Conformational changes and loose packing promote E. coli Tryptophanase cold lability

Background  

Oligomeric enzymes can undergo a reversible loss of activity at low temperatures. One such enzyme is tryptophanase (Trpase) from Escherichia coli. Trpase is a pyridoxal phosphate (PLP)-dependent tetrameric enzyme with a Mw of 210 kD. PLP is covalently bound through an enamine bond to Lys270 at the active site. The incubation of holo E. coli Trpases at 2°C for 20 h results in breaking this enamine bond and PLP release, as well as a reversible loss of activity and dissociation into dimers. This sequence of events is termed cold lability and its understanding bears relevance to protein stability and shelf life.     

Identification of proteins that form specific complexes with the highly conserved protein Translin in Schizosaccharomyces pombe

Translin is a single-stranded DNA and RNA binding protein that has a high affinity for G-rich sequences. TRAX is a Translin paralog that associates with Translin. Both Translin and TRAX were highly conserved in eukaryotes. The nucleic acid binding form of Translin is a barrel-shaped homo-octamer. A Translin–TRAX hetero-octamer having a similar structure also binds nucleic acids. Previous reports suggested that Translin may be involved in chromosomal translocations, telomere metabolism and the control of mRNA transport and translation. More recent studies have indicated that Translin–TRAX hetero-octamers are involved in RNA silencing. To gain a further insight into the functions of Translin, we have undertaken to systematically search for proteins with which it forms specific complexes in living cells. Here we report the results of such a search conducted in the fission yeast Schizosaccharomyces pombe, a suitable model system. This search was carried out by affinity purification and immuno-precipitation techniques, combined with differential labeling of the intracellular proteins with the stable isotopes ¹⁵N and ¹⁴N. We identified for the first time two proteins containing an RNA Recognition Motif (RRM), which are specifically associated with the yeast Translin: (1) the pre-mRNA-splicing factor srp1 that belongs to the highly conserved SR family of proteins and (2) vip1, a protein conserved in fungi. Our data also support the presence of RNA in these intracellular complexes. Our experimental approach should be generally applicable to studies of weak intracellular protein–protein interactions and provides a clear distinction between false positive vs. truly interacting proteins.     

Predicting Disease Risk Using Bootstrap Ranking and Classification Algorithms

Genome-wide association studies (GWAS) are widely used to search for genetic loci that underlie human disease. Another goal is to predict disease risk for different individuals given their genetic sequence. Such predictions could either be used as a “black box” in order to promote changes in life-style and screening for early diagnosis, or as a model that can be studied to better understand the mechanism of the disease. Current methods for risk prediction typically rank single nucleotide polymorphisms (SNPs) by the p-value of their association with the disease, and use the top-associated SNPs as input to a classification algorithm. However, the predictive power of such methods is relatively poor. To improve the predictive power, we devised BootRank, which uses bootstrapping in order to obtain a robust prioritization of SNPs for use in predictive models. We show that BootRank improves the ability to predict disease risk of unseen individuals in the Wellcome Trust Case Control Consortium (WTCCC) data and results in a more robust set of SNPs and a larger number of enriched pathways being associated with the different diseases. Finally, we show that combining BootRank with seven different classification algorithms improves performance compared to previous studies that used the WTCCC data. Notably, diseases for which BootRank results in the largest improvements were recently shown to have more heritability than previously thought, likely due to contributions from variants with low minimum allele frequency (MAF), suggesting that BootRank can be beneficial in cases where SNPs affecting the disease are poorly tagged or have low MAF. Overall, our results show that improving disease risk prediction from genotypic information may be a tangible goal, with potential implications for personalized disease screening and treatment.     

SARS-CoV-2 Serological testing in frontline health workers in Zimbabwe

BackgroundIn order to protect health workers from SARS-CoV-2, there is need to characterise the different types of patient facing health workers. Our first aim was to determine both the infection status and seroprevalence of SARS-CoV-2 in health workers. Our second aim was to evaluate the occupational and demographic predictors of seropositivity to inform the country’s infection prevention and control (IPC) strategy.Methods and principal findingsWe invited 713 staff members at 24 out of 35 health facilities in the City of Bulawayo in Zimbabwe. Compliance to testing was defined as the willingness to uptake COVID-19 testing by answering a questionnaire and providing samples for both antibody testing and PCR testing. SARS-COV-2 antibodies were detected using a rapid diagnostic test kit and SAR-COV-2 infection was determined by real-time (RT)-PCR. Of the 713 participants, 635(89%) consented to answering the questionnaire and providing blood sample for antibody testing while 560 (78.5%) agreed to provide nasopharyngeal swabs for the PCR SARS-CoV-2 testing. Of the 635 people (aged 18–73) providing a blood sample 39.1% reported a history of past COVID-19 symptoms while 14.2% reported having current symptoms of COVID-19. The most-prevalent co-morbidity among this group was hypertension (22.0%) followed by asthma (7.0%) and diabetes (6.0%). The SARS-CoV-2 sero-prevalence was 8.9%. Of the 560 participants tested for SARS-CoV-2 infection, 2 participants (0.36%) were positive for SAR-CoV-2 infection by PCR testing. None of the SARS-CoV-2 antibody positive people were positive for SAR-CoV-2 infection by PCR testing.Conclusion and interpretationIn addition to clinical staff, several patient-facing health workers were characterised within Zimbabwe’s health system and the seroprevalence data indicated that previous exposure to SAR-CoV-2 had occurred across the full spectrum of patient-facing staff with nurses and nurse aides having the highest seroprevalence. Our results highlight the need for including the various health workers in IPC strategies in health centres to ensure effective biosecurity and biosafety.     

Colon stem cell and crypt dynamics exposed by cell lineage reconstruction

Stem cell dynamics in vivo are often being studied by lineage tracing methods. Our laboratory has previously developed a retrospective method for reconstructing cell lineage trees from somatic mutations accumulated in microsatellites. This method was applied here to explore different aspects of stem cell dynamics in the mouse colon without the use of stem cell markers. We first demonstrated the reliability of our method for the study of stem cells by confirming previously established facts, and then we addressed open questions. Our findings confirmed that colon crypts are monoclonal and that, throughout adulthood, the process of monoclonal conversion plays a major role in the maintenance of crypts. The absence of immortal strand mechanism in crypts stem cells was validated by the age-dependent accumulation of microsatellite mutations. In addition, we confirmed the positive correlation between physical and lineage proximity of crypts, by showing that the colon is separated into small domains that share a common ancestor. We gained new data demonstrating that colon epithelium is clustered separately from hematopoietic and other cell types, indicating that the colon is constituted of few progenitors and ruling out significant renewal of colonic epithelium from hematopoietic cells during adulthood. Overall, our study demonstrates the reliability of cell lineage reconstruction for the study of stem cell dynamics, and it further addresses open questions in colon stem cells. In addition, this method can be applied to study stem cell dynamics in other systems.