A mathematical model of adult subventricular neurogenesis.

Neurogenesis has been the subject of active research in recent years and many authors have explored the phenomenology of the process, its regulation and its purported purpose. Recent developments in bioluminescent imaging (BLI) allow direct in vivo imaging of neurogenesis, and in order to interpret the experimental results, mathematical models are necessary. This study proposes such a mathematical model that describes adult mammalian neurogenesis occurring in the subventricular zone and the subsequent migration of cells through the rostral migratory stream to the olfactory bulb (OB). This model assumes that a single chemoattractant is responsible for cell migration, secreted both by the OB and in an endocrine fashion by the cells involved in neurogenesis. The solutions to the system of partial differential equations are compared with the physiological rodent process, as previously documented in the literature and quantified through the use of BLI, and a parameter space is described, the corresponding solution to which matches that of the rodent model. A sensitivity analysis shows that this parameter space is stable to perturbation and furthermore that the system as a whole is sloppy. A large number of parameter sets are stochastically generated, and it is found that parameter spaces corresponding to physiologically plausible solutions generally obey constraints similar to the conditions reported in vivo. This further corroborates the model and its underlying assumptions based on the current understanding of the investigated phenomenon. Concomitantly, this leaves room for further quantitative predictions pertinent to the design of future proposed experiments

Changes in interstitial fluid flow, mass transport and the bone cell response in microgravity and normogravity

This study was internally funded. Author J.A.'s work was supported by the National Aeronautics and Space Administration [grant No. 80NSSC21M0309] issued through the NASA Office of STEM Engagement

Assessing interactions between the associations of common genetic susceptibility variants, reproductive history and body mass index with breast cancer risk in the breast cancer association consortium: a combined case-control study.

INTRODUCTION: Several common breast cancer genetic susceptibility variants have recently been identified. We aimed to determine how these variants combine with a subset of other known risk factors to influence breast cancer risk in white women of European ancestry using case-control studies participating in the Breast Cancer Association Consortium. METHODS: We evaluated two-way interactions between each of age at menarche, ever having had a live birth, number of live births, age at first birth and body mass index (BMI) and each of 12 single nucleotide polymorphisms (SNPs) (10q26-rs2981582 (FGFR2), 8q24-rs13281615, 11p15-rs3817198 (LSP1), 5q11-rs889312 (MAP3K1), 16q12-rs3803662 (TOX3), 2q35-rs13387042, 5p12-rs10941679 (MRPS30), 17q23-rs6504950 (COX11), 3p24-rs4973768 (SLC4A7), CASP8-rs17468277, TGFB1-rs1982073 and ESR1-rs3020314). Interactions were tested for by fitting logistic regression models including per-allele and linear trend main effects for SNPs and risk factors, respectively, and single-parameter interaction terms for linear departure from independent multiplicative effects. RESULTS: These analyses were applied to data for up to 26,349 invasive breast cancer cases and up to 32,208 controls from 21 case-control studies. No statistical evidence of interaction was observed beyond that expected by chance. Analyses were repeated using data from 11 population-based studies, and results were very similar. CONCLUSIONS: The relative risks for breast cancer associated with the common susceptibility variants identified to date do not appear to vary across women with different reproductive histories or body mass index (BMI). The assumption of multiplicative combined effects for these established genetic and other risk factors in risk prediction models appears justified.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

SNPeffect 4.0: on-line prediction of molecular and structural effects of protein-coding variants

Single nucleotide variants (SNVs) are, together with copy number variation, the primary source of variation in the human genome and are associated with phenotypic variation such as altered response to drug treatment and susceptibility to disease. Linking structural effects of non-synonymous SNVs to functional outcomes is a major issue in structural bioinformatics. The SNPeffect database (http://snpeffect.switchlab.org) uses sequence- and structure-based bioinformatics tools to predict the effect of protein-coding SNVs on the structural phenotype of proteins. It integrates aggregation prediction (TANGO), amyloid prediction (WALTZ), chaperone-binding prediction (LIMBO) and protein stability analysis (FoldX) for structural phenotyping. Additionally, SNPeffect holds information on affected catalytic sites and a number of post-translational modifications. The database contains all known human protein variants from UniProt, but users can now also submit custom protein variants for a SNPeffect analysis, including automated structure modeling. The new meta-analysis application allows plotting correlations between phenotypic features for a user-selected set of variants

Whole-genome sequencing reveals a coding non-pathogenic variant tagging a non-coding pathogenic hexanucleotide repeat expansion in C9orf72 as cause of amyotrophic lateral sclerosis

Motor neuron degeneration in amyotrophic lateral sclerosis (ALS) has a familial cause in 10% of patients. Despite significant advances in the genetics of the disease, many families remain unexplained. We performed whole-genome sequencing in five family members from a pedigree with autosomal-dominant classical ALS. A family-based elimination approach was used to identify novel coding variants segregating with the disease. This list of variants was effectively shortened by genotyping these variants in 2 additional unaffected family members and 1500 unrelated population-specific controls. A novel rare coding variant in SPAG8 on chromosome 9p13.3 segregated with the disease and was not observed in controls. Mutations in SPAG8 were not encountered in 34 other unexplained ALS pedigrees, including 1 with linkage to chromosome 9p13.2–23.3. The shared haplotype containing the SPAG8 variant in this small pedigree was 22.7 Mb and overlapped with the core 9p21 linkage locus for ALS and frontotemporal dementia. Based on differences in coverage depth of known variable tandem repeat regions between affected and non-affected family members, the shared haplotype was found to contain an expanded hexanucleotide (GGGGCC)n repeat in C9orf72 in the affected members. Our results demonstrate that rare coding variants identified by whole-genome sequencing can tag a shared haplotype containing a non-coding pathogenic mutation and that changes in coverage depth can be used to reveal tandem repeat expansions. It also confirms (GGGGCC)n repeat expansions in C9orf72 as a cause of familial ALS

An Evolutionary Trade-Off between Protein Turnover Rate and Protein Aggregation Favors a Higher Aggregation Propensity in Fast Degrading Proteins

We previously showed the existence of selective pressure against protein aggregation by the enrichment of aggregation-opposing ‘gatekeeper’ residues at strategic places along the sequence of proteins. Here we analyzed the relationship between protein lifetime and protein aggregation by combining experimentally determined turnover rates, expression data, structural data and chaperone interaction data on a set of more than 500 proteins. We find that selective pressure on protein sequences against aggregation is not homogeneous but that short-living proteins on average have a higher aggregation propensity and fewer chaperone interactions than long-living proteins. We also find that short-living proteins are more often associated to deposition diseases. These findings suggest that the efficient degradation of high-turnover proteins is sufficient to preclude aggregation, but also that factors that inhibit proteasomal activity, such as physiological ageing, will primarily affect the aggregation of short-living proteins

MutDB: update on development of tools for the biochemical analysis of genetic variation

Understanding how genetic variation affects the molecular function of gene products is an emergent area of bioinformatic research. Here, we present updates to MutDB (http://www.mutdb.org), a tool aiming to aid bioinformatic studies by integrating publicly available databases of human genetic variation with molecular features and clinical phenotype data. MutDB, first developed in 2002, integrates annotated SNPs in dbSNP and amino acid substitutions in Swiss-Prot with protein structural information, links to scores that predict functional disruption and other useful annotations. Though these functional annotations are mainly focused on nonsynonymous SNPs, some information on other SNP types included in dbSNP is also provided. Additionally, we have developed a new functionality that facilitates KEGG pathway visualization of genes containing SNPs and a SNP query tool for visualizing and exporting sets of SNPs that share selected features based on certain filters

Cognitive impairment in young adults following cerebellar stroke:Prevalence and longitudinal course

Introduction: Cognitive impairment is a well-known result of a stroke, but for cerebellar stroke in young patients detailed knowledge on the nature and extent of cognitive deficits is limited. This study examined the prevalence and course of cognitive impairment in a large cohort of patients with cerebellar stroke.Methods: Sixty young (18-49 years) cerebellar stroke patients completed extensive neuropsychological assessments in the subacute (&lt;9 months post-stroke) and/or chronic phase (≥9 months post-stroke). Performance and course were assessed using standardized scores and Reliable Change Index analyses. Associations between cognitive deficits and lesion locations were explored using subtraction analyses, and associations with subjective cognitive complaints and fatigue were examined.Results: Sixty patients (52% male) were included with a mean age at event of 43.1 years. Cognitive impairment was observed in 60.3% of patients in the subacute phase and 51.2% during the chronic phase. Deficits were most frequent for visuo-spatial skills and executive functioning (42.5-54.6%). Both improvement and decline were observed over time, in 17.9% and 41.0% of participants, respectively. Cognitive deficits seem to be associated with lesions in certain cerebellar regions, however, no distinct correlation was found for a specific subregion. Subjective cognitive complaints were present in the majority of participants (61-80.5%) and positively correlated with fatigue in both phases (ρ = -.661 and ρ = -.757, p &lt; .001, respectively).Discussion: Cognitive impairment in cerebellar stroke patients is common, with deficits most pronounced for visuo-spatial skills and executive functioning, as in line with the Cerebellar Cognitive Affective Syndrome. The course of cognitive performance was heterogenous, with cognitive decline despite the fact that no recurrent strokes occurred. No clear association between lesion location and cognitive deficits was observed. Subjective cognitive complaints and fatigue were prevalent and positively correlated. Clinicians could use this information to actively screen for and better inform patients about possible cognitive sequalae.</p

Improved Detection of Rare Genetic Variants for Diseases

Technology advances have promoted gene-based sequencing studies with the aim of identifying rare mutations responsible for complex diseases. A complication in these types of association studies is that the vast majority of non-synonymous mutations are believed to be neutral to phenotypes. It is thus critical to distinguish potential causative variants from neutral variation before performing association tests. In this study, we used existing predicting algorithms to predict functional amino acid substitutions, and incorporated that information into association tests. Using simulations, we comprehensively studied the effects of several influential factors, including the sensitivity and specificity of functional variant predictions, number of variants, and proportion of causative variants, on the performance of association tests. Our results showed that incorporating information regarding functional variants obtained from existing prediction algorithms improves statistical power under certain conditions, particularly when the proportion of causative variants is moderate. The application of the proposed tests to a real sequencing study confirms our conclusions. Our work may help investigators who are planning to pursue gene-based sequencing studies