Assessment of stress tolerance acquisition in the heat-tolerant derivative strains of Bifidobacterium animalis subsp. lactis BB-12 and Lactobacillus rhamnosus GG

[Aims] The purpose of this study was to investigate the heat-shock response at molecular level in Lactobacillus rhamnosus GG, Bifidobacterium animalis subsp. lactis BB-12 and their heat-tolerant derivatives and to characterize the changes that make the derivatives more robust in terms of heat stress.[Methods and results] The study strains were exposed for 2 h to a heat-shock treatment, Bif. animalis subsp. lactis BB-12 and its derivative at 50°C and the Lact. rhamnosus GG and its derivative at 60°C. Protein synthesis before and after heat shock was examined using proteomics and RT-qPCR. The analysis revealed that the regulation of seven proteins in both strain pairs was modified as a response to heat or between the original and the derivative strain. The comparison of wild-type strains and the heat-tolerant derivatives suggests that the acquisition of heat tolerance in the Bif. animalis subsp. lactis BB-12 derivative is due to a slightly increased constitutive level of chaperones, while in Lact. rhamnosus GG derivative, the main reason seems to be a higher ability to induce the production of chaperones.[Conclusions] This study revealed possible markers of heat tolerance in B. lactis and Lact. rhamnosus strains.[Significance and impact of study] This study increases our knowledge on how Lactobacillus and Bifidobacterium strains may acquire heat tolerance. These findings may be useful for improving the heat tolerance of existing probiotic strains as well as screening new heat-tolerant strains.This study was funded by TEKES (the Finnish Funding Agency for Technology and Innovation) grant number 398/31/2009.Peer Reviewe

MYC-Induced miR-203b-3p and miR-203a-3p Control Bc1-xL Expression and Paclitaxel Sensitivity in Tumor Cells

Taxanes are chemotherapeutic agents used in the treatment of solid tumors, particularly of breast, ovarian, and lung origin. However, patients show divergent therapy responses, and the molecular determinants of taxane sensitivity have remained elusive. Especially the signaling pathways that promote death of the taxane-treated cells are poorly characterized. Here we describe a novel part of a signaling route in which c-Myc enhances paclitaxel sensitivity through upregulation of miR-203b-3p and miR-203a-3p; two clustered antiapoptosis protein BcI-xL controlling microRNAs. In vitro, the miR-203b-3p decreases the expression of BcI-xL by direct targeting of the gene's mRNA 3'UTR. Notably, overexpression of the miR-203b-3p changed the fate of paclitaxel-treated breast and ovarian cancer cells from mitotic slippage to cell death. In breast tumors, high expression of the miR-203b-3p and MYC was associated with better therapy response and patient survival. Interestingly, in the breast tumors, MYC expression correlated negatively with BCL2L1 expression but positively with miR-203b-3p and miR-203a-3p. Finally, silencing of MYC suppressed the transcription of both miRNAs in breast tumor cells. Pending further validation, these results may assist in patient stratification for taxane therapy.Peer reviewe

A carbohydrate-active enzyme (CAZy) profile links successful metabolic specialization of Prevotella to its abundance in gut microbiota

Gut microbiota participates in diverse metabolic and homeostatic functions related to health and well-being. Its composition varies between individuals, and depends on factors related to host and microbial communities, which need to adapt to utilize various nutrients present in gut environment. We profiled fecal microbiota in 63 healthy adult individuals using metaproteomics, and focused on microbial CAZy (carbohydrate-active) enzymes involved in glycan foraging. We identified two distinct CAZy profiles, one with many Bacteroides-derived CAZy in more than one-third of subjects (n = 25), and it associated with high abundance of Bacteroides in most subjects. In a smaller subset of donors (n = 8) with dietary parameters similar to others, microbiota showed intense expression of Prevotella-derived CAZy including exo-beta-(1,4)-xylanase, xylan-1,4-beta-xylosidase, alpha-L-arabinofuranosidase and several other CAZy belonging to glycosyl hydrolase families involved in digestion of complex plant-derived polysaccharides. This associated invariably with high abundance of Prevotella in gut microbiota, while in subjects with lower abundance of Prevotella, microbiota showed no Prevotella-derived CAZy. Identification of Bacteroides- and Prevotella-derived CAZy in microbiota proteome and their association with differences in microbiota composition are in evidence of individual variation in metabolic specialization of gut microbes affecting their colonizing competence

Variation of Absorption Angstrom Exponent in Aerosols From Different Emission Sources

The absorption Angstrom exponent (AAE) describes the spectral dependence of light absorption by aerosols. AAE is typically used to differentiate between different aerosol types for example., black carbon, brown carbon, and dust particles. In this study, the variation of AAE was investigated mainly in fresh aerosol emissions from different fuel and combustion types, including emissions from ships, buses, coal-fired power plants, and residential wood burning. The results were assembled to provide a compendium of AAE values from different emission sources. A dual-spot aethalometer (AE33) was used in all measurements to obtain the light absorption coefficients at seven wavelengths (370-950 nm). AAE(470/950) varied greatly between the different emission sources, ranging from -0.2 +/- 0.7 to 3.0 +/- 0.8. The correlation between the AAE(470/950) and AAE(370-950) results was good (R-2 = 0.95) and the mean bias error between these was 0.02. In the ship engine exhaust emissions, the highest AAE(470/950) values (up to 2.0 +/- 0.1) were observed when high sulfur content heavy fuel oil was used, whereas low sulfur content fuels had the lowest AAE(470/950) (0.9-1.1). In the diesel bus exhaust emissions, AAE(470/950) increased in the order of acceleration (0.8 +/- 0.1), deceleration (1.1 +/- 0.1), and steady driving (1.2 +/- 0.1). In the coal-fired power plant emissions, the variation of AAE(470/950) was substantial (from -0.1 +/- 2.1 to 0.9 +/- 1.6) due to the differences in the fuels and flue gas cleaning conditions. Fresh wood-burning derived aerosols had AAE(470/950) from 1.1 +/- 0.1 (modern masonry heater) to 1.4 +/- 0.1 (pellet boiler), lower than typically associated with wood burning, while the burn cycle phase affected AAE variation.Peer reviewe

Smoking is a predictor of complications in all types of surgery : a machine learning-based big data study

Background: Machine learning algorithms are promising tools for smoking status classification in big patient data sets. Smoking is a risk factor for postoperative complications in major surgery. Whether this applies to all surgery is unknown. The aims of this retrospective cohort study were to develop a machine learning algorithm for clinical record-based smoking status classification and to determine whether smoking and former smoking predict complications in all surgery types. Methods: All surgeries performed in a Finnish hospital district from 1 January 2015 to 31 December 2019 were analysed. Exclusion criteria were age below 16 years, unknown smoking status, and unknown ASA class. A machine learning algorithm was developed for smoking status classification. The primary outcome was 90-day overall postoperative complications in all surgeries. Secondary outcomes were 90-day overall complications in specialties with over 10 000 surgeries and critical complications in all surgeries. Results: The machine learning algorithm had precisions of 0.958 for current smokers, 0.974 for ex-smokers, and 0.95 for never-smokers. The sample included 158 638 surgeries. In adjusted logistic regression analyses, smokers had increased odds of overall complications (odds ratio 1.17; 95 per cent c.i. 1.14 to 1.20) and critical complications (odds ratio 1.21; 95 per cent c.i. 1.14 to 1.29). Corresponding odds ratios of ex-smokers were 1.09 (95 per cent c.i. 1.06 to 1.13) and 1.09 (95 per cent c.i. 1.02 to 1.17). Smokers had increased odds of overall complications in all specialties with over 10 000 surgeries. ASA class was the most important complication predictor. Conclusion: Machine learning algorithms are feasible for smoking status classification in big surgical data sets. Current and former smoking predict complications in all surgery types.Peer reviewe

Pilot symbol structure optimization for future cellular high-speed scenarios

Demands for wireless communication are ever growing and researchers and engineers at the field of telecommunications all over the world are working to meet those demands. There is a great deal to improve and even more ways to accomplish those improvements. This Master’s Thesis is focused on one of those demands that should be fulfilled to achieve the Fifth Generation (5G) wireless system requirements. TheMaster’s Thesis is a study of pilot structure optimization for high-speed scenarios. Pilot symbols, also known as reference symbols, are multiplexed with data symbols. Pilot symbols do not carry data. Instead, pilots help to retrieve information about frequency- and time-selectivity type of channel properties affected, e.g., by User Equipment (UE) speed. These channel properties are essential to channel estimation. If the pilot structure is not optimized to report accurately enough of them, channel estimator functions poorly. Pilot structure optimization is performed for a channel model, which has the most similar scattering environment compared to high-speed train channel scenario, which is one possible scenario where high UE-speed is realistic. International Telecommunication Union’s (ITU) Rural Macro Line-of-Sight (RMaLOS) is the closest reference channel model applicable to the high-speed scenario. When UE speed is high (hundreds of kilometers per hour) channel is tend to flatten in frequency. This fact explains why RMA channel model is the closest reference for actual high speed channel. Optimized pilot structure has to be able to estimate channel at lower UE speeds, where channel can be extremely frequency-selective. This implies that optimized pilot structure has to have frequency-tracking properties. Pilot structure optimization is carried out by simulating the performance of several pilot structures. The performance of a selected pilot structure can be evaluated via performance of the channel estimation where the chosen pilot structure is deployed. A Wiener-filter channel estimator has been implemented to enable accurate performance simulations. Several pilot structures were compared against each other and two superior structures were found. Superiority of these two structures is based on dense Doppler-tracking while maintaining a few crucial pilots in frequency domain to ensure channel estimation in frequency-selective channels. Both structures perform quite equally through the simulations and thus the structure with a smaller overhead was chosen as the most optimal pilot structure.Vaatimukset langattomalle tiedonsiirrolle kasvavat päivä päivältä. Tutkijat ja insinöörit ympäri maailmaa työskentelevät uusien teknologioiden parissa, yrittäen löytää uusia ratkaisuja, joilla nämä vaatimukset voitaisiin täyttää. Parannusvaatimuksia uusiin teknologioihin on paljon, ja tekotapoja vielä enemmän. Tämä diplomityö keskittyy ratkaisemaan yhtä näistä vaatimuksista, joita langattomien järjestelmien viides sukupolvi (5G) asettaa. Tämän diplomityön aiheena on pilottisymbolirakenteen optimointi suurnopeusskenaarioon. Pilottisymbolit, myös referenssi-symbolit, on limitetty aika-taajuus-tasoon yhdessä datasymbolien kanssa. Pilottisymbolit eivät kuitenkaan kuljeta dataa, vaan auttavat arvioimaan radiokanavan taajuus- ja aikaselektiivisyysominaisuuksia, joihin vaikuttaa mm. päätelaitteen nopeus. Jos pilottisymbolirakennetta ei ole optimoitu informoimaan kanavaestimaattoria niistä tarpeeksi tarkasti, toimii kanavaestimaattori huonosti. Optimointi tehdään kanavamallille, jonka säteily-ympäristö soveltuu parhaiten suurnopeusjunalle. Valittu kanavamalli on Kansainvälisen Televiestintäliiton (ITU):n Rural Macro Line-of-Sight (RMaLOS). Kun päätelaitteen nopeus on suuri (satoja kilometrejä tunnissa), on kanavalle ominaista muuttua tasaiseksi taajuustasossa. Tällä voidaan selittää, miksi RMa referenssi-kanava on lähimpänä oikeaa korkeanopeuskanavaa. Optimoidun pilottirakenteen täytyy pystyä estimoimaan kanava myös matalissa päätelaitteen nopeuksissa, jolloin kanava voi olla erittäinkin taajuusselektiivinen. Tämä tarkoittaa sitä, että optimoidussa pilottirakenteessa täytyy olla pilotteja myös taajuustasossa. Pilottirakenteen optimointi suoritetaan vertailemalla useiden pilottirakenteiden suorituskykyjä. Pilottirakenteen suorituskyky tulee parhaiten esille vertailemalla eri pilottirakenteilla toteutettujen kanavaestimaattorien suorituskykyjä. Työssä on toteutettu Wiener-suodatinta käyttävä kanavaestimaattori, jotta kanavaestimointi olisi mahdollisimman tarkka. Simulaatio-osiossa useita eri pilottirakenteita verrattiin toisiinsa. Näiden simulaatioiden avulla löydettiin kaksi ylivoimaisesti parasta pilottirakennetta. Näiden pilottirakenteiden ylivertaisuus perustuu suureen määrään pilotteja aikatasossa, allokoiden kuitenkin tarpeeksi pilotteja myös taajuustasoon, jotta kanavaestimointi taajuusselektiivisessä kanavassa olisi mahdollista. Molemmat rakenteet suoriutuivat lähes yhtä hyvin kaikista simulaatioista, ja niinpä rakenne jolla on pienemmät pilottimääristä johtuvat kustannukset valittiin optimaalisimmaksi

Functional Proteomics

Data-independent acquisition (DIA) mode of mass spectrometry, such as the SWATH-MS technology, enables accurate and consistent measurement of proteins, which is crucial for comparative proteomics studies. However, there is lack of free and easy to implement data analysis protocols that can handle the different data processing steps from raw spectrum files to peptide intensity matrix and its downstream analysis. Here, we provide a data analysis protocol, named diatools, covering all these steps from spectral library building to differential expression analysis of DIA proteomics data. The data analysis tools used in this protocol are open source and the protocol is distributed at Docker Hub as a complete software environment that supports Linux, Windows, and macOS operating systems.</p