Brian Caffrey

Nomadic lifestyle of Lactobacillus plantarum revealed by comparative genomics of 54 strains isolated from different habitats

The ability of bacteria to adapt to diverse environmental conditions is well-known. The process of bacterial adaptation to a niche has been linked to large changes in the genome content, showing that many bacterial genomes reflect the constraints imposed by their habitat. However, some highly versatile bacteria are found in diverse habitats that almost share nothing in common. Lactobacillus plantarum is a lactic acid bacterium that is found in a large variety of habitat. With the aim of unravelling the link between evolution and ecological versatility of L. plantarum, we analysed the genomes of 54 L. plantarum strains isolated from different environments. Comparative genome analysis identified a high level of genomic diversity and plasticity among the strains analysed. Phylogenomic and functional divergence studies coupled with gene-trait matching analyses revealed a mixed distribution of the strains, which was uncoupled from their environmental origin. Our findings revealed the absence of specific genomic signatures marking adaptations of L. plantarum towards the diverse habitats it is associated with. This suggests fundamentally similar trends of genome evolution in L. plantarum, which occur in a manner that is apparently uncoupled from ecological constraint and reflects the nomadic lifestyle of this species.

Cell-Cycle Proteins Control Production of Neutrophil Extracellular Traps

Neutrophils are essential for immune defense and can respond to infection by releasing chromatin in the form of neutrophil extracellular traps (NETs). Here we show that NETs are induced by mitogens and accompanied by induction of cell-cycle markers, including phosphorylation of the retinoblastoma protein and lamins, nuclear envelope breakdown, and duplication of centrosomes. We identify cyclin-dependent kinases 4 and 6 (CDK4/6) as essential regulators of NETs and show that the response is inhibited by the cell-cycle inhibitor p21Cip. CDK6, in neutrophils, is required for clearance of the fungal pathogen Candida albicans. Our data describe a function for CDK4/6 in immunity.

Genome-wide Chromatin Profiling of Legionella pneumophila-Infected Human Macrophages Reveals Activation of the Probacterial Host Factor TNFAIP2.

BACKGROUND Legionella pneumophila is a causative agent of severe pneumonia. Infection leads to a broad host cell response, as evident, for example, on the transcriptional level. Chromatin modifications, which control gene expression, play a central role in the transcriptional response to L. pneumophila METHODS We infected human-blood-derived macrophages (BDMs) with L. pneumophila and used chromatin immunoprecipitation followed by sequencing to screen for gene promoters with the activating histone 4 acetylation mark. RESULTS We found the promoter of tumor necrosis factor α-induced protein 2 (TNFAIP2) to be acetylated at histone H4. This factor has not been characterized in the pathology of L. pneumophila TNFAIP2 messenger RNA and protein were upregulated in response to L. pneumophila infection of human-BDMs and human alveolar epithelial (A549) cells. We showed that L. pneumophila-induced TNFAIP2 expression is dependent on the NF-κB transcription factor. Importantly, knock down of TNFAIP2 led to reduced intracellular replication of L. pneumophila Corby in A549 cells. CONCLUSIONS Taken together, genome-wide chromatin analysis of L. pneumophila-infected macrophages demonstrated induction of TNFAIP2, a NF-κB-dependent factor relevant for bacterial replication.

Identification of 170 New Long Noncoding RNAs in Schistosoma mansoni

Long noncoding RNAs (lncRNAs) are transcripts generally longer than 200 nucleotides with no or poor protein coding potential, and most of their functions are also poorly characterized. Recently, an increasing number of studies have shown that lncRNAs can be involved in various critical biological processes such as organism development or cancer progression. Little, however, is known about their effects in helminths parasites, such as Schistosoma mansoni. Here, we present a computational pipeline to identify and characterize lncRNAs from RNA-seq data with high confidence from S. mansoni adult worms. Through the utilization of different criteria such as genome localization, exon number, gene length, and stability, we identified 170 new putative lncRNAs. All novel S. mansoni lncRNAs have no conserved synteny including human and mouse. These closest protein coding genes were enriched in 10 significant Gene Ontology terms related to metabolism, transport, and biosynthesis. Fifteen putative lncRNAs showed differential expression, and three displayed sex-specific differential expressions in praziquantel sensitive and resistant adult worm couples. Together, our method can predict a set of novel lncRNAs from the RNA-seq data. Some lncRNAs are shown to be differentially expressed suggesting that those novel lncRNAs can be given high priority in further functional studies focused on praziquantel resistance.

Computational Modeling of miRNA Biogenesis

Over the past few years it has been observed, thanks in no small part to high-throughput methods, that a large proportion of the human genome is transcribed in a tissue- and time-specific manner. Most of the detected transcripts are non-coding RNAs and their functional consequences are not yet fully understood. Among the different classes of non-coding transcripts, microRNAs (miRNAs) are small RNAs that post-transcriptionally regulate gene expression. Despite great progress in understanding the biological role of miRNAs, our understanding of how miRNAs are regulated and processed is still developing. High-throughput sequencing data have provided a robust platform for transcriptome-level, as well as gene-promoter analyses. In silico predictive models help shed light on the transcriptional and post-transcriptional regulation of miRNAs, including their role in gene regulatory networks. Here we discuss the advances in computational methods that model different aspects of miRNA biogeneis, from transcriptional regulation to post-transcriptional processing. In particular, we show how the predicted miRNA promoters from PROmiRNA, a miRNA promoter prediction tool, can be used to identify the most probable regulatory factors for a miRNA in a specific tissue. As differential miRNA post-transcriptional processing also affects gene-regulatory networks, especially in diseases like cancer, we also describe a statistical model proposed in the literature to predict efficient miRNA processing from sequence features.

Computational Modeling of miRNA Biogenesis, in Mathematical Models in Biology

Over the past few years it has been observed, thanks in no small part to high-throughput methods, that a large proportion of the human genome is transcribed in a tissue- and time-specific manner. Most of the detected transcripts are non-coding RNAs and their functional consequences are not yet fully understood. Among the different classes of non-coding transcripts, microRNAs (miRNAs) are small RNAs that post-transcriptionally regulate gene expression. Despite great progress in understanding the biological role of miRNAs, our understanding of how miRNAs are regulated and processed is still developing. High-throughput sequencing data have provided a robust platform for transcriptome-level, as well as gene-promoter analyses. In silico predictive models help shed light on the transcriptional and post-transcriptional regulation of miRNAs, including their role in gene regulatory networks. Here we discuss the advances in computational methods that model different aspects of miRNA biogeneis, from transcriptional regulation to post-transcriptional processing. In particular, we show how the predicted miRNA promoters from PROmiRNA, a miRNA promoter prediction tool, can be used to identify the most probable regulatory factors for a miRNA in a specific tissue. As differential miRNA post-transcriptional processing also affects gene-regulatory networks, especially in diseases like cancer, we also describe a statistical model proposed in the literature to predict efficient miRNA processing from sequence features.

Computational modeling of microRNA Biogenesis

Over the past few years it has been observed, thanks in no small part to high-throughput methods, that a large proportion of the human genome is transcribed in a tissue- and time-specific manner. Most of the detected transcripts are non-coding RNAs and their functional consequences are not yet fully understood. Among the different classes of non-coding transcripts, microRNAs (miRNAs) are small RNAs that post-transcriptionally regulate gene expression. Despite great progress in understanding the biological role of miRNAs, our understanding of how miRNAs are regulated and processed is still developing. High-throughput sequencing data have provided a robust platform for transcriptome-level, as well as gene-promoter analyses. In silico predictive models help shed light on the transcriptional and post-transcriptional regulation of miRNAs, including their role in gene regulatory networks. Here we discuss the advances in computational methods that model different aspects of miRNA biogeneis, from transcriptional regulation to post-transcriptional processing. In particular, we show how the predicted miRNA promoters from PROmiRNA, a miRNA promoter prediction tool, can be used to identify the most probable regulatory factors for a miRNA in a specific tissue. As differential miRNA post-transcriptional processing also affects gene-regulatory networks, especially in diseases like cancer, we also describe a statistical model proposed in the literature to predict efficient miRNA processing from sequence features.