Internal tandem duplications of the Flt3 gene (Flt3/ITDs) are present in about 18% of all AML cases and are therefore one of the most frequent somatic gene mutations in AML. Little is known about the role of Flt3/ITDs in leukemogenesis or their clinical relevance. In this study we compared 18 samples with Flt3/ITDs and 63 AML samples without these mutations with respect to clinical prognosis, cytokine responsiveness, progenitor cell content and repopulation in the NOD/SCID mouse. We found that in patients with a mutation CR rates are reduced (P = 0.03) and relapse rates are increased (P = 0.01), indicating the prognostic importance of Flt3/ITDs. This is also emphasized by the finding that in patients under the age of 60 years, as well as in older patients the event-free survival was more unfavorable for the mutant patients (P = 0.003 and P = 0.03, respectively). At diagnosis Flt3/ITD and non-mutant AML bone marrow samples did not differ in their progenitor/stem cell frequencies. Cobblestone area forming cell (CAFC) subsets showed a similar frequency distribution in mutant and nonmutant samples. In 7-day liquid cultures, Flt3/ITD samples showed a reduced growth in response to a variety of myeloid growth factors. In contrast, Flt3/ITD samples displayed a higher ability to engraft the NOD/SCID bone marrow with leukemic cells. Together these data show that the Flt3/ITD represents an important diagnostic marker for patient prognosis, and that the presence of these mutations is associated with altered proliferative ability of progenitors in vivo and in vitro. Leukemia (2000) 14, 675-683.

PurposeThe Lifelines COVID-19 cohort was set up to assess the psychological and societal impacts of the COVID-19 pandemic and investigate potential risk factors for COVID-19 within the Lifelines prospective population cohort.ParticipantsParticipants were recruited from the 140 000 eligible participants of Lifelines and the Lifelines NEXT birth cohort, who are all residents of the three northern provinces of the Netherlands. Participants filled out detailed questionnaires about their physical and mental health and experiences on a weekly basis starting in late March 2020, and the cohort consists of everyone who filled in at least one questionnaire in the first 8 weeks of the project.Findings to date>71 000 unique participants responded to the questionnaires at least once during the first 8 weeks, with >22 000 participants responding to seven questionnaires. Compiled questionnaire results are continuously updated and shared with the public through the Corona Barometer website. Early results included a clear signal that younger people living alone were experiencing greater levels of loneliness due to lockdown, and subsequent results showed the easing of anxiety as lockdown was eased in June 2020.Future plansQuestionnaires were sent on a (bi)weekly basis starting in March 2020 and on a monthly basis starting July 2020, with plans for new questionnaire rounds to continue through 2020 and early 2021. Questionnaire frequency can be increased again for subsequent waves of infections. Cohort data will be used to address how the COVID-19 pandemic developed in the northern provinces of the Netherlands, which environmental and genetic risk factors predict disease susceptibility and severity and the psychological and societal impacts of the crisis. Cohort data are linked to the extensive health, lifestyle and sociodemographic data held for these participants by Lifelines, a 30-year project that started in 2006, and to data about participants held in national databases.

Data availabilitySummary statistics generated by COVID-19 Host Genetics Initiative are available online (https://www.covid19hg.org/results/r6/). The analyses described here use the freeze 6 data. The COVID-19 Host Genetics Initiative continues to regularly release new data freezes. Summary statistics for samples from individuals of non-European ancestry are not currently available owing to the small individual sample sizes of these groups, but the results for 23 loci lead variants are reported in Supplementary Table 3. Individual-level data can be requested directly from the authors of the contributing studies, listed in Supplementary Table 1.

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The COVID−19 pandemic has affected billions of people around the world not only through the infection itself but also through its wider impact on public health and daily life. To assess the effects of the pandemic, a team of researchers across a wide range of disciplines developed and implemented the Lifelines COVID−19 questionnaire, leading to the development of the Lifelines COVID−19 cohort. This cohort is recruited from participants of the Lifelines prospective population cohort and the Lifelines NEXT birth cohort, and participants were asked to fill out detailed questionnaires about their physical and mental health and experiences on a weekly basis starting in late March of 2020 and on a bi−weekly basis staring in June 2020. The Lifelines region covers the three Northern provinces of the Netherlands−Drenthe, Groningen and Friesland−which together account for about 10% of the Dutch population. To date, >70,000 people have responded to the questionnaires at least once, and the questionnaire program is still ongoing. Data collected by the questionnaires will be used to address four aspects of the outbreak: (1) how the COVID−19 pandemic developed in the three northern provinces of the Netherlands, (2) which environmental risk factors predict disease susceptibility and severity, (3) which genetic risk factors predict disease susceptibility and severity and (4) what are the psychological and societal impacts of the crisis.

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BackgroundExpression quantitative trait loci (eQTL) studies have shown how genetic variants affect downstream gene expression. To identify the upstream regulatory processes, single-cell data can be used. Single-cell data also offers the unique opportunity to reconstruct personalized co-expression networks—by exploiting the large number of cells per individual, we can identify SNPs that alter co-expression patterns (co-expression QTLs, co-eQTLs) using a limited number of individuals.ResultsTo tackle the large multiple testing burden associated with a genome-wide analysis (i.e. the need to assess all combinations of SNPs and gene pairs), we conducted a co-eQTL meta-analysis across four scRNA-seq peripheral blood mononuclear cell datasets from three studies (reflecting 173 unique participants and 1 million cells) using a novel filtering strategy followed by a permutation-based approach. Before analysis, we evaluated the co-expression patterns to be used for co-eQTL identification using different external resources. The subsequent analysis identified a robust set of cell-type-specific co-eQTLs for 72 independent SNPs that affect 946 gene pairs, which we then replicated in a large bulk cohort. These co-eQTLs provide novel insights into how disease-associated variants alter regulatory networks. For instance, one co-eQTL SNP, rs1131017, that is associated with several autoimmune diseases affects the co-expression of RPS26 with other ribosomal genes. Interestingly, specifically in T cells, the SNP additionally affects co-expression of RPS26 and a group of genes associated with T cell-activation and autoimmune disease. Among these genes, we identified enrichment for targets of five T-cell-activation-related transcriptional factors whose binding sites harbor rs1131017. This reveals a previously overlooked process and pinpoints potential regulators that could explain the association of rs1131017 with autoimmune diseases.ConclusionOur co-eQTL results highlight the importance of studying gene regulation at the context-specific level to understand the biological implications of genetic variation. With the expected growth of sc-eQTL datasets, our strategy—combined with our technical guidelines—will soon identify many more co-eQTLs, further helping to elucidate unknown disease mechanisms.