A New Strategy for Efficient Retrospective Data Analyses for Designer Benzodiazepines in Large LC-HRMS Datasets

The expanding and dynamic market of new psychoactive substances (NPSs) poses challenges for laboratories worldwide. The retrospective data analysis (RDA) of previously analyzed samples for new targets can be used to investigate analytes missed in the first data analysis. However, RDA has historically been unsuitable for routine evaluation because reprocessing and reevaluating large numbers of forensic samples are highly work- and time-consuming. In this project, we developed an efficient and scalable retrospective data analysis workflow that can easily be tailored and optimized for groups of NPSs. The objectives of the study were to establish a retrospective data analysis workflow for benzodiazepines in whole blood samples and apply it on previously analyzed driving-under-the-influence-of-drugs (DUID) cases. The RDA workflow was based on a training set of hits in ultrahigh-performance liquid chromatography–quadrupole time-of-flight–mass spectrometry (UHPLC-QTOF-MS) data files, corresponding to common benzodiazepines that also had been analyzed with a complementary UHPLC–tandem mass spectrometry (MS/MS) method. Quantitative results in the training set were used as the true condition to evaluate whether a hit in the UHPLC-QTOF-MS data file was true or false positive. The training set was used to evaluate and set filters. The RDA was used to extract information from 47 DBZDs in 13,514 UHPLC-QTOF-MS data files from DUID cases analyzed from 2014 to 2020, with filters on the retention time window, count level, and mass error. Sixteen designer and uncommon benzodiazepines (DBZDs) were detected, where 47 identifications had been confirmed by using complementary methods when the case was open (confirmed positive finding), and 43 targets were not reported when the case was open (tentative positive finding). The most common tentative and confirmed findings were etizolam (n = 26), phenazepam (n = 13), lorazepam (n = 9), and flualprazolam (n = 8). This method efficiently found DBZDs in previously acquired UHPLC-QTOF-MS data files, with only nine false-positive hits. When the standard of an emerging DBZD becomes available, all previously acquired DUID data files can be screened in less than 1 min. Being able to perform a fast and accurate retrospective data analysis across previously acquired data files is a major technological advancement in monitoring NPS abuse

Membranes by the Numbers

Many of the most important processes in cells take place on and across membranes. With the rise of an impressive array of powerful quantitative methods for characterizing these membranes, it is an opportune time to reflect on the structure and function of membranes from the point of view of biological numeracy. To that end, in this article, I review the quantitative parameters that characterize the mechanical, electrical and transport properties of membranes and carry out a number of corresponding order of magnitude estimates that help us understand the values of those parameters.Comment: 27 pages, 12 figure

Characterization of Drosophila melanogaster JmjC+N histone demethylases

In this article, we characterize histone demethylase activity of the entire family of JmjC+N proteins of Drosophila melanogaster. Our results show that Lid (little imaginal discs), which is structurally homologous to JARID1, demethylates H3K4me3. However, contrary to what would be inferred from its demethylase activity, lid contributes to the establishment of transcriptionally competent chromatin states as: (i) is required for histone H3 acetylation; (ii) contributes to expression of the homoeotic gene Ultrabithorax (Ubx); and (iii) antagonizes heterochromatin-mediated gene silencing (PEV). These results, which are consistent with the identification of lid as a trithorax group (trxG) gene, are discussed in the context of current models for the contribution of H3K4me3 to the regulation of gene expression. Here, we also show that the two Drosophila JMJD2 homologues, dJMJD2(1)/CG15835 and dJMJD2(2)/CG33182, are capable of demethylating both H3K9me3 and H3K36me3. dJMJD2(1)/CG15835 regulates heterochromatin organization, as its over-expression induces spreading of HP1, out of heterochromatin, into euchromatin, without affecting the actual pattern of histone modifications of heterochromatin. dJMJD2(1)/CG15835 is excluded from heterochromatin and localizes to multiple euchromatic sites, where it regulates H3K36 methylation. These results indicate that dJMJD2(1)/CG15835 contributes to delimit hetero- and euchromatic territories through the regulation of H3K36 methylation in euchromatin. On the other hand, dJARID2/CG3654 shows no demethylase activity on H3K4me3, H3K9me3, H3K27me3, H3K36me3 and H4K20me3

Finding one's way in proteomics: a protein species nomenclature

Our knowledge of proteins has greatly improved in recent years, driven by new technologies in the fields of molecular biology and proteome research. It has become clear that from a single gene not only one single gene product but many different ones - termed protein species - are generated, all of which may be associated with different functions. Nonetheless, an unambiguous nomenclature for describing individual protein species is still lacking. With the present paper we therefore propose a systematic nomenclature for the comprehensive description of protein species. The protein species nomenclature is flexible and adaptable to every level of knowledge and of experimental data in accordance with the exact chemical composition of individual protein species. As a minimum description the entry name (gene name + species according to the UniProt knowledgebase) can be used, if no analytical data about the target protein species are available

Measurement of qubits

We describe in detail the theory underpinning the measurement of density matrices of a pair of quantum two-level systems (qubits). Our particular emphasis is on qubits realized by the two polarization degrees of freedom of a pair of entangled photons generated in a down-conversion experiment; however, the discussion applies in general, regardless of the actual physical realization. Two techniques are discussed, namely, a tomographic reconstruction (in which the density matrix is linearly related to a set of measured quantities) and a maximum likelihood technique which requires numerical optimization (but has the advantage of producing density matrices that are always non-negative definite). In addition, a detailed error analysis is presented, allowing errors in quantities derived from the density matrix, such as the entropy or entanglement of formation, to be estimated. Examples based on down-conversion experiments are used to illustrate our results

Immune Reconstitution During the First Year of Antiretroviral Therapy of HIV-1-Infected Adults in Rural Burkina Faso

There are no data on the outcome of highly active antiretroviral therapy (HAART) in HIV-infected adults in rural Burkina Faso. We therefore assessed CD4+ T-cell counts and HIV-1 plasma viral load (VL), the proportion of naive T-cells (co-expressing CCR7 and CD45RA) and T-cell activation (expression of CD95 or CD38) in 61 previously untreated adult patients from Nouna, Burkina Faso, at baseline and 2 weeks, 1, 3, 6, 9 and 12 months after starting therapy. Median CD4+ T-cell counts increased from 174 (10th-90th percentile: 33-314) cells/µl at baseline to 300 (114- 505) cells/µl after 3 months and 360 (169-562) cells/µl after 12 months of HAART. Median VL decreased from 5.8 (4.6- 6.6) log10 copies/ml at baseline to 1.6 (1.6-2.3) log10 copies/ml after 12 months. Early CD4+ T-cell recovery was accompanied by a reduction of the expression levels of CD95 and CD38 on T-cells. Out of 42 patients with complete virological follow-up under HAART, 19 (45%) achieved concordant good immunological (gain of ≥100 CD4+ T-cells/µl above baseline) and virological (undetectable VL) responses after 12 months of treatment (intention-to-treat analysis). Neither a decreased expression of the T-cell activation markers CD38 and CD95, nor an increase in the percentage of naive T-cells reliably predicted good virological treatment responses in patients with good CD4+ T-cell reconstitution. Repeated measurement of CD4+ T-cell counts during HAART remains the most important parameter for immunologic monitoring. Substitution of repeated VL testing by determination of T-cell activation levels (e.g., CD38 expression on CD8+ T-cells) should be applied with caution