Homozygous mutation in CSF1R causes brain abnormalities, neurodegeneration, and dysosteosclerosis (BANDDOS).

Introduction: The CSF1R gene encodes the receptor for colony-stimulating factor-1, the macrophage, and monocyte-specific growth factor. Mutations in this gene cause hereditary diffuse leukoencephalopathy with spheroids (HDLS) with autosomal dominant inheritance and BANDDOS (Brain Abnormalities, Neurodegeneration, and Dysosteosclerosis) with autosomal recessive inheritance. Methods: Targeted gene sequencing was performed on the genomic DNA samples of the deceased patient and a fetus along with ten healthy members of his family to identify the disease-causing mutation. Bioinformatics tools were used to study the mutation effect on protein function and structure. To predict the effect of the mutation on the protein, various bioinformatics tools were applied. Results: A novel homozygous variant was identified in the gene CSF1R, c.2498C>T; p.T833M in exon 19, in the index patient and the fetus. Furthermore, some family members were heterozygous for this variant, while they had not any symptoms of the disease. In silico analysis indicated this variant has a detrimental effect on CSF1R. It is conserved among humans and other similar species. The variant is located within the functionally essential PTK domain of the receptor. However, no structural damage was introduced by this substitution. Conclusion: In conclusion, regarding the inheritance pattern in the family and clinical manifestations in the index patient, we propose that the mentioned variant in the CSF1R gene may cause BANDDOS.

Perseus plugin "Metis" for metabolic-pathway-centered quantitative multi-omics data analysis for static and time-series experimental designs.

We introduce Metis, a new plugin for the Perseus software aimed at analyzing quantitative multi-omics data based on metabolic pathways. Data from different omics types are connected through reactions of a genome-scale metabolic-pathway reconstruction. Metabolite concentrations connect through the reactants, while transcript, protein, and protein post-translational modification (PTM) data are associated through the enzymes catalyzing the reactions. Supported experimental designs include static comparative studies and time-series data. As an example for the latter, we combine circadian mouse liver multi-omics data and study the contribution of cycles of phosphoproteome and metabolome to enzyme activity regulation. Our analysis resulted in 52 pairs of cycling phosphosites and metabolites connected through a reaction. The time lags between phosphorylation and metabolite peak show non-uniform behavior, indicating a major contribution of phosphorylation in the modulation of enzymatic activity.

MaxDIA enables library-based and library-free data-independent acquisition proteomics.

MaxDIA is a software platform for analyzing data-independent acquisition (DIA) proteomics data within the MaxQuant software environment. Using spectral libraries, MaxDIA achieves deep proteome coverage with substantially better coefficients of variation in protein quantification than other software. MaxDIA is equipped with accurate false discovery rate (FDR) estimates on both library-to-DIA match and protein levels, including when using whole-proteome predicted spectral libraries. This is the foundation of discovery DIA-hypothesis-free analysis of DIA samples without library and with reliable FDR control. MaxDIA performs three- or four-dimensional feature detection of fragment data, and scoring of matches is augmented by machine learning on the features of an identification. MaxDIA's bootstrap DIA workflow performs multiple rounds of matching with increasing quality of recalibration and stringency of matching to the library. Combining MaxDIA with two new technologies-BoxCar acquisition and trapped ion mobility spectrometry-both lead to deep and accurate proteome quantification.

Genetic basis of acephalic spermatozoa syndrome, and intracytoplasmic sperm injection outcomes in infertile men: a systematic scoping review.

PURPOSE: Acephalic spermatozoa syndrome (ASS) is known as a severe type of teratozoospermia, defined as semen composed of mostly headless spermatozoa that affect male fertility. In this regard, this systematic review aimed to discuss gene variants associated with acephalic spermatozoa phenotype as well as the clinical outcomes of intracytoplasmic sperm injection (ICSI) treatment for the acephalic spermatozoa-associated male infertility. METHODS: A systematic search was performed on PubMed, Embase, Scopus, and Ovid databases until May 17, 2020. This systematic scoping review was reported in terms of the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) statement. RESULTS: Twenty articles were included in this systematic review. Whole-exome and Sanger sequencing have helped in the identification of variants in SUN5, PMFBP1, BRDT, TSGA10, DNAH6, HOOK1, and CEP112 genes as possible causes of this phenotype in humans. The results of the ICSI are conflicting due to both positive and negative reports of ICSI outcomes. CONCLUSION: ASS has a genetic origin, and several genetic alterations related to the pathogenesis of this anomaly have been recently identified. Notably, only SUN5 and PMFBP1 mutations are well-known to be implicated in ASS. Accordingly, more functional studies are needed to confirm the pathogenicity of other variants. ICSI could provide a promising treatment for acephalic spermatozoa-associated male infertility. Besides the importance of sperm head-tail junction integrity, some other factors, whether within the sperm cell or female factors, may be involved in the ICSI outcome.

Revisiting the complex architecture of ALS in Turkey: Expanding genotypes, shared phenotypes, molecular networks, and a public variant database.

The last decade has proven that amyotrophic lateral sclerosis (ALS) is clinically and genetically heterogeneous, and that the genetic component in sporadic cases might be stronger than expected. This study investigates 1,200 patients to revisit ALS in the ethnically heterogeneous yet inbred Turkish population. Familial ALS (fALS) accounts for 20% of our cases. The rates of consanguinity are 30% in fALS and 23% in sporadic ALS (sALS). Major ALS genes explained the disease cause in only 35% of fALS, as compared with ~70% in Europe and North America. Whole exome sequencing resulted in a discovery rate of 42% (53/127). Whole genome analyses in 623 sALS cases and 142 population controls, sequenced within Project MinE, revealed well-established fALS gene variants, solidifying the concept of incomplete penetrance in ALS. Genome-wide association studies (GWAS) with whole genome sequencing data did not indicate a new risk locus. Coupling GWAS with a coexpression network of disease-associated candidates, points to a significant enrichment for cell cycle- and division-related genes. Within this network, literature text-mining highlights DECR1, ATL1, HDAC2, GEMIN4, and HNRNPA3 as important genes. Finally, information on ALS-related gene variants in the Turkish cohort sequenced within Project MinE was compiled in the GeNDAL variant browser (www.gendal.org).

MicroRNAs association with azoospermia, oligospermia, asthenozoospermia, and teratozoospermia: a systematic review.

Infertility is a major health problem across the world. One of the main reasons for male infertility are defects in sperm. Semen analysis is the most common test utilized to evaluate male fertility and since it suffers from multiple drawbacks, reproduction scientists have tried to find new molecular markers for detecting sperm defects. MicroRNAs (miRNAs) are small molecules in cells which take part in regulating gene expression. Various studies have confirmed miRNAs to have a role in defining multiple sperm characteristics, including sperm count, motility, and morphology. In this paper, we have systematically reviewed the role of miRNAs in infertile men with sperm defects including azoospermia, oligospermia, asthenozoospermia, and teratozoospermia. Also, we have assembled various bioinformatics tools to come up with a pipeline for predicting novel miRNAs which could possibly participate in sperm count, motility, and morphology. Also, related KEGG and GO terms for predicted miRNAs have been included in order to highlight their role in sperm function. Our study emphasizes the potential role of miRNAs in male infertility and provides a general overview for future studies aiming to find robust molecular markers for this condition.

A new report of autoinflammation and PLCG2-associated antibody deficiency and immune dysregulation (APLAID) with a homozygous pattern from Iran.

Autoinflammation and PLCG2-associated antibody deficiency and immune dysregulation (APLAID) is an autosomal dominant autoinflammatory disease characterized by episodic skin, musculoskeletal, ophthalmic and gastrointestinal tract symptoms. Here we report an 11-year-old girl with a history of repeated episodes of fever, myalgia, arthralgia, abdominal pain, and urticarial rash in the trunk and limbs. Chest and pelvic X-Ray, sacroiliac joints MRI, brain MRI and abdominal CT scan were normal. Anti-nuclear antibody, Rheumatoid factor, cryoglobulin, ANCA/PR3, p-ANCA/MPO, anti-smooth muscle antibody and anti-mitochondrial antibody were negative. Serology for cytomegalovirus, Epstein-Barr, hepatitis B, hepatitis C, and HIV viruses was negative. Serum immunoglobulins were in the normal range. Genetic analysis for familial Mediterranean fever syndrome was negative. Whole exome sequencing was carried out to identify the genetic cause of our patient. We identified a homozygous missense variant (c.579C > G, p. His193Gln) in exon 7 of the PLCG2 gene. Bioinformatic analysis and clinical symptoms suggests this variant to be pathogenic in the homozygous state for APLAID and thus probably acting in an autosomal recessive manner. Our bioinformatic analysis also showed this novel mutation to have detrimental effects on the 3D structure of the PLCG2 protein, which is well conserved among many other similar species.

Visualization and Analysis of miRNAs Implicated in Amyotrophic Lateral Sclerosis Within Gene Regulatory Pathways.

MicroRNAs (miRNAs), approximately 22 nucleotides long, post-transcriptionally active gene expression regulators, play active roles in modulating cellular processes. Gene regulation and miRNA regulation are intertwined and the main aim of this study is to facilitate the analysis of miRNAs within gene regulatory pathways. VANESA enables the reconstruction of biological pathways and supports visualization and simulation. To support integrative miRNA and gene pathway analyses, a custom database of experimentally proven miRNAs, integrating data from miRBase, TarBase and miRTarBase, was added to DAWIS-M.D., which is the main data source for VANESA. Analysis of human KEGG pathways within DAWIS-M.D. showed that 661 miRNAs (~1/3 recorded human miRNAs) lead to 65,474 interactions. hsa-miR-335-5p targets most genes in our system (2,544); while the most targeted gene (with 71 miRNAs) is NUFIP2 (Nuclear Fragile X Mental Retardation Protein Interacting Protein 2). Amyotrophic Lateral Sclerosis (ALS), a complex neurodegenerative disease, was chosen as a proof of concept model. Using our system, it was possible to reduce the initially several hundred genes and miRNAs associated with ALS to eight genes, 19 miRNAs and 31 interactions. This highlights the effectiveness of the implemented system to distill important information from otherwise hard to access, highly convoluted and vast regulatory networks.

Characterization of the c9orf72 GC-rich low complexity sequence in two cohorts of Italian and Turkish ALS cases.

Large expansions of a noncoding GGGGCC repeat in the C9orf72 gene are the main cause of amyotrophic lateral sclerosis (ALS). The GGGGCC repeat is contiguous with another GC-rich region. Recent studies reported a significantly higher frequency of insertions/deletions within the GC-rich region in patients carrying the GGGGCC expansion. A GTGGT motif comprised within the GC-rich region, which joins two 100% GC sequences, was frequently deleted, supporting the hypothesis that these deletions could make the region more prone to slippage and pathological expansion. To confirm this hypothesis, we sequenced the GC-rich region adjacent the GGGGCC repeat in ALS patients, 116 C9orf72 expansion carriers, 219 non-carriers, and 223 healthy controls, from Italian and Turkish cohorts. Deletions were significantly more frequent in C9orf72 expansion carriers (6%) compared to non-carrier ALS patients (0.46%, OR =14.00, 95% CI =1.71-306.59, p = 0.003), to controls (0%, OR =16.29, 95% CI =2.12-725.99, p = 4.86 × 10-4) and to the whole cohort of non-carriers (0.2%, OR =28.51, 95% CI =3.47-618.91, p = 9.58 × 10-5). Among expansion carriers, deletions with or without the GTGGT motif were equally distributed (4 vs. 3). The frequency of insertions was not statistically different between C9orf72 expansion carriers and any other group including the whole cohort of non-carriers (p = 0.439, Fisher's exact test). Our data confirmed the association between deletions within GC-rich region and the GGGGCC expansion in Italian and Turkish cases, although we did not confirm a role of the GTGGT element deletion. Further studies will be therefore necessary to assess the causal relationships between contiguous deletions of the GC-rich region and the GGGGCC expansion.

Elevated Global DNA Methylation Is Not Exclusive to Amyotrophic Lateral Sclerosis and Is Also Observed in Spinocerebellar Ataxia Types 1 and 2.

Adult-onset neurological disorders are caused and influenced by a multitude of different factors, including epigenetic modifications. Here, using an ELISA kit selected upon careful testing, we investigated global 5-methylcytosine (5-mC) levels in sporadic and familial amyotrophic lateral sclerosis (sALS and fALS), spinocerebellar ataxia types 1 and 2 (SCA1 and SCA2), Huntington's disease, Friedreich's ataxia, and myotonic dystrophy type 1. We report a significant elevation in global 5-mC levels of about 2-7% on average for sALS (p < 0.01 [F(1, 243) = 9.159, p = 0.0027]) and various forms of fALS along with SCA1 (p < 0.01 [F(1, 83) = 11.285], p = 0.0012) and SCA2 (p < 0.001 [F(1, 122) = 29.996, p = 0.0001]) when compared to age- and sex-matched healthy controls. C9orf72 expansion carrier ALS patients exhibit the highest global 5-mC levels along with C9orf72 promoter hypermethylation. We failed to measure global 5-hydroxymethylcytosine (5-hmC) levels in blood, probably due to the very low levels of 5-hmC and the limitations of the commercially available ELISA kits. Our results point towards a role for epigenetics modification in ALS, SCA1, and SCA2, and help conclude a dispute on the global 5-mC levels in sALS blood.

Visualization and Analysis of MicroRNAs within KEGG Pathways using VANESA.

MicroRNAs (miRNAs) are small RNA molecules which are known to take part in post-transcriptional regulation of gene expression. Here, VANESA, an existing platform for reconstructing, visualizing, and analysis of large biological networks, has been further expanded to include all experimentally validated human miRNAs available within miRBase, TarBase and miRTarBase. This is done by integrating a custom hybrid miRNA database to DAWIS-M.D., VANESA's main data source, enabling the visualization and analysis of miRNAs within large biological pathways such as those found within the Kyoto Encyclopedia of Genes and Genomes (KEGG). Interestingly, 99.15 % of human KEGG pathways either contain genes which are targeted by miRNAs or harbor them. This is mainly due to the high number of interaction partners that each miRNA could have (e.g.: hsa-miR-335-5p targets 2544 genes and 71 miRNAs target NUFIP2). We demonstrate the usability of our system by analyzing the measles virus KEGG pathway as a proof-of-principle model and further highlight the importance of integrating miRNAs (both experimentally validated and predicted) into biological networks for the elucidation of novel miRNA-mRNA interactions of biological importance.

What computational non-targeted mass spectrometry-based metabolomics can gain from shotgun proteomics.

Computational workflows for mass spectrometry-based shotgun proteomics and untargeted metabolomics share many steps. Despite the similarities, untargeted metabolomics is lagging behind in terms of reliable fully automated quantitative data analysis. We argue that metabolomics will strongly benefit from the adaptation of successful automated proteomics workflows to metabolomics. MaxQuant is a popular platform for proteomics data analysis and is widely considered to be superior in achieving high precursor mass accuracies through advanced nonlinear recalibration, usually leading to five to ten-fold better accuracy in complex LC-MS/MS runs. This translates to a sharp decrease in the number of peptide candidates per measured feature, thereby strongly improving the coverage of identified peptides. We argue that similar strategies can be applied to untargeted metabolomics, leading to equivalent improvements in metabolite identification.

Search for SCA2 blood RNA biomarkers highlights Ataxin-2 as strong modifier of the mitochondrial factor PINK1 levels.

Ataxin-2 (ATXN2) polyglutamine domain expansions of large size result in an autosomal dominantly inherited multi-system-atrophy of the nervous system named spinocerebellar ataxia type 2 (SCA2), while expansions of intermediate size act as polygenic risk factors for motor neuron disease (ALS and FTLD) and perhaps also for Levodopa-responsive Parkinson's disease (PD). In view of the established role of ATXN2 for RNA processing in periods of cell stress and the expression of ATXN2 in blood cells such as platelets, we investigated whether global deep RNA sequencing of whole blood from SCA2 patients identifies a molecular profile which might serve as diagnostic biomarker. The bioinformatic analysis of SCA2 blood global transcriptomics revealed various significant effects on RNA processing pathways, as well as the pathways of Huntington's disease and PD where mitochondrial dysfunction is crucial. Notably, an induction of PINK1 and PARK7 expression was observed. Conversely, expression of Pink1 was severely decreased upon global transcriptome profiling of Atxn2-knockout mouse cerebellum and liver, in parallel to strong effects on Opa1 and Ghitm, which encode known mitochondrial dynamics regulators. These results were validated by quantitative PCR and immunoblots. Starvation stress of human SH-SY5Y neuroblastoma cells led to a transcriptional phasic induction of ATXN2 in parallel to PINK1, and the knockdown of one enhanced the expression of the other during stress response. These findings suggest that ATXN2 may modify the known PINK1 roles for mitochondrial quality control and autophagy during cell stress. Given that PINK1 is responsible for autosomal recessive juvenile PD, this genetic interaction provides a concept how the degeneration of nigrostriatal dopaminergic neurons and the Parkinson phenotype may be triggered by ATXN2 mutations.

Computational methods for microRNA target prediction.

MicroRNAs (miRNAs) are important players in gene regulation. The final and maybe the most important step in their regulatory pathway is the targeting. Targeting is the binding of the miRNA to the mature RNA via the RNA-induced silencing complex. Expression patterns of miRNAs are highly specific in respect to external stimuli, developmental stage, or tissue. This is used to diagnose diseases such as cancer in which the expression levels of miRNAs are known to change considerably. Newly identified miRNAs are increasing in number with every new release of miRBase which is the main online database providing miRNA sequences and annotation. Many of these newly identified miRNAs do not yet have identified targets. This is especially the case in animals where the miRNA does not bind to its target as perfectly as it does in plants. Valid targets need to be identified for miRNAs in order to properly understand their role in cellular pathways. Experimental methods for target validations are difficult, expensive, and time consuming. Having considered all these facts it is of crucial importance to have accurate computational miRNA target predictions. There are many proposed methods and algorithms available for predicting targets for miRNAs, but only a few have been developed to become available as independent tools and software. There are also databases which collect and store information regarding predicted miRNA targets. Current approaches to miRNA target prediction produce a huge amount of false positive and an unknown amount of false negative results, and thus the need for better approaches is evermore evident. This chapter aims to give some detail about the current tools and approaches used for miRNA target prediction, provides some grounds for their comparison, and outlines a possible future.

Can MiRBase provide positive data for machine learning for the detection of MiRNA hairpins?

Experimental detection and validation of miRNAs is a tedious, time-consuming, and expensive process. Computational methods for miRNA gene detection are being developed so that the number of candidates that need experimental validation can be reduced to a manageable amount. Computational methods involve homology-based and ab inito algorithms. Both approaches are dependent on positive and negative training examples. Positive examples are usually derived from miRBase, the main resource for experimentally validated miRNAs. We encountered some problems with miRBase which we would like to report here. Some problems, among others, we encountered are that folds presented in miRBase are not always the fold with the minimum free energy; some entries do not seem to conform to expectations of miRNAs, and some external accession numbers are not valid. In addition, we compared the prediction accuracy for the same negative dataset when the positive data came from miRBase or miRTarBase and found that the latter led to more precise prediction models. We suggest that miRBase should introduce some automated facilities for ensuring data quality to overcome these problems.