Biology subjects

Alexander, P.

Publications and source records attributed to Alexander, P..

5 recordsLinked to original sources

bioRxiv2025

The characteristic of epithelial-specific phenotypes and immunosuppressive microenvironment in the context of tumour budding in colorectal cancer

BackgroundTumour budding (TB), defined as a small cluster of up to four cells at the invasive front of the tumour, is a well-established independent and robust prognostic biomarker in colorectal cancer (CRC). This is strongly associated with adverse clinicopathological features and poor survival outcomes. Despite its clinical relevance, the precise underlying mechanism responsible for TB phenomenon remains unclear. MethodsMulti-omic approaches from bulk, regional GeoMx and Spatial Molecular Imager (SMI) RNA were used to identify the underlying mechanism of TB and its possible correlation with tumour microenvironment (TME) in CRC tissue. The results were validated using immunohistochemistry (IHC) and multiplex immunofluorescence (mIF) staining. ResultsPatients with high TB experience worse outcomes and associate with adverse clinical factors across two independent CRC cohorts. Bulk and regional RNA expression analyses reveal that tumours with high TB are significantly enriched for TNF- and TGF-{beta} signatures in both cohorts. Single cell CosMx SMI analysis confirmed TB cells exhibit higher expression of these signatures than adjacent invasive edge tumour cells. Elevated cyclinD1 expression was also observed within TB, and high cyclinD1 levels tend to experience poorer CRC prognosis. Furthermore, regional bulk RNA expression within the non-tumour (PanCK-) invasive edge areas demonstrated that tumours exhibiting high TB revealed the significantly differential expressions of immune-related genes (e.g. CD3, NKG7, IL6, CXCR6, CD47, IFNAR1 and VSIR). Single cell CosMx SMI analysis revealed that cancer-associated fibroblasts (CAFs) were physically the closest cells to TB cells. This spatial proximity was confirmed at the protein level using mIF, where the distance from TB to CD68+ macrophages predicted significantly poorer CRC outcomes. ConclusionThis multi-omic study confirms the prognostic significance of TB in CRC patients across two independent cohorts. Our findings highlight that TNF- and TGF-{beta} signalling play a crucial role in budding cells development by regulating cyclinD1. Furthermore, the transcriptomic analysis reveals an immunosuppressive niche characterised by reduced immune activity and close spatial interactions with CAFs and macrophages Ultimately, this study provides valuable insight into TBs underlying mechanism and its complex interactions within the TME. This could provide a foundation for developing targeted therapeutic strategies in CRC.

cancer biology↗

bioRxiv2025

Biophysical Basis of the in vivo Electroretinogram of the Mouse: Current Source Density Analysis of Genetically and Pharmacologically Isolated Rod photoreceptor-driven Currents

To better understand the molecular basis of the mouse electroretinogram (ERG) we have developed a biophysical model of the rod photoreceptor layers ionic mechanisms and applied current source-density (CSD) analysis to predict the genetically and pharmacologically isolated rod ERG a-wave. The saturating a-wave is characterized by a rapid relaxation ({tau} ~ 6 ms) from its maximum that has been hypothesized to be caused by HCN1 channel opening consequent to light-triggered sustained hyperpolarization, or by extracellular flow of capacitive current during hyperpolarization. To test these hypotheses, the CSD model included an ensemble of 11 rods with cell body locations spanning the outer nuclear layer, and with ionic mechanisms -- including CNG channels, NCKX in the outer segment, NKX, Kv2.1 channels in the inner segment, and HCN1 throughout the "non-outer segment" -- fully specified as to axial distributions, voltage dependencies, and the extracellular conductivity of the photoreceptor layer extracellular space. Predicting with CSD the steady-state axial distributions of rod dark current and transretinal potential, and the spatio-temporal response of the activation phase of the rods light response to intense stimuli, the analysis confirmed the Robson-Frishman hypothesis that extracellular capacitive current flowing towards the inner segment upon the rapid closure of CNG current plays a major role in shaping the early a-wave. The CSD analysis also reveals that extracellular current from HCN1 channels opened by the hyperpolarization contributes a net extracellular current flowing toward the ONL that contributes materially to the saturated a-wave relaxation and to setting its plateau level.

biophysics↗

bioRxiv2022

Structure of the SHOC2-MRAS-PP1C complex provides insights into RAF activation and Noonan syndrome

SHOC2 acts as a strong synthetic lethal interactor with MEK inhibitors in multiple KRAS cancer cell lines. SHOC2 forms a heterotrimeric complex with MRAS and PP1C that is essential for regulating RAF and MAPK-pathway activation by dephosphorylating a specific phosphoserine on RAF kinases. Here we present the high-resolution crystal structure of SHOC2-MRAS-PP1C (SMP) complex and apo-SHOC2. Our structures reveal that SHOC2, MRAS and PP1C form a stable ternary complex where all three proteins synergistically interact with each other. Our results show that dephosphorylation of RAF substrates by PP1C is enhanced upon interacting with SHOC2 and MRAS. The SMP complex only forms when MRAS is in an active state and is dependent on SHOC2 functioning as a scaffolding protein in the complex by bringing PP1C and MRAS together. Our results provide structural insights into the role of the SMP complex in RAF activation, how mutations found in Noonan syndrome enhance the complex formation and reveal new avenues for therapeutic interventions.

biophysics↗

bioRxiv2022

cLD: Rare-variant disequilibrium between genomic regions identifies novel genomic interactions

Linkage disequilibrium (LD) is a fundamental concept in genetics; critical for studying genetic associations and molecular evolution. However, LD measurements are only reliable for common genetic variants, leaving low-frequency variants unanalyzed. In this work, we introduce cumulative LD (cLD), a stable statistic that captures the rare-variant LD between genetic regions, which reflects more biological interactions between variants, in addition to lack of recombination. We derived the theoretical variance of cLD using delta methods to demonstrate its higher stability than LD for rare variants. This property is also verified by bootstrapped simulations using real data. In application, we find cLD reveals an increased genetic association between genes in 3D chromatin interactions, a phenomenon recently reported negatively by calculating standard LD between common variants. Additionally, we show that cLD is higher between gene pairs reported in interaction databases, identifies unreported protein-protein interactions, and reveals interacting genes distinguishing case/control samples in association studies.

bioinformatics↗

bioRxiv2018

Multivariate directed connectivity analysis (MDCA) enables decoding of directed interactions in neural and social networks

Many scientific fields currently face the daunting task of studying the dynamics of complex networks. For example, while we know that the rich mental phenomena of humans and other animals are mediated by complex systems of neural circuits in the brain, the mechanistic links between these biological networks and the functions that they mediate are poorly understood. Here we present a novel class of methods, termed multivariate directed connectivity analysis, to investigate network dynamics via patterns of directed interactions between network nodes. We validate these methods using simulated data and apply them to three real-world datasets, two neuroscientific and one investigating the 2016 US presidential candidates influence on the social media service Twitter. We find that these methods enable novel understanding of how information processing is distributed across networks. The methods are generally applicable to the study of dynamic information networks in biological, computational, and other fields of research.

neuroscience↗