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Cells accumulate mutations as we age, and these mutations can be a source of diseases such as cancer. How cells containing mutations evolve, are maintained, and proliferate within the body has not been well characterized. Using a quantitative framework, Watson et al. applied population genetic theory to estimate mutation accumulation in cells in blood from sequencing data derived from nearly 50,000 healthy individuals (see the Perspective by Curtis). By evaluating how mutations differ between blood cell populations, a phenomenon known as clonal hematopoiesis, the researchers could observe how recurrent mutations can drive certain clonal lineages to high frequencies within an individual. The risk of specific mutations, some of which are associated with leukemias, rising to high frequencies may therefore be a function of cellular selection and the age at which the mutation originated. Science , this issue p. 1449 ; see also p. 1426 Blood sequencing data from ~50,000 individuals reveals how mutation, genetic drift, and fitness differences shape the diversity of healthy blood. Somatic mutations acquired in healthy tissues as we age are major determinants of cancer risk. Whether variants confer a fitness advantage or rise to detectable frequencies by chance remains largely unknown. Blood sequencing data from ~50,000 individuals reveal how mutation, genetic drift, and fitness shape the genetic diversity of healthy blood (clonal hematopoiesis). We show that positive selection, not drift, is the major force shaping clonal hematopoiesis, provide bounds on the number of hematopoietic stem cells, and quantify the fitness advantages of key pathogenic variants, at single-nucleotide resolution, as well as the distribution of fitness effects (fitness landscape) within commonly mutated driver genes. These data are consistent with clonal hematopoiesis being driven by a continuing risk of mutations and clonal expansions that become increasingly detectable with age.

Mosaic chromosomal alterations (mCAs) are common in cancers and can arise decades before diagnosis. A quantitative understanding of the rate at which these events occur, and their functional consequences, could improve cancer risk prediction and our understanding of somatic evolution. Using mCA clone size estimates from the blood of approximately 500,000 UK Biobank participants, we estimate mutation rates and fitness consequences of acquired gain, loss and copy-neutral loss of heterozygosity events. Most mCAs have moderate to high fitness effects but occur at a low rate, being more than tenfold less common than equivalently fit single-nucleotide variants. Notable exceptions are mosaic loss of X and Y, which we estimate have roughly 1,000-fold higher mutation rates than autosomal mCAs. Although the way in which most mCAs increase in prevalence with age is consistent with constant growth rates, some mCAs exhibit different behavior, suggesting that their fitness may depend on inherited variants, extrinsic factors or distributions of fitness effects. A survey of the fitness effects conferred by mosaic chromosomal alterations (mCAs) in UK Biobank shows that most mCAs—despite being relatively infrequent—are associated with increased fitness. Mosaic loss of the sex chromosomes was more common but these events afforded only small fitness gains.

The repeated failures of amyloid-targeting therapies have challenged our narrow understanding of Alzheimer’s disease (AD) pathogenesis and inspired wide-ranging investigations into the underlying mechanisms of disease. Increasing evidence indicates that AD develops from an intricate web of biochemical and cellular processes that extend far beyond amyloid and tau accumulation. This growing recognition surrounding the diversity of AD pathophysiology underscores the need for holistic systems-based approaches to explore AD pathogenesis. Here we describe how network-based proteomics has emerged as a powerful tool and how its application to the AD brain has provided an informative framework for the complex protein pathophysiology underlying the disease. Furthermore, we outline how the AD brain network proteome can be leveraged to advance additional scientific and translational efforts, including the discovery of novel protein biomarkers of disease.

Background Despite being twice as likely to get Alzheimer’s disease (AD), African Americans have been grossly underrepresented in AD research. While emerging evidence indicates that African Americans with AD have lower cerebrospinal fluid (CSF) levels of Tau compared to Caucasians, other differences in AD CSF biomarkers have not been fully elucidated. Here, we performed unbiased proteomic profiling of CSF from African Americans and Caucasians with and without AD to identify both common and divergent AD CSF biomarkers. Methods Multiplex tandem mass tag-based mass spectrometry (TMT-MS) quantified 1,840 proteins from 105 control and 98 AD patients of which 100 identified as Caucasian while 103 identified as African American. We used differential protein expression and co-expression approaches to assess how changes in the CSF proteome are related to race and AD. Co-expression network analysis organized the CSF proteome into 14 modules associated with brain cell-types and biological pathways. A targeted mass spectrometry method, selected reaction monitoring (SRM), with heavy labeled internal standards was used to measure a panel of CSF module proteins across a subset of African Americans and Caucasians with or without AD. A receiver operating characteristic (ROC) curve analysis assessed the performance of each protein biomarker in differentiating controls and AD by race. Results Consistent with previous findings, the increase of Tau levels in AD was greater in Caucasians than in African Americans by both immunoassay and TMT-MS measurements. CSF modules which included 14–3-3 proteins (YWHAZ and YWHAG) demonstrated equivalent disease-related elevations in both African Americans and Caucasians with AD, whereas other modules demonstrated more profound disease changes within race. Modules enriched with proteins involved with glycolysis and neuronal/cytoskeletal proteins, including Tau, were more increased in Caucasians than in African Americans with AD. In contrast, a module enriched with synaptic proteins including VGF, SCG2, and NPTX2 was significantly lower in African Americans than Caucasians with AD. Following SRM and ROC analysis, VGF, SCG2, and NPTX2 were significantly better at classifying African Americans than Caucasians with AD. Conclusions Our findings provide insight into additional protein biomarkers and pathways reflecting underlying brain pathology that are shared or differ by race.

Genetic alterations under positive selection in healthy tissues have implications for cancer risk. However, total levels of positive selection across the genome remain unknown. Passenger mutations are influenced by all driver mutations, regardless of type or location in the genome. Therefore, the total number of passengers can be used to estimate the total number of drivers—including unidentified drivers outside of cancer genes that are traditionally missed. Here we analyze the variant allele frequency spectrum of synonymous mutations from healthy blood and esophagus to quantify levels of missing positive selection. In blood, we find that only 30% of passengers can be explained by single-nucleotide variants in driver genes, suggesting high levels of positive selection for mutations elsewhere in the genome. In contrast, more than half of all passengers in the esophagus can be explained by just the two driver genes NOTCH1 and TP53 , suggesting little positive selection elsewhere. Synonymous passenger mutations are used to measure levels of positive selection in healthy blood and esophagus. This approach can quantify missing selection due to unidentified drivers.

Almost all individuals with Down Syndrome (DS) develop Alzheimer’s disease (AD) by mid to late life. However, the degree to which AD in DS shares pathological changes with sporadic late-onset AD (LOAD) and autosomal dominant AD (ADAD) beyond core AD biomarkers such as amyloid-β (Aβ) and tau is unknown. Here, we used proteomics of cerebrospinal fluid from individuals with DS ( n  = 229) in the Down Alzheimer Barcelona Neuroimaging Initiative (DABNI) cohort to assess the evolution of AD pathophysiology from asymptomatic to dementia stages and compared the proteomic biomarker changes in DS to those observed in LOAD and ADAD. Although many proteomic alterations were shared across DS, LOAD, and ADAD, DS demonstrated more severe changes in immune-related proteins, extracellular matrix pathways, and plasma proteins likely related to blood-brain barrier dysfunction compared to LOAD. These changes were present in young adults with DS prior to the onset of Aβ or tau pathology, suggesting they are associated with trisomy 21 and may serve as risk factors for DSAD. DSAD showed an earlier increase in markers of axonal and white matter pathology and earlier changes in markers potentially associated with cerebral amyloid angiopathy compared to ADAD. The unique features of DSAD may have important implications for treatment strategies in this population. Down syndrome causes extensive Alzheimer’s disease pathology in all individuals and has been instrumental in development of the amyloid hypothesis in AD. Here, the authors use proteomics on Down syndrome spinal fluid and brain tissues to illustrate the common and unique changes in DSAD compared to other genetic forms of AD and the more common late-onset form of the disease.

Alzheimer’s disease (AD) is the most common form of dementia, with cerebrospinal fluid (CSF) β-amyloid (Aβ), total Tau, and phosphorylated Tau (pTau) providing the most sensitive and specific biomarkers for diagnosis. However, these diagnostic biomarkers do not reflect the complex changes in AD brain beyond amyloid (A) and Tau (T) pathologies. Here, we report a selected reaction monitoring mass spectrometry (SRM-MS) method with isotopically labeled standards for relative protein quantification in CSF. Biomarker positive (AT+) and negative (AT−) CSF pools were used as quality controls (QCs) to assess assay precision. We detected 62 peptides (51 proteins) with an average coefficient of variation (CV) of ~13% across 30 QCs and 133 controls (cognitively normal, AT−), 127 asymptomatic (cognitively normal, AT+) and 130 symptomatic AD (cognitively impaired, AT+). Proteins that could distinguish AT+ from AT− individuals included SMOC1, GDA, 14-3-3 proteins, and those involved in glycolysis. Proteins that could distinguish cognitive impairment were mainly neuronal proteins (VGF, NPTX2, NPTXR, and SCG2). This demonstrates the utility of SRM-MS to quantify CSF protein biomarkers across stages of AD.

Background/Objectives: Research on the relationship between digital media use in adolescence and mental health outcomes in young adulthood remains unclear. This study aims to (1) assess how trajectories of digital media use from adolescence to young adulthood predict mental health outcomes and (2) identify factors in adolescence that contribute to digital media use trajectories. Methods: Participants (Mage = 15.53 years; 56.86% female; 66.89% White) from the National Longitudinal Study of Adolescent and Adult Health database provided digital media use data across Waves I–IV. At Wave I, participants self-reported parental support, family connectedness, face-to-face interactions with peers, and self-esteem. At Wave IV, participants self-reported anxiety and depression diagnoses, depressive symptomology, suicidal ideation and attempts, and short-term and working memory. General linear and logistic regression models assessed the relationships. Results: Four trajectory groups emerged: Group 1 “increase” (9.97%), Group 2 “low” (73.36%), Group 3 “decrease” (13.94%), and Group 4 “high” (2.73%). Individuals in Group 4 experienced decreased short-term memory compared to individuals in Group 2. The odds of a suicide attempt in the past 12 months were significantly higher for individuals in Groups 3 and 4 compared to Group 2. Conclusions: Patterns of digital media use from adolescence to young adulthood may contribute to suicide attempts and short-term memory in young adulthood, highlighting the need for interventions to reduce screen time. Non-significant findings highlight the need for additional research aimed at clarifying these relationships and identifying factors in early adolescence that may contribute to digital media use trajectories.

Background Amyotrophic Lateral Sclerosis (ALS), a neurodegenerative disease involving loss of motor neurons, typically results in death within 3–5 years of disease onset. Although roughly 10% of cases can be linked to a specific inherited mutation (e.g., C9orf72 hexanucleotide repeat expansion or SOD1 mutation), the cause(s) of most cases are unknown. Consequently, there is a critical need for biomarkers that reflect disease onset and progression across ALS subgroups. Methods We employed tandem mass tag mass spectrometry (TMT-MS) based proteomics on cerebrospinal fluid (CSF) to identify and quantify 2105 proteins from sporadic, C9orf72, and SOD1 ALS patients, asymptomatic C9orf72 expansion carriers, and controls ( N  = 101). To verify trends in our Emory University cohort we used data-independent acquisition (DIA-MS) on an expanded, four center cohort. This expanded cohort of 259 individuals included 50 sporadic ALS (sALS), 43 C9orf72 ALS, 22 SOD1 ALS, 72 asymptomatic gene carriers (59 C9orf72 and 13 SOD1) and 72 age-matched controls. We identified 2330 proteins and used differential protein abundance and network analyses to determine how protein profiles vary across disease subtypes in ALS CSF. Results Differential abundance and co-expression network analysis identified proteomic differences between ALS and control, as well as differentially abundant proteins between sporadic, C9orf72 and SOD1 ALS. A panel of proteins differentiated forms of ALS that are indistinguishable in a clinical setting. An additional panel differentiated asymptomatic from symptomatic C9orf72 and SOD1 mutation carriers, marking a pre-symptomatic proteomic signature of genetic forms of ALS. Leveraging this large, multicenter cohort, we validated our ALS CSF network and identified ALS-specific proteins and network modules. Conclusions This study represents a comprehensive analysis of the CSF proteome across sporadic and genetic causes of ALS that resolves differences among these ALS subgroups and also identifies proteins that distinguish symptomatic from asymptomatic gene carriers. These new data point to varying pathogenic pathways that result in an otherwise clinically indistinguishable disease.

INTRODUCTION Marmosets spontaneously develop pathological hallmarks of Alzheimer's disease (AD) including amyloid beta plaques. However, tau expression in the marmoset brain has been understudied. METHODS Isoforms of tau were examined by western blot, mass spectrometry, immunofluorescence, and immunohistochemical staining. RESULTS 3R and 4R tau isoforms are expressed in marmoset brains at both the transcript and protein levels across ages. Mass spectrometry analysis revealed that tau peptides in marmoset corresponded to the 3R and 4R peptides in human brain, with 3R predominating at birth and an ≈40%:60% 3R:4R ratios in adolescents and adults; tau was distributed widely in neurons, with localization in the soma and synaptic regions. Phosphorylation residues were observed on Threonine (Thr) Thr181, Thr217, Thr231, Serine (Ser) Ser202/Thr205, and Ser396/Ser404. DISCUSSION Our results confirm both 3R and 4R tau isoform expression and phosphorylation residues in the marmoset brain, and emphasize the significance of marmosets with natural expression of AD‐related hallmarks as important translational models for AD. Highlights We report comprehensive characterization of tau isoform expression in marmoset brains across the lifespan. 3R and 4R tau isoforms are expressed in marmoset brains at both the transcript and protein levels across ages. These data emphasize the significance of marmosets with natural expression of primate‐specific traits that are important for the study of Alzheimer's disease.