Biology subjects

Zajac, N.

Publications and source records attributed to Zajac, N..

5 recordsLinked to original sources

bioRxiv2025

A combination of phenotypic responses and genetic adaptations enables Staphylococcus aureus to withstand inhibitory molecules secreted by Pseudomonas aeruginosa

Staphylococcus aureus and Pseudomonas aeruginosa frequently co-occur in infections, and there is evidence that their interactions can negatively affect disease outcomes. P. aeruginosa is known to be dominant, often compromising S. aureus through the secretion of inhibitory compounds. We previously demonstrated that S. aureus can become resistant to growth-inhibitory compounds during experimental evolution. While resistance arose rapidly, the underlying mechanisms were not obvious as only a few genetic mutations were associated with resistance, while ample phenotypic changes occurred. We thus hypothesize that resistance may result from a combination of phenotypic responses and genetic adaptation. Here, we tested this hypothesis using proteomics. We first focused on an evolved strain that acquired a single mutation in tcyA (encoding a transmembrane transporter unit) upon exposure to P. aeruginosa supernatant. We show that this mutation leads to a complete abolishment of transporter synthesis, which confers moderate protection against PQS and selenocystine, two toxic compounds produced by P. aeruginosa. However, this genetic effect was minor compared to the fundamental phenotypic changes observed at the proteome level when both ancestral and evolved S. aureus strains were exposed to P. aeruginosa supernatant. Major changes involved the downregulation of virulence factors, metabolic pathways, membrane transporters, and the upregulation of ROS scavengers and an efflux pump. Our results suggest that the observed multi-variate phenotypic response is a powerful adaptive strategy, offering instant protection against competitors in fluctuating environments and reducing the need for hard-wired genetic adaptations. ImportanceDifferent bacterial pathogens can co-occur in infections, where they interact with one another and influence disease severity. Previous research showed that pathogens can evolve and adapt to co-infecting species. Here, we show that evolution through genetic mutations and selection are not necessarily required to change pathogen behavior. Instead, we found that the human pathogen Staphylococcus aureus is able to plastically respond to the presence of Pseudomonas aeruginosa, a competing pathogen. Through proteomics and metabolomics, we demonstrate that S. aureus undergoes substantial proteomic alterations in response to P. aeruginosa by down-regulating virulence factor expression, changing metabolism, and mounting protective measures against toxic compounds. Our work highlights that pathogens possess sophisticated mechanisms to respond to competitors to secure growth and survival in polymicrobial infections. We predict such plastic responses to have significant impacts on infection outcomes.

microbiology↗

bioRxiv2025

Deep visual multi-omics profiling reveals mechanisms that underly cancer cell differentiation and aggressiveness in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) exhibits significant intra-tumoral heterogeneity (ITH) at both morphological and genetic levels, complicating treatment and contributing to disease progression. Among these, ccRCCs with focal rhabdoid differentiation stand out as highly aggressive tumors distinguished by cells with unique morphological features. However, the correlation between distinct morphological phenotypes, specific molecular alterations, and their influence on tumor behavior remains poorly understood. In this study, we integrated advanced AI-based image analysis with single-cell isolation and multi-omics profiling to dissect the link between clinically relevant morphological and molecular features of ccRCC cells. Using a novel digital pathology workflow, we quantified low-grade, high-grade, and rhabdoid morphologies in ccRCC diagnostic images with unprecedented precision. Subsequently, isolation of two sets of 1,000 morphologically distinct cells for detailed mRNA and protein expression analyses, revealed significant increasing dysregulation associating with higher histopathological grades. Rhabdoid ccRCC cells (grade 4) demonstrated unique molecular profiles, including upregulated FOXM1-driven proliferation, disrupted cell-matrix interactions, and enhanced immune evasion pathways. Despite high T-cell infiltration in rhabdoid areas, we identified a rhabdoid-specific immunosuppressive network driven by cytokines, IFN-beta, and integrin signaling, likely contributing to T-cell exhaustion. Rhabdoid ccRCC cells develop a distinct immunosuppressive signaling network, involving PD-L1 and novel immunomodulatory factors such as CD38 and ITGB2. These findings provide a basis for novel therapeutic strategies targeting these pathways in combination with immunotherapy to improve outcomes for patients with aggressive rhabdoid ccRCC. Key PointsO_LIccRCC is characterized by well-established morphological heterogeneity but the correlation with the underlying molecular aberrations remained elusive. C_LIO_LIBy integrating AI-based image analysis with single cell isolation and deep multi-omics profiling, we dissect the molecular intricacies of ccRCC, from targeted collection of 1,000 morphologically distinct cells. C_LIO_LIOur results demonstrate significant dysregulation of gene and protein expression correlating with higher histopathological grades in ccRCC. C_LIO_LIAggressive ccRCC cells with rhabdoid differentiation (grade 4) display distinct molecular profiles, as they upregulate FOXM1-mediated proliferation, ECM remodeling and the immune evasion responses, suggesting new therapeutic avenues enhancing ICI efficacy in these patients. C_LI

cancer biology↗

bioRxiv2024

Heterogeneous and Novel Transcript Expression in Single Cells of Patient-Derived ccRCC Organoids

Splicing is often dysregulated in cancer, leading to alterations in the expression of canonical and alternative splice isoforms. This complex phenomenon can be revealed by an in-depth understanding of cellular heterogeneity at the single-cell level. Recent advances in single-cell long- read sequencing technologies enable comprehensive transcriptome sequencing at the single-cell level. In this study, we have generated single-cell long-read sequencing of Patient-Derived Organoid (PDO) cells of clear-cell Renal Cell Carcinoma (ccRCC), an aggressive and lethal form of cancer that arises in kidney tubules. We have used the Multiplexed Arrays Sequencing (MAS-ISO-Seq) protocol of PacBio to sequence full-length transcripts exceptionally deep across 2,599 single cells to obtain the most comprehensive view of the alternative landscape of ccRCC to date. On average, we uncovered 86,182 transcripts across PDOs, of which 31,531 (36.6%) were previously uncharacterized. In contrast to known transcripts, many of these novel isoforms appear to exhibit cell-specific expression. Nonetheless, >50% of these novel transcripts were predicted to possess a complete protein-coding open reading frame. This finding suggests a biological role for these transcripts within kidney cells. Moreover, an analysis of the most dominant transcript switching events between ccRCC and non-ccRCC cells revealed that many switching events were cell and sample-specific, underscoring the heterogeneity of alternative splicing events in ccRCC. Overall, our research elucidates the intricate transcriptomic architecture of ccRCC, potentially exposing the mechanisms underlying its aggressive phenotype and resistance to conventional cancer therapies.

cancer biology↗

bioRxiv2024

Comparison of Single-cell Long-read and Short-read Transcriptome Sequencing of Patient-derived Organoid Cells of ccRCC: Quality Evaluation of the MAS-ISO-seq Approach

Single-cell RNA sequencing is used in profiling gene expression differences between cells. Short-read sequencing platforms provide high throughput and high-quality information at the gene-level, but the technique is hindered by limited read length, failing in providing an understanding of the cell heterogeneity at the isoform level. This gap has recently been addressed by the long-read sequencing platforms that provide the opportunity to preserve full-length transcript information during sequencing. To objectively evaluate the information obtained from both methods, we sequenced four samples of patient-derived organoid cells of clear cell renal cell carcinoma and one healthy sample of kidney organoid cells on Illumina Novaseq 6000 and PacBio Sequel IIe. For both methods, for each sample, the cDNA was derived from the same 10x Genomics 3 single-cell gene expression cDNA library. Here we present the technical characteristics of both datasets and compare cell metrics and gene-level information. We show that the two methods largely overlap in the results but we also identify sources of variability which present a set of advantages and disadvantages to both methods.

genomics↗

bioRxiv2023

The impact of PCR duplication on RNA-seq data generated using NovaSeq 6000, NovaSeq X, AVITI and G4 sequencers.

RNA sequencing (RNA-seq) is a powerful technology for gene expression and functional genomics profiling. Expression profiles generated using this approach can be impacted by the methods utilised for cDNA library generation. Selection of the optimal parameters for each step during the protocol are crucial for acquisition of high-quality data. Polymerase chain reaction (PCR) amplification of transcripts is a common step in many RNA-seq protocols and, if not optimised, high PCR duplicate proportions can be generated, resulting in the inflation of transcript counts and introduction of bias. In this study, we investigate the impact of input amount and PCR cycle number on the PCR duplication rate and on the RNA-seq data quality using a broad range of inputs (1 ng -1,000 ng) for RNA-seq library preparation with unique molecular identifiers (UMIs) and sequencing the data on four different short-read sequencing platforms: Illumina NovaSeq 6000, Illumina NovaSeq X, Element Biosciences AVITI, and Singular Genomics G4. Across all platforms, samples of input amounts greater than 125 ng had a negligible PCR duplication rate and the number of PCR cycles did not have a significant effect on data quality. However, for input amounts lower than 125ng we observed a strong negative correlation between input amount and the proportion of PCR duplicates; between 34% and 96% of reads were discarded via deduplication. Fortunately, UMIs were effective for removing in silico PCR duplicates without removing valuable biological information. Removal of PCR duplicates resulted in more comparable gene expression obtained from the different PCR cycles. Data generated with each of the four sequencing platforms presented similar associations between starting material amount and the number of PCR cycles on PCR duplicates, a similar number of genes detected, and comparable gene expression profiles. However, the sequencers using conversion kits for Illumina libraries (AVITI, G4) exhibited lower adapter dimer abundance across all input amounts, but also a higher PCR duplication rate in very low input amounts (<15ng). Overall, this study showed that the choice of input amount and number of PCR cycles are important parameters for obtaining high-quality RNA-seq data across all sequencing platforms. UMI deduplication is an effective way to remove PCR duplicates, improving the data quality and removing any variation caused by the conversion kits.

genomics↗