Effective public response to a pandemic relies upon accurate measurement of the extent and dynamics of an outbreak. Viral genome sequencing has emerged as a powerful approach to link seemingly unrelated cases, and large-scale sequencing surveillance can inform on critical epidemiological parameters. Here, we report the analysis of 864 SARS-CoV-2 sequences from cases in the New York City metropolitan area during the COVID-19 outbreak in Spring 2020. The majority of cases had no recent travel history or known exposure, and genetically linked cases were spread throughout the region. Comparison to global viral sequences showed that early transmission was most linked to cases from Europe. Our data are consistent with numerous seeds from multiple sources and a prolonged period of unrecognized community spreading. This work highlights the complementary role of genomic surveillance in addition to traditional epidemiological indicators.

The clinical condition COVID-19, caused by SARS-CoV-2, was declared a pandemic by the WHO in March 2020. Currently, there are more than 5 million cases worldwide, and the pandemic has increased exponentially in many countries, with different incidences and death rates among regions/ethnicities and, intriguingly, between sexes. In addition to the many factors that can influence these discrepancies, we suggest a biological aspect, the genetic variation at the viral S protein receptor in human cells, ACE2 (angiotensin I-converting enzyme 2), which may contribute to the worse clinical outcome in males and in some regions worldwide. We performed exomics analysis in native and admixed South American populations, and we also conducted in silico genomics databank investigations in populations from other continents. Interestingly, at least ten polymorphisms in coding, noncoding and regulatory sites were found that can shed light on this issue and offer a plausible biological explanation for these epidemiological differences. In conclusion, there are ACE2 polymorphisms that could influence epidemiological discrepancies observed among ancestry and, moreover, between sexes.

A complex landscape of genomic regulatory elements underpins patterns of metazoan gene expression, yet it has been technically difficult to disentangle composite regulatory elements within their endogenous genomic context. Expression of the Sox2 transcription factor (TF) in mouse embryonic stem cells (mESCs) depends on a distal regulatory cluster of DNase I hypersensitive sites (DHSs), but the contributions of individual DHSs and their degree of independence remain a mystery. Here, we comprehensively analyze the regulatory architecture of the Sox2 locus in mESCs using Big-IN to scarlessly deliver payloads ranging up to 143 kb, permitting deletions, rearrangements and inversions of single or multiple DHSs, and surgical alterations to individual TF recognition sequences. Multiple independent mESC clones were derived for each payload, extensively sequence-verified, and profiled for expression of Sox2 specifically from the engineered allele. We find that a single core DHS comprising a handful of key TF recognition sequences is sufficient to sustain significant expression in mESCs, though its contribution is modulated by additional DHSs. Moreover, their overall activity is influenced by specific DHS order and/or orientation effects. We built a highly predictive model for locus regulation which includes nonlinear components indicating both synergy and redundancy among. Our results suggest that composite regulatory elements and their influence on gene expression can be resolved to a tractable set of sequence features using synthetic regulatory genomics.

Effective public response to a pandemic relies upon accurate measurement of the extent and dynamics of an outbreak. Viral genome sequencing has emerged as a powerful approach to link seemingly unrelated cases, and large-scale sequencing surveillance can inform on critical epidemiological parameters. Here, we report the analysis of 236 SARS-CoV2 sequences from cases in the New York City metropolitan area during the initial stages of the 2020 COVID-19 outbreak. The majority of cases throughout the region had no recent travel history or known exposure, and genetically linked cases were spread throughout the region. Comparison to global viral sequences showed that the majority were most related to cases from Europe. Our data are consistent with numerous seed transmissions from multiple sources and a prolonged period of unrecognized community spreading. This work highlights the complementary role of real-time genomic surveillance in addition to traditional epidemiological indicators.

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The molecular mechanisms behind aneurysmal subarachnoid haemorrhage (aSAH) are still poorly understood. Expression patterns of miRNAs may help elucidate the post-transcriptional gene expression in aSAH. Here, we evaluate the global miRNAs expression profile (miRnome) of patients with aSAH to identify potential biomarkers. We collected 33 peripheral blood samples (27 patients with cerebral aneurysm, collected 7 to 10 days after the haemorrhage, when usually is the cerebral vasospasm risk peak, and six controls). Then, were performed small RNA sequencing using an Illumina Next Generation Sequencing (NGS) platform. Differential expression analysis identified eight differentially expressed miRNAs. Among them, three were identified being up-regulated, and five down-regulated. miR-486-5p was the most abundant expressed and is associated with poor neurological admission status. In silico miRNA gene target prediction showed 148 genes associated with at least two differentially expressed miRNAs. Among these, THBS1 and VEGFA, known to be related to thrombospondin and vascular endothelial growth factor. Moreover, MYC gene was found to be regulated by four miRNAs, suggesting an important role in aneurysmal subarachnoid haemorrhage. Additionally, 15 novel miRNAs were predicted being expressed only in aSAH, suggesting possible involvement in aneurysm pathogenesis. These findings may help the identification of novel biomarkers of clinical interest.

The manifestation of the COVID-19 varies from absence of symptoms to Severe Acute Respiratory Syndrome. The epidemiological data indicate that infection and mortality rates are greater in European populations in comparison with eastern Asians. To test if epidemiological patterns may be partly determined by human genetic variation, we investigated, by exomic and databank analyses, the variability found in the TMPRSS2 gene in populations from different continents, since this gene is fundamental to virus access into human cells. The functional variants revealed low diversity. The analyses of the variation in the modifiers of gene expression indicate that the European populations may have much higher levels of pulmonary expression of the TMPRSS2 gene and would be more vulnerable to infection by SARS-CoV-2. By contrast, the pulmonary expression of the TMPRSS2 may be reduced in the populations from East Asia, which implies that they are less susceptible to the virus infection and, these genetic features might also favor their better outcomes. The presented data, if confirmed, indicates a potential genetic contribution of TMPRSS2 to individual susceptibility to viral infection, and might also influence COVID-19 outcome.

It is estimated that 10 to 20% of all genes in the human genome encode cell surface proteins and due to their subcellular localization these proteins represent excellent targets for cancer diagnosis and therapeutics. Therefore, a precise characterization of the surfaceome set in different types of tumor is needed. Using TCGA data from 15 different tumor types and a new method to identify cancer genes, the S-score, we identified several potential therapeutic targets within the surfaceome set. This allowed us to expand a previous analysis from us and provided a clear characterization of the human surfaceome in the tumor landscape. Moreover, we present evidence that a three-gene set—WNT5A, CNGA2, and IGSF9B—can be used as a signature associated with shorter survival in breast cancer patients. The data made available here will help the community to develop more efficient diagnostic and therapeutic tools for a variety of tumor types.