Evaluating the feasibility of Cas9 overexpression in 3T3-L1 cells for generation of genetic knock-out adipocyte cell lines

Cell lines recapitulating physiological processes can represent alternatives to animal or human studies. The 3T3-L1 cell line is used to mimic adipocyte function and differentiation. Since transfection of 3T3-L1 cells is difficult, we used a modified 3T3-L1 cell line overexpressing Cas9 for a straightforward generation of gene knock-outs. As an example, we intended to generate 3T3-L1 cell lines deficient for adhesion G protein-coupled receptors Gpr64/Adgr2 and Gpr126/Adgr6 using the CRISPR/Cas approach. Surprisingly, all the generated knock-out as well as scramble control cell lines were unresponsive to isoprenaline in respect to adiponectin secretion and lipolysis in contrast to the wild type 3T3-L1 cells. We, therefore, analysed the properties of these stable Cas9-overexpressing 3T3-L1 cells. We demonstrate that this commercially available cell line exhibits dysfunction in cAMP signalling pathways as well as reduced insulin sensitivity independent of gRNA transfection. We tried transient transfection of plasmids harbouring Cas9 as well as direct introduction of the Cas9 protein as alternate approaches to the stable expression of this enzyme. We find that transfection of the Cas9 protein is not only feasible but also does not impair adipogenesis and, therefore, represents a preferable alternative to achieve genetic knock-out.</p

Substitutions in HARs recovered from the array capture experiment (Sidrón) and two published hominin genomes (Neandertal and Denisova).

<p>(<b>A</b>) Number of substitutions on the human lineage recovered in each dataset. (<b>B</b>) Fraction of substitutions recovered in each dataset and all datasets together. (<b>C</b>) Pie charts of substitutions from all possible overlaps among datasets (agree: datasets have same state; disagree: at least two datasets differ).</p

Temporal clustering analysis of HAR substitutions and protein-coding genes.

<p>(<b>A</b>) Clustering ratios for recent substitutions. HAR ratios computed using all datasets, and changes in genes computed from Neandertal and Denisova genomes. Non-syn gene = non-synonymous substitutions in full-length protein sequences; syn gene = synonymous substitutions in full-length protein sequences; syn exon = synonymous substitutions in individual exon sequences. (<b>B</b>) Clustering ratios for old substitutions. In both panels, the red line shows ratio = 1, which indicates an absence of clustering. Error bars are 95% confidence intervals calculated from an empirical distribution after repeating the sample procedure 1,000 times.</p

Percentages of fixed positions for old and recent substitutions.

<p>The red points show the percentages calculated for HARs, the horizontal black line shows the genome-wide percentage, and the orange shaded areas show 95% confidence intervals for the genome-wide percentage calculated from an empirical distribution after randomly sampling genome-wide HAR-sized elements 1,000 times.</p

Percentage of substitutions in HARs and other functional categories classified as recent.

<p>(<b>A</b>) Recent substitutions in HARs. Boxplots represent all substitutions and substitutions from A/T to G/C (W2S) and from G/C to A/T (S2W) base pairs, respectively. Recent substitutions are defined as those found in modern humans but none of the ancient hominins (determined using Sidrón, Neandertal and Denisova datasets). The red lines show the genome-wide averages. (<b>B</b>) Recent substitutions in different functional categories. Boxplots represent HARs and other categories of genomic elements. Substitutions in HARs are defined as in (a) while substitutions in other categories are defined using either the Neandertal or Denisova genome. In both panels, the red line shows the genome-wide average percentage of recent substitutions (12%). Error bars for HARs are 95% confidence intervals calculated from an empirical bootstrap distribution (1,000 iterations). Error bars for other functional categories are 95% binomial confidence intervals. Colors explained in inset.</p

Temporal clustering analysis of HARs.

<p>HARs were split in two bins according to their percentage of W2S substitutions: 0 to 50% and >50%. (<b>A</b>) Clustering ratios for recent substitutions of each type. (<b>B</b>) Clustering ratios for old substitutions of each type. In both panels, the red lines and confidence intervals are calculated as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032877#pone-0032877-g004" target="_blank">Figure 4</a>.</p

Genome-wide association study of long COVID

Infections can lead to persistent symptoms and diseases such as shingles after varicella zoster or rheumatic fever after streptococcal infections. Similarly, severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) infection can result in long coronavirus disease (COVID), typically manifesting as fatigue, pulmonary symptoms and cognitive dysfunction. The biological mechanisms behind long COVID remain unclear. We performed a genome-wide association study for long COVID including up to 6,450 long COVID cases and 1,093,995 population controls from 24 studies across 16 countries. We discovered an association of FOXP4 with long COVID, independent of its previously identified association with severe COVID-19. The signal was replicated in 9,500 long COVID cases and 798,835 population controls. Given the transcription factor FOXP4’s role in lung physiology and pathology, our findings highlight the importance of lung function in the pathophysiology of long COVID.</p

COVID-19 Host Genetics Initiative. A first update on mapping the human genetic architecture of COVID-19

The COVID-19 pandemic continues to pose a major public health threat, especially in countries with low vaccination rates. To better understand the biological underpinnings of SARS-CoV-2 infection and COVID-19 severity, we formed the COVID-19 Host Genetics Initiative1. Here we present a genome-wide association study meta-analysis of up to 125,584 cases and over 2.5 million control individuals across 60 studies from 25 countries, adding 11 genome-wide significant loci compared with those previously identified2. Genes at new loci, including SFTPD, MUC5B and ACE2, reveal compelling insights regarding disease susceptibility and severity.</p

COVID-19 Host Genetics Initiative. A first update on mapping the human genetic architecture of COVID-19

The COVID-19 pandemic continues to pose a major public health threat, especially in countries with low vaccination rates. To better understand the biological underpinnings of SARS-CoV-2 infection and COVID-19 severity, we formed the COVID-19 Host Genetics Initiative1. Here we present a genome-wide association study meta-analysis of up to 125,584 cases and over 2.5 million control individuals across 60 studies from 25 countries, adding 11 genome-wide significant loci compared with those previously identified2. Genes at new loci, including SFTPD, MUC5B and ACE2, reveal compelling insights regarding disease susceptibility and severity.</p