Psychiatrization of Society: A Conceptual Framework and Call for Transdisciplinary Research

Purpose: Worldwide, there have been consistently high or even rising incidences of diagnosed mental disorders and increasing mental healthcare service utilization over the last decades, causing a growing burden for healthcare systems and societies. While more individuals than ever are being diagnosed and treated as mentally ill, psychiatric knowledge, and practices affect the lives of a rising number of people, gain importance in society as a whole and shape more and more areas of life. This process can be described as the progressing psychiatrization of society. Methods: This article is a conceptual paper, focusing on theoretical considerations and theory development. As a starting point for further research, we suggest a basic model of psychiatrization, taking into account its main sub-processes as well as its major top-down and bottom-up drivers. Results: Psychiatrization is highly complex, diverse, and global. It involves various protagonists and its effects are potentially harmful to individuals, to societies and to public healthcare. To better understand, prevent or manage its negative aspects, there is a need for transdisciplinary research, that empirically assesses causes, mechanisms, and effects of psychiatrization. Conclusion: Although psychiatrization has highly ambivalent effects, its relevance mainly derives from its risks: While individuals with minor disturbances of well-being might be subjected to overdiagnosis and overtreatment, psychiatrization could also result in undermining mental healthcare provision for the most severely ill by promoting the adaption of services to the needs and desires of the rather mild cases. On a societal level, psychiatrization might boost medical interventions which incite individual coping with social problems, instead of encouraging long-term political solutions

Microarray amplification bias: loss of 30% differentially expressed genes due to long probe – poly(A)-tail distances

BACKGROUND: Laser microdissection microscopy has become a rising tool to assess gene expression profiles of pure cell populations. Given the low yield of RNA, a second round of amplification is usually mandatory to yield sufficient amplified-RNA for microarray approaches. Since amplification induces truncation of RNA molecules, we studied the impact of a second round of amplification on identification of differentially expressed genes in relation to the probe - poly(A)-tail distances. RESULTS: Disagreement was observed between gene expression profiles acquired after a second round of amplification compared to a single round. Thirty percent of the differentially expressed genes identified after one round of amplification were not detected after two rounds. These inconsistent genes have a significant longer probe - poly(A)-tail distance. qRT-PCR on unamplified RNA confirmed differential expression of genes with a probe - poly(A)-tail distance >500 nucleotides appearing only after one round of amplification. CONCLUSION: Our data demonstrate a marked loss of 30% of truly differentially expressed genes after a second round of amplification. Therefore, we strongly recommend improvement of amplification procedures and importance of microarray probe design to allow detection of all differentially expressed genes in case of limited amounts of RNA

CAG Repeat Size Influences the Progression Rate of Spinocerebellar Ataxia Type 3

Objective: In spinocerebellar ataxia type 3/Machado-Joseph disease (SCA3/MJD), the expanded cytosine adenine guanine (CAG) repeat in ATXN3 is the causal mutation, and its length is the main factor in determining the age at onset (AO) of clinical symptoms. However, the contribution of the expanded CAG repeat length to the rate of disease progression after onset has remained a matter of debate, even though an understanding of this factor is crucial for experimental data on disease modifiers and their translation to clinical trials and their design. Methods: Eighty-two Dutch patients with SCA3/MJD were evaluated annually for 15 years using the International Cooperative Ataxia Rating Scale (ICARS). Using linear growth curve models, ICARS progression rates were calculated and tested for their relation to the length of the CAG repeat expansion and to the residual age at onset (RAO): The difference between the observed AO and the AO predicted on the basis of the CAG repeat length. Results: On average, ICARS scores increased 2.57 points/year of disease. The length of the CAG repeat was positively correlated with a more rapid ICARS progression, explaining 30% of the differences between patients. Combining both the length of the CAG repeat and RAO as comodifiers explained up to 47% of the interpatient variation in ICARS progression. Interpretation: Our data imply that the length of the expanded CAG repeat in ATXN3 is a major determinant of clinical decline, which suggests that CAG-dependent molecular mechanisms similar to those responsible for disease onset also contribute to the rate of disease progression in SCA3/MJD. ANN NEUROL 2020

Geographic clustering of testicular cancer incidence in the northern part of The Netherlands

Geographic variations in testicular cancer incidence may be caused by differences in environmental factors, genetic factors, or both. In the present study, geographic patterns of age-adjusted testicular cancer incidence rates (IRs) in 12 provinces in The Netherlands in the period 1989–1995 were analysed. In addition, the age-adjusted IR of testicular cancer by degree of urbanization was evaluated. Cancer incidence data were obtained from the Netherlands Cancer Registry. The overall annual age-adjusted IR of testicular cancer in The Netherlands in the period 1989–1995 was 4.4 per 100 000 men. The province Groningen in the north of the country showed the highest annual IR with 5.8 per 100 000 men, which was higher (P < 0.05) than the overall IR in The Netherlands (incidence rate ratio (IRR) 1.3, 95% confidence interval (CI) 1.1–1.6). The highest IR in Groningen was seen for both seminomas and non-seminomas. In addition, Groningen showed the highest age-specific IRs in all relevant younger age groups (15–29, 30–44 and 45–59 years), illustrating the consistency of data. The province Friesland, also situated in the northern part of the country, showed the second highest IR of testicular cancer with 5.3 cases per 100 000 men per year (IRR 1.2, 95% Cl 1.0–1.5, not significant). This mainly resulted from the high IR of seminoma in Friesland. Analysis of age-adjusted IRs of testicular cancer by degree of urbanization in The Netherlands showed no urban–rural differences at analysis of all histological types combined, or at separate analyses of seminomas and non-seminomas. Geographic clustering of testicular cancer seems to be present in the rural north of The Netherlands with some stable founder populations, which are likely to share a relatively high frequency of genes from common ancestors including genes possibly related to testicular cancer. Although this finding does not exclude the involvement of shared environmental factors in the aetiology of testicular cancer, it may also lend support to a genetic susceptibility to testicular cancer development. Testicular cancer cases in stable founder populations seem particularly suitable for searching for testicular cancer susceptibility genes because such genes are likely to be more frequent among affected men in such populations. © 1999 Cancer Research Campaig

Evidence Based Selection of Housekeeping Genes

For accurate and reliable gene expression analysis, normalization of gene expression data against housekeeping genes (reference or internal control genes) is required. It is known that commonly used housekeeping genes (e.g. ACTB, GAPDH, HPRT1, and B2M) vary considerably under different experimental conditions and therefore their use for normalization is limited. We performed a meta-analysis of 13,629 human gene array samples in order to identify the most stable expressed genes. Here we show novel candidate housekeeping genes (e.g. RPS13, RPL27, RPS20 and OAZ1) with enhanced stability among a multitude of different cell types and varying experimental conditions. None of the commonly used housekeeping genes were present in the top 50 of the most stable expressed genes. In addition, using 2,543 diverse mouse gene array samples we were able to confirm the enhanced stability of the candidate novel housekeeping genes in another mammalian species. Therefore, the identified novel candidate housekeeping genes seem to be the most appropriate choice for normalizing gene expression data

A New Perspective on Transcriptional System Regulation (TSR): Towards TSR Profiling

It has been hypothesized that the net expression of a gene is determined by the combined effects of various transcriptional system regulators (TSRs). However, characterizing the complexity of regulation of the transcriptome is a major challenge. Principal component analysis on 17,550 heterogeneous human microarray experiments revealed that 50 orthogonal factors (hereafter called TSRs) are able to capture 64% of the variability in expression in a wide range of experimental conditions and tissues. We identified gene clusters controlled in the same direction and show that gene expression can be conceptualized as a process influenced by a fairly limited set of TSRs. Furthermore, TSRs can be linked to biological functions, as we demonstrate a strong relation between TSR-related gene clusters and biological functionality as well as cellular localization, i.e. gene products of similarly regulated genes by a specific TSR are located in identical parts of a cell. Using 3,934 diverse mouse microarray experiments we found striking similarities in transcriptional system regulation between human and mouse. Our results give biological insights into regulation of the cellular transcriptome and provide a tool to characterize expression profiles with highly reliable TSRs instead of thousands of individual genes, leading to a >500-fold reduction of complexity with just 50 TSRs. This might open new avenues for those performing gene expression profiling studies

Survival-Related Profile, Pathways, and Transcription Factors in Ovarian Cancer

Ate van der Zee and colleagues analyze the gene expression profiles of ovarian cancer samples from 157 patients, and identify an 86-gene expression profile that seems to predict overall survival

Unraveling the Regulatory Mechanisms Underlying Tissue-Dependent Genetic Variation of Gene Expression

It is known that genetic variants can affect gene expression, but it is not yet completely clear through what mechanisms genetic variation mediate this expression. We therefore compared the cis-effect of single nucleotide polymorphisms (SNPs) on gene expression between blood samples from 1,240 human subjects and four primary non-blood tissues (liver, subcutaneous, and visceral adipose tissue and skeletal muscle) from 85 subjects. We characterized four different mechanisms for 2,072 probes that show tissue-dependent genetic regulation between blood and non-blood tissues: on average 33.2% only showed cis-regulation in non-blood tissues; 14.5% of the eQTL probes were regulated by different, independent SNPs depending on the tissue of investigation. 47.9% showed a different effect size although they were regulated by the same SNPs. Surprisingly, we observed that 4.4% were regulated by the same SNP but with opposite allelic direction. We show here that SNPs that are located in transcriptional regulatory elements are enriched for tissue-dependent regulation, including SNPs at 3′ and 5′ untranslated regions (P = 1.84×10−5 and 4.7×10−4, respectively) and SNPs that are synonymous-coding (P = 9.9×10−4). SNPs that are associated with complex traits more often exert a tissue-dependent effect on gene expression (P = 2.6×10−10). Our study yields new insights into the genetic basis of tissue-dependent expression and suggests that complex trait associated genetic variants have even more complex regulatory effects than previously anticipated

Self-reported race/ethnicity in the age of genomic research: its potential impact on understanding health disparities

This review explores the limitations of self-reported race, ethnicity, and genetic ancestry in biomedical research. Various terminologies are used to classify human differences in genomic research including race, ethnicity, and ancestry. Although race and ethnicity are related, race refers to a person’s physical appearance, such as skin color and eye color. Ethnicity, on the other hand, refers to communality in cultural heritage, language, social practice, traditions, and geopolitical factors. Genetic ancestry inferred using ancestry informative markers (AIMs) is based on genetic/genomic data. Phenotype-based race/ethnicity information and data computed using AIMs often disagree. For example, self-reporting African Americans can have drastically different levels of African or European ancestry. Genetic analysis of individual ancestry shows that some self-identified African Americans have up to 99% of European ancestry, whereas some self-identified European Americans have substantial admixture from African ancestry. Similarly, African ancestry in the Latino population varies between 3% in Mexican Americans to 16% in Puerto Ricans. The implication of this is that, in African American or Latino populations, self-reported ancestry may not be as accurate as direct assessment of individual genomic information in predicting treatment outcomes. To better understand human genetic variation in the context of health disparities, we suggest using “ancestry” (or biogeographical ancestry) to describe actual genetic variation, “race” to describe health disparity in societies characterized by racial categories, and “ethnicity” to describe traditions, lifestyle, diet, and values. We also suggest using ancestry informative markers for precise characterization of individuals’ biological ancestry. Understanding the sources of human genetic variation and the causes of health disparities could lead to interventions that would improve the health of all individuals