Blood cytokine analysis suggests that SARS-CoV-2 infection results in a sustained tumour promoting environment in cancer patients

Cytokines, chemokines and (angiogenic) growth factors (CCGs) have been shown to play an intricate role in the progression of both solid and haematological malignancies. Recent studies have shown that SARS-CoV-2 infection leads to worse outcome in cancer patients, especially in haematological malignancy patients. Here, we investigated how SARS-CoV-2 infection impacts the already altered CCG levels in solid or haematological malignancies, specifically whether there is a protective effect or rather a potentially higher risk for major COVID-19 complications in cancer patients due to elevated CCGs linked to cancer progression. Serially analysing immune responses with 55 CCGs in cancer patients under active treatment with or without SARS-CoV-2 infection, we first showed that cancer patients without SARS-CoV-2 infection (n=54) demonstrate elevated levels of 35 CCGs compared to the non-cancer, non-infected control group of health care workers (n=42). Of the 35 CCGs, 19 were common to both solid and haematological malignancy groups and comprised previously described cytokines such as IL-6, TNF-, IL-1Ra, IL-17A, and VEGF, but also several less well described cytokines/chemokines such as Fractalkine, Tie-2, and T cell chemokine CTACK. Importantly, we show here that 7 CCGs are significantly altered in SARS-CoV-2 exposed cancer patients (n=52). Of these TNF-, IFN-{beta}, TSLP and sVCAM-1, identified to be elevated in haematological cancers, are also known tumour-promoting factors. Longitudinal analysis conducted over 3 months showed persistence of several tumour-promoting CCGs in SARS-CoV-2 exposed cancer patients. These data urge for increased vigilance for haematological malignancy patients as a part of long COVID follow-up.

Employing whole genome mapping for optimal de novo assembly of bacterial genomes.

BACKGROUND: De novo genome assembly can be challenging due to inherent properties of the reads, even when using current state-of-the-art assembly tools based on de Bruijn graphs. Often users are not bio-informaticians and, in a black box approach, utilise assembly parameters such as contig length and N50 to generate whole genome sequences, potentially resulting in mis-assemblies. FINDINGS: Utilising several assembly tools based on de Bruijn graphs like Velvet, SPAdes and IDBA, we demonstrate that at the optimal N50, mis-assemblies do occur, even when using the multi-k-mer approaches of SPAdes and IDBA. We demonstrate that whole genome mapping can be used to identify these mis-assemblies and can guide the selection of the best k-mer size which yields the highest N50 without mis-assemblies. CONCLUSIONS: We demonstrate the utility of whole genome mapping (WGM) as a tool to identify mis-assemblies and to guide k-mer selection and higher quality de novo genome assembly of bacterial genomes.