Low Prevalence of Lactase Persistence in Bronze Age Europe Indicates Ongoing Strong Selection over the Last 3,000 Years

Lactase persistence (LP), the continued expression of lactase into adulthood, is the most strongly selected single gene trait over the last 10,000 years in multiple human populations. It has been posited that the primary allele causing LP among Eurasians, rs4988235-A [1], only rose to appreciable frequencies during the Bronze and Iron Ages [2, 3], long after humans started consuming milk from domesticated animals. This rapid rise has been attributed to an influx of people from the Pontic-Caspian steppe that began around 5,000 years ago [4, 5]. We investigate the spatiotemporal spread of LP through an analysis of 14 warriors from the Tollense Bronze Age battlefield in northern Germany (∼3,200 before present, BP), the oldest large-scale conflict site north of the Alps. Genetic data indicate that these individuals represent a single unstructured Central/Northern European population. We complemented these data with genotypes of 18 individuals from the Bronze Age site Mokrin in Serbia (∼4,100 to ∼3,700 BP) and 37 individuals from Eastern Europe and the Pontic-Caspian Steppe region, predating both Bronze Age sites (∼5,980 to ∼3,980 BP). We infer low LP in all three regions, i.e., in northern Germany and South-eastern and Eastern Europe, suggesting that the surge of rs4988235 in Central and Northern Europe was unlikely caused by Steppe expansions. We estimate a selection coefficient of 0.06 and conclude that the selection was ongoing in various parts of Europe over the last 3,000 years

TFPI-2 is a putative tumor suppressor gene frequently inactivated by promoter hypermethylation in nasopharyngeal carcinoma

<p>Abstract</p> <p>Background</p> <p>Epigenetic silencing of tumor suppressor genes play important roles in NPC tumorgenesis. Tissue factor pathway inhibitor-2 (TFPI-2), is a protease inhibitor. Recently, <it>TFPI-2 </it>was suggested to be a tumor suppressor gene involved in tumorigenesis and metastasis in some cancers. In this study, we investigated whether <it>TFPI-2 </it>was inactivated epigenetically in nasopharyngeal carcinoma (NPC).</p> <p>Methods</p> <p>Transcriptional expression levels of <it>TFPI-2 </it>was evaluated by RT-PCR. Methylation status were investigated by methylation specific PCR and bisulfate genomic sequencing. The role of <it>TFPI-2 </it>as a tumor suppressor gene in NPC was addressed by re-introducing <it>TFPI-2 </it>expression into the NPC cell line CNE2.</p> <p>Results</p> <p><it>TFPI-2 </it>mRNA transcription was inactivated in NPC cell lines. <it>TFPI-2 </it>was aberrantly methylated in 66.7% (4/6) NPC cell lines and 88.6% (62/70) of NPC primary tumors, but not in normal nasopharyngeal epithelia. <it>TFPI-2 </it>expression could be restored in NPC cells after demethylation treatment. Ectopic expression of TFPI-2 in NPC cells induced apoptosis and inhibited cell proliferation, colony formation and cell migration.</p> <p>Conclusions</p> <p>Epigenetic inactivation of <it>TFPI-2 </it>by promoter hypermethylation is a frequent and tumor specific event in NPC. <it>TFPI-2 </it>might be considering as a putative tumor suppressor gene in NPC.</p

Foreground modelling via Gaussian process regression: An application to HERA data

The key challenge in the observation of the redshifted 21-cm signal from cosmic reionization is its separation from the much brighter foreground emission. Such separation relies on the different spectral properties of the two components, although, in real life, the foreground intrinsic spectrum is often corrupted by the instrumental response, inducing systematic effects that can further jeopardize the measurement of the 21-cm signal. In this paper, we use Gaussian Process Regression to model both foreground emission and instrumental systematics in $\sim 2$ hours of data from the Hydrogen Epoch of Reionization Array. We find that a simple co-variance model with three components matches the data well, giving a residual power spectrum with white noise properties. These consist of an "intrinsic" and instrumentally corrupted component with a coherence-scale of 20 MHz and 2.4 MHz respectively (dominating the line of sight power spectrum over scales $k_{\parallel} \le 0.2$ h cMpc$^{-1}$) and a baseline dependent periodic signal with a period of $\sim 1$ MHz (dominating over $k_{\parallel} \sim 0.4 - 0.8$h cMpc$^{-1}$) which should be distinguishable from the 21-cm EoR signal whose typical coherence-scales is $\sim 0.8$ MHz

Foreground modelling via Gaussian process regression: An application to HERA data

The key challenge in the observation of the redshifted 21-cm signal from cosmic reionization is its separation from the much brighter foreground emission. Such separation relies on the different spectral properties of the two components, although, in real life, the foreground intrinsic spectrum is often corrupted by the instrumental response, inducing systematic effects that can further jeopardize the measurement of the 21-cm signal. In this paper, we use Gaussian Process Regression to model both foreground emission and instrumental systematics in $\sim 2$ hours of data from the Hydrogen Epoch of Reionization Array. We find that a simple co-variance model with three components matches the data well, giving a residual power spectrum with white noise properties. These consist of an "intrinsic" and instrumentally corrupted component with a coherence-scale of 20 MHz and 2.4 MHz respectively (dominating the line of sight power spectrum over scales $k_{\parallel} \le 0.2$ h cMpc$^{-1}$) and a baseline dependent periodic signal with a period of $\sim 1$ MHz (dominating over $k_{\parallel} \sim 0.4 - 0.8$h cMpc$^{-1}$) which should be distinguishable from the 21-cm EoR signal whose typical coherence-scales is $\sim 0.8$ MHz

Understanding tumor heterogeneity as functional compartments - superorganisms revisited

Compelling evidence broadens our understanding of tumors as highly heterogeneous populations derived from one common progenitor. In this review we portray various stages of tumorigenesis, tumor progression, self-seeding and metastasis in analogy to the superorganisms of insect societies to exemplify the highly complex architecture of a neoplasm as a system of functional "castes.