Biology subjects

Krishnan, A.

Publications and source records attributed to Krishnan, A..

At least 91 records · Page 5Linked to original sources

bioRxiv2021

Systematic tissue annotations of -omics samples by modeling unstructured metadata

There are currently >1.3 million human -omics samples that are publicly available. This valuable resource remains acutely underused because discovering particular samples from this ever-growing data collection remains a significant challenge. The major impediment is that sample attributes are routinely described using varied terminologies written in unstructured natural language. We propose a natural-language-processing-based machine learning approach (NLP-ML) to infer tissue and cell-type annotations for -omics samples based only on their free-text metadata. NLP-ML works by creating numerical representations of sample descriptions and using these representations as features in a supervised learning classifier that predicts tissue/cell-type terms. Our approach significantly outperforms an advanced graph-based reasoning annotation method (MetaSRA) and a baseline exact string matching method (TAGGER). Model similarities between related tissues demonstrate that NLP-ML models capture biologically-meaningful signals in text. Additionally, these models correctly classify tissue-associated biological processes and diseases based on their text descriptions alone. NLP-ML models are nearly as accurate as models based on gene-expression profiles in predicting sample tissue annotations but have the distinct capability to classify samples irrespective of the -omics experiment type based on their text metadata. Python NLP-ML prediction code and trained tissue models are available at https://github.com/krishnanlab/txt2onto.

bioinformatics↗

bioRxiv2021

Revisiting the role of Toxoplasma gondii ERK7 in the maintenance and stability of the apical complex

Toxoplasma gondii ERK7 is known to contribute to the integrity of the apical complex and to be involved only in the final step of the conoid biogenesis. In the absence of ERK7, mature parasites lose their conoid complex and are unable to glide, invade or egress from host cells. In contrast to a previous report, we show here that depletion of ERK7 phenocopies the depletion of the apical cap proteins AC9 or AC10. The absence of ERK7 leads to the loss of the apical polar ring, the disorganization of the basket of subpellicular microtubules and an impairment in micronemes secretion. Ultra-expansion microscopy (U-ExM) coupled to NHS-Ester staining on intracellular parasites offers an unprecedented level of resolution and highlights the disorganization of the rhoptries as well as the dilated plasma membrane at the apical pole in the absence of ERK7. Comparative proteomics analysis of wild-type and ERK7 or AC9 depleted parasites led to the disappearance of known, predicted, as well as putative novel components of the apical complex. In contrast, the absence of ERK7 led to an accumulation of microneme proteins, resulting from the defect in exocytosis of the organelles. ImportanceThe conoid is an enigmatic, dynamic organelle positioned at the apical tip of the coccidian subgroup of the Apicomplexa, close to the apical polar ring (APR) from which the subpellicular microtubules (SPTMs) emerge and at the site of microneme and rhoptry secretory organelles exocytosis. In Toxoplasma gondii, the conoid protrudes concomitantly to microneme secretion, during egress, motility and invasion. The conditional depletion of the apical cap structural protein AC9 or AC10 leads to a disorganization of SPTMs as well as the loss of APR and conoid that result in microneme secretion defect and block in motility, invasion and egress. We show here that depletion of the kinase ERK7 phenocopies completely AC9 and AC10 mutants. Moreover, the combination of ultrastructure expansion microscopy with an NHS ester staining revealed that ERK7 depleted parasites exhibit a dilated apical plasma membrane and a mis-positioning of the rhoptry organelles.

microbiology↗

bioRxiv2021

Behavioral context shapes vocal sequences in two anuran species with different repertoire sizes

Acoustic signals in animals serve to convey context-dependent information to receivers. Birds and mammals combine diverse sounds into complex sequences to communicate, but these sequences largely remain understudied in other taxa. Anuran vocalizations are a prominent feature of their life history, and function in defense of territories and to attract mates. However, despite the spectacular diversity of anurans in tropical regions of the world, vocal diversity and communication strategies remain relatively poorly studied. Specifically, studies of vocal sequences and context-dependent vocal patterns in frogs remain few. Here, we investigated the context-dependent vocal repertoire and the use of vocal sequences by two anuran species belonging to different lineages, both endemic to the hyper-diverse Western Ghats of India. By recording vocal sequences both when frogs were alone and in the presence of a territorial rival, we present evidence that both species modify their vocal repertoire according to context. Specifically, one species appends notes to generate more complex sequences, whereas the other shifts to different note types, resulting in different sequences for different contexts. Thus, despite differences in repertoire size, both frog species are capable of adjusting the temporal sequence of vocalizations to communicate in different contexts. This study highlights the need for further studies of insular frogs, to understand how diversification across these continental islands has influenced the evolution of vocal repertoires, vocal sequence patterns and communication systems. Lay SummaryAnimals employ complex sequences of acoustic signals to communicate in diverse behavioral contexts. Here, we demonstrate that two frog species with different vocal repertoires both modify the sequence of note emissions in the presence of a territorial rival. These patterns demonstrate that anurans are capable of complex shifts in the patterns of their vocalization, to communicate different messages to different receivers. Our findings demonstrate the value of studying behavioral diversity in tropical regions.

animal behavior and cognition↗

bioRxiv2021

Progressive and predictive markers of disease evolution: platelet transcriptome in chronic myeloproliferative neoplasms

Predicting disease progression remains a particularly challenging endeavor in chronic degenerative disorders and cancer, thus limiting early detection, risk stratification, and preventive interventions. Here, profiling the spectrum of chronic myeloproliferative neoplasms (MPNs) as a model, we identify the blood platelet transcriptome as a proxy for highly sensitive progression biomarkers that also enables prediction of advanced disease via machine learning algorithms. Using RNA sequencing (RNA-seq), we derive disease-relevant gene expression in purified platelets from 120 peripheral blood samples constituting two time-separated cohorts of patients diagnosed with one of three MPN subtypes at sample acquisition - essential thrombocythemia, ET (n=24), polycythemia vera, PV (n=33), and primary or post ET/PV secondary myelofibrosis, MF (n=42), and healthy donors (n=21). The MPN platelet transcriptome reveals an incremental molecular reprogramming that is independent of patient driver mutation status or therapy and discriminates each clinical phenotype. Leveraging this dataset that shows a characteristic progressive expression gradient across MPN, we develop a machine learning model (Lasso-penalized regression) and predict advanced subtype MF at high accuracy and under two conditions of external validation: i) temporal: our two Stanford cohorts, AUC-ROC of 0.96; and ii) geographical: independently published data of an additional n=25 MF and n=46 healthy donors, AUC-ROC of 0.97). Lasso-derived signatures offer a robust core set of < 5 MPN transcriptome markers that are progressive in expression. Mechanistic insights from our data highlight impaired protein homeostasis as a prominent driver of MPN evolution, with persistent integrated stress response. We also identify JAK inhibitor-specific signatures and other interferon, proliferation, and proteostasis-associated markers as putative targets for MPN-directed therapy. Our platelet transcriptome snapshot of chronic MPNs demonstrates a proof of principle for disease risk stratification and progression beyond genetic data alone, with potential utility in other progressive disorders. HighlightsLeveraging two independent and mutually validating MPN patient cohorts, we identify progressive transcriptomic markers that also enable externally validated prediction in MPNs. Our platelet RNA-Seq data identifies impaired protein homeostasis as prominent in MPN progression and offers putative targets of therapy. VISUAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=135 HEIGHT=200 SRC="FIGDIR/small/435190v3_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@72dc16org.highwire.dtl.DTLVardef@cf5096org.highwire.dtl.DTLVardef@b3d460org.highwire.dtl.DTLVardef@3c0ddc_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2021

Combinatorial patterns of gene expression changes contribute to variable expressivity of the developmental delay-associated 16p12.1 deletion

BackgroundRecent studies have suggested that individual variants do not sufficiently explain the variable expressivity of phenotypes observed in complex disorders. For example, the 16p12.1 deletion is associated with developmental delay and neuropsychiatric features in affected individuals, but is inherited in >90% of cases from a mildly-affected parent. While children with the deletion are more likely to carry additional "second-hit" variants than their parents, the mechanisms for how these variants contribute to phenotypic variability are unknown. MethodsWe performed detailed clinical assessments, whole-genome sequencing, and RNA sequencing of lymphoblastoid cell lines for 32 individuals in five large families with multiple members carrying the 16p12.1 deletion. We identified contributions of the 16p12.1 deletion and "second-hit" variants towards a range of expression changes in deletion carriers and their family members, including differential expression, outlier expression, alternative splicing, allele-specific expression, and expression-quantitative trait loci analyses. ResultsWe found that the deletion dysregulates multiple autism and brain development genes such as FOXP1, ANK3, and MEF2. Carrier children also showed an average of 5,323 gene expression changes compared with one or both parents, which matched with 33/39 observed developmental phenotypes. We identified significant enrichments for 13/25 classes of "second-hit" variants in genes with expression changes, where 4/25 variant classes were only enriched when inherited from the non-carrier parent, including loss-of-function SNVs and large duplications. In 11 instances, including for ZEB2 and SYNJ1, gene expression was synergistically altered by both the deletion and inherited "second-hits" in carrier children. Finally, brain-specific interaction network analysis showed strong connectivity between genes carrying "second-hits" and genes with transcriptome alterations in deletion carriers. ConclusionsOur results suggest a potential mechanism for how "second-hit" variants modulate expressivity of complex disorders such as the 16p12.1 deletion through transcriptomic perturbation of gene networks important for early development. Our work further shows that family-based assessments of transcriptome data are highly relevant towards understanding the genetic mechanisms associated with complex disorders.

genomics↗

bioRxiv2020

Ecological specialisation and evolutionary reticulation in extant Hyaenidae

During the Miocene, Hyaenidae was a highly diverse family of Carnivora that has since been severely reduced to four extant genera, each of which contains only a single species. These species include the bone-cracking spotted, striped, and brown hyenas, and the specialised insectivorous aardwolf. Previous genome studies have analysed the evolutionary histories of the spotted and brown hyenas, but little is known about the remaining two species. Moreover, the genomic underpinnings of scavenging and insectivory, defining traits of the extant species, remain elusive. To tackle these questions, we generated an aardwolf genome and analysed it together with those from the other three species. We provide new insights into the evolutionary relationships between the species, the genomic underpinnings of their scavenging and insectivorous lifestyles, and their respective genetic diversities and demographic histories. High levels of phylogenetic discordance within the family suggest gene flow between the aardwolf lineage and the ancestral brown/striped hyena lineage. Genes related to immunity and digestion in the bone-cracking hyenas and craniofacial development in the aardwolf showed the strongest signals of selection in their respective lineages, suggesting putative key adaptations to carrion or termite feeding. We also found a family-wide expansion in olfactory receptor genes suggesting that an acute sense of smell was a key early adaptation for the Hyaenidae family. Finally, we report very low levels of genetic diversity within the brown and striped hyenas despite no signs of inbreeding, which we putatively link to their similarly slow decline in Neover the last [~]2 million years. We found much higher levels of genetic diversity in both the spotted hyena and aardwolf and more stable population sizes through time. Taken together, these findings highlight how ecological specialisation can impact the evolutionary history, demographics, and adaptive genetic changes of a lineage.

evolutionary biology↗

bioRxiv2020

Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data

BackgroundConstructing gene coexpression networks is a powerful approach for analyzing high-throughput gene expression data towards module identification, gene function prediction, and disease-gene prioritization. While optimal workflows for constructing coexpression networks - including good choices for data pre-processing, normalization, and network transformation - have been developed for microarray-based expression data, such well-tested choices do not exist for RNA-seq data. Almost all studies that compare data processing/normalization methods for RNA-seq focus on the end goal of determining differential gene expression. ResultsHere, we present a comprehensive benchmarking and analysis of 30 different workflows, each with a unique set of normalization and network transformation methods, for constructing coexpression networks from RNA-seq datasets. We tested these workflows on both large, homogenous datasets (Genotype-Tissue Expression project) and small, heterogeneous datasets from various labs (submitted to the Sequence Read Archive). We analyzed the workflows in terms of aggregate performance, individual method choices, and the impact of multiple dataset experimental factors. Our results demonstrate that between-sample normalization has the biggest impact, with trimmed mean of M-values or upper quartile normalization producing networks that most accurately recapitulate known tissue-naive and tissue-specific gene functional relationships. ConclusionsBased on this work, we provide concrete recommendations on robust procedures for building an accurate coexpression network from an RNA-seq dataset. In addition, researchers can examine all the results in great detail at https://krishnanlab.github.io/norm_for_RNAseq_coexp to make appropriate choices for coexpression analysis based on the experimental factors of their RNA-seq dataset.

genomics↗

bioRxiv2020

Functional assessment of the "two-hit" model for neurodevelopmental defects in Drosophila and X. laevis

We previously identified a deletion on chromosome 16p12.1 that is mostly inherited and associated with multiple neurodevelopmental outcomes, where severely affected probands carried an excess of rare pathogenic variants compared to mildly affected carrier parents. We hypothesized that the 16p12.1 deletion sensitizes the genome for disease, while "second-hits" in the genetic background modulate the phenotypic trajectory. To test this model, we examined how neurodevelopmental defects conferred by knockdown of individual 16p12.1 homologs are modulated by simultaneous knockdown of homologs of "second-hit" genes in Drosophila melanogaster and Xenopus laevis. We observed that knockdown of 16p12.1 homologs affect multiple phenotypic domains, leading to delayed developmental timing, seizure susceptibility, brain alterations, abnormal dendrite and axonal morphology, and cellular proliferation defects. Compared to genes within the 16p11.2 deletion, which has higher de novo occurrence, 16p12.1 homologs were less likely to interact with each other in Drosophila models or a human brain-specific interaction network, suggesting that interactions with "second-hit" genes may confer higher impact towards neurodevelopmental phenotypes. Assessment of 212 pairwise interactions in Drosophila between 16p12.1 homologs and 76 homologs of patient-specific "second-hit" genes (such as ARID1B and CACNA1A), genes within neurodevelopmental pathways (such as PTEN and UBE3A), and transcriptomic targets (such as DSCAM and TRRAP) identified genetic interactions in 63% of the tested pairs. In 11 out of 15 families, homologs of patient-specific "second-hits" enhanced or suppressed the phenotypic effects of one or many 16p12.1 homologs. In fact, homologs of SETD5 synergistically interacted with homologs of MOSMO in both Drosophila and X. laevis, leading to modified cellular and brain phenotypes, as well as axon outgrowth defects that were not observed with knockdown of either individual homolog. Our results suggest that several 16p12.1 genes sensitize the genome towards neurodevelopmental defects, and complex interactions with "second-hit" genes determine the ultimate phenotypic manifestation. Author SummaryCopy-number variants, or deletions and duplications in the genome, are associated with multiple neurodevelopmental disorders. The developmental delay-associated 16p12.1 deletion is mostly inherited, and severely affected children carry an excess of "second-hits" variants compared to mildly affected carrier parents, suggesting that additional variants modulate the clinical manifestation. We studied this "two-hit" model using Drosophila and Xenopus laevis, and systematically tested how homologs of "second-hit" genes modulate neurodevelopmental defects observed for 16p12.1 homologs. We observed that 16p12.1 homologs independently led to multiple neurodevelopmental features and weakly interacted with each other, suggesting that interactions with "second-hit" homologs potentially have a higher impact towards neurodevelopmental defects than interactions between 16p12.1 homologs. We tested 212 pairwise interactions of 16p12.1 homologs with "second-hit" homologs and genes within conserved neurodevelopmental pathways, and observed modulation of neurodevelopmental defects caused by 16p12.1 homologs in 11 out of 15 families, and 16/32 of these changes could be attributed to genetic interactions. Interestingly, we observed that SETD5 homologs interacted with homologs of MOSMO, which conferred additional neuronal phenotypes not observed with knockdown of individual homologs. We propose that the 16p12.1 deletion sensitizes the genome to multiple neurodevelopmental defects, and complex interactions with "second-hit" genes determine the clinical trajectory of the disorder.

genomics↗

bioRxiv2020

Convergent acoustic community structure in South Asian dry and wet grassland birds

Although the study of bird acoustic communities has great potential to provide valuable conservation data, many aspects of their assembly and dynamics remain poorly understood. Grassland habitats in South Asia comprise distinct biomes with a unique avifauna, presenting an opportunity to address how community-level patterns in acoustic signal space arise. Similarity in signal space of different grassland bird communities may be due to phylogenetic similarity, or because different bird groups partition the acoustic resource, resulting in convergent distributions in signal space. Here, we quantify the composition, signal space and phylogenetic diversity of bird acoustic communities from the dry semiarid grasslands of Northwest India and the wet floodplain grasslands of Northeast India. We find that acoustic communities occupying these distinct biomes exhibit convergent signal space. However, dry grasslands exhibit higher phylogenetic diversity, and the two communities are not phylogenetically more similar than expected by chance. The Sylvioidea encompasses half the species in the wet grassland acoustic community, with an expanded signal space compared to the dry grasslands. Thus, dry and wet grassland communities are convergent in signal space despite differences in phylogenetic diversity. We therefore hypothesize that different clades colonizing grasslands partition the acoustic resource, resulting in convergent community structure across biomes. Many of the birds we recorded are highly threatened, and acoustic monitoring will support conservation measures in these imperiled, yet poorly-studied habitats.

ecology↗

bioRxiv2020

PecanPy: a fast, efficient, and parallelized Python implementation of node2vec

Learning low-dimensional representations (embeddings) of nodes in large graphs is key to applying machine learning on massive biological networks. Node2vec is the most widely used method for node embedding. However, its original Python and C++ implementations scale poorly with network density, failing for dense biological networks with hundreds of millions of edges. We have developed PecanPy, a new Python implementation of node2vec that uses cache-optimized compact graph data structures and precomputing/parallelization to result in fast, high-quality node embeddings for biological networks of all sizes and densities. PecanPy software and documentation are available at https://github.com/krishnanlab/pecanpy.

bioinformatics↗

bioRxiv2020

Evoked pain intensity representation is distributed across brain systems: A multistudy mega-analysis

Information is coded in the brain at different scales for different phenomena: locally, distributed across regions and networks, and globally. For pain, the scale of representation is controversial. Although generally believed to be an integrated cognitive and sensory phenomenon implicating diverse brain systems, quantitative characterizations of which regions and networks are sufficient to represent pain are lacking. In this meta-analysis (or mega-analysis) using data from 289 participants across 10 studies, we use model comparison combined with multivariate predictive models to investigate the spatial scale and location of acute pain representation. We compare models based on (a) a single most pain-predictive module, either previously identified elementary regions or a single best large-scale cortical resting-state network module; (b) selected cortical-subcortical systems related to evoked pain in prior literature ( multi-system models); and (c) a model spanning the full brain. We estimate the accuracy of pain intensity predictions using cross validation (7 studies) and subsequently validate in three independent holdout studies. All spatial scales convey information about pain intensity, but distributed, multi-system models better characterize pain representations than any individual region or network (e.g. multisystem models explain >20% more of individual subject pain ratings than the best elementary region). Full brain models showed no predictive advantage over multi-system models. These findings quantify the extent that representation of evoked pain experience is distributed across multiple cortical and subcortical systems, show that pain representation is not circumscribed by any elementary region or conical network, and provide a blueprint for identifying the spatial scale of information in other domains. Significance StatementWe define modular, multisystem and global views of brain function, use multivariate fMRI decoding to characterize pain representations at each level, and provide evidence for a multisystem representation of evoked pain. We further show that local views necessarily exclude important components of pain representation, while a global full brain representation is superfluous, even though both are viable frameworks for representing pain. These findings quantitatively juxtapose and reconcile divergent conclusions from evoked pain studies within a generalized neuroscientific framework, and provide a blueprint for investigating representational architecture for diverse brain processes.

neuroscience↗

bioRxiv2020

A ubiquitin-based mechanism for the oligogenic inheritance of heterotaxy and heart defects

The etiology of congenital heart defects (CHDs), amongst the most common human birth defects, is poorly understood partly because of its complex genetic architecture. Here we show that two genes previously implicated in CHDs, Megf8 and Mgrn1, interact genetically and biochemically to regulate the strength of Hedgehog signaling in target cells. MEGF8, a single-pass transmembrane protein, and MGRN1, a RING superfamily E3 ligase, assemble to form a transmembrane ubiquitin ligase complex that catalyzes the ubiquitination and degradation of the Hedgehog pathway transducer Smoothened. Homozygous Megf8 and Mgrn1 mutations increased Smoothened abundance and elevated sensitivity to Hedgehog ligands. While mice heterozygous for loss-of-function Megf8 or Mgrn1 mutations were normal, double heterozygous embryos exhibited an incompletely penetrant syndrome of CHDs with heterotaxy. Thus, genetic interactions between components of a receptor-like ubiquitin ligase complex that tunes morphogen signaling strength can cause a birth defect syndrome inherited in an oligogenic pattern.

developmental biology↗

bioRxiv2020

Co-expression signatures of combinatorial gene regulation

Gene co-expression analyses provide a powerful tool to determine gene associations. The interaction of transcription factors (TFs) with their target genes is an essential step in gene regulation, yet to what extent TFs-target gene associations are recovered in co-expression studies remains unclear. Using the wealth of data available for Arabidopsis, we show here that protein-DNA interactions are overall poor indicators of TF-target co-expression, yet the inclusion of TF-TF interaction information significantly enhance co-expression signals. These results highlight the impact of combinatorial gene control on such gene association networks. We integrated this information to predict higher-order regulatory complexes, which are difficult to identify experimentally. We demonstrate that genes strongly co-expressed with a TF are also enriched in indirect targets. Our results have significant implications on the empirical understanding of complex gene regulatory networks and transcription factor function, and the significance of co-expression from the perspective of protein-protein and protein-DNA interactions.

systems biology↗

bioRxiv2020

A Flexible, Interpretable, and Accurate Approach for Imputing the Expression of Unmeasured Genes

While there are >2 million publicly-available human microarray gene-expression profiles, these profiles were measured using a variety of platforms that each cover a pre-defined, limited set of genes. Therefore, key to reanalyzing and integrating this massive data collection are methods that can computationally reconstitute the complete transcriptome in partially-measured microarray samples by imputing the expression of unmeasured genes. Current state-of-the-art imputation methods are tailored to samples from a specific platform and rely on gene-gene relationships regardless of the biological context of the target sample. We show that sparse regression models that capture sample-sample relationships (termed SampleLASSO), built on-the-fly for each new target sample to be imputed, outperform models based on fixed gene relationships. Extensive evaluation involving three machine learning algorithms (LASSO, k-nearest-neighbors, and deep-neural-networks), two gene subsets (GPL96-570 and LINCS), and three imputation tasks (within and across microarray/RNA-seq) establishes that SampleLASSO is the most accurate model. Additionally, we demonstrate the biological interpretability of this method by showing that, for imputing a target sample from a certain tissue, SampleLASSO automatically leverages training samples from the same tissue. Thus, SampleLASSO is a simple, yet powerful and flexible approach for harmonizing large-scale gene-expression data.

bioinformatics↗

bioRxiv2020

True S-cones are concentrated in the ventral mouse retina for color detection in the upper visual field

Color, an important visual cue for survival, is encoded by comparing signals from photoreceptors with different spectral sensitivities. The mouse retina expresses a short wavelength-sensitive and a middle/long wavelength-sensitive opsin (S- and M-opsin), forming opposing, overlapping gradients along the dorsal-ventral axis. Here, we analyzed the distribution of all cone types across the entire retina for two commonly used mouse strains. We found, unexpectedly, that true S-cones (S-opsin only) are highly concentrated (up to 30% of cones) in ventral retina. Moreover, S-cone bipolar cells (SCBCs) are also skewed towards ventral retina, with wiring patterns matching the distribution of true S-cones. In addition, true S-cones in the ventral retina form clusters, which may augment synaptic input to SCBCs. Such a unique true S-cone pattern forms a basis for mouse color vision, likely reflecting evolutionary adaption to enhance color coding for the upper visual field suitable for mices habitat and behavior.

evolutionary biology↗

bioRxiv2020

Novel Electrodes for Reliable EEG Recordings on Coarse and Curly Hair

EEG is a powerful and affordable brain sensing and imaging tool used extensively for the diagnosis of neurological disorders (e.g. epilepsy), brain computer interfacing, and basic neuroscience. Unfortunately, most EEG electrodes and systems are not designed to accommodate coarse and curly hair common in individuals of African descent. This can lead to poor quality data that might be discarded in scientific studies after recording from a broader population set, and for clinical diagnoses, lead to an uncomfortable and/or emotionally taxing experience, and, in the worst cases, misdiagnosis. In this work, we design a system to explicitly accommodate coarse and curly hair, and demonstrate that, across time, our electrodes, in conjunction with appropriate braiding, attain substantially (~10x) lower impedance than state-of-the-art systems. This builds on our prior work that demonstrated that braiding hair in patterns consistent with the clinical standard 10-20 arrangement leads to improved impedance with existing systems.

bioengineering↗

bioRxiv2019

Daratumumab induces mechanisms of immune activation through CD38+ NK cell targeting

Daratumumab (Dara), a multiple myeloma (MM) therapy, is an antibody against the surface receptor CD38, which is expressed not only on plasma cells but also on NK cells and monocytes. Correlative data have highlighted the immune-modulatory role of Dara, despite the paradoxical observation that Dara regimens decrease the frequency of total NK cells. Here we show that, despite this reduction, NK cells play a pivotal role in Dara anti-MM activity. CD38 on NK cells is essential for Dara-induced immune modulation, and its expression is restricted to NK cells with effector function. We also show that Dara induces rapid CD38 protein degradation associated with NK cell activation, leaving an activated CD38-negative NK cell population. CD38+ NK cell targeting by Dara also promotes monocyte activation, inducing an increase in T cell costimulatory molecules (CD86/80) and enhancing anti-MM phagocytosis activity ex-vivo and in vivo. In support of Daras immunomodulating role, we show that MM patients that discontinued Dara therapy because of progression maintain targetable unmutated surface CD38 expression on their MM cells, but retain effector cells with impaired cellular immune function. In summary, we report that CD38+ NK cells may be an unexplored therapeutic target for priming the immune system of MM patients.

cancer biology↗

bioRxiv2019

A generalizable multivariate brain pattern for interpersonal guilt

Feeling guilty when we have wronged another is a crucial aspect of prosociality, but its neurobiological bases are elusive. Although multivariate patterns of brain activity show promise for developing brain measures linked to specific emotions, it is less clear whether brain activity can be trained to detect more complex social emotional states such as guilt. Here, we identified a distributed Guilt-Related Brain Signature (GRBS) across two independent neuroimaging datasets that used interpersonal interactions to evoke guilt. This signature discriminated conditions associated with interpersonal guilt from closely matched control conditions in a cross-validated training sample (N = 24; Chinese population) and in an independent test sample (N = 19; Swiss population). However, it did not respond to observed or experienced pain, or recalled guilt. Moreover, the GRBS only exhibited weak spatial similarity with other brain signatures of social affective processes, further indicating the specificity of the brain state it represents. These findings provide a step towards developing biological markers of social emotions, which could serve as important tools to investigate guilt-related brain processes in both healthy and clinical populations.

neuroscience↗