that patients with advanced AMD had elevated sC5b-9 compared to those with non-advanced AMD, although this was not associated with the P167S polymorphism. Electron microscopy of membrane attack complexes (MAC) generated using recombinantly produced wild type or P167S C9 demonstrated identical MAC ring structures. In functional assays, the P167S variant displayed a higher propensity to polymerise and a small increase in its ability to induce haemolysis of sheep erythrocytes when added to C9-depleted serum. The demonstration that this C9 P167S AMD risk polymorphism displays increased polymerisation and functional activity provides a rationale for the gene therapy trials of sCD59 to inhibit the terminal pathway of complement in AMD that are underway.

Description: Motivation As the generation of complex single-cell RNA sequencing datasets becomes more commonplace it is the responsibility of researchers to provide access to these data in a way that can be easily explored and shared. Whilst it is often the case that data is deposited for future bioinformatic analysis many studies do not release their data in a way that is easy to explore by non-computational researchers. Results In order to help address this we have developed ShinyCell, an R package that converts single-cell RNA sequencing datasets into explorable and shareable interactive interfaces. These interfaces can be easily customised in order to maximise their usability and can be easily uploaded to online platforms to facilitate wider access to published data. Availability ShinyCell is available at https://github.com/SGDDNB/ShinyCell and https://figshare.com/projects/ShinyCell/100439.

Description: Motivation Genomic selection (GS) is currently deemed the most effective approach to speed up breeding of agricultural varieties. It has been recognized that consideration of multiple traits in GS can improve accuracy of prediction for traits of low heritability. However, since GS forgoes statistical testing with the idea of improving predictions, it does not facilitate mechanistic understanding of the contribution of particular single nucleotide polymorphisms (SNP). Results Here we propose a L2,1-norm regularized multivariate regression model and devise a fast and efficient iterative optimization algorithm, called L2,1-joint, applicable in multi-trait GS. The usage of the L2,1-norm facilitates variable selection in a penalized multivariate regression that considers the relation between individuals, when the number of SNPs is much larger than the number of individuals. The capacity for variable selection allows us to define master regulators that can be used in a multi-trait GS setting to dissect the genetic architecture of the analyzed traits. Our comparative analyses demonstrate that the proposed model is a favorable candidate compared to existing state-of-the-art approaches. Prediction and variable selection with data sets from Brassica napus, wheat and Arabidopsis thaliana diversity panels are conducted to further showcase the performance of the proposed model. Availability and implementation The model is implemented using R programming language and the code is freely available from https://github.com/alainmbebi/L21-norm-GS. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Summary Finding informative predictive features in high dimensional biological case-control datasets is challenging. The Extreme Pseudo-Sampling (EPS) algorithm offers a solution to the challenge of feature selection via a combination of deep learning and linear regression models. First, using a variational autoencoder, it generates complex latent representations for the samples. Second, it classifies the latent representations of cases and controls via logistic regression. Third, it generates new samples (pseudo-samples) around the extreme cases and controls in the regression model. Finally, it trains a new regression model over the upsampled space. The most significant variables in this regression are selected. We present an open-source implementation of the algorithm that is easy to set up, use, and customize. Our package enhances the original algorithm by providing new features and customizability for data preparation, model training and classification functionalities. We believe the new features will enable the adoption of the algorithm for a diverse range of datasets. Availability The software package for Python is available online at https://github.com/roohy/eps

Description: Motivation There are high demands for joint genotyping of structural variations with short-read sequencing, but efficient and accurate genotyping in population scale is a challenging task. Results We developed muCNV that aggregates per-sample summary pileups for joint genotyping of 〉 100,000 samples. Pilot results show very low Mendelian inconsistencies. Applications to large-scale projects in cloud show the computational efficiencies of muCNV genotyping pipeline. Availability muCNV is publicly available for download at: https://github.com/gjun/muCNV Supplementary information Supplementary data are available at Bioinformatics online.

Description: Motivation Molecular property prediction is a hot topic in recent years. Existing graph-based models ignore the hierarchical structures of molecules. According to the knowledge of chemistry and pharmacy, the functional groups of molecules are closely related to its physio-chemical properties and binding affinities. So, it should be helpful to represent molecular graphs by fragments that contain functional groups for molecular property prediction. Results In this paper, to boost the performance of molecule property prediction, we first propose a definition of molecule graph fragments that may be or contain functional groups, which are relevant to molecular properties, then develop a fragment-oriented multi-scale graph attention network for molecular property prediction, which is called FraGAT. Experiments on several widely-used benchmarks are conducted to evaluate FraGAT. Experimental results show that FraGAT achieves state-of-the-art predictive performance in most cases. Furthermore, our case studies showthat when the fragments used to represent the molecule graphs contain functional groups, the model can make better predictions. This conforms to our expectation and demonstrates the interpretability of the proposed model. Availability and implementation The code and data underlying this work are available in GitHub, at https://github.com/ZiqiaoZhang/FraGAT. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Motivation The Anatomical Therapeutic Chemical (ATC) system is an official classification system established by the World Health Organization for medicines. Correctly assigning ATC classes to given compounds is an important research problem in drug discovery, which can not only discover the possible active ingredients of the compounds, but also infer theirs therapeutic, pharmacological, and chemical properties. Results In this paper, we develop an end-to-end multi-label classifier called CGATCPred to predict 14 main ATC classes for given compounds. In order to extract rich features of each compound, we use the deep Convolutional Neural Network (CNN) and shortcut connections to represent and learn the seven association scores between the given compound and others. Moreover, we construct the correlation graph of ATC classes and then apply graph convolutional network (GCN) on the graph for label embedding abstraction. We use all label embedding to guide the learning process of compound representation. As a result, by using the Jackknife test, CGATCPred obtain reliable Aiming of 81.94%, Coverage of 82.88%, Accuracy 80.81%, Absolute True 76.58% and Absolute False 2.75%, yielding significantly improvements compared to exiting multi-label classifiers. Availability The codes of CGATCPred are available at https://github.com/zhc940702/CGATCPred and https://zenodo.org/record/4552917. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Motivation Sequence motif discovery algorithms can identify novel sequence patterns that perform biological functions in DNA, RNA and protein sequences—for example, the binding site motifs of DNA-and RNA-binding proteins. Results The STREME algorithm presented here advances the state-of-the-art in ab initio motif discovery in terms of both accuracy and versatility. Using in vivo DNA (ChIP-seq) and RNA (CLIP-seq) data, and validating motifs with reference motifs derived from in vitro data, we show that STREME is more accurate, sensitive and thorough than several widely used algorithms (DREME, HOMER, MEME, Peak-motifs) and two other representative algorithms (ProSampler and Weeder). STREME’s capabilities include the ability to find motifs in datasets with hundreds of thousands of sequences, to find both short and long motifs (from 3 to 30 positions), to perform differential motif discovery in pairs of sequence datasets, and to find motifs in sequences over virtually any alphabet (DNA, RNA, protein and user-defined alphabets). Unlike most motif discovery algorithms, STREME reports a useful estimate of the statistical significance of each motif it discovers. STREME is easy to use individually via its web server or via the command line, and is completely integrated with the widely-used MEME Suite of sequence analysis tools. The name STREME stands for “Simple, Thorough, Rapid, Enriched Motif Elicitation”. Availability The STREME web server and source code are provided freely for non-commercial use at http://meme-suite.org.

Description: Motivation Understanding the mechanisms by which the zebrafish pectoral fin develops is expected to produce insights on how vertebrate limbs grow from a 2D cell layer to a 3D structure. Two mechanisms have been proposed to drive limb morphogenesis in tetrapods: a growth-based morphogenesis with a higher proliferation rate at the distal tip of the limb bud than at the proximal side, and directed cell behaviors that include elongation, division and migration in a nonrandom manner. Based on quantitative experimental biological data at the level of individual cells in the whole developing organ, we test the conditions for the dynamics of pectoral fin early morphogenesis. Results We found that during the development of the zebrafish pectoral fin, cells have a preferential elongation axis that gradually aligns along the proximodistal axis (PD) of the organ. Based on these quantitative observations, we build a center-based cell model enhanced with a polarity term and cell proliferation to simulate fin growth. Our simulations resulted in 3D fins similar in shape to the observed ones, suggesting that the existence of a preferential axis of cell polarization is essential to drive fin morphogenesis in zebrafish, as observed in the development of limbs in the mouse, but distal tip-based expansion is not. Availability Upon publication, biological data will be available at http://bioemergences.eu/modelingFin, and source code at https://github.com/guijoe/MaSoFin. Supplementary information Supplementary data are included in this manuscript.

Description: Motivation Knowledge manipulation of Gene Ontology (GO) and Gene Ontology Annotation (GOA) can be done primarily by using vector representation of GO terms and genes. Previous studies have represented GO terms and genes or gene products in Euclidean space to measure their semantic similarity using an embedding method such as the Word2Vec-based method to represent entities as numeric vectors. However, this method has the limitation that embedding large graph-structured data in the Euclidean space cannot prevent a loss of information of latent hierarchies, thus precluding the semantics of GO and GOA from being captured optimally. On the other hand, hyperbolic spaces such as the Poincaré balls are more suitable for modeling hierarchies, as they have a geometric property in which the distance increases exponentially as it nears the boundary because of negative curvature. Results In this paper, we propose hierarchical representations of GO and genes (HiG2Vec) by applying Poincaré embedding specialized in the representation of hierarchy through a two-step procedure: GO embedding and gene embedding. Through experiments, we show that our model represents the hierarchical structure better than other approaches and predicts the interaction of genes or gene products similar to or better than previous studies. The results indicate that HiG2Vec is superior to other methods in capturing the GO and gene semantics and in data utilization as well. It can be robustly applied to manipulate various biological knowledge. Availability https://github.com/JaesikKim/HiG2Vec Supplementary information Supplementary data are available at Bioinformatics online.

Description: Motivation Facing the increasing gap between high-throughput sequence data and limited functional insights, computational protein function annotation provides a high-throughput alternative to experimental approaches. However, current methods can have limited applicability while relying on protein data besides sequences, or lack generalizability to novel sequences, species and functions. Results To overcome aforementioned barriers in applicability and generalizability, we propose a novel deep learning model using only sequence information for proteins, named Transformer-based protein function Annotation through joint sequence–Label Embedding (TALE). For generalizability to novel sequences we use self attention-based transformers to capture global patterns in sequences. For generalizability to unseen or rarely seen functions (tail labels), we embed protein function labels (hierarchical GO terms on directed graphs) together with inputs/features (1D sequences) in a joint latent space. Combining TALE and a sequence similarity-based method, TALE+ outperformed competing methods when only sequence input is available. It even outperformed a state-of-the-art method using network information besides sequence, in two of the three gene ontologies. Furthermore, TALE and TALE+ showed superior generalizability to proteins of low similarity, new species, or rarely annotated functions compared to training data, revealing deep insights into the protein sequence–function relationship. Ablation studies elucidated contributions of algorithmic components toward the accuracy and the generalizability. Availability The data, source codes and models are available at https://github.com/Shen-Lab/TALE Supplementary information Supplementary data are available at Bioinformatics online.

Description: Motivation Random sampling of metabolic fluxes can provide a comprehensive description of the capabilities of a metabolic network. However, current sampling approaches do not model thermodynamics explicitly, leading to inaccurate predictions of an organism’s potential or actual metabolic operations. Results We present a probabilistic framework combining thermodynamic quantities with steady-state flux constraints to analyze the properties of a metabolic network. It includes methods for probabilistic metabolic optimization and for joint sampling of thermodynamic and flux spaces. Applied to a model of E. coli, we use the methods to reveal known and novel mechanisms of substrate channeling, and to accurately predict reaction directions and metabolite concentrations. Interestingly, predicted flux distributions are multimodal, leading to discrete hypotheses on E. coli’s metabolic capabilities. Availability Python and MATLAB packages available at https://gitlab.com/csb.ethz/pta. Supplementary information Supplementary data are available at Bioinformatics online.

Description: DNA damage and repair activity are often assessed in blood s#38les from humans in different types of molecular epidemiology studies. However, it is not always feasible to analyse the s#38les on the day of collection without any type of storage. For instance, certain studies use repeated s#38ling of cells from the same subject or s#38les from different subjects collected at different time-points, and it is desirable to analyse all these s#38les in the same comet assay experiment. In addition, flawless comet assay analyses on frozen s#38les opens up for the possibility of using this technique on biobank material. In this article we discuss the use of cryopreserved peripheral blood mononuclear cells (PBMCs), buffy coat (BC) and whole blood (WB) for analysis of DNA damage and repair using the comet assay. The published literature and the authors’ experiences indicate that various types of blood s#38les can be cryopreserved with only minor effect on the basal level of DNA damage. There is evidence to suggest that WB and PBMCs can be cryopreserved for several years without much effect on the level of DNA damage. However, care should be taken when cryopreserving WB and BCs. It is possible to use either fresh or frozen s#38les of blood cells, but results from fresh and frozen cells should not be used in the same dataset. The article outlines detailed protocols for the cryopreservation of PBMCs, BCs and WB s#38les.

Description: Motivation Thanks to the increasing availability of drug-drug interactions (DDI) datasets and large biomedical knowledge graphs (KGs), accurate detection of adverse DDI using machine learning models becomes possible. However, it remains largely an open problem how to effectively utilize large and noisy biomedical KG for DDI detection. Due to its sheer size and amount of noise in KGs, it is often less beneficial to directly integrate KGs with other smaller but higher quality data (e.g., experimental data). Most of existing approaches ignore KGs altogether. Some tries to directly integrate KGs with other data via graph neural networks with limited success. Furthermore most previous works focus on binary DDI prediction whereas the multi-typed DDI pharmacological effect prediction is more meaningful but harder task. Results To fill the gaps, we propose a new method SumGNN: knowledge summarization graph neural network, which is enabled by a subgraph extraction module that can efficiently anchor on relevant subgraphs from a KG, a self-attention based subgraph summarization scheme to generate reasoning path within the subgraph, and a multi-channel knowledge and data integration module that utilizes massive external biomedical knowledge for significantly improved multi-typed DDI predictions. SumGNN outperforms the best baseline by up to 5.54%, and performance gain is particularly significant in low data relation types. In addition, SumGNN provides interpretable prediction via the generated reasoning paths for each prediction. Availability The code is available in the supplementary. Supplementary information Supplementary data are available at Bioinformatics online.

Description: RNA-binding proteins (RBPs) play essential roles in diverse cellular processes through post-transcriptional regulation of RNAs. The subcellular localization of RBPs is thus under tight control, the breakdown of which is associated with aberrant cytoplasmic accumulation of nuclear RBPs such as TDP-43 and FUS, well-known pathological markers for amyotrophic lateral sclerosis and frontotemporal dementia (ALS/FTD). Here, we report in Drosophila model for ALS/FTD that nuclear accumulation of a cytoplasmic RBP, Staufen, may be a new pathological feature. We found that in Drosophila C4da neurons expressing PR36, one of the arginine-rich dipeptide repeat proteins (DPRs), Staufen accumulated in the nucleus in Importin- and RNA-dependent manner. Notably, expressing Staufen with exogenous NLS—but not with mutated endogenous NLS—potentiated PR-induced dendritic defect, suggesting that nuclear-accumulated Staufen can enhance PR toxicity. PR36 expression increased Fibrillarin staining in the nucleolus, which was enhanced by heterozygous mutation of stau (stau+/−), a gene that codes Staufen. Furthermore, knockdown of fib, which codes Fibrillarin, exacerbated retinal degeneration mediated by PR toxicity, suggesting that increased amount of Fibrillarin by stau+/− is protective. Stau+/− also reduced the amount of PR-induced nuclear-accumulated Staufen and mitigated retinal degeneration and rescued viability of flies expressing PR36. Taken together, our data show that nuclear accumulation of Staufen in neurons may be an important pathological feature contributing to the pathogenesis of ALS/FTD.

Description: Background Genome-wide association studies (GWAS) of kidney function have uncovered hundreds of loci, primarily in populations of European ancestry. We have undertaken the first continental African GWAS of estimated glomerular filtration rate (eGFR), a measure of kidney function used to define chronic kidney disease (CKD). Method We conducted GWAS of eGFR in 3288 East Africans from the Uganda General Population Cohort (GPC) and replicated in 8224 African Americans from the Women's Health Initiative. Loci attaining genome-wide significant evidence for association (p 

Description: In the current study, we investigated the protective role of citalopram against cognitive decline, impaired mitochondrial dynamics, defective mitochondrial biogenesis, defective autophagy, mitophagy, and synaptic dysfunction in APP transgenic mouse model of Alzheimer’s disease (ad). We treated 12-month-old wild-type (WT) and age-matched transgenic APP mice with citalopram for 2 months. Using Morris Water Maze and rotarod tests, quantitative RT-PCR, immunoblotting, biochemical methods and transmission electron microscopy methods, we assessed cognitive behavior, RNA and protein levels of mitochondrial dynamics, biogenesis, autophagy, mitophagy, synaptic, ad-related and neurogenesis genes in wild-type and APP mice treated and untreated with citalopram. Citalopram-treated APP mice relative to citalopram-untreated APP mice, exhibited improved cognitive behavior. Increased levels of mRNA associated with mitochondrial fission and ad-related genes; decreased levels of fusion, biogenesis, autophagy, mitophagy, synaptic and neurogenesis genes were found in APP mice relative to WT mice. However, APP mice treated with citalopram compared to citalopram-untreated APP mice, revealed reduced levels of the mitochondrial fission and ad-related genes and increased fusion, biogenesis, autophagy, mitophagy, synaptic and neurogenesis genes. Our protein data agrees with the mRNA levels. Transmission electron microscopy revealed significantly increased mitochondrial numbers and reduced mitochondrial length in APP mice; these were reversed in citalopram-treated APP mice. Further, Golgi-cox staining analysis revealed reduced dendritic spines in APP mice relative to WT mice. However, citalopram-treated APP mice showed significantly increased dendritic spines, indicating that citalopram enhances spine density, synaptic activity and improved cognitive function in APP mice. These findings suggest that citalopram reduces cognitive decline, Aβ levels, and mitochondrial and synaptic toxicities and may have a strong protective role against mutant APP and Aβ-induced injuries in patients with depression, anxiety, and ad.

Description: Motivation Most protein-structure superimposition tools consider only Cartesian coordinates. Yet, much of biology happens on the surface of proteins, which is why proteins with shared ancestry and similar function often have comparable surface shapes. Superposition of proteins based on surface shape can enable comparison of highly divergent proteins, identify convergent evolution and enable detailed comparison of surface features and binding sites. Results We present ZEAL, an interactive tool to superpose global and local protein structures based on their shape resemblance using 3D (Zernike-Canterakis) functions to represent the molecular surface. In a benchmark study of structures with the same fold, we show that ZEAL outperforms two other methods for shape-based superposition. In addition, alignments from ZEAL was of comparable quality to the coordinate-based superpositions provided by TM-align. For comparisons of proteins with limited sequence and backbone-fold similarity, where coordinate-based methods typically fail, ZEAL can often find alignments with substantial surface-shape correspondence. In combination with shape-based matching, ZEAL can be used as a general tool to study relationships between shape and protein function. We identify several categories of protein functions where global shape similarity is significantly more likely than expected by random chance, when comparing proteins with little similarity on the fold level. In particular, we find that global surface shape similarity is particular common among DNA binding proteins. Availability ZEAL can be used online at https://andrelab.org/zeal or as a standalone program with command line or graphical user interface. Source files and installers are available at https://github.com/Andre-lab/ZEAL Supplementary information Supplementary data are available at Bioinformatics online.

Description: Keratoconus is a common corneal defect with a complex genetic basis. By whole exome sequencing of affected members from 11 multiplex families of European ancestry, we identified 23 rare, heterozygous, potentially pathogenic variants in 8 genes. These include nonsynonymous single amino acid substitutions in HSPG2, EML6 and CENPF in two families each, and, in NBEAL2, LRP1B, PIK3CG and MRGPRD in three families each; ITGAX had nonsynonymous single amino acid substitutions in two families and an indel with a base substitution producing a nonsense allele in the third family. Only HSPG2, EML6 and CENPF have been associated with ocular phenotypes previously. With the exception of MRGPRD and ITGAX, we detected the transcript and encoded protein of the remaining genes in the cornea and corneal cell cultures. Cultured stromal cells showed cytoplasmic punctate staining of NBEAL2, staining of the fibrillar cytoskeletal network by EML6, while CENPF localized to the basal body of primary cilia. We inhibited the expression of HSPG2, EML6, NBEAL2, and CENPF in stromal cell cultures and assayed for the expression of COL1A1 as a readout of corneal matrix production. An upregulation in COL1A1 after siRNA inhibition indicated their functional link to stromal cell biology. For ITGAX, encoding a leukocyte integrin, we assayed its level in the sera of 3 affected families compared to 10 unrelated controls to detect an increase in all affecteds. Our study identified genes that regulate the cytoskeleton, protein trafficking and secretion, barrier tissue function and response to injury and inflammation, as being relevant to keratoconus.

Description: Motivation For network-assisted analysis, which has become a popular method of data mining, network construction is a crucial task. Network construction relies on the accurate quantification of direct associations among variables. The existence of multiscale associations among variables presents several quantification challenges, especially when quantifying nonlinear direct interactions. Results In this study, the multiscale part mutual information (MPMI), based on part mutual information (PMI) and nonlinear partial association (NPA), was developed for effectively quantifying nonlinear direct associations among variables in networks with multiscale associations. First, we defined the MPMI in theory and derived its five important properties. Second, an experiment in a three-node network was carried out to numerically estimate its quantification ability under two cases of strong associations. Third, experiments of the MPMI and comparisons with the PMI, NPA and conditional mutual information were performed on simulated datasets and on datasets from DREAM challenge project. Finally, the MPMI was applied to real datasets of glioblastoma and lung adenocarcinoma to validate its effectiveness. Results showed that the MPMI is an effective alternative measure for quantifying nonlinear direct associations in networks, especially those with multiscale associations. Availability The source code of MPMI is available online at https://github.com/CDMB-lab/MPMI. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Since the mid-1970s there have been many reports that purport to implicate aluminium in the etiology of neurodegenerative disease. After several decades of research, the role of aluminium in such disease remains controversial and is not the subject of this review. However, if aluminium is implicated in such disease then it follows that there must be a toxicological mechanism, or mode of action, and many researchers have investigated various potential mechanisms including the involvement of oxidative damage, cytotoxicity, and genotoxicity. This paper reviews many of the publications of studies using various salts of aluminium and various genotoxicity endpoints, both in vitro and in vivo, with a focus on oxidative damage. The conclusion of this review is that the majority, if not all, of the publications that report positive results have serious technical flaws and/or implausible findings, and consequently should contribute little or no weight to a weight of evidence (WoE) argument. There are many high quality, GLP compliant genotoxicity studies, that follow relevant OECD test guidelines and the ECHA integrated mutagenicity testing strategy, on several salts of aluminium; all demonstrate clear negative results for both in vitro and in vivo genotoxicity. In addition, the claim for an oxidative mode of action for aluminium can be shown to be spurious. This review concludes that there are no reliable studies that demonstrate a potential for genotoxicity, or oxidative mode of action, for aluminium.

Description: Background Benign breast disease (BBD) is a strong breast cancer risk factor but identifying patients that might develop invasive breast cancer remains a challenge. Methods By applying machine-learning to digitized H&E-stained biopsies and computer-assisted thresholding to mammograms obtained circa BBD diagnosis, we generated quantitative tissue composition metrics and determined their association with future invasive breast cancer diagnosis. Archival breast biopsies and mammograms were obtained for women (18-86 years of age) in a case-control study, nested within a cohort of 15,395 BBD patients from Kaiser Permanente Northwest (1970-2012), followed through mid-2015. Cases (n = 514) who developed incident invasive breast cancer and controls (n = 514) were matched on BBD diagnosis age and plan membership duration. All statistical tests were 2-sided. Results Increasing epithelial area on the BBD biopsy was associated with increasing breast cancer risk (Odds ratio [OR]Q4 vs Q1=1.85, 95% confidence interval [CI] = 1.13-3.04; Ptrend=0.02). Conversely, increasing stroma was associated with decreased risk in non-proliferative, but not proliferative, BBD (Pheterogeneity=0.002). Increasing epithelium-to-stroma proportion [ORQ4 vs Q1=2.06, 95% CI = 1.28-3.33; Ptrend=0.002) and percent mammographic density (MBD) (ORQ4 vs Q1=2.20, 95% CI = 1.20-4.03; Ptrend=0.01) were independently and strongly predictive of increased breast cancer risk. In combination, women with high epithelium-to-stroma proportion/high MBD had substantially higher risk than those with low epithelium-to-stroma proportion/low MBD [OR = 2.27, 95% CI = 1.27-4.06; Ptrend=0.005), particularly among women with non-proliferative (Ptrend=0.01) versus proliferative (Ptrend=0.33) BBD. Conclusion Among BBD patients, increasing epithelium-to-stroma proportion on BBD biopsies and percent MBD at BBD diagnosis were independently and jointly associated with increasing breast cancer risk. These findings were particularly striking for women with non-proliferative disease (comprising approximately 70% of all BBD patients), for whom relevant predictive biomarkers are lacking.

Description: Motivation Ribosome Profiling (Ribo-seq) has revolutionized the study of RNA translation by providing information on ribosome positions across all translated RNAs with nucleotide-resolution. Yet several technical limitations restrict the sequencing depth of such experiments, the most common of which is the overabundance of rRNA fragments. Various strategies can be employed to tackle this issue, including the use of commercial rRNA depletion kits. However, as they are designed for more standardized RNAseq experiments, they may perform suboptimally in Ribo-seq. In order to overcome this, it is possible to use custom biotinylated oligos complementary to the most abundant rRNA fragments, however currently no computational framework exists to aid the design of optimal oligos. Results Here, we first show that a major confounding issue is that the rRNA fragments generated via Ribo-seq vary significantly with differing experimental conditions, suggesting that a “one-size-fits-all” approach may be inefficient. Therefore we developed Ribo-ODDR, an oligo design pipeline integrated with a user-friendly interface that assists in oligo selection for efficient experiment-specific rRNA depletion. Ribo-ODDR uses preliminary data to identify the most abundant rRNA fragments, and calculates the rRNA depletion efficiency of potential oligos. We experimentally show that Ribo-ODDR designed oligos outperform commercially available kits and lead to a significant increase in rRNA depletion in Ribo-seq. Availability Ribo-ODDR is freely accessible at https://github.com/fallerlab/Ribo-ODDR Supplementary information Supplementary data are available at Bioinformatics online.

Description: Summary Many experimental approaches have been developed to identify transcription start sites (TSS) from genomic scale data. However, experiment specific biases lead to large numbers of false-positive calls. Here, we present our integrative approach iTiSS, which is an accurate and generic TSS caller for any TSS profiling experiment in eukaryotes, and substantially reduces the number of false positives by a joint analysis of several complementary datasets. Availability and implementation iTiSS is platform independent and implemented in Java (v1.8) and is freely available at https://www.erhard-lab.de/software and https://github.com/erhard-lab/iTiSS. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Bacterial infection is a highly complex biological process involving a dynamic interaction between the invading microorganism and the host. Specifically, intracellular pathogens seize control over the host cellular processes including membrane dynamics, actin cytoskeleton, phosphoinositide metabolism, intracellular trafficking and immune defense mechanisms to promote their host colonization. To accomplish such challenging tasks, virulent bacteria deploy unique species-specific secreted effectors to evade and/or subvert cellular defense surveillance mechanisms to establish a replication niche. However, despite superficially similar infection strategies, diverse Rickettsia species utilize different effector repertoires to promote host colonization. This review will discuss our current understandings on how different Rickettsia species deploy their effector arsenal to manipulate host cellular processes to promote their intracytosolic life within the mammalian host.

Description: Adaptor protein 2 (AP2), a heterotetrameric complex comprising AP2α, AP2β2, AP2μ2 and AP2σ2 subunits, is ubiquitously expressed and involved in endocytosis and trafficking of membrane proteins, such as the calcium-sensing receptor (CaSR), a G-protein coupled receptor that signals via Gα11. Mutations of CaSR, Gα11 and AP2σ2, encoded by AP2S1, cause familial hypocalciuric hypercalcaemia types 1–3 (FHH1–3), respectively. FHH3 patients have heterozygous AP2S1 missense Arg15 mutations (p.Arg15Cys, p.Arg15His or p.Arg15Leu) with hypercalcaemia, which may be marked and symptomatic, and occasional hypophosphataemia and osteomalacia. To further characterise the phenotypic spectrum and calcitropic pathophysiology of FHH3, we used CRISPR/Cas9 genome editing to generate mice harbouring the AP2S1 p.Arg15Leu mutation, which causes the most severe FHH3 phenotype. Heterozygous (Ap2s1+/L15) mice were viable, and had marked hypercalcaemia, hypermagnesaemia, hypophosphataemia, and increases in alkaline phosphatase activity and fibroblast growth factor-23. Plasma 1,25-dihydroxyvitamin D was normal, and no alterations in bone mineral density or bone turnover were noted. Homozygous (Ap2s1L15/L15) mice invariably died perinatally. Co-immunoprecipitation studies showed that the AP2S1 p.Arg15Leu mutation impaired protein–protein interactions between AP2σ2 and the other AP2 subunits, and also with the CaSR. Cinacalcet, a CaSR positive allosteric modulator, decreased plasma calcium and parathyroid hormone concentrations in Ap2s1+/L15 mice, but had no effect on the diminished AP2σ2-CaSR interaction in vitro. Thus, our studies have established a mouse model that is representative for FHH3 in humans, and demonstrated that the AP2S1 p.Arg15Leu mutation causes a predominantly calcitropic phenotype, which can be ameliorated by treatment with cinacalcet.

Description: Summary Once folded, natural protein molecules have few energetic conflicts within their polypeptide chains. Many protein structures do however contain regions where energetic conflicts remain after folding, i.e. they are highly frustrated. These regions, kept in place over evolutionary and physiological timescales, are related to several functional aspects of natural proteins such as protein–protein interactions, small ligand recognition, catalytic sites and allostery. Here, we present FrustratometeR, an R package that easily computes local energetic frustration on a personal computer or a cluster. This package facilitates large scale analysis of local frustration, point mutants and molecular dynamics (MD) trajectories, allowing straightforward integration of local frustration analysis into pipelines for protein structural analysis. Availability and implementation https://github.com/proteinphysiologylab/frustratometeR. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Summary MitoFlex is a linux-based mitochondrial genome analysis toolkit, which provides a complete workflow of raw data filtering, de novo assembly, mitochondrial genome identification and annotation for animal high throughput sequencing data. The overall performance was compared between MitoFlex and its analogue MitoZ, in terms of protein coding gene recovery, memory consumption and processing speed. Availability MitoFlex is available at https://github.com/Prunoideae/MitoFlex under GPLv3 license. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Transcriptome-wide association study (TWAS) is an important integrative method for identifying genes that are causally associated with phenotypes. A key step of TWAS involves the construction of expression prediction models for every gene in turn using its cis-SNPs as predictors. Different TWAS methods rely on different models for gene expression prediction and each such model makes a distinct modeling assumption that is often suitable for a particular genetic architecture underlying expression. However, the genetic architectures underlying gene expression vary across genes throughout the transcriptome. Consequently, different TWAS methods may be beneficial in detecting genes with distinct genetic architectures. Here, we develop a new method, HMAT, which aggregates TWAS association evidence obtained across multiple gene expression prediction models by leveraging the harmonic mean p value combination strategy. Because each expression prediction model is suited to capture a particular genetic architecture, aggregating TWAS associations across prediction models as in HMAT improves accurate expression prediction and enables subsequent powerful TWAS analysis across the transcriptome. A key feature of HMAT is its ability to accommodate the correlations among different TWAS test statistics and produce calibrated p values after aggregation. Through numerical simulations, we illustrated the advantage of HMAT over commonly used TWAS methods as well as ad-hoc p value combination rules such as Fisher’s method. We also applied HMAT to analyze summary statistics of nine common diseases. In the real data applications, HMAT was on average 30.6% more powerful compared to the next best method, detecting many new disease-associated genes that were otherwise not identified by existing TWAS approaches. In conclusion, HMAT represents a flexible and powerful TWAS method that enjoys robust performance across a range of genetic architectures underlying gene expression.

Description: Motivation Breast cancer is one of the leading causes of cancer deaths among women worldwide. It is necessary to develop new breast cancer drugs because of the shortcomings of existing therapies. The traditional discovery process is time-consuming and expensive. Repositioning of clinically approved drugs has emerged as a novel approach for breast cancer therapy. However, serendipitous or experiential repurposing cannot be used as a routine method. Results In this study, we proposed a graph neural network model GraphRepur based on GraphSAGE for drug repurposing against breast cancer. GraphRepur integrated two major classes of computational methods, drug network-based and drug signature-based. The differentially expressed genes of disease, drug-exposure gene expression data, and the drug-drug links information were collected. By extracting the drug signatures and topological structure information contained in the drug relationships, GraphRepur can predict new drugs for breast cancer, outperforming previous state-of-the-art approaches and some classic machine learning methods. The high-ranked drugs have indeed been reported as new uses for breast cancer treatment recently. Availability The source code of our model and datasets are available at: https://github.com/cckamy/GraphRepur and https://figshare.com/articles/software/GraphRepur_Breast_Cancer_Drug_Repurposing/14220050 Supplementary information Supplementary data are available at Bioinformatics online.

Description: Summary LocusZoom.js is a JavaScript library for creating interactive web-based visualizations of genetic association study results. It can display one or more traits in the context of relevant biological data (such as gene models and other genomic annotation), and allows interactive refinement of analysis models (by selecting linkage disequilibrium reference panels, identifying sets of likely causal variants, or comparisons to the GWAS catalog). It can be embedded in web pages to enable data sharing and exploration. Views can be customized and extended to display other data types such as phenome-wide association study (PheWAS) results, chromatin co-accessibility, or eQTL measurements. A new web upload service harmonizes datasets, adds annotations, and makes it easy to explore user-provided result sets. Availability and implementation LocusZoom.js is open-source software under a permissive MIT license. Code and documentation are available at: https://github.com/statgen/locuszoom/. Installable packages for all versions are also distributed via NPM. Additional features are provided as standalone libraries to promote reuse. Use with your own GWAS results at https://my.locuszoom.org/. Supplementary information Supplementary data are available at Bioinformatics online.

Description: We study the selection of adjustment sets for estimating the interventional mean under a point exposure dynamic treatment regime, that is, a treatment rule that depends on the subject’s covariates. We assume a non-parametric causal graphical model with, possibly, hidden variables and at least one adjustment set comprised of observable variables. We provide the definition of a valid adjustment set for a point exposure dynamic treatment regime, which generalizes the existing definition for a static intervention. We show that there exists an adjustment set, referred to as optimal minimal, that yields the non-parametric estimator of the interventional mean with the smallest asymptotic variance among those that are based on observable minimal adjustment sets. An observable minimal adjustment set is a valid adjustment set such that all its variables are observable and the removal of any variable from it destroys validity. We provide similar optimality results for the class of observable minimum adjustment sets, that is, valid observable adjustment sets of minimum cardinality among the observable adjustment sets. Moreover, we show that if either no variables are hidden or if all the observable variables are ancestors of either treatment, outcome, or the variables that are used to decide treatment, a globally optimal adjustment set exists. We provide polynomial time algorithms to compute the globally optimal, optimal minimal, and optimal minimum adjustment sets. Because static interventions can be viewed as a special case of dynamic regimes, all our results also apply for static interventions.

Description: Previously, we identified missense mutations in CCNF that are causative of familial and sporadic amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Hallmark features of these diseases include the build-up of insoluble protein aggregates as well as the mislocalisation of proteins such as TDP-43. In recent years, the dysregulation of SFPQ has also emerged as a pathological hallmark of ALS/FTD. CCNF encodes for the protein cyclin F, a substrate recognition component of an E3-ubiquitin ligase. We have previously shown that ALS/FTD-linked mutations in CCNF cause disruptions to overall protein homeostasis that leads to a build-up of K48-linked ubiquitylated proteins as well as defects in autophagic machinery. To investigate further processes that may be affected by cyclin F, we used a protein-proximity ligation method, known as BioID, standard immunoprecipitations and mass spectrometry (MS) to identify novel interaction partners of cyclin F and infer further process that may be affected by the ALS/FTD-causing mutation. Results demonstrate that cyclin F closely associates with proteins involved with RNA metabolism as well as a number of RNA binding proteins previously linked to ALS/FTD, including splicing factor proline and glutamine rich (SFPQ). Notably, the overexpression of cyclin F(S621G) led to the aggregation and altered subcellular distribution of SFPQ in HEK293 cells, whilst leading to altered degradation in primary neurons. Overall, our data links ALS/FTD-causing mutations in CCNF to converging pathological features of ALS/FTD and provides a link between defective protein degradation systems and the pathological accumulation of a protein involved in RNA processing and metabolism.

Description: Rickettsiae are obligate intracellular bacteria that can cause life-threatening illnesses and are among the oldest known vector-borne pathogens. Members of this genus are extraordinarily diverse and exhibit a broad host range. To establish intracellular infection, Rickettsia species undergo complex, multistep life cycles that are encoded by heavily streamlined genomes. As a result of reductive genome evolution, rickettsiae are exquisitely tailored to their host cell environment but cannot survive extracellularly. This host-cell dependence makes for a compelling system to uncover novel host-pathogen biology, but it has also hindered experimental progress. Consequently, the molecular details of rickettsial biology and pathogenesis remain poorly understood. With recent advances in molecular biology and genetics, the field is poised to start unraveling the molecular mechanisms of these host-pathogen interactions. Here, we review recent discoveries that have shed light on key aspects of rickettsial biology. These studies have revealed that rickettsiae subvert host cells using mechanisms that are distinct from other better-studied pathogens, underscoring the great potential of the Rickettsia genus for revealing novel biology. We also highlight several open questions as promising areas for future study and discuss the path toward solving the fundamental mysteries of this neglected and emerging human pathogen.

Description: The cat flea, Ctenocephalides felis, is an arthropod vector capable of transmitting several human pathogens including Rickettsia species. Earlier studies identified Rickettsia felis in the salivary glands of the cat flea and transmission of rickettsiae during arthropod feeding. The saliva of hematophagous insects contains multiple biomolecules with anticlotting, vasodilatory, and immunomodulatory activities. Notably, the exact role of salivary factors in the molecular interaction between flea-borne rickettsiae and their insect host is still largely unknown. To determine if R. felis modulates gene expression in the cat flea salivary gland, cat fleas were infected with R. felis and transcription patterns of selected salivary gland-derived factors, including antimicrobial peptides and flea-specific antigens, were assessed. Salivary glands were microdissected from infected and control cat fleas at different time points after exposure and total RNA was extracted and subjected to reverse-transcriptase quantitative PCR for gene expression analysis. During the experimental 10 day feeding period, a dynamic change in gene expression of immunity-related transcripts and salivary antigens between the two experimental groups was detected. The data indicated that defensin-2 (Cf-726), glycine-rich antimicrobial peptide (Cf-83), salivary antigens (Cf-169 and Cf-65), and deorphanized peptide (Cf-75) are flea-derived factors responsive to rickettsial infection.

Description: Background More than half of breast cancer cases are diagnosed among women aged younger than 62 years, which may result in employment challenges. This study examined whether cancer-related employment disruption was associated with increased financial hardship in a national U.S. study of women with breast cancer. Methods Women with breast cancer who were enrolled in the Sister or Two Sister Studies completed a survivorship survey in 2012. Employment disruption was defined as stopping work completely or working fewer hours after diagnosis. Financial hardship was defined as 1) experiencing financial problems paying for cancer care, 2) borrowing money/incurring debt, or 3) filing for bankruptcy because of cancer. Prevalence ratios and 95% confidence intervals for the association between employment disruption and financial hardship were estimated using multivariable Poisson regression with robust variance. Results We analyzed data from women employed at diagnosis (n = 1,628). Women were a median age of 48 years at diagnosis and 5.6 years from diagnosis at survey completion. Overall, 27.3% of women reported employment disruption (15.4% stopped working; 11.9% reduced hours), and 21.0% experienced financial hardship (16.0% had difficulty paying for care; 12.6% borrowed money/incurred debt; 1.8% filed for bankruptcy). In adjusted analysis, employment disruption was associated with nearly twice the prevalence of financial hardship (prevalence ratio = 1.93, 95% confidence interval = 1.58 to 2.35). Conclusions Women experiencing employment disruptions after breast cancer may be more vulnerable to financial hardship. Findings highlight the need to target risk factors for employment disruption, facilitate return to work or ongoing employment, and mitigate financial consequences after cancer.

Description: The human pulmonary environment is complex, containing a matrix of cells, including fibroblasts, epithelial cells, interstitial macrophages, alveolar macrophages and neutrophils. When confronted with foreign material or invading pathogens, these cells mount a robust response. Nevertheless, many bacterial pathogens with an intracellular lifecycle stage exploit this environment for replication and survival. These include, but are not limited to, Coxiella burnetii, Legionella pneumophila, Yersinia pestis, Mycobacterium tuberculosis and Staphylococcus aureus. Currently, few human disease-relevant model systems exist for studying host–pathogen interactions during these bacterial infections in the lung. Here, we present two novel infection platforms, human alveolar macrophages (hAMs) and human precision-cut lung slices (hPCLS), along with an up-to-date synopsis of research using said models. Additionally, alternative uses for these systems in the absence of pathogen involvement are presented, such as tissue banking and further characterization of the human lung environment. Overall, hAMs and hPCLS allow novel human disease-relevant investigations that other models, such as cell lines and animal models, cannot completely provide.

Description: Motivation R Experiment objects such as the SummarizedExperiment or SingleCellExperiment are data containers for storing one or more matrix-like assays along with associated row and column data. These objects have been used to facilitate the storage and analysis of high-throughput genomic data generated from technologies such as single-cell RNA sequencing. One common computational task in many genomics analysis workflows is to perform subsetting of the data matrix before applying down-stream analytical methods. For example, one may need to subset the columns of the assay matrix to exclude poor-quality samples or subset the rows of the matrix to select the most variable features. Traditionally, a second object is created that contains the desired subset of assay from the original object. However, this approach is inefficient as it requires the creation of an additional object containing a copy of the original assay and leads to challenges with data provenance. Results To overcome these challenges, we developed an R package called ExperimentSubset, which is a data container that implements classes for efficient storage and streamlined retrieval of assays that have been subsetted by rows and/or columns. These classes are able to inherently provide data provenance by maintaining the relationship between the subsetted and parent assays. We demonstrate the utility of this package on a single-cell RNA-seq dataset by storing and retrieving subsets at different stages of the analysis while maintaining a lower memory footprint. Overall, the ExperimentSubset is a flexible container for the efficient management of subsets. Availability and implementation ExperimentSubset package is available at Bioconductor: https://bioconductor.org/packages/ExperimentSubset/ and Github: https://github.com/campbio/ExperimentSubset. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Summary In this article, we introduce a hierarchical clustering and Gaussian mixture model with expectation-maximization (EM) algorithm for detecting copy number variants (CNVs) using whole exome sequencing (WES) data. The R shiny package ‘HCMMCNVs’ is also developed for processing user-provided bam files, running CNVs detection algorithm and conducting visualization. Through applying our approach to 325 cancer cell lines in 22 tumor types from Cancer Cell Line Encyclopedia (CCLE), we show that our algorithm is competitive with other existing methods and feasible in using multiple cancer cell lines for CNVs estimation. In addition, by applying our approach to WES data of 120 oral squamous cell carcinoma (OSCC) samples, our algorithm, using the tumor sample only, exhibits more power in detecting CNVs as compared with the methods using both tumors and matched normal counterparts. Availability and implementation HCMMCNVs R shiny software is freely available at github repository https://github.com/lunching/HCMM_CNVs.and Zenodo https://doi.org/10.5281/zenodo.4593371. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Summary The need for an efficient and cost-effective method is compelling in biomolecular NMR. To tackle this problem, we have developed the Poky suite, the revolutionized platform with boundless possibilities for advancing research and technology development in signal detection, resonance assignment, structure calculation, and relaxation studies with the help of many automation and user interface tools. This software is extensible and scalable by scripting and batching as well as providing modern graphical user interfaces and a diverse range of modules right out of the box. Availability Poky is freely available to non-commercial users at https://poky.clas.ucdenver.edu. Supplementary information Supplementary data are available at Bioinformatics online.

Description: GGGGCC repeats in a non-coding region of the C9orf72 gene have been identified as a major genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia. We previously showed that the GGGGCC expanded repeats alone were sufficient to cause neurodegeneration in Drosophila. Recent evidence indicates that GGGGCC expanded repeats can modify various gene transcriptomes. To determine the role of these genes in GGGGCC-mediated neurotoxicity, we screened an established Drosophila model expressing GGGGCC expanded repeats in this study. Our results showed that knockdown of the DNA topoisomerase II (Top2) gene can specifically modulate GGGGCC-associated neurodegeneration of the eye. Furthermore, chemical inhibition of Top2 or siRNA-induced Top2 downregulation could alleviate the GGGGCC-mediated neurotoxicity in Drosophila assessed by eye neurodegeneration and locomotion impairment. By contrast, upregulated Top2 levels were detected in Drosophila strains, and moreover, TOP2A level was also upregulated in Neuro-2a cells expressing GGGGCC expanded repeats, as well as in the brains of Sod1G93A model mice. This indicated that elevated levels of TOP2A may be involved in a pathway common to the pathophysiology of distinct ALS forms. Moreover, through RNA-sequencing, a total of 67 genes, involved in the pathways of intracellular signaling cascades, peripheral nervous system development, et al, were identified as potential targets of TOP2A to modulate GGGGCC-mediated neurodegeneration.

Description: Human neurodegenerative proteinopathies are disorders associated with abnormal protein depositions in brain neurons. They include polyglutamine (polyQ) conditions such as Huntington’s disease (HD) and α-synucleinopathies such as Parkinson’s disease (PD). Overexpression of NMNAT/Nma1, an enzyme in the NAD+ biosynthetic salvage pathway, acts as an efficient suppressor of proteotoxicities in yeast, fly, and mouse models. Screens in yeast models of HD and PD allowed us to identify three additional enzymes of the same pathway that achieve similar protection against proteotoxic stress: Npt1, Pnc1, and Qns1. The mechanism by which these proteins maintain proteostasis has not been identified. Here, we report that their ability to maintain proteostasis in yeast models of HD and PD is independent of their catalytic activity and does not require cellular protein quality control systems such as the proteasome or autophagy. Furthermore, we show that, under proteotoxic stress, the four proteins are recruited as molecular chaperones with holdase and foldase activities. The NAD+ salvage proteins act by preventing misfolding and, together with the Hsp90 chaperone, promoting the refolding of extended polyQ domains and α-synuclein (α-Syn). Our results illustrate the existence of an evolutionarily conserved strategy of repurposing or moonlighting housekeeping enzymes under stress conditions to maintain proteostasis. We conclude that the entire salvage NAD+ biosynthetic pathway links NAD+ metabolism and proteostasis and emerges as a target for therapeutics to combat age-associated neurodegenerative proteotoxicities.

Description: Mutations in CHCHD10, coding for a mitochondrial intermembrane space protein, are a rare cause of autosomal dominant amyotrophic lateral sclerosis (ALS). Mutation-specific toxic gain of function or haploinsufficiency models have been proposed to explain pathogenicity. To decipher the metabolic dysfunction associated with the haploinsufficient p.R15L variant we integrated transcriptomic, metabolomic and proteomic data sets in patient cells subjected to an energetic stress that forces the cells to rely on oxidative phosphorylation for ATP production. Patient cells had a complex I deficiency that resulted in an increased NADH/NAD+ ratio, diminished TCA cycle activity, a reorganization of one carbon metabolism, and an increased AMP/ATP ratio leading to phosphorylation of AMPK and inhibition of mTORC1. These metabolic changes activated the unfolded protein response (UPR) in the ER through the IRE1/XBP1 pathway, upregulating downstream targets including ATF3, ATF4, CHOP and EGLN3, and two cytokine markers of mitochondrial disease, GDF15 and FGF21. Activation of the mitochondrial UPR was mediated through an upregulation of the transcription factors ATF4 and ATF5, leading to increased expression of mitochondrial proteases and heat shock proteins. There was a striking transcriptional up regulation of at least seven dual specific phosphatases, associated with an almost complete dephosphorylation of JNK isoforms, suggesting a concerted deactivation of MAP kinase pathways. This study demonstrates that loss of CHCHD10 function elicits an energy deficit that activates unique responses to nutrient stress in both the mitochondria and ER, which may contribute to the selective vulnerability of motor neurons.

Description: Motivation Breast cancer is a very heterogeneous disease and there is an urgent need to design computational methods that can accurately predict the prognosis of breast cancer for appropriate therapeutic regime. Recently, deep learning-based methods have achieved great success in prognosis prediction, but many of them directly combine features from different modalities that may ignore the complex inter-modality relations. In addition, existing deep learning-based methods do not take intra-modality relations into consideration that are also beneficial to prognosis prediction. Therefore, it is of great importance to develop a deep learning-based method that can take advantage of the complementary information between intra-modality and inter-modality by integrating data from different modalities for more accurate prognosis prediction of breast cancer. Results We present a novel unified framework named genomic and pathological deep bilinear network (GPDBN) for prognosis prediction of breast cancer by effectively integrating both genomic data and pathological images. In GPDBN, an inter-modality bilinear feature encoding module is proposed to model complex inter-modality relations for fully exploiting intrinsic relationship of the features across different modalities. Meanwhile, intra-modality relations that are also beneficial to prognosis prediction, are captured by two intra-modality bilinear feature encoding modules. Moreover, to take advantage of the complementary information between inter-modality and intra-modality relations, GPDBN further combines the inter- and intra-modality bilinear features by using a multi-layer deep neural network for final prognosis prediction. Comprehensive experiment results demonstrate that the proposed GPDBN significantly improves the performance of breast cancer prognosis prediction and compares favorably with existing methods. Availabilityand implementation GPDBN is freely available at https://github.com/isfj/GPDBN. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Motivation Co-expression networks are a powerful gene expression analysis method to study how genes co-express together in clusters with functional coherence that usually resemble specific cell type behaviour for the genes involved. They can be applied to bulk-tissue gene expression profiling and assign function, and usually cell type specificity, to a high percentage of the gene pool used to construct the network. One of the limitations of this method is that each gene is predicted to play a role in a specific set of coherent functions in a single cell type (i.e. at most we get a single for each gene). We present here GMSCA (Gene Multifunctionality Secondary Co-expression Analysis), a software tool that exploits the co-expression paradigm to increase the number of functions and cell types ascribed to a gene in bulk-tissue co-expression networks. Results We applied GMSCA to 27 co-expression networks derived from bulk-tissue gene expression profiling of a variety of brain tissues. Neurons and glial cells (microglia, astrocytes and oligodendrocytes) were considered the main cell types. Applying this approach, we increase the overall number of predicted triplets by 46.73%. Moreover, GMSCA predicts that the SNCA gene, traditionally associated to work mainly in neurons, also plays a relevant function in oligodendrocytes. Availability The tool is available at GitHub, https://github.com/drlaguna/GMSCA as open-source software. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Summary MMseqs2 taxonomy is a new tool to assign taxonomic labels to metagenomic contigs. It extracts all possible protein fragments from each contig, quickly retains those that can contribute to taxonomic annotation, assigns them with robust labels and determines the contig’s taxonomic identity by weighted voting. Its fragment extraction step is suitable for the analysis of all domains of life. MMseqs2 taxonomy is 2–18× faster than state-of-the-art tools and also contains new modules for creating and manipulating taxonomic reference databases as well as reporting and visualizing taxonomic assignments. Availability and implementation MMseqs2 taxonomy is part of the MMseqs2 free open-source software package available for Linux, macOS and Windows at https://mmseqs.com. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Motivation The post-transcriptional epigenetic modification on mRNA is an emerging field to study the gene regulatory mechanism and their association with diseases. Recently developed high-throughput sequencing technology named Methylated RNA Immunoprecipitation Sequencing (MeRIP-seq) enables one to profile mRNA epigenetic modification transcriptome-wide. A few computational methods are available to identify transcriptome-wide mRNA modification, but they are either limited by over-simplified model ignoring the biological variance across replicates or suffer from low accuracy and efficiency. Results In this work, we develop a novel statistical method, based on an empirical Bayesian hierarchical model, to identify mRNA epigenetic modification regions from MeRIP-seq data. Our method accounts for various sources of variations in the data through rigorous modeling, and applies shrinkage estimation by borrowing informations from transcriptome-wide data to stabilize the parameter estimation. Simulation and real data analyses demonstrate that our method is more accurate, robust and efficient than the existing peak calling methods. Availability Our method TRES is implemented as an R package and is freely available on Github at https://github.com/ZhenxingGuo0015/TRES. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Chlamydia trachomatis is the most commonly reported sexually transmitted infection in the United States. The high prevalence of infection and lack of a vaccine indicate a critical knowledge gap surrounding the host's response to infection and how to effectively generate protective immunity. The immune response to C. trachomatis is complex, with cells of the adaptive immune system playing a crucial role in bacterial clearance. Here, we discuss the CD4+ and CD8+ T cell response to Chlamydia, the importance of antigen specificity and the role of memory T cells during the recall response. Ultimately, a deeper understanding of protective immune responses is necessary to develop a vaccine that prevents the inflammatory diseases associated with Chlamydia infection.

Description: Motivation Identifying meaningful cancer driver genes in a cohort of tumors is a challenging task in cancer genomics. Although existing studies have identified known cancer drivers, most of them focus on detecting coding drivers with mutations. It is acknowledged that non-coding drivers can regulate driver mutations to promote cancer growth. In this work, we propose a novel node importance based network analysis (NIBNA) framework to detect coding and non-coding cancer drivers. We hypothesize that cancer drivers are crucial to the formation of community structures in cancer network, and removing them from the network greatly perturbs the network structure thereby critically affecting the functioning of the network. NIBNA detects cancer drivers using a three-step process; first, a condition-specific network is built by incorporating gene expression data and gene networks, second, the community structures in the network are estimated and third, a centrality-based metric is applied to compute node importance. Results We apply NIBNA to the BRCA dataset and it outperforms existing state-of-art methods in detecting coding cancer drivers. NIBNA also predicts 265 miRNA drivers and majority of these drivers have been validated in literature. Further we apply NIBNA to detect cancer subtype-specific drivers and several predicted drivers have been validated to be associated with cancer subtypes. Lastly, we evaluate NIBNA’s performance in detecting epithelial-mesenchymal transition (EMT) drivers, and we confirmed 8 coding and 13 miRNA drivers in the list of known genes. Availability The source code can be accessed at: https://github.com/mandarsc/NIBNA. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Motivation A common way to summarize sequencing datasets is to quantify data lying within genes or other genomic intervals. This can be slow and can require different tools for different input file types. Results Megadepth is a fast tool for quantifying alignments and coverage for BigWig and BAM/CRAM input files, using substantially less memory than the next-fastest competitor. Megadepth can summarize coverage within all disjoint intervals of the Gencode V35 gene annotation for more than 19 000 GTExV8 BigWig files in approximately 1 h using 32 threads. Megadepth is available both as a command-line tool and as an R/Bioconductor package providing much faster quantification compared to the rtracklayer package. Availability and implementation https://github.com/ChristopherWilks/megadepth, https://bioconductor.org/packages/megadepth. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Motivation Bio-entity Coreference Resolution focuses on identifying the coreferential links in biomedical texts, which is crucial to complete bio-events’ attributes and interconnect events into bio-networks. Previously, as one of the most powerful tools, deep neural network-based general domain systems are applied to the biomedical domain with domain-specific information integration. However, such methods may raise much noise due to its insufficiency of combining context and complex domain-specific information. Results In this paper, we explore how to leverage the external knowledge base in a fine-grained way to better resolve coreference by introducing a knowledge-enhanced Long Short Term Memory network (LSTM), which is more flexible to encode the knowledge information inside the LSTM. Moreover, we further propose a knowledge attention module to extract informative knowledge effectively based on contexts. The experimental results on the BioNLP and CRAFT datasets achieve state-of-the-art performance, with a gain of 7.5 F1 on BioNLP and 10.6 F1 on CRAFT. Additional experiments also demonstrate superior performance on the cross-sentence coreferences. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Motivation Adverse drug–drug interactions (DDIs) are crucial for drug research and mainly cause morbidity and mortality. Thus, the identification of potential DDIs is essential for doctors, patients and the society. Existing traditional machine learning models rely heavily on handcraft features and lack generalization. Recently, the deep learning approaches that can automatically learn drug features from the molecular graph or drug-related network have improved the ability of computational models to predict unknown DDIs. However, previous works utilized large labeled data and merely considered the structure or sequence information of drugs without considering the relations or topological information between drug and other biomedical objects (e.g. gene, disease and pathway), or considered knowledge graph (KG) without considering the information from the drug molecular structure. Results Accordingly, to effectively explore the joint effect of drug molecular structure and semantic information of drugs in knowledge graph for DDI prediction, we propose a multi-scale feature fusion deep learning model named MUFFIN. MUFFIN can jointly learn the drug representation based on both the drug-self structure information and the KG with rich bio-medical information. In MUFFIN, we designed a bi-level cross strategy that includes cross- and scalar-level components to fuse multi-modal features well. MUFFIN can alleviate the restriction of limited labeled data on deep learning models by crossing the features learned from large-scale KG and drug molecular graph. We evaluated our approach on three datasets and three different tasks including binary-class, multi-class and multi-label DDI prediction tasks. The results showed that MUFFIN outperformed other state-of-the-art baselines. Availability and implementation The source code and data are available at https://github.com/xzenglab/MUFFIN.

Description: Motivation A major challenge in analyzing cancer patient transcriptomes is that the tumors are inherently heterogeneous and evolving. We analyzed 214 bulk RNA samples of a longitudinal, prospective ovarian cancer cohort and found that the sample composition changes systematically due to chemotherapy and between the anatomical sites, preventing direct comparison of treatment-naive and treated samples. Results To overcome this, we developed PRISM, a latent statistical framework to simultaneously extract the sample composition and cell-type-specific whole-transcriptome profiles adapted to each individual sample. Our results indicate that the PRISM-derived composition-free transcriptomic profiles and signatures derived from them predict the patient response better than the composite raw bulk data. We validated our findings in independent ovarian cancer and melanoma cohorts, and verified that PRISM accurately estimates the composition and cell-type-specific expression through whole-genome sequencing and RNA in situ hybridization experiments. Availabilityand implementation https://bitbucket.org/anthakki/prism. Supplementary information Supplementary data are available at Bioinformatics online.

Description: Motivation Investigating the relationships between two sets of variables helps to understand their interactions and can be done with canonical correlation analysis (CCA). However, the correlation between the two sets can sometimes depend on a third set of covariates, often subject-related ones such as age, gender or other clinical measures. In this case, applying CCA to the whole population is not optimal and methods to estimate conditional CCA, given the covariates, can be useful. Results We propose a new method called Random Forest with Canonical Correlation Analysis (RFCCA) to estimate the conditional canonical correlations between two sets of variables given subject-related covariates. The individual trees in the forest are built with a splitting rule specifically designed to partition the data to maximize the canonical correlation heterogeneity between child nodes. We also propose a significance test to detect the global effect of the covariates on the relationship between two sets of variables. The performance of the proposed method and the global significance test is evaluated through simulation studies that show it provides accurate canonical correlation estimations and well-controlled Type-1 error. We also show an application of the proposed method with EEG data. Availability and implementation RFCCA is implemented in a freely available R package on CRAN (https://CRAN.R-project.org/package=RFCCA). Supplementary information Supplementary data are available at Bioinformatics online.

Description: Major depressive disorder (MDD) is a common comorbidity in chronic obstructive pulmonary disease (COPD), affecting up to 57% of patients with COPD. While the comorbidity of COPD and MDD is well established, the causal relationship between these two diseases is unclear. A large-scale electronic health record (EHR) clinical biobank and genome-wide association study (GWAS) summary statistics for MDD and lung function traits were used to investigate potential shared underlying genetic susceptibility between COPD and MDD. Linkage disequilibrium score regression was used to estimate genetic correlation between phenotypes. Polygenic scores (PRS) for MDD and lung function traits were developed and used to perform a phenome-wide association study (PheWAS). Multi-trait-based conditional and joint analysis identified single nucleotide polymorphisms (SNPs) influencing both lung function and MDD. We found genetic correlations between MDD and all lung function traits were small and not statistically significant. A PRS-MDD was significantly associated with an increased risk of COPD in a PheWAS (odds ratio [OR] = 1.12, 95% confidence interval [CI]: 1.09–1.16) when adjusting for age, sex, and genetic ancestry, but this relationship became attenuated when controlling for smoking history (OR = 1.08, 95% CI: 1.04–1.13). No significant associations were found between the lung function PRS and MDD. Multi-trait-based conditional and joint analysis identified three SNPs that may contribute to both traits, two of which were previously associated with mood disorders and COPD. Our findings suggest that the observed relationship between COPD and MDD may not be driven by a strong shared genetic architecture.

Description: Motivation Polypharmacy side effects should be carefully considered for new drug development. However, considering all the complex drug–drug interactions that cause polypharma-cy side effects is challenging. Recently, graph neural network (GNN) models have handled these complex interactions successfully and shown great predictive perfor-mance. Nevertheless, the GNN models have difficulty providing intelligible factors of the prediction for biomedical and pharmaceutical domain experts. Method A novel approach, graph feature attention network (GFAN), is presented for inter-pretable prediction of polypharmacy side effects by emphasizing target genes differ-ently. To artificially simulate polypharmacy situations, where two different drugs are taken together, we formulated a node classification problem by using the concept of line graph in graph theory. Results Experiments with benchmark datasets validated interpretability of the GFAN and demonstrated competitive performance with the graph attention network in a previous work. And the specific cases in the polypharmacy side effect prediction experiments showed that the GFAN model is capable of very sensitively extracting the target genes for each side effect prediction. Availability and implementation https://github.com/SunjooBang/Polypharmacy-side-effect-prediction

Description: Previous studies have indicated important roles for NIMA-related kinase 1 (NEK1) in modulating DNA damage checkpoints and DNA repair capacity. To broadly assess the contributions of NEK1 to genotoxic stress and mitochondrial functions, we characterized several relevant phenotypes of NEK1 CRISPR knockout (KO) and WT HAP1 cells. Our studies revealed that NEK1 KO cells resulted in increased apoptosis and hypersensitivity to the alkylator methyl methanesulfonate, the radiomimetic bleomycin, and UVC light, yet increased resistance to the crosslinker cisplatin. Mitochondrial functionalities were also altered in NEK1 KO cells, with phenotypes of reduced mitophagy, increased total mitochondria, elevated levels of reactive oxygen species, impaired complex I activity, and higher amounts of mitochondrial DNA damage. RNA-seq transcriptome analysis coupled with qRT-PCR studies comparing NEK1 KO cells with NEK1 overexpressing cells revealed that the expression of genes involved in DNA repair pathways, such as base excision repair, nucleotide excision repair, and double-strand break repair, are altered in a way that might influence genotoxin resistance. Together, our studies underline and further support that NEK1 serves as a hub signaling kinase in response to DNA damage, modulating DNA repair capacity, mitochondrial activity and cell fate determination.

Description: The PHF6 mutation c.1024C 〉 T; p.R342X, is a recurrent cause of Börjeson-Forssman-Lehmann Syndrome (BFLS), a neurodevelopmental disorder characterized by moderate–severe intellectual disability, truncal obesity, gynecomastia, hypogonadism, long tapering fingers, and large ears (MIM#301900). Here, we generated transgenic mice with the identical substitution (R342X mice) using CRISPR technology. We show that the p.R342X mutation causes a reduction in PHF6 protein levels, in both human and mice, from nonsense mediated decay and nonsense-associated alternative splicing, respectively. MRI studies indicated that R342X mice had a reduced brain volume on a mixed genetic background but developed hydrocephaly and a high incidence of postnatal death on a C57BL/6 background. Cortical development proceeded normally while hippocampus and hypothalamus relative brain volumes were altered. A hypoplastic anterior pituitary was also observed that likely contributes to the small size of the R342X mice. Behavior testing demonstrated deficits in associative learning, spatial memory and an anxiolytic phenotype. Taken together, the R342X mice represent a good preclinical model of BFLS that will allow further dissection of PHF6 function and disease pathogenesis.

Description: DNA mismatch repair (MMR) proteins play an important role in maintaining genome stability, both in somatic and in germline cells. Loss of MLH1, a central MMR protein, leads to infertility and to microsatellite instability (MSI) in spermatocytes, however, the effect of Mlh1 heterozygosity in germline genome stability remains unexplored. To test the effect of Mlh1 heterozygosity on MSI in mature sperm, we combined mouse genetics with single-molecule PCR that detects allelic changes at unstable microsatellites. We discovered 4.5% and 5.9% MSI in sperm of 4- and 12-month old Mlh1  +/- mice, respectively, and that Mlh1 promoter methylation in Mlh1  +/- sperm correlated with higher MSI. No such elevated MSI was seen in non-proliferating somatic cells. Additionally, we show contrasting dynamics of deletions versus insertions at unstable microsatellites (mononucleotide repeats) in sperm.

Description: The 2019 and 2020 sporadic outbreaks of Yellow fever (YF) in Sub-Saharan Africa (SSA) countries had raised a lot of global health concerns. This article aims to narratively review the vector biology, YF vaccination program, environmental factors and climatic changes, and to understand how they could facilitate the re-emergence of YF. This study comprehensively reviewed articles that focused on the interplay and complexity of YF Virus (YFV) vector diversity/competence, YF vaccine immunodynamics, and climatic changes impacts on YFV transmission as they influence the 2019/2020 sporadic outbreaks in Sub-Saharan Africa (SSA). Based on available reports, vectorial migration, climatic changes, and YF immunization level could be reasons for the re-emergence of YF at the community and national levels. Essentially, the drivers of YFV infection due to spillover are moderately constant. However, changes in land use and landscape have been shown to influence sylvan-to-urban spillover. Furthermore, increased precipitation and warmer temperatures due to climate change, are likely to broaden the range of mosquitoes' habitat. The 2019/2020 YF outbreaks in SSA is basically a result of inadequate vaccination campaigns, YF surveillance and vector control. Consequently, and most importantly, adequate immunization coverage must be implemented and properly achieved under the responsibility of the public health stakeholders.

Description: Background Although it is well documented that adolescents and young adults (AYAs) with cancer have low participation in cancer clinical trials (CCTs), the underlying reasons are not well understood. We utilized the NCI Community Oncology Research Program (NCORP) network to identify barriers and facilitators to AYA CCT enrollment, and strategies to improve enrollment at community-based and minority/underserved sites. Methods We performed one-on-one semi-structured qualitative interviews with stakeholders (NCORP Site Principle Investigators, NCORP Administrators, Physicians involved in enrollment, Lead Clinical Research Associates or Clinical Research Nurses, Nurse Navigators, Regulatory Research Associates, Patient Advocates) in the AYA CCT enrollment process. NCORP sites that included high- and low-AYA enrolling affiliate sites and were diverse in geography and department representation (eg, pediatrics, medical oncology) were invited to participate. All interviews were recorded and transcribed. Themes related to barriers and facilitators and strategies to improve enrollment were identified. Results We conducted 43 interviews across 10 NCORP sites. Eleven barriers and 13 facilitators to AYA enrollment were identified. Main barriers included perceived limited trial availability and eligibility, physician gatekeeping, lack of provider and research staff time, and financial constraints. Main facilitators and strategies to improve AYA enrollment included having a patient screening process, physician endorsement of trials, an “AYA champion” on site, and strong communication between medical and pediatric oncology. Conclusions Stakeholders identified several opportunities to address barriers contributing to low AYA CCT enrollment at community-based and minority/underserved sites. Results of this study will inform development and implementation of targeted interventions to increase AYA CCT enrollment.

Description: Patients, practitioners, and policymakers are increasingly concerned about the delivery of ineffective or low-value clinical practices in cancer care settings. Research is needed on how to effectively de-implement these types of practices from cancer care. In this commentary, we spotlight the National Cancer Institute (NCI) Community Oncology Research Program (NCORP), a national network of community oncology practices, and elaborate on how it is an ideal infrastructure for conducting rigorous, real-world research on de-implementation. We describe key, multi-level issues that affect de-implementation and also serve as a guidepost for developing strategies to drive de-implementation. We describe optimal study designs for testing de-implementation strategies and elaborate on how and why the NCORP network is uniquely positioned to conduct rigorous and impactful de-implementation trials. The number and diversity of affiliated community oncology care sites, coupled with the overall objective of improving cancer care delivery, make the NCORP an opportune infrastructure for advancing de-implementation research while simultaneously improving the care of millions of cancer patients nationwide.

Description: Gene networks for disorders of social behavior provide the mechanisms critical for identifying therapeutic targets and biomarkers. Large behavioral phenotypic effects of small human deletions make the positive sociality of Williams syndrome (WS) ideal for determining transcriptional networks for social dysfunction currently based on DNA variations for disorders such as Autistic spectrum disorder (ASD). Consensus on WS networks has been elusive due to the need for larger cohort size, sensitive genome wide detection, and analytic tools. We report a core set of WS network perturbations in a cohort of 58 individuals (34 with typical, six atypical deletions and 18 controls). Genome-wide exon-level expression arrays robustly detected changes in differentially expressed gene (DEG) transcripts from WS deleted genes that ranked in the top 11 of 12 122 transcripts, validated by qRT-PCR, RNASeq and Western blots. WS DEG’s were strictly dosed in the full but not the atypical deletions that revealed a breakpoint position effect on non-deleted CLIP2, a caveat for current phenotypic mapping based on CNV. Network analyses tested the role of the top WS DEG’s in the dendritic spine, employing GeneMANIA to harmonize WS DEGs with comparable query gene-sets. The results indicate perturbed actin cytoskeletal signaling analogous to the excitatory dendritic spine. Independent Protein–Protein Interaction analyses of top WS DEGs generated a 100-node graph annotated topologically revealing three interacting pathways, MAPK, IGF1-PI3K-AKT–mTOR/insulin, and actin signaling at the synapse. The results indicate striking similarity of WS transcriptional networks to GWAS-based ASD risk suggesting common network dysfunction for these disorders of divergent sociality.

Description: Summary A problem of major interest in network data analysis is to explain the strength of connections using context information. To achieve this, we introduce a novel approach named network-supervised dimension reduction by projecting covariates onto low-dimensional spaces for revealing the linkage pattern, without assuming a model.We propose a new loss function for estimating the parameters in the resulting linear projection, based on the notion that closer proximity in the low-dimension projection renders stronger connections. Interestingly, the convergence rate of our estimator is shown to depend on a network effect factor which is the smallest number that can partition a graph in a way similar to the graph coloring problem. Our methodology has interesting connections to principal component analysis and linear discriminant analysis, which we exploit for clustering and community detection. The methodology developed is further illustrated by numerical experiments and the analysis of a pulsar candidates data in astronomy.

Description: Motivation The prediction performance of Cox proportional hazard model suffers when there are only few uncensored events in the training data. Results We propose a Sparse-Group regularized Cox regression method to improve the prediction performance of large-scale and high-dimensional survival data with few observed events. Our approach is applicable when there is one or more other survival responses that 1. has a large number of observed events; 2. share a common set of associated predictors with the rare event response. This scenario is common in the UK Biobank (Sudlow et al., 2015) dataset where records for a large number of common and less prevalent diseases of the same set of individuals are available. By analyzing these responses together, we hope to achieve higher prediction performance than when they are analyzed individually. To make this approach practical for large-scale data, we developed an accelerated proximal gradient optimization algorithm as well as a screening procedure inspired by Qian et al. (2020). Availability https://github.com/rivas-lab/multisnpnet-Cox Supplementary information Supplementary data are available at Bioinformatics online.

Description: Several reports have suggested that genetic susceptibility contributes to the development and progression of diabetic retinopathy. We aimed to identify genetic loci that confer susceptibility to diabetic retinopathy in Japanese patients with type 2 diabetes. We analysed 5 790 508 single nucleotide polymorphisms (SNPs) in 8880 Japanese patients with type 2 diabetes, 4839 retinopathy cases and 4041 controls, as well as 2217 independent Japanese patients with type 2 diabetes, 693 retinopathy cases, and 1524 controls. The results of these two genome-wide association studies (GWAS) were combined with an inverse variance meta-analysis (Stage-1), followed by de novo genotyping for the candidate SNP loci (p 

Description: ß-Synuclein (ß-Syn) has long been considered to be an attenuator for the neuropathological effects caused by the Parkinson’s disease-related α-Synuclein (α-Syn) protein. However, recent studies demonstrated that overabundant ß-Syn can form aggregates and induce neurodegeneration in CNS neurons in vitro and in vivo, albeit at a slower pace as compared to α-Syn. Here we demonstrate that ß-Syn mutants V70M, detected in a sporadic case of Dementia with Lewy Bodies (DLB), and P123H, detected in a familial case of DLB, robustly aggravate the neurotoxic potential of ß-Syn. Intriguingly, the two mutations trigger mutually exclusive pathways. ß-Syn V70M enhances morphological mitochondrial deterioration and degeneration of dopaminergic and non-dopaminergic neurons, but has no influence on neuronal network activity. Conversely, ß-Syn P123H silences neuronal network activity, but does not aggravate neurodegeneration. ß-Syn WT, V70M and P123H formed proteinase K (PK) resistant intracellular fibrils within neurons, albeit with less stable C-termini as compared to α-Syn. Under cell free conditions, ß-Syn V70M demonstrated a much slower pace of fibril formation as compared to WT ß-Syn, and P123H fibrils present with a unique phenotype characterized by large numbers of short, truncated fibrils. Thus, it is possible that V70M and P123H cause structural alterations in ß-Syn, that are linked to their distinct neuropathological profiles. The extent of the lesions caused by these neuropathological profiles is almost identical to that of overabundant α-Syn, and thus likely to be directly involved into etiology of DLB. Over all, this study provides insights into distinct disease mechanisms caused by mutations of ß-Syn.