A novel time-lapse imaging method for studying developing bacterial biofilms

In nature, bacteria prevailingly reside in the form of biofilms. These elaborately organized surface‑bound assemblages of bacterial cells show numerous features of multicellular organization. We recently showed that biofilm growth is a true developmental process, which resembles developmental processes in multicellular eukaryotes. To study the biofilm growth in a fashion of eukaryotic ontogeny, it is essential to define dynamics and critical transitional phases of this process. The first step in this endeavor is to record the gross morphological changes of biofilm ontogeny under standardized conditions. This visual information is instrumental in guiding the sampling strategy for the later omics analyses of biofilm ontogeny. However, none of the currently available visualizations methods is specifically tailored for recording gross morphology across the whole biofilm development. To address this void, here we present an affordable Arduino‑based approach for time‑lapse visualization of complete biofilm ontogeny using bright field stereomicroscopy with episcopic illumination. The major challenge in recording biofilm development on the air–solid interphase is water condensation, which compromises filming directly through the lid of a Petri dish. To overcome these trade‑offs, we developed an Arduino microcontroller setup which synchronizes a robotic arm, responsible for opening and closing the Petri dish lid, with the activity of a stereomicroscope‑mounted camera and lighting conditions. We placed this setup into a microbiological incubator that maintains temperature and humidity during the biofilm growth. As a proof‑of‑principle, we recorded biofilm development of five Bacillus subtilis strains that show different morphological and developmental dynamics

Metagenomics-based disease prediction

Razvoj novih tehnologija sekvenciranja omogućio je metagenomske analize, tj. analize uzoraka izravno prikupljenih iz okoliša bez potrebe za uzgojem pojedinih vrsta u laboratorijskim uvjetima. U okviru ovog diplomskog rada istražen je problem predviđanja bolesti tehnikama strojnog učenja iz podataka o mikrobiomu prikupljenih metagenomskim analizama. Rad sadrži pregled postojeće literature, i rezultate evaluacije modela stroja potpornih vektora (SVM), AdaBoost (jačanje stabla odluka), slučajne šume, i umjetnih neuronskih mreža na tri skupa podataka koji sadrže kontrolne uzorke i uzorke koji su pogođeni cirozom jetre, karcinomom debelog crijeva i dijabetesom tipa 2. Najbolji postignuti F1 rezultati su: 0.89 na skupu podataka za cirozu jetre (AdaBoost), 0.81 na skupu podataka za karcinom debelog crijeva (AdaBoost), i 0.76 na skupu podataka za dijabetes tipa 2 (SVM).The development of high-throughput sequencing technologies has enabled large-scale metagenomic analyses, i.e. direct analyses of all genomes in samples with no need for the cultivation of specific species. In this thesis, the problem of machine learning based disease prediction from metagenomic microbiome data is addressed. The thesis contains a survey of recent literature, and the evaluation of support vector machine, AdaBoost (boosting decision trees), random forest, and artificial neural network models on three different datasets containing control samples and samples affected with liver cirrhosis, colorectal cancer, and type two diabetes. The best F1 scores are: 0.89 on the liver cirrhosis dataset (AdaBoost), 0.81 on the colorectal cancer dataset (AdaBoost), and 0.76 on the type two diabetes dataset (SVM)

Metagenomics-based disease prediction

Razvoj novih tehnologija sekvenciranja omogućio je metagenomske analize, tj. analize uzoraka izravno prikupljenih iz okoliša bez potrebe za uzgojem pojedinih vrsta u laboratorijskim uvjetima. U okviru ovog diplomskog rada istražen je problem predviđanja bolesti tehnikama strojnog učenja iz podataka o mikrobiomu prikupljenih metagenomskim analizama. Rad sadrži pregled postojeće literature, i rezultate evaluacije modela stroja potpornih vektora (SVM), AdaBoost (jačanje stabla odluka), slučajne šume, i umjetnih neuronskih mreža na tri skupa podataka koji sadrže kontrolne uzorke i uzorke koji su pogođeni cirozom jetre, karcinomom debelog crijeva i dijabetesom tipa 2. Najbolji postignuti F1 rezultati su: 0.89 na skupu podataka za cirozu jetre (AdaBoost), 0.81 na skupu podataka za karcinom debelog crijeva (AdaBoost), i 0.76 na skupu podataka za dijabetes tipa 2 (SVM).The development of high-throughput sequencing technologies has enabled large-scale metagenomic analyses, i.e. direct analyses of all genomes in samples with no need for the cultivation of specific species. In this thesis, the problem of machine learning based disease prediction from metagenomic microbiome data is addressed. The thesis contains a survey of recent literature, and the evaluation of support vector machine, AdaBoost (boosting decision trees), random forest, and artificial neural network models on three different datasets containing control samples and samples affected with liver cirrhosis, colorectal cancer, and type two diabetes. The best F1 scores are: 0.89 on the liver cirrhosis dataset (AdaBoost), 0.81 on the colorectal cancer dataset (AdaBoost), and 0.76 on the type two diabetes dataset (SVM)

Finding overlapping reads for de novo genome assembly

Određivanje preklapanja je prvi korak de novo sastavljanja genoma metodom preklapanje-razmještaj-konsenzus . Za određivanje preklapanja koristio sam kombinirani pristup. Pristup koristi prošireno sufiksno polje za pronalazak identičnih preklapanja i dinamičko programiranje za poravnanje dijelova očitanja između identičnih preklapanja. Ovakav način određivanja preklapanja omogućava kompromis između kvalitete određenih preklapanja i vremena izvođenja.Finding overlaps is the first step of de novo genome assembly using Overlap-Layout-Consensus method. To determine overlaps I used a combined approach. The approach uses enhanced suffix array for finding identical overlaps and dynamic programming to align parts of reads between identical overlaps. This method allows a trade off between the quality of determined overlaps and runtime

Finding overlapping reads for de novo genome assembly

Određivanje preklapanja je prvi korak de novo sastavljanja genoma metodom preklapanje-razmještaj-konsenzus . Za određivanje preklapanja koristio sam kombinirani pristup. Pristup koristi prošireno sufiksno polje za pronalazak identičnih preklapanja i dinamičko programiranje za poravnanje dijelova očitanja između identičnih preklapanja. Ovakav način određivanja preklapanja omogućava kompromis između kvalitete određenih preklapanja i vremena izvođenja.Finding overlaps is the first step of de novo genome assembly using Overlap-Layout-Consensus method. To determine overlaps I used a combined approach. The approach uses enhanced suffix array for finding identical overlaps and dynamic programming to align parts of reads between identical overlaps. This method allows a trade off between the quality of determined overlaps and runtime

Metagenomics-based disease prediction

Razvoj novih tehnologija sekvenciranja omogućio je metagenomske analize, tj. analize uzoraka izravno prikupljenih iz okoliša bez potrebe za uzgojem pojedinih vrsta u laboratorijskim uvjetima. U okviru ovog diplomskog rada istražen je problem predviđanja bolesti tehnikama strojnog učenja iz podataka o mikrobiomu prikupljenih metagenomskim analizama. Rad sadrži pregled postojeće literature, i rezultate evaluacije modela stroja potpornih vektora (SVM), AdaBoost (jačanje stabla odluka), slučajne šume, i umjetnih neuronskih mreža na tri skupa podataka koji sadrže kontrolne uzorke i uzorke koji su pogođeni cirozom jetre, karcinomom debelog crijeva i dijabetesom tipa 2. Najbolji postignuti F1 rezultati su: 0.89 na skupu podataka za cirozu jetre (AdaBoost), 0.81 na skupu podataka za karcinom debelog crijeva (AdaBoost), i 0.76 na skupu podataka za dijabetes tipa 2 (SVM).The development of high-throughput sequencing technologies has enabled large-scale metagenomic analyses, i.e. direct analyses of all genomes in samples with no need for the cultivation of specific species. In this thesis, the problem of machine learning based disease prediction from metagenomic microbiome data is addressed. The thesis contains a survey of recent literature, and the evaluation of support vector machine, AdaBoost (boosting decision trees), random forest, and artificial neural network models on three different datasets containing control samples and samples affected with liver cirrhosis, colorectal cancer, and type two diabetes. The best F1 scores are: 0.89 on the liver cirrhosis dataset (AdaBoost), 0.81 on the colorectal cancer dataset (AdaBoost), and 0.76 on the type two diabetes dataset (SVM)

Finding overlapping reads for de novo genome assembly

Određivanje preklapanja je prvi korak de novo sastavljanja genoma metodom preklapanje-razmještaj-konsenzus . Za određivanje preklapanja koristio sam kombinirani pristup. Pristup koristi prošireno sufiksno polje za pronalazak identičnih preklapanja i dinamičko programiranje za poravnanje dijelova očitanja između identičnih preklapanja. Ovakav način određivanja preklapanja omogućava kompromis između kvalitete određenih preklapanja i vremena izvođenja.Finding overlaps is the first step of de novo genome assembly using Overlap-Layout-Consensus method. To determine overlaps I used a combined approach. The approach uses enhanced suffix array for finding identical overlaps and dynamic programming to align parts of reads between identical overlaps. This method allows a trade off between the quality of determined overlaps and runtime

<b>Macroevolutionary dynamics of gene family gain and loss along multicellular eukaryotic lineages.</b> <b>(Data)</b>

Paper: Mirjana Domazet-Lošo, Tin Široki, Korina Šimičević, Tomislav Domazet-Lošo (2022) Macroevolutionary dynamics of gene family gain and loss along multicellular eukaryotic lineages.Supplementary Data 6 - GO enrichments, gene family gain H. sapiensSupplementary Data 7 - GO enrichments, gene family gain D. melanogasterSupplementary Data 8 - GO enrichments, gene family gain S. cerevisiaeSupplementary Data 9 - GO enrichments, gene family gain A. thalianaSupplementary Data 10 - GO enrichments, gene family loss H. sapiensSupplementary Data 11 - GO enrichments, gene family loss D. melanogasterSupplementary Data 12 - GO enrichments, gene family loss S. cerevisiaeSupplementary Data 13 - GO enrichments, gene family loss A. thalianaDescription Supplementary Data 6-13: The enrichment of GO functional categories in gained and lost gene families along the H. sapiens, D. melanogaster, S.cerevisiae and A. thaliana lineages. Functional enrichments were calculated using the set of gained and lost gene families along H. sapiens, D. melanogaster, S. cerevisiae and A. thaliana lineages (x-axis). The gene families are reconstructed with MMSeq2 cluster using a range of c-values (0 to 0.8, y-axis). Solid circles depict significant enrichments of a GO term in gene families gained at a particular phylostratum. The size of circles is proportional to an enrichment value estimated by log-odds, while the shades of blue (gain) or red (loss) correspond to p-values. The significance of enrichments was estimated by hypergeometric test corrected for multiple comparisons. Only enrichments with p </p

Embryo-Like Features in Developing Bacillus subtilis Biofilms

Correspondence between evolution and development has been discussed for more than two centuries. Recent work reveals that phylogeny-ontogeny correlations are indeed present in developmental transcriptomes of eukaryotic clades with complex multicellularity. Nevertheless, it has been largely ignored that the pervasive presence of phylogeny-ontogeny correlations is a hallmark of development in eukaryotes. This perspective opens a possibility to look for similar parallelisms in biological settings where developmental logic and multicellular complexity are more obscure. For instance, it has been increasingly recognized that multicellular behavior underlies biofilm formation in bacteria. However, it remains unclear whether bacterial biofilm growth shares some basic principles with development in complex eukaryotes. Here we show that the ontogeny of growing Bacillus subtilis biofilms recapitulates phylogeny at the expression level. Using time-resolved transcriptome and proteome profiles, we found that biofilm ontogeny correlates with the evolutionary measures, in a way that evolutionary younger and more diverged genes were increasingly expressed toward later timepoints of biofilm growth. Molecular and morphological signatures also revealed that biofilm growth is highly regulated and organized into discrete ontogenetic stages, analogous to those of eukaryotic embryos. Together, this suggests that biofilm formation in Bacillus is a bona fide developmental process comparable to organismal development in animals, plants, and fungi. Given that most cells on Earth reside in the form of biofilms and that biofilms represent the oldest known fossils, we anticipate that the widely adopted vision of the first life as a single-cell and free-living organism needs rethinking.CC BY-NC 4.0</p