Cognition, function, and prevalent dementia in centenarians and near-centenarians: An individual participant data (IPD) meta-analysis of 18 studies.

INTRODUCTION: There are limited data on prevalence of dementia in centenarians and near-centenarians (C/NC), its determinants, and whether the risk of dementia continues to rise beyond 100. METHODS: Participant-level data were obtained from 18 community-based studies (N = 4427) in 11 countries that included individuals ≥95 years. A harmonization protocol was applied to cognitive and functional impairments, and a meta-analysis was performed. RESULTS: The mean age was 98.3 years (SD = 2.67); 79% were women. After adjusting for age, sex, and education, dementia prevalence was 53.2% in women and 45.5% in men, with risk continuing to increase with age. Education (OR 0.95;0.92-0.98) was protective, as was hypertension (odds ratio [OR] 0.51;0.35-0.74) in five studies. Dementia was not associated with diabetes, vision and hearing impairments, smoking, and body mass index (BMI). DISCUSSION: Among the exceptional old, dementia prevalence remains higher in the older participants. Education was protective against dementia, but other factors for dementia-free survival in C/NC remain to be understood.

Exome sequencing identifies rare damaging variants in ATP8B4 and ABCA1 as risk factors for Alzheimer's disease.

Alzheimer's disease (AD), the leading cause of dementia, has an estimated heritability of approximately 70%1. The genetic component of AD has been mainly assessed using genome-wide association studies, which do not capture the risk contributed by rare variants2. Here, we compared the gene-based burden of rare damaging variants in exome sequencing data from 32,558 individuals-16,036 AD cases and 16,522 controls. Next to variants in TREM2, SORL1 and ABCA7, we observed a significant association of rare, predicted damaging variants in ATP8B4 and ABCA1 with AD risk, and a suggestive signal in ADAM10. Additionally, the rare-variant burden in RIN3, CLU, ZCWPW1 and ACE highlighted these genes as potential drivers of respective AD-genome-wide association study loci. Variants associated with the strongest effect on AD risk, in particular loss-of-function variants, are enriched in early-onset AD cases. Our results provide additional evidence for a major role for amyloid-ß precursor protein processing, amyloid-ß aggregation, lipid metabolism and microglial function in AD.

Mining for osteogenic surface topographies: In silico design to in vivo osseo-integration.

Stem cells respond to the physicochemical parameters of the substrate on which they grow. Quantitative material activity relationships - the relationships between substrate parameters and the phenotypes they induce - have so far poorly predicted the success of bioactive implant surfaces. In this report, we screened a library of randomly selected designed surface topographies for those inducing osteogenic differentiation of bone marrow-derived mesenchymal stem cells. Cell shape features, surface design parameters, and osteogenic marker expression were strongly correlated in vitro. Furthermore, the surfaces with the highest osteogenic potential in vitro also demonstrated their osteogenic effect in vivo: these indeed strongly enhanced bone bonding in a rabbit femur model. Our work shows that by giving stem cells specific physicochemical parameters through designed surface topographies, differentiation of these cells can be dictated.

Analysis of high-throughput screening reveals the effect of surface topographies on cellular morphology.

Surface topographies of materials considerably impact cellular behavior as they have been shown to affect cell growth, provide cell guidance, and even induce cell differentiation. Consequently, for successful application in tissue engineering, the contact interface of biomaterials needs to be optimized to induce the required cell behavior. However, a rational design of biomaterial surfaces is severely hampered because knowledge is lacking on the underlying biological mechanisms. Therefore, we previously developed a high-throughput screening device (TopoChip) that measures cell responses to large libraries of parameterized topographical material surfaces. Here, we introduce a computational analysis of high-throughput materiome data to capture the relationship between the surface topographies of materials and cellular morphology. We apply robust statistical techniques to find surface topographies that best promote a certain specified cellular response. By augmenting surface screening with data-driven modeling, we determine which properties of the surface topographies influence the morphological properties of the cells. With this information, we build models that predict the cellular response to surface topographies that have not yet been measured. We analyze cellular morphology on 2176 surfaces, and find that the surface topography significantly affects various cellular properties, including the roundness and size of the nucleus, as well as the perimeter and orientation of the cells. Our learned models capture and accurately predict these relationships and reveal a spectrum of topographies that induce various levels of cellular morphologies. Taken together, this novel approach of high-throughput screening of materials and subsequent analysis opens up possibilities for a rational design of biomaterial surfaces.

Somatic mutations found in the healthy blood compartment of a 115-yr-old woman demonstrate oligoclonal hematopoiesis.

The somatic mutation burden in healthy white blood cells (WBCs) is not well known. Based on deep whole-genome sequencing, we estimate that approximately 450 somatic mutations accumulated in the nonrepetitive genome within the healthy blood compartment of a 115-yr-old woman. The detected mutations appear to have been harmless passenger mutations: They were enriched in noncoding, AT-rich regions that are not evolutionarily conserved, and they were depleted for genomic elements where mutations might have favorable or adverse effects on cellular fitness, such as regions with actively transcribed genes. The distribution of variant allele frequencies of these mutations suggests that the majority of the peripheral white blood cells were offspring of two related hematopoietic stem cell (HSC) clones. Moreover, telomere lengths of the WBCs were significantly shorter than telomere lengths from other tissues. Together, this suggests that the finite lifespan of HSCs, rather than somatic mutation effects, may lead to hematopoietic clonal evolution at extreme ages.

Molecular mechanisms of biomaterial-driven osteogenic differentiation in human mesenchymal stromal cells.

Calcium phosphate (CaP) based ceramics are used as bone graft substitutes in the treatment of bone defects. The physico-chemical properties of these materials determine their bioactivity, meaning that molecular and cellular responses in the body will be tuned accordingly. In a previous study, we compared two porous CaP ceramics, hydroxyapatite (HA) and ß-tricalcium phosphate (TCP), which, among other properties, differ in their degradation behaviour in vitro and in vivo, and we demonstrated that the more degradable ß-TCP induced more bone formation in a heterotopic model in sheep. This is correlated to in vitro data, where human bone marrow derived mesenchymal stromal cells (MSC) exhibited higher expression of osteogenic differentiation markers, such as osteopontin, osteocalcin and bone sialoprotein, when cultured in ß-TCP than in HA. More recently, we also showed that this effect could be mimicked in vitro by exposure of MSC to high concentrations of calcium ions (Ca(2+)). To further correlate surface physico-chemical dynamics of HA and ß-TCP ceramics with the molecular response of MSC, we followed Ca(2+) release and surface changes in time as well as cell attachment and osteogenic differentiation of MSC on these ceramics. Within 24 hours, we observed differences in cell morphology, with MSC cultured in ß-TCP displaying more pronounced attachment and spreading than cells cultured in HA. In the same time frame, ß-TCP induced expression of G-protein coupled receptor (GPCR) 5A and regulator of G-protein signaling 2, revealed by DNA microarray analysis. These genes, associated with the protein kinase A and GPCR signaling pathways, may herald the earliest response of MSC to bone-inducing ceramics.

Predicting the therapeutic efficacy of MSC in bone tissue engineering using the molecular marker CADM1.

Mesenchymal stromal cells (hMSCs) are advancing into the clinic but the therapeutic efficacy of hMSCs faces the problem of donor variability. In bone tissue engineering, no reliable markers have been identified which are able to predict the bone-forming capacity of hMSCs prior to implantation. To this end, we isolated hMSCs from 62 donors and characterized systematically their in vitro lineage differentiation capacity, gene expression signature and in vivo capacity for ectopic bone formation. Our data confirms the large variability of in vitro differentiation capacity which did not correlate with in vivo ectopic bone formation. Using DNA microarray analysis of early passage hMSCs we identified a diagnostic bone-forming classifier. In fact, a single gene, CADM1, strongly correlated with the bone-forming capacity of hMSCs and could be used as a reliable in vitro diagnostic marker. Furthermore, data mining of genes expressed correlating with in vivo bone formation represented involvement in neurogenic processes and Wnt signaling. We will apply our data set to predict therapeutic efficacy of hMSCs and to gain novel insight in the process of bone regeneration. Our bio-informatics driven approach may be used in other fields of cell therapy to establish diagnostic markers for clinical efficacy.

Detecting recurrent gene mutation in interaction network context using multi-scale graph diffusion.

BACKGROUND: Delineating the molecular drivers of cancer, i.e. determining cancer genes and the pathways which they deregulate, is an important challenge in cancer research. In this study, we aim to identify pathways of frequently mutated genes by exploiting their network neighborhood encoded in the protein-protein interaction network. To this end, we introduce a multi-scale diffusion kernel and apply it to a large collection of murine retroviral insertional mutagenesis data. The diffusion strength plays the role of scale parameter, determining the size of the network neighborhood that is taken into account. As a result, in addition to detecting genes with frequent mutations in their genomic vicinity, we find genes that harbor frequent mutations in their interaction network context. RESULTS: We identify densely connected components of known and putatively novel cancer genes and demonstrate that they are strongly enriched for cancer related pathways across the diffusion scales. Moreover, the mutations in the clusters exhibit a significant pattern of mutual exclusion, supporting the conjecture that such genes are functionally linked. Using multi-scale diffusion kernel, various infrequently mutated genes are found to harbor significant numbers of mutations in their interaction network neighborhood. Many of them are well-known cancer genes. CONCLUSIONS: The results demonstrate the importance of defining recurrent mutations while taking into account the interaction network context. Importantly, the putative cancer genes and networks detected in this study are found to be significant at different diffusion scales, confirming the necessity of a multi-scale analysis.

A mesenchymal stromal cell gene signature for donor age.

Human aging is associated with loss of function and regenerative capacity. Human bone marrow derived mesenchymal stromal cells (hMSCs) are involved in tissue regeneration, evidenced by their capacity to differentiate into several lineages and therefore are considered the golden standard for cell-based regeneration therapy. Tissue maintenance and regeneration is dependent on stem cells and declines with age and aging is thought to influence therapeutic efficacy, therefore, more insight in the process of aging of hMSCs is of high interest. We, therefore, hypothesized that hMSCs might reflect signs of aging. In order to find markers for donor age, early passage hMSCs were isolated from bone marrow of 61 donors, with ages varying from 17-84, and clinical parameters, in vitro characteristics and microarray analysis were assessed. Although clinical parameters and in vitro performance did not yield reliable markers for aging since large donor variations were present, genome-wide microarray analysis resulted in a considerable list of genes correlating with human age. By comparing the transcriptional profile of aging in human with the one from rat, we discovered follistatin as a common marker for aging in both species. The gene signature presented here could be a useful tool for drug testing to rejuvenate hMSCs or for the selection of more potent, hMSCs for cell-based therapy.

An algorithm-based topographical biomaterials library to instruct cell fate.

It is increasingly recognized that material surface topography is able to evoke specific cellular responses, endowing materials with instructive properties that were formerly reserved for growth factors. This opens the window to improve upon, in a cost-effective manner, biological performance of any surface used in the human body. Unfortunately, the interplay between surface topographies and cell behavior is complex and still incompletely understood. Rational approaches to search for bioactive surfaces will therefore omit previously unperceived interactions. Hence, in the present study, we use mathematical algorithms to design nonbiased, random surface features and produce chips of poly(lactic acid) with 2,176 different topographies. With human mesenchymal stromal cells (hMSCs) grown on the chips and using high-content imaging, we reveal unique, formerly unknown, surface topographies that are able to induce MSC proliferation or osteogenic differentiation. Moreover, we correlate parameters of the mathematical algorithms to cellular responses, which yield novel design criteria for these particular parameters. In conclusion, we demonstrate that randomized libraries of surface topographies can be broadly applied to unravel the interplay between cells and surface topography and to find improved material surfaces.