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Kannan, A.

Publications and source records attributed to Kannan, A..

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Cell-to-cell and genome-to-genome variability of Adenovirus transcription tuned by the cell cycle

In clonal cultures, not all cells are equally susceptible to virus infection. Underlying mechanisms of infection variability are poorly understood. Here, we developed image-based single cell measurements to scrutinize the heterogeneity of adenovirus (AdV) infection. AdV delivers, transcribes and replicates a linear double-stranded DNA genome in the nucleus. We measured the abundance of viral transcripts by single-molecule RNA fluorescence in situ hybridization (FISH), and the incoming ethynyl-deoxy-cytidine (EdC)-tagged viral genome by copper(I)-catalyzed azide-alkyne cycloaddition (click) reaction. The early transcripts increased from 2-12 hours, the late ones from 12-23 hours post infection (pi), indicating distinct accumulation kinetics. Surprisingly, the expression of the immediate early transactivator gene E1A only moderately correlated with the number of viral genomes in the cell nucleus, although the incoming viral DNA remained largely intact until 7 hours pi. Genome-to-genome heterogeneity was found at the level of viral transcription, as indicated by colocalization with the large intron containing early region E4 transcripts, uncorrelated to the multiplicity of incoming genomes in the nucleus. In accordance, individual genomes exhibited heterogeneous replication activity, as shown by single-strand DNA-FISH and immunocytochemistry. These results indicate that the variability in viral gene expression and replication are not due to defective genomes but due to host cell heterogeneity. By analyzing the cell cycle state, we found that G1 cells exhibited the highest E1A expression, and significantly increased the correlation between E1A expression and viral genome copy numbers. This combined image-based single molecule procedure is ideally suited to explore the cell-to-cell variability in viral infection, including transcriptional activators and repressors, RNA splicing mechanisms, and the impact of the 3-dimensional nuclear topology on gene regulation. Author SummaryAdenoviruses (AdV) are ubiquitous pathogens in vertebrates. They persist in infected people, and cause unpredictable outbreaks, morbidity and mortality across the globe. Here we report that the common human AdV type C5 (AdV-C5) gives rise to considerable infection variability at the level of single cells in culture, and that a major underlying reason is the cell-to-cell heterogeneity. By combining sensitive single molecule in situ technology for detecting the incoming viral DNA and newly synthesized viral transcripts we show that viral gene expression is heterogeneous between infected human cells, as well as individual genomes. We report a moderate correlation between the number of viral genomes in the nucleus and immediate early E1A transcripts. This correlation is increased in the G1 phase of the cell cycle, where the E1A transcripts were found to be more abundant than in any other cell cycle phase. Our results demonstrate the importance of cell-to-cell variability measurements for understanding transcription and replication in viral infections.

microbiology

Double-stranded RNA bending by AU-tract sequences

Sequence-dependent structural deformations of the DNA double helix (dsDNA) have been extensively studied, where adenine tracts (A-tracts) provide a striking example for global bending in the molecule. In contrast to dsDNA, much less is known about how the nucleotide sequence affects bending deformations of double-stranded RNA (dsRNA). Using all-atom microsecond long molecular dynamics simulations we found a sequence motif consisting of alternating adenines and uracils, or AU-tracts, that bend the dsRNA helix by locally compressing the major groove. We experimentally tested this prediction using atomic force microscopy (AFM) imaging of long dsRNA molecules containing phased AU-tracts. AFM images revealed a clear intrinsic bend in these AU-tracts molecules, as quantified by a significantly lower persistence length compared to dsRNA molecules of arbitrary sequence. The bent structure of AU-tracts here described might play a role in sequence-specific recognition of dsRNAs by dsRNA-interacting proteins or impact the folding of RNA into intricate tertiary and quaternary structures.

biophysics

Comparative genomics for the elucidation of multidrug resistance (MDR) in Candida lusitaniae

Multidrug resistance (MDR) has emerged in hospitals due to the use of several agents administered in combination or sequentially to the same individual. We reported earlier MDR in Candida lusitaniae during therapy with amphotericin B (AmB), azoles and candins. We used here comparative genomic approaches between the initial susceptible isolate and 4 other isolates with different MDR profiles. From a total of 18 non-synonymous SNPs (NSS) in genome comparisons with the initial isolate, six could be associated with MDR. One of the SNPs occurred in a putative transcriptional activator (MRR1) resulting in a V668G substitution in isolates resistant to azoles and 5-fluorocytosine (5-FC). We demonstrated by gene editing that MMR1 acted by upregulation of MFS7 (a multidrug transporter) in the presence of the V668G substitution. MFS7 itself mediated not only azole resistance but also 5-FC resistance, which represents a novel resistance mechanism for this drug class. Three other distinct NSS occurred in FKS1 (a glucan synthase that is targeted by candins) in three candin-resistant isolates. Lastly, two other NSS in ERG3 and ERG4 (ergosterol biosynthesis) resulting in non-sense mutations were revealed in AmB-resistant isolates, one of which accumulated the 2 ERG NSS. AmB-resistant isolates lacked ergosterol and exhibited sterol profiles consistent with ERG3 and ERG4 defects. In conclusion, this genome analysis combined with genetics and metabolomics helped to decipher the resistance profiles identified in this clinical case. MDR isolates accumulated 6 different mutations conferring resistance to all antifungal agents used in medicine. This case study illustrates the capacity of C. lusitaniae to rapidly adapt under drug pressure within the host.\n\nImportanceAntifungal resistance is an inevitable phenomenon when fungal pathogens get exposed to antifungal drugs. These drugs can be grouped in 4 distinct classes (azoles, candins, polyenes, pyrimidine analogs) and are used in different clinical settings. Failures in therapy implicates the sequential or combined use of these different drug classes, which can result in some cases in the development of multidrug resistance (MDR). MDR is particularly challenging in the clinic since it drastically reduces possible treatment alternatives. In this study, we report the rapid development of MDR in Candida lusitaniae in a patient, which became resistant to all known antifungal agents used up to now in medicine. To understand how MDR developed in C. lusitaniae, whole genome sequencing followed by comparative genome analysis was undertaken in sequential MDR isolates. This helped to detect all specific mutations linked to drug resistance and explained the different MDR patterns exhibited by the clinical isolates.

microbiology

Machine learning enables detection of early-stage colorectal cancer by whole-genome sequencing of plasma cell-free DNA

BackgroundBlood-based methods using cell-free DNA (cfDNA) are under development as an alternative to existing screening tests. However, early-stage detection of cancer using tumor-derived cfDNA has proven challenging because of the small proportion of cfDNA derived from tumor tissue in early-stage disease. A machine learning approach to discover signatures in cfDNA, potentially reflective of both tumor and non-tumor contributions, may represent a promising direction for the early detection of cancer.\n\nMethodsWhole-genome sequencing was performed on cfDNA extracted from plasma samples (N=546 colorectal cancer and 271 non-cancer controls). Reads aligning to protein-coding gene bodies were extracted, and read counts were normalized. cfDNA tumor fraction was estimated using IchorCNA. Machine learning models were trained using k-fold cross-validation and confounder-based cross-validation to assess generalization performance.\n\nResultsIn a colorectal cancer cohort heavily weighted towards early-stage cancer (80% stage I/II), we achieved a mean AUC of 0.92 (95% CI 0.91-0.93) with a mean sensitivity of 85% (95% CI 83-86%) at 85% specificity. Sensitivity generally increased with tumor stage and increasing tumor fraction. Stratification by age, sequencing batch, and institution demonstrated the impact of these confounders and provided a more accurate assessment of generalization performance.\n\nConclusionsA machine learning approach using cfDNA achieved high sensitivity and specificity in a large, predominantly early-stage, colorectal cancer cohort. The possibility of systematic technical and institution-specific biases warrants similar confounder analyses in other studies. Prospective validation of this machine learning method and evaluation of a multi-analyte approach are underway.

cancer biology