Biology subjects

Kandala, S.

Publications and source records attributed to Kandala, S..

5 recordsLinked to original sources

bioRxiv2022

Chronic chromosome instability induced by Plk1 results in immune suppression in breast cancer

Chromosomal instability (CIN), the inability to correctly segregate chromosomes during cell division, is a common characteristic of solid tumors. CIN contributes to tumor evolution by promoting intratumor heterogeneity, thus facilitating resistance to cancer therapies. In vitro studies have demonstrated that cells with complex karyotypes are recognized and eliminated by natural killer (NK) cells. Paradoxically, it has also been observed that human tumors with high levels of CIN have an immunosuppressive phenotype. It remains unclear which CIN-associated molecular features alter immune recognition during tumor evolution. Previous studies with Polo-like kinase 1 (Plk1) overexpression in Her2-positive breast tumors, resulted in increased levels of CIN and delayed tumorigenesis. Using this mouse model, we show that high CIN tumors activate a senescence-associated secretory phenotype (SASP) and become immune evasive by activating RELB signaling and upregulating PD-L1 in a non-cell-autonomous manner. Single-cell RNA sequencing of immune cells from early-stage induced mammary glands revealed that macrophages, NK cells, B cells and regulatory T cells are programmed to a suppressive phenotype during tumor development. In human tumors, we further establish the importance of RELB/p38 signaling in understanding the interplay between CIN and the immune system, highlighting the need for novel adjuvant therapies in the context of chromosomally unstable tumors.

cancer biology↗

bioRxiv2021

Individualized precision targeting of dorsal attention and default mode networks with rTMS in traumatic brain injury-associated depression

BackgroundAt the group level, antidepressant efficacy of rTMS targets is inversely related to their normative connectivity with subgenual anterior cingulate cortex (sgACC). Individualized connectivity may yield better targets, particularly in patients with neuropsychiatric disorders who may have aberrant connectivity. However, sgACC connectivity shows poor test-retest reliability at the individual level. Individualized resting-state network mapping (RSNM) can reliably map inter-individual variability in brain network organization. ObjectiveTo identify individualized RSNM-based rTMS targets that reliably target the sgACC connectivity profile. MethodsWe used RSNM to identify network-based rTMS targets in 10 healthy controls and 13 individuals with traumatic brain injury-associated depression (TBI-D). These "RSNM targets" were compared with consensus structural targets and targets based on individualized anti-correlation with a group-mean-derived sgACC region ("anti-group-mean sgACC targets"). The TBI-D cohort was randomized to receive active (n=9) or sham (n=4) rTMS to RSNM targets. ResultsThe group-mean sgACC connectivity profile was reliably estimated by individualized correlation with default mode network (DMN) and anti-correlation with dorsal attention network (DAN). Individualized RSNM targets were then identified based on DAN anti-correlation and DMN correlation. Counterintuitively, anti-correlation with the group-mean sgACC connectivity profile was stronger and more reliable for RSNM-derived targets than for "anti-group-mean sgACC targets". Improvement in depression after RSNM-targeted rTMS was predicted by target anti-correlation with the portions of sgACC. Active treatment led to increased connectivity within and between several relevant regions. ConclusionsRSNM may enable reliable individualized rTMS targeting, although further research is needed to determine whether this personalized approach can improve clinical outcomes.

neuroscience↗

bioRxiv2018

Transdiagnostic multimodal neuroimaging in psychosis: structural, resting-state, and task MRI correlates of cognitive control

BackgroundPsychotic disorders, including schizophrenia and bipolar disorder, are associated with impairments in regulation of goal-directed behavior, termed cognitive control. Cognitive control related neural alterations have been studied in psychosis. However, studies are typically unimodal and relationships across modalities of brain function and structure remain unclear. Thus, we performed transdiagnostic multimodal analyses to examine cognitive control related neural variation in psychosis.\n\nMethodsStructural, resting, and working memory task imaging and behavioral data for 31 controls, 27 bipolar, and 23 schizophrenia patients were collected and processed identically to the Human Connectome Project (HCP), enabling identification of relationships with prior multimodal work. Two cognitive control related independent components (ICs) derived from the HCP using multiset canonical correlation analysis + joint independent component analysis (mCCA+jICA) were used to predict performance in psychosis. de novo mCCA+jICA was performed, and resultant IC weights were correlated with cognitive control.\n\nResultsA priori ICs significantly predicted cognitive control in psychosis (3/5 modalities significant). De novo mCCA+jICA identified an IC correlated with cognitive control that also discriminated groups. Structural contributions included insular, somatomotor, cingulate, and visual regions; task contributions included precentral, posterior parietal, cingulate, and visual regions; and resting-state contributions highlighted canonical network organization. Follow-up analyses suggested de novo correlations with cognitive control were primarily influenced by schizophrenia patients.\n\nConclusionsA priori components partially predicted performance in transdiagnostic psychosis and de novo analyses identified novel contributions in somatomotor and visual regions in schizophrenia. Together, results suggest joint contributions across modalities related to cognitive control across the healthy-to-psychosis spectrum.

neuroscience↗

bioRxiv2017

rTMS with individualized resting-state network mapping for neuropsychiatric sequelae of repetitive traumatic brain injury in a retired NFL player

The recent advent of individualized resting-state network mapping (RSNM) has revealed substantial inter-individual variability in anatomical localization of brain networks identified using resting-state functional MRI (rsfMRI). Such variability may be particularly important after repetitive traumatic brain injury (TBI), which is associated with treatment-resistant depression. RSNM enables personalized targeting of repetitive transcranial magnetic stimulation (rTMS), a focal brain stimulation technique that relieves depression when administered over dorsolateral prefrontal cortex.\n\nRSNM was used to identify left/right dorsolateral prefrontal rTMS targets with maximal difference between dorsal attention network and default mode network (DMN) correlations. These targets were spatially distinct from those identified by prior methods. The method was evaluated by administering twenty sessions of left-sided excitatory and right-sided inhibitory rTMS to a retired NFL defensive lineman with progressive treatment-resistant neuropsychiatric disturbances. Treatment led to improvement in Montgomery-Asberg Depression Rating Scale (72%), cognitive testing, and headache scales. In comparison with healthy individuals and subjects with TBI-associated depression, baseline rsfMRI revealed substantially elevated DMN connectivity with medial temporal lobe (MTL). Serial rsfMRI scans showed gradual improvement in MTL-DMN connectivity and stimulation site connectivity with subgenual anterior cingulate cortex. This highlights the possibility of individualized neuromodulation and biomarker-based monitoring for neuropsychiatric sequelae of repetitive TBI.

neuroscience↗

bioRxiv2017

Multimodal Neural Correlates Of Cognitive Control In The Human Connectome Project

Cognitive control is a construct that refers to the set of functions that enable decisionmaking and task performance through the representation of task states, goals, and rules. The neural correlates of cognitive control have been studied in humans using a wide variety of neuroimaging modalities, including structural MRI, resting-state fMRI, and task-based fMRI. The results from each of these modalities independently have implicated the involvement of a number of brain regions in cognitive control, including dorsal prefrontal cortex, and frontal parietal and cingulo-opercular brain networks. However, it is not clear how the results from a single modality relate to results in other modalities. Recent developments in multimodal image analysis methods provide an avenue for answering such questions and could yield more integrated models of the neural correlates of cognitive control. In this study, we used multiset canonical correlation analysis with joint independent component analysis (mCCA+jICA) to identify multimodal patterns of variation related to cognitive control. We used two independent cohorts of participants from the Human Connectome Project, each of which had data from four imaging modalities. We replicated the findings from the first cohort in the second cohort using both independent and predictive analyses. The independent analyses identified a component in each cohort that was highly similar to the other and significantly correlated with cognitive control performance. The replication by prediction analyses identified two independent components that were significantly correlated with cognitive control performance in the first cohort and significantly predictive of performance in the second cohort. These components identified positive relationships across the modalities in neural regions related to both dynamic and stable aspects of task control, including regions in both the frontal-parietal and cingulo-opercular networks, as well as regions hypothesized to be modulated by cognitive control signaling, such as visual cortex. Taken together, these results illustrate the potential utility of multi-modal analyses in identifying the neural correlates of cognitive control across different indicators of brain structure and function.

neuroscience↗