Biology subjects

Sultana, T.

Publications and source records attributed to Sultana, T..

3 recordsLinked to original sources

bioRxiv2023

Investigating the association of resting-state brain effective connectivity with basic negative emotions

The neurotic personality has an impact on the regulation of basic negative emotions such as anger, fear, and sadness. There has been extensive research in search of functional connectivity biomarkers of neuroticism and basic negative emotions but there is a lack of research based on effective connectivity. In the current research, we intended to determine the significance of causal interaction of three large-scale resting-state networks - default mode, salience, and executive networks - to predict neuroticism and basic negative emotions. In this study, a large-scale human connectome project dataset comprising functional MRI scans and self-reported scores of neuroticism and negative emotions of 1079 subjects, was utilized. Spectral dynamic causal modelling and parametric empirical Bayes was used to estimate the subject-level effective connectivity parameters and their group-level associations with the neuroticism and emotional scores. Leave-one-out cross-validation using parametric empirical Bayes was employed for prediction analysis. Our results for heightened emotions showed that the self-connection of right hippocampus can predict individuals with high fear, self-connections of dorsal anterior cingulate cortex, posterior cingulate cortex and left dorsolateral prefrontal cortex can predict individuals with high sadness. High anger, low sadness, and neuroticism scores of any emotion category except low fear, could not be predicted using triple network effective connectivity. Our findings revealed that the causal (directed) connections of the resting-state triple network can potentially serve as a connectomic signature for people with high and low fear, high sadness, low anger, and neuroticism with low fear.

neuroscience↗

bioRxiv2022

Robust eIF4B levels undermine invasive growth and immune evasion mechanisms in murine triple negative breast cancer models.

Dysregulated protein synthesis is seen in many aggressive cancers, including metastatic breast cancer. However, the specific contributions of certain translation initiation factors to in vivo disease remain undefined. This is particularly true of eIF4B, an RNA-binding protein and cofactor of the RNA helicase eIF4A and associated eIF4F cap-binding complex. While eIF4A, eIF4G, and eIF4E are well-known to contribute to the progression of many cancer types including metastatic breast cancers, the role played by eIF4B in breast cancer remains relatively unclear. We therefore explored how naturally divergent and experimentally modulated eIF4B levels impact tumor growth and progression in well-characterized murine triple negative breast cancer (TNBC) models. Surprisingly, we found that higher eIF4B levels in mouse and human breast cancers were associated with less aggressive phenotypes. shRNA-mediated eIF4B knockdown in TNBC lines failed to markedly alter proliferation and global translation in the cells in vitro and only modestly hindered their growth as primary mammary tumors growth in mice. However, eIF4B knockdown significantly enhanced invasive growth in vitro and exacerbated both tumor burden and mortality relative to nontargeting shRNA controls in a model of metastatic disease. Analysis of eIF4B levels and breast cancer patient survival reinforced a link to better outcomes. Interestingly, low eIF4B expression was also associated with more formidable immune evasion in vitro and in vivo, implicating a novel immunomodulatory role for this factor in the malignant setting that suggests a mode of action beyond its historical role as a co-activator of eIF4A/F. Significance StatementMetastasis is the leading cause of cancer-related mortality. Despite many advances in our understanding of this complex process and the molecular and cellular events involved, mechanisms that allow secondary tumors to arise and persist remain incompletely understood. Uncharacterized metastatic determinants active at the level of translational control may be exploitable as novel therapy targets or biomarkers predicting a tumors potential for spread and recurrence. Here we describe previously unrecognized consequences of dysregulated eIF4B levels in murine breast cancer that shed light on how this translation initiation factor contributes to disease outcomes. Our findings suggest that eIF4B levels direct metastatic risk and immune evasion, and further study should establish its value in personalized treatment decisions and development of future therapies.

cancer biology↗

bioRxiv2022

Changes in both top-down and bottom-up effective connectivity drive visual hallucinations in Parkinson's disease

Visual hallucinations are common in Parkinsons disease and are associated with poorer quality of life and higher risk of dementia. An important and influential model that is widely accepted as an explanation for the mechanism of visual hallucinations in Parkinsons disease and other Lewy-body diseases is that these arise due to aberrant hierarchical processing, with impaired bottom-up integration of sensory information and overweighting of top-down perceptual priors within the visual system. This hypothesis has been driven by behavioural data and supported indirectly by observations derived from regional activation and correlational measures using neuroimaging. However, until now, there was no evidence from neuroimaging for differences in causal influences between brain regions measured in patients with Parkinsons hallucinations. This is in part because previous resting-state studies focus on functional connectivity, which is inherently undirected in nature and cannot test hypotheses about directionality of connectivity. Spectral dynamic causal modelling is a Bayesian framework that allows the inference of effective connectivity - defined as the directed (causal) influence that one region exerts on another region - from resting-state functional MRI data. In the current study, we utilise spectral dynamic causal modelling to estimate effective connectivity within the resting-state visual network in our cohort of 15 Parkinsons disease visual hallucinators, and 75 Parkinsons disease non-hallucinators. We find that visual hallucinators display decreased bottom-up effective connectivity from the lateral geniculate nucleus to primary visual cortex and increased top-down effective connectivity from left prefrontal cortex to primary visual cortex and medial thalamus, as compared to non-hallucinators. Importantly, we find that the pattern of effective connectivity is predictive of the presence of visual hallucinations and associated with their severity within the hallucinating group. This is the first study to provide evidence, using resting state effective connectivity, to support a model of aberrant hierarchical predictive processing as the mechanism for visual hallucinations in Parkinsons disease.

neuroscience↗