Biology subjects

Nichols, S.

Publications and source records attributed to Nichols, S..

8 recordsLinked to original sources

bioRxiv2026

Transmission strategy modulates parasite biogeography in an island-colonising bird

Parasites occur in every ecosystem, although their dispersal is often constrained by the availability of hosts or vectors. Here, we explore how variation in parasite life history traits, particularly transmission strategy, may influence their distributions. Specifically, we test whether a variety of parasites ad-here to the rules of island biogeography, and whether their distributions vary with transmission strategy. We utilised broad-spectrum parasite detection from existing Whole Genome Sequence (WGS) data to characterise parasites with varying transmission strategies from the blood of a passerine bird, the silvereye (Zosterops lateralis), sampled across 25 islands in the South Pacific and from five of the states in mainland Australia. Overall, parasite richness was higher on mainland Australia compared to islands and decreased with distance of islands from the Australian continent. However, these patterns were dependent on transmission strategy. For parasites transmitted by flying insect vectors, richness decreased on islands compared to the mainland. However, increasing isolation from the mainland among islands had little further impact. On the other hand, richness of directly transmitted parasites and those requiring another intermediate host declined sharply with increasing distance from the mainland. While islands may act as an initial barrier to colonisation for parasites relying on flying insect vectors, their highly dispersive vectors may subsequently reduce the impact of increasing isolation distance on richness. Our work underscores the importance of considering parasite life-histories and their transmission strategies for understanding the processes that shape parasite communities on islands.

ecology↗

bioRxiv2026

Life cycle complexity shapes parasite sharing amongst migratory and resident hosts

Migratory animals are potentially important parasite dispersers. However, understanding the impact of host migration on parasite distributions is challenging because variation in parasite life histories can mediate dispersal potential. Furthermore, studies of parasite dispersal via host migration often compare parasites in different species of migrant and resident hosts, despite differences in host phylogeny, ecology, and exposure history, which can complicate ascertaining the impact of migration. The Tasmanian population of silvereye (Zosterops lateralis lateralis) exhibits partial migration. Some members of the population overwinter on the Australian mainland, where they co-occur with residents of another silvereye subspecies (Z. l. cornwalli). Utilising targeted PCR, metabarcoding and high-throughput sequencing, we characterise a range of parasite genera from blood and faecal samples in Tasmanian resident, migrant and mainland resident birds. Greater parasite richness was documented in migrant individuals, possibly reflecting exposure to varied parasite faunas at stopovers or during overwintering. The increased richness was primarily driven by parasites with simple life cycles. Reduced sharing of parasites with complex life cycles amongst migratory and resident host populations may be explained by ecological constraints in establishing transmission, such as vector or secondary host availability. Our study exemplifies the utility of molecular methods for characterising multi-parasite systems and makes use of a single host species to provide insights on patterns of parasite distributions that result from migratory movements. Knowing how parasite life histories constrain or promote dispersal via regular host migration will be essential for predicting future patterns of pathogen dispersal under climate change and shifting migratory regimes.

ecology↗

bioRxiv2024

Genomic alterations and transcriptional phenotypes in circulating tumor DNA and matched metastatic tumor

BackgroundProfiling circulating cell-free DNA (cfDNA) has become a fundamental practice in cancer medicine, but the effectiveness of cfDNA at elucidating tumor-derived molecular features has not been systematically compared to standard single-lesion tumor biopsies in prospective cohorts of patients. The use of plasma instead of tissue to guide therapy is particularly attractive for patients with small cell lung cancer (SCLC), a cancer whose aggressive clinical course making it exceedingly challenging to obtain tumor biopsies. MethodsHere, a prospective cohort of 49 plasma samples obtained before, during, and after treatment from 20 patients with recurrent SCLC, we study cfDNA low pass whole genome (0.1X coverage) and exome (130X) sequencing in comparison with time-point matched tumor, characterized using exome and transcriptome sequencing. ResultsDirect comparison of cfDNA versus tumor biopsy reveals that cfDNA not only mirrors the mutation and copy number landscape of the corresponding tumor but also identifies clinically relevant resistance mechanisms and cancer driver alterations not found in matched tumor biopsies. Longitudinal cfDNA analysis reliably tracks tumor response, progression, and clonal evolution. Genomic sequencing coverage of plasma DNA fragments around transcription start sites shows distinct treatment-related changes and captures the expression of key transcription factors such as NEUROD1 and REST in the corresponding SCLC tumors, allowing prediction of SCLC neuroendocrine phenotypes and treatment responses. Conclusions These findings have important implications for non-invasive stratification and subtype-specific therapies for patients with SCLC, now treated as a single disease.

cancer biology↗

bioRxiv2024

Microenvironment Shapes Cell State, Plasticity, and Heterogeneity of Small Cell Lung Cancer

Small-cell lung cancer (SCLC) is the most fatal form of lung cancer. Intra-tumoral heterogeneity, marked by neuroendocrine (NE) and non-neuroendocrine (non-NE) cell states, defines SCLC, but the drivers of SCLC plasticity are poorly understood. To map the landscape of SCLC tumor microenvironment (TME), we apply spatially resolved transcriptomics and quantitative mass spectrometry-based proteomics to metastatic SCLC tumors obtained via rapid autopsy. The phenotype and overall composition of non-malignant cells in the tumor microenvironment (TME) exhibits substantial variability, closely mirroring the tumor phenotype, suggesting TME-driven reprogramming of NE cell states. We identify cancer-associated fibroblasts (CAF) as a crucial element of SCLC TME heterogeneity, contributing to immune exclusion, and predicting exceptionally poor prognosis. Together, our work provides a comprehensive map of SCLC tumor and TME ecosystems, emphasizing their pivotal role in SCLCs adaptable nature, opening possibilities for re-programming the intercellular communications that shape SCLC tumor states.

cancer biology↗

bioRxiv2022

Direct Cryo-ET observation of platelet deformation induced by SARS-CoV-2 Spike protein

SARS-CoV-2 is a novel coronavirus responsible for the COVID-19 pandemic. Its high pathogenicity is due to SARS-CoV-2 spike protein (S protein) contacting host-cell receptors. A critical hallmark of COVID-19 is the occurrence of coagulopathies. Here, we report the direct observation of the interactions between S protein and platelets. Live imaging showed that the S protein triggers platelets to deform dynamically, in some cases, leading to their irreversible activation. Strikingly, cellular cryo-electron tomography revealed dense decorations of S protein on the platelet surface, inducing filopodia formation. Hypothesizing that S protein binds to filopodia-inducing integrin receptors, we tested the binding to RGD motif-recognizing platelet integrins and found that S protein recognizes integrin v{beta}3. Our results infer that the stochastic activation of platelets is due to weak interactions of S protein with integrin, which can attribute to the pathogenesis of COVID-19 and the occurrence of rare but severe coagulopathies.

cell biology↗

bioRxiv2022

Subtyping of Small Cell Lung Cancer using plasma cell-free nucleosomes

Emerging data on small cell lung cancer (SCLC), an aggressive malignancy with exceptionally poor prognosis, support subtypes driven by distinct transcription regulators, which engender unique therapeutic vulnerabilities. However, the translational potential of these observations is limited by access to tumor biopsies. Here, we leverage chromatin immunoprecipitation of cell-free nucleosomes carrying active chromatin modifications followed by sequencing (cfChIP-seq) on 442 plasma samples from individuals with advanced SCLC, neuroendocrine carcinomas (NEC), non-SCLC cancers, and healthy adults. Beyond providing reliable estimates of SCLC circulating free DNA tumor fraction, cfChIP-seq captures the unique epigenetic states of SCLC tissue- and cell-of-origin. Comparison of cfChIP-seq signals to matched tumor transcriptomes reveals genome-wide concordance, establishing a direct link between gene expression in the tumor and plasma cell-free nucleosomes. Exploiting this link, we develop a classifier that discriminates between SCLC lineage-defining transcription factor subtypes based on cfChIP-seq data. This work sets the stage to non-invasively profile SCLC transcriptomes using plasma cfDNA histone modifications.

cancer biology↗

bioRxiv2021

Modelling TGFβR and Hh pathway regulation of prognostic matrisome molecules in ovarian cancer

In a multi-level deconstruction of omental metastases, we previously identified a prognostic matrisome gene expression signature in high-grade serous ovarian cancer (HGSOC) and twelve other malignancies. Here, our aim was to understand how six of these extracellular matrix, ECM, molecules, COL11A1, COMP, FN1, VCAN, CTSB and COL1A1, are up-regulated in cancer. Using biopsies, we identified significant associations between TGF{beta}R activity, Hedgehog signalling and these ECM molecules and then studied the associations in mono-, co- and tri-culture. Activated omental fibroblasts produced more matrix than malignant cells, directed by TGF{beta}R and Hedgehog signalling crosstalk. We reconstructed omental metastases in tri-culture of HGSOC cells, omental fibroblasts and adipocytes. This combination was sufficient to generate all six ECM proteins and the matrisome expression signature. TGF{beta}R and Hedgehog inhibitor combinations attenuated fibroblast activation, gel remodelling and ECM remodelling in these models. The tri-culture model reproduces key features of omental metastases and allows study of diseased-associated ECM.

cancer biology↗

bioRxiv2017

A novel data-driven model for real-time influenza forecasting

We provide data-driven machine learning methods that are capable of making real-time influenza forecasts that integrate the impacts of climatic factors and geographical proximity to achieve better forecasting performance. The key contributions of our approach are both applying deep learning methods and incorporation of environmental and spatio-temporal factors to improve the performance of the influenza forecasting models. We evaluate the method on Influenza Like Illness (ILI) counts and climatic data, both publicly available data sets. Our proposed method outperforms existing known influenza forecasting methods in terms of their Mean Absolute Percentage Error and Root Mean Square Error. The key advantages of the proposed data-driven methods are as following: (1) The deep-learning model was able to effectively capture the temporal dynamics of flu spread in different geographical regions, (2) The extensions to the deep-learning model capture the influence of external variables that include the geographical proximity and climatic variables such as humidity, temperature, precipitation and sun exposure in future stages, (3) The model consistently performs well for both the city scale and the regional scale on the Google Flu Trends (GFT) and Center for Disease Control (CDC) flu counts. The results offer a promising direction in terms of both data-driven forecasting methods and capturing the influence of spatio-temporal and environmental factors for influenza forecasting methods.

epidemiology↗