Comparison of Statistical Tests and Power Analysis for Phosphoproteomics Data.

Advances in protein tagging and mass spectrometry have enabled generation of large quantitative proteome and phosphoproteome data sets, for identifying differentially expressed targets in case-control studies. The power study of statistical tests is critical for designing strategies for effective target identification and control of experimental cost. Here, we develop a simulation framework to generate realistic phospho-peptide data with known changes between cases and controls. Using this framework, we quantify the performance of traditional t-tests, Bayesian tests, and the ranking-by-fold-change test. Bayesian tests, which share variance information among peptides, outperform the traditional t-tests. Although ranking-by-fold-change has similar power as the Bayesian tests, its type I error rate cannot be properly controlled without proper permutation analysis; therefore, simply relying on the ranking likely brings false positives. Two-sample Bayesian tests considering dependencies between intensity and variance are superior for data sets with complex variance. While increasing the sample size enhances the statistical tests' performance, balanced controls and cases are recommended over a one-side weighted group. Further, higher peptide standard deviations require higher fold changes to achieve the same statistical power. Together, these results highlight the importance of model-informed experimental design and principled statistical analyses when working with large-scale proteomics and phosphoproteomics data.

Potential Role of Glyphosate, Glyphosate-Based Herbicides, and AMPA in Breast Cancer Development: A Review of Human and Human Cell-Based Studies.

The potential connection between exposure to glyphosate and glyphosate-based herbicides (GBHs) and breast cancer risk is a topic of research that is rapidly gaining the public's attention due to the conflicting reports surrounding glyphosate's potential carcinogenicity. In this review, we synthesize the current published biomedical literature works that have explored associations of glyphosate, its metabolite, aminomethylphosphonic acid (AMPA), and GBHs with breast cancer risk in humans and human cell-based models. Using PubMed as our search engine, we identified a total of 14 articles that were included in this review. In the four human studies, urinary glyphosate and/or AMPA were associated with breast cancer risk, endocrine disruption, oxidative stress biomarkers, and changes in DNA methylation patterns. Among most of the 10 human cell-based studies, glyphosate exhibited endocrine disruption, induced altered gene expression, increased DNA damage, and altered cell viability, while GBHs were more cytotoxic than glyphosate alone. In summary, numerous studies have shown glyphosate, AMPA, and GBHs to have potential carcinogenic, cytotoxic, or endocrine-disruptive properties. However, more human studies need to be conducted in order for more definitive and supported conclusions to be made on their potential effects on breast cancer risk.

Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil.

Cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in Manaus, Brazil, resurged in late 2020 despite previously high levels of infection. Genome sequencing of viruses sampled in Manaus between November 2020 and January 2021 revealed the emergence and circulation of a novel SARS-CoV-2 variant of concern. Lineage P.1 acquired 17 mutations, including a trio in the spike protein (K417T, E484K, and N501Y) associated with increased binding to the human ACE2 (angiotensin-converting enzyme 2) receptor. Molecular clock analysis shows that P.1 emergence occurred around mid-November 2020 and was preceded by a period of faster molecular evolution. Using a two-category dynamical model that integrates genomic and mortality data, we estimate that P.1 may be 1.7- to 2.4-fold more transmissible and that previous (non-P.1) infection provides 54 to 79% of the protection against infection with P.1 that it provides against non-P.1 lineages. Enhanced global genomic surveillance of variants of concern, which may exhibit increased transmissibility and/or immune evasion, is critical to accelerate pandemic responsiveness.

Genomics and epidemiology of a novel SARS-CoV-2 lineage in Manaus, Brazil.

Cases of SARS-CoV-2 infection in Manaus, Brazil, resurged in late 2020, despite high levels of previous infection there. Through genome sequencing of viruses sampled in Manaus between November 2020 and January 2021, we identified the emergence and circulation of a novel SARS-CoV-2 variant of concern, lineage P.1, that acquired 17 mutations, including a trio in the spike protein (K417T, E484K and N501Y) associated with increased binding to the human ACE2 receptor. Molecular clock analysis shows that P.1 emergence occurred around early November 2020 and was preceded by a period of faster molecular evolution. Using a two-category dynamical model that integrates genomic and mortality data, we estimate that P.1 may be 1.4-2.2 times more transmissible and 25-61% more likely to evade protective immunity elicited by previous infection with non-P.1 lineages. Enhanced global genomic surveillance of variants of concern, which may exhibit increased transmissibility and/or immune evasion, is critical to accelerate pandemic responsiveness.

Genomics and epidemiology of the P 1 SARS-CoV-2 lineage in Manaus, Brazil

Cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in Manaus, Brazil, resurged in late 2020 despite previously high levels of infection. Genome sequencing of viruses sampled in Manaus between November 2020 and January 2021 revealed the emergence and circulation of a novel SARS-CoV-2 variant of concern. Lineage P.1 acquired 17 mutations, including a trio in the spike protein (K417T, E484K, and N501Y) associated with increased binding to the human ACE2 (angiotensin-converting enzyme 2) receptor. Molecular clock analysis shows that P.1 emergence occurred around mid-November 2020 and was preceded by a period of faster molecular evolution. Using a two-category dynamical model that integrates genomic and mortality data, we estimate that P.1 may be 1.7- to 2.4-fold more transmissible and that previous (non-P.1) infection provides 54 to 79% of the protection against infection with P.1 that it provides against non-P.1 lineages. Enhanced global genomic surveillance of variants of concern, which may exhibit increased transmissibility and/or immune evasion, is critical to accelerate pandemic responsiveness.