A Computational Framework for Host-Pathogen Protein-Protein Interactions

Infectious diseases cause millions of illnesses and deaths every year, and raise great health concerns world widely. How to monitor and cure the infectious diseases has become a prevalent and intractable problem. Since the host-pathogen interactions are considered as the key infection processes at the molecular level for infectious diseases, there have been a large amount of researches focusing on the host-pathogen interactions towards the understanding of infection mechanisms and the development of novel therapeutic solutions. For years, the continuously development of technologies in biology has benefitted the wet lab-based experiments, such as small-scale biochemical, biophysical and genetic experiments and large-scale methods (for example yeast-two-hybrid analysis and cryogenic electron microscopy approach). As a result of past decades of efforts, there has been an exploded accumulation of biological data, which includes multi omics data, for example, the genomics data and proteomics data. Thus, an initiative review of omics data has been conducted in Chapter 2, which has exclusively demonstrated the recent update of ‘omics’ study, particularly focusing on proteomics and genomics. With the high-throughput technologies, the increasing amount of ‘omics’ data, including genomics and proteomics, has even further boosted. An upsurge of interest for data analytics in bioinformatics comes as no surprise to the researchers from a variety of disciplines. Specifically, the astonishing rate at which genomics and proteomics data are generated leads the researchers into the realm of ‘Big Data’ research. Chapter 2 is thus developed to providing an update of the omics background and the state-of-the-art developments in the omics area, with a focus on genomics data, from the perspective of big data analytics..

The future of zoonotic risk prediction

In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open data, equity and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges? This article is part of the theme issue 'Infectious disease macroecology: parasite diversity and dynamics across the globe'.Peer reviewe

Asymmetrical flow field-flow fractionation in virus purification

Viruses are the most abundant entities in the biosphere, the estimated amount of viruses is more than 10^30. The number is incomprehensible and exceeds the amount of host cells at least by one order of magnitude. Viruses are extremely diverse entities by means of morphologies, sizes, genomes and biochemical and biophysical properties. As obligate parasites, viruses can only be propagated in living cells. This sets challenges for the virus purification, since the starting material contains host and growth media derived impurities. Medical applications such as phage therapy, vaccine development, and gene therapy require large amounts of highly purified viruses and virus-like particles (VLPs). Nanotechnology utilizes viruses and VLPs as building blocks for nanoscale materials and devices and also requires virus purification methods which maintain the biophysical and biochemical properties of the particles. Viruses are often purified with combinations of different methods. The most common ones are precipitation and ultracentrifugation. Precipitation does not lead into high purities and is generally applied as a pre-step for purification. Ultracentrifugation leads to high purity but it exposes viruses to high shear forces possibly leading to losses of infectivity. The large size of many viruses may restrict utilization of traditional chromatography. However, monolithic matrices are applicable for virus purification. In this work asymmetrical flow field-flow fractionationation (AF4) method was developed for virus purification. AF4 is a highly versatile size-based separation method applicable for samples with sizes ranging between ~1‒500 nm. The separation in AF4 is conducted with the aid of liquid flows. Solid stationary phase is not applied at all, thus no strong interactions during the separation occur making the method gentle. Several parameters in the AF4 system are adjustable, making the method highly versatile and an attractive alternative for virus purification. In this study, AF4 conditions were optimized for purification of six prokaryotic viruses, having different morphologies and properties. Analytical sample channel and preparative UV-detector were utilized. Yields of infective viruses were high and purity levels comparable to the ones obtained with a method based on precipitation and ultracentrifugation. AF4 was proven to be applicable for all tested viruses, also the ones requiring high ionic strength conditions were amenable for AF4 purification. The AF4-method is fast and obtained virus preparations were homogenous. As the system is highly versatile, it is expected that it can be tailored for other viruses as well, to meet the further needs of virus purification.Virusten määrä ympäristössämme on tähtitieteellinen. Viruspartikkeleita on arvioitu olevan biosfäärissä jopa 10^30 kappaletta, näinollen ylittäen isäntäsolujen määrän jopa kymmenkertaisesti. Virukset ovat hyvin kirjava joukko niin morfologioiden, kokojen, genomien kuin biokemiallisten ja – fysikaalisten ominaisuuksiensa suhteen. Kuitenkin, virukset voivat lisääntyä ainoastaan elävissä soluissa. Tämä asettaa omat haasteensa viruspartikkelien puhdistamiselle, sillä lähtömateriaali sisältää isäntäsolusta ja kasvatusalusta peräisin olevia epäpuhtauksia. Lääketieteen sovellutukset, kuten faagiterapia, rokotekehitys ja geeniterapia tarvitsevat suuria määriä korkean puhtaustason viruksia ja viruksen kaltaisia partikkeleita. Nanoteknologiassa korkean puhtaustason viruksia ja viruksen kaltaisia partikkeleita käytetään rakennuspalikoina nanomittaluokan materiaaleissa. Lääketiede, nanoteknologia ja muut viruksia hyödyntävät alat tarvitsevat nopeita ja tehokkaita viruspuhdistusmenetelmiä, jotka säilyttävät viruksen ominaisuudet ja johtavat korkeisiin saantoihin. Useimmiten viruspuhdistuksessa yhdistellään eri menetelmiä. Yleisimpiä näistä menetelmistä ovat saostus ja ultrasentrifugaatio. Saostus ei yksin johda korkeaan puhtaustasoon ja sitä käytetäänkin tavallisesti puhdistuksen ensimmäisenä askeleena. Ultrasentrifugoinnilla sen sijaan saavutetaan korkea puhtausaste, mutta altistetaan virukset koville sentrifugointivoimille. Tämä saattaa johtaa partikkelien vahingoittumiseen ja infektiivisyyden menettämiseen. Virukset ovat makromolekulaarisia komplekseja, joten niiden koko rajoittaa useiden perinteisten kromatografiamenetelmien käyttöä niiden puhdistuksessa. Tässä työssä kehitettiin viruspuhdistusmenetelmä asymmetrista virtauskenttäfraktiointia (AF4) hyödyntäen. AF4 on hyvin monipuolinen, kokoon perustuva erottelumenetelmä näytteille kokoluokassa ~1‒500 nm. Erottelu saadaan aikaiseksi nestevirtauksilla eikä menetelmässä käytetä lainkaan kiinteä stationäärifaasia. AF4-erottelu onkin erityisen hellävarainen menetelmä, sillä voimakkaita vuorovaikutuksia näytteen ja laitteiston välillä ei synny. Lukuisat AF4-laitteiston ominaisuudet ovat säädettävissä näytteen erityispiirteille sopiviksi, tehden menetelmästä houkuttelevan vaihtoehdon viruspuhdistuksessa. Tässä projektissa AF4-olosuhteet optimoitiin viruspuhdistuksen tarpeisiin kuutta prokaryoottivirusta hyödyntäen. Optimointiin käytetyt virukset edustivat eri morfologiatyyppejä hyvin erilaisine ominaisuuksineen. Laitteistossa käytettiin analyyttista näytekanavaa ja preparatiivista UV-detektoria. Virussaannot olivat korkeita ja puhtaustaso vastasi saostuksen ja ultrasentrifugoinnin yhdistelmällä saavutettavaa puhtautta. Osoitimme AF4-menetelmän soveltuvan kaikille testatuille viruksille, myös niille, jotka vaativat korkeita suolapitoisuuksia säilyttääkseen infektiivisyytensä. AF4-menetelmä on nopea ja puhdistetut viruspreparaatit laadultaan homogeenisia. Perinteinen, saostukseen ja ultrasentrifugointiin pohjautuva puhdistusmenetelmä on työläs ja aikaa vievä. AF4-menetelmällä puhdistusaika lyheni merkittävästi ja korkeiden saantojen vuoksi lähtömateriaalin tarve väheni moninkertaisesti. Koska AF4-menetelmä on helposti muokattavissa, sen voi olettaa soveltuvan myös muille kuin työssä testatuille viruksille ja eri tieteenalojen viruspuhdistuksen tarpeisiin

The SIB Swiss Institute of Bioinformatics’ resources : focus on curated databases

The SIB Swiss Institute of Bioinformatics provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article

The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article

Cage row arrangement affects the performance of laying hens in the hot humid tropics

Although the traditional cage system of housing laying hens is gradually being faced out due to welfare reasons, cages are still common in most developing tropical countries in different arrangements. In a 12-week experiment, the effects of a three cage row arrangement on hen-day production and egg qualities of Shaver Brown hens was studied. Data were collected from 2 layer sheds housing 9,000 hens in a 3-cage row arrangement (southern row, northern row and middle row) with 3,000 hens per row. Data were analysed for a randomized complete block design where cage rows were the treatments and weeks the blocks. Results showed no significant effects of cage row arrangement on feed intake, hen-day production, per cent yolk and Haugh unit (P>0.05). Egg weight, egg mass and per cent shell were significantly reduced and feed conversion ratio increased on the middle row (P<0.05). Egg weight, egg mass, per cent shell and feed conversion ratio did not differ between the side rows (P>0.05). These results suggest that battery cage row arrangement may not affect the rate of lay but egg weight, egg mass and efficiency of feed utilisation may be adversely affected in hens housed in the middle row. These findings have both economic and welfare implications

The future of zoonotic risk prediction

In the light of the urgency raised by the COVID-19 pandemic, global investment in wildlife virology is likely to increase, and new surveillance programmes will identify hundreds of novel viruses that might someday pose a threat to humans. To support the extensive task of laboratory characterization, scientists may increasingly rely on data-driven rubrics or machine learning models that learn from known zoonoses to identify which animal pathogens could someday pose a threat to global health. We synthesize the findings of an interdisciplinary workshop on zoonotic risk technologies to answer the following questions. What are the prerequisites, in terms of open data, equity and interdisciplinary collaboration, to the development and application of those tools? What effect could the technology have on global health? Who would control that technology, who would have access to it and who would benefit from it? Would it improve pandemic prevention? Could it create new challenges? This article is part of the theme issue ‘Infectious disease macroecology: parasite diversity and dynamics across the globe’.NSF BII 2021909; the University of Toronto EEB Fellowship; the Wellcome Trust; the National Institute of Allergy and Infectious Diseases of the National Institutes of Health and the Defense Threat Reduction Agency.http://rstb.royalsocietypublishing.orgam2022Medical Virolog

The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases.

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article