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Although platinum‐based drugs are widely used chemotherapeutics for cancer treatment, the determinants of tumor cell responsiveness remain poorly understood. We show that the loss of subunits LRRC8A and LRRC8D of the heteromeric LRRC8 volume‐regulated anion channels (VRACs) increased resistance to clinically relevant cisplatin/carboplatin concentrations. Under isotonic conditions, about 50% of cisplatin uptake depended on LRRC8A and LRRC8D, but neither on LRRC8C nor on LRRC8E. Cell swelling strongly enhanced LRRC8‐dependent cisplatin uptake, bolstering the notion that cisplatin enters cells through VRAC. LRRC8A disruption also suppressed drug‐induced apoptosis independently from drug uptake, possibly by impairing VRAC‐dependent apoptotic cell volume decrease. Hence, by mediating cisplatin uptake and facilitating apoptosis, VRAC plays a dual role in the cellular drug response. Incorporation of the LRRC8D subunit into VRAC substantially increased its permeability for cisplatin and the cellular osmolyte taurine, indicating that LRRC8 proteins form the channel pore. Our work suggests that LRRC8D‐containing VRACs are crucial for cell volume regulation by an important organic osmolyte and may influence cisplatin/carboplatin responsiveness of tumors.

Modern chromatography-based metabolomics measurements generate large amounts of data in the form of abundances of metabolites. An increasingly popular way of representing and analyzing such data is by means of association networks. Ideally, such a network can be interpreted in terms of the underlying biology. A property of chromatography-based metabolomics data is that the measurement error structure is complex: apart from the usual (random) instrumental error there is also correlated measurement error. This is intrinsic to the way the samples are prepared and the analyses are performed and cannot be avoided. The impact of correlated measurement errors on (partial) correlation networks can be large and is not always predictable. The interplay between relative amounts of uncorrelated measurement error, correlated measurement error and biological variation defines this impact. Using chromatography-based time-resolved lipidomics data obtained from a human intervention study we show how partial correlation based association networks are influenced by correlated measurement error. We show how the effect of correlated measurement error on partial correlations is different for direct and indirect associations. For direct associations the correlated measurement error usually has no negative effect on the results, while for indirect associations, depending on the relative size of the correlated measurement error, results can become unreliable. The aim of this paper is to generate awareness of the existence of correlated measurement errors and their influence on association networks. Time series lipidomics data is used for this purpose, as it makes it possible to visually distinguish the correlated measurement error from a biological response. Underestimating the phenomenon of correlated measurement error will result in the suggestion of biologically meaningful results that in reality rest solely on complicated error structures. Using proper experimental designs that allow for the quantification of the size of correlated and uncorrelated errors, can help to identify suspicious connections in association networks constructed from (partial) correlations. © 2014 Elsevier B.V. Chemicals/CAS: lipid, 66455-18-3; lysophosphatidylcholine, 93794-93-5

Veel zaken in ons lichaam worden voor een deel gereguleerd door hormonen. Een voorbeeld hiervan zijn de stress- en energieniveaus in angstige situaties. Zulke stress en energiehormonen bereiden het lichaam voor op een vecht- of vluchtreactie door het lichaam te activeren en onze hersenen te bespelen bepaalde keuzes te maken. Hetzelfde mechanisme is ervoor verantwoordelijk dat wij van een slaaptoestand naar een wakkere toestand overgaan in de ochtend. Wakker worden is een goed gereguleerde cyclus die iedere dag herhaald wordt en met behulp van biologische klokken iedere dag rond dezelfde tijd gebeurt. Dit soort cycli zijn niet beperkt tot stresshormonen en wakker worden, het hele ensemble van hormonen voert een gecoördineerde dans op in hun niveaus gedurende de dag. Sommige van de hormonen gaan graag samen met anderen terwijl andere hormonen duidelijk gescheiden van elkaar worden om niet elkaars functie te beïnvloeden. De wetenschappelijke kennis van welke hormonen samen of juist niet samen dansen is beperkt. Daniël Vis legt methoden uit om niveauverschillen te detecteren in experimentele gegevens. Ook introduceert hij andere methoden om te detecteren hoe hormonen samen dansen. Hij illustreert dit door te tonen dat de dans in hormonale niveaus anders is bij patiënten in vergelijking met gezonde controlepersonen. Ook zijn er systematische verschillen tussen dunne en dikke mensen. Dit betekent dat een beter begrip van hoe de hormonale dans wordt opgevoerd een factor is waar rekening mee moet worden gehouden bij het behandelen van ziekten.

Data sets resulting from metabolomics, proteomics, or metabolic profiling experiments are usually complex. This type of data contains underlying factors, such as time, doses, or combinations thereof. Classical biostatistics methods do not take into account the structure of such complex data sets. However, incorporating this structure into the data analysis is important for understanding the biological information in these data sets. We describe ANOVA simultaneous component analysis (ASCA), a method capable of dealing with complex multivariate data sets containing an underlying experimental design. It is a generalization of analysis of variance (ANOVA) for univariate data to the multivariate case. The method allows for easy interpretation of the variation induced by the different factors of the design. The method is illustrated with a data set from a metabolomics experiment with time and dose factors.