Biology subjects

Brock, J.

Publications and source records attributed to Brock, J..

9 recordsLinked to original sources

bioRxiv2026

Weak supervision of H&E slides reveals systems-level biology and functional states that govern therapeutic resistance

Precision oncology lacks scalable methods to identify the mechanisms that mediate therapeutic resistance for individual patients. Resistance often arises from focal cellular niches that are obscured by bulk profiling and costly to resolve with multi-omics. Here, we show that deep learning (DL), applied to routine histology, can localize focal tissue regions enriched for therapeutically relevant biology. Using 3111 breast cancer H&E slides with matched bulk transcriptomics, we trained weakly-supervised DL models to infer activities of immune, metabolic, and tumor-intrinsic phenotypes implicated in therapeutic resistance (AUROC>0.80; PCC>0.64). Accurate inference of these phenotypes should identify tissue regions enriched for the corresponding biological signal. Therefore, we validated phenotype inference and spatial localization with complementary analyses. Tissue-matched multiplexed immunofluorescence showed concordance between inferred immune states and corresponding cell fractions (p=0.006-0.106). Across multi-institutional cohorts, model-derived phenotypes recovered expected relationships with therapeutic outcomes (p<0.045). Finally, in a blinded evaluation, pathologists confirmed that model-derived high-attention regions were enriched for phenotype-specific morphology (p<2.408*10-5). Because evaluated phenotypes represent diverse mechanisms of resistance across therapeutic modalities, these findings provide a foundation for resistance-directed localization using therapeutic outcomes as supervision. By directing deep profiling toward model-prioritized regions, this framework could enable scalable nomination of candidate mediators of resistance for subsequent functional validation across real-world patient populations. One sentence summaryWeakly supervised deep learning localizes focal tissue regions enriched for therapeutically relevant biology in routine histology, thus offering a scalable strategy to study therapeutic resistance across large patient populations.

biophysics↗

bioRxiv2025

ESPWA: a deep learning-enabled tool for precision-based use of endocrine therapy in resource-limited settings

Immunohistochemistry for estrogen receptor (ER) expression is often unavailable in low-and-middle-income countries (LMICs), leading to empiric use of endocrine therapy (ET) and unnecessary toxicity in ER-negative patients. To address this unmet need, we developed ESPWA, a deep-learning model trained on 3448 H&E slides and tissue-matched ER status from breast cancer patients treated at Zanmi Lasante (ZL), Haiti. A model trained on The Cancer Genome Atlas (TCGA) exhibited substantial domain shift when applied to the ZL cohort, with AUROCs dropping from 0.846 on TCGA cross-validation to 0.671 on the ZL cohort. In contrast, ESPWA demonstrated improved performance on ZL cross-validation (AUROC=0.790; p=0.005). In an independent test set of 134 Haitian patients with parallel slides prepared and scanned in Mirebalais Hospital (Haiti) and Brigham and Womens Hospital, ESPWA was robust to variations in slide preparation, quality, and scanners, achieving AUROCs of 0.794 on BWH-prepared WSIs and 0.805 on Mirebalais-prepared WSIs. Prospective studies using ESPWA are underway in sub-Saharan Africa to evaluate its utility in informing precision-based use of ET.

cancer biology↗

bioRxiv2024

Structure, dynamics and evolution of the Candida albicans multi-drug resistance ABC transporter CDR1

The pleiotropic drug resistance transporter Cdr1 from Candida albicans plays a crucial role in antifungal resistance. Here, we present high-resolution cryo-electron microscopy structures of C. albicans Cdr1 in multiple functional states: nucleotide-bound, substrate-bound, and apo forms. The 3.5 [A] resolution structure of Cdr1 in complex with ATP and ADP reveals the molecular details of its asymmetric nucleotide-binding sites (NBS), with ATP bound to the deviant NBS1 and ADP to the canonical NBS2. Structures of Cdr1 bound to rhodamine 6G (3.4 [A]) and Oregon Green 488 (3.5 [A]) in complex with these nucleotides elucidate the pleiotropic substrate-binding pocket and highlight how nucleotide exchange drives conformational change required for transport. Additionally, we determined a 3.7 [A] resolution structure of Cdr1 in the detergent LMNG without nucleotides, as well as a 3.5 [A] resolution structure with nucleotides but no substrate, representing an apo state. We complemented these structural insights with molecular dynamics simulations to understand substrate binding dynamics, ancestral sequence reconstruction to trace the evolution of key functional motifs, and analysis of clinical isolates from sequence databases to identify potential resistance-associated variations. Comparison of these structures provides new insights into the conformational changes associated with the transport cycle of this asymmetric ABC transporter, revealing how ATP binding at the deviant NBS1 allosterically regulates the canonical NBS2, driving ATPase activity. This work significantly advances our understanding of the molecular mechanisms underlying multidrug resistance in pathogenic fungi and provides a structural and evolutionary framework for the rational design of Cdr1 inhibitors to combat antifungal resistance.

microbiology↗

bioRxiv2024

Optimization of H9c2 differentiation leads to calcium-active and striated cardiac cells without addition of retinoic acid.

As a reliable alternative to animal testing in cardiovascular research, it is crucial to improve differentiation of immortalized cell lines. In this study, we focused on optimizing the differentiation efficiency of the H9c2 cell line into cardiomyocytes using a high-throughput, automated image processing approach. While previous studies used protocols involving retinoic acid to enhance cardiac differentiation, we applied a simplified medium composition that results in higher differentiation rates. Along that line, we differentiated H9c2 cells into cardiomyocytes, which not only showed sarcomere-characteristic striation but also periodic intracellular calcium signaling for the first time. As a second step, we examined the potential application of polyacrylamide hydrogels (E = 12 kPa) with defined fibronectin coating densities. The optimum fibronectin density of 2.6 {micro}g/cm2 found for single cells was investigated to further improve the differentiation efficiency. However, the differentiation and proliferation dynamics dominate the adhesion forces between the cells and the hydrogel, and thus, result in premature clustering and detachment. In conclusion, we identified an optimized differentiation protocol and provided a basis for the further investigation necessary to potentially use hydrogels as natural cell environment, aiming to raise the differentiation efficiency even more.

developmental biology↗

bioRxiv2024

High-throughput optimisation of protein secretion in yeast via an engineered biosensor.

Secretion of high value proteins and enzymes is fundamental to the synthetic biology economy; allowing continuous fermentation during production and protein purification without cell lysis. Most eukaryotic protein secretion is encoded by an N-terminal signal peptide; however, the strong impact of signal peptide sequence variation on the secretion efficiency of a given protein is not well defined. Despite high natural signal peptide sequence diversity, most recombinant protein secretion systems employ only a few well characterised signal peptides. Additionally, the selection of promoters and terminators can significantly affect secretion efficiency, yet screening numerous genetic constructs for optimal sequences remains inefficient. Here, we have adapted a yeast G-protein coupled receptor biosensor, to measure the concentration of a peptide tag that is co-secreted with any protein of interest. Protein secretion efficiency can thus be quantified via the induction of a fluorescent reporter that is upregulated downstream of receptor activation. This enables high-throughput screening of over 6000 combinations of promoters, signal peptides and terminators, assembled using one-pot Combinatorial Golden Gate cloning. We demonstrate this biosensor can quickly identify best combinations for secretion and quantify secretion levels. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=188 HEIGHT=200 SRC="FIGDIR/small/594099v2_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@87d612org.highwire.dtl.DTLVardef@1f73d24org.highwire.dtl.DTLVardef@cf35a7org.highwire.dtl.DTLVardef@1cb8a3b_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2022

Hearing the world differently: Examining predictive coding accounts of autism using MEG

Predictive coding accounts of autism suggest that autistic perception is characterised by divergent precision weighting. The precise nature of this divergence, however, is debated. Here, we sought to disentangle competing predictive coding accounts of autism by testing them at a neural level. To this end, we used paediatric magnetoencephalography to record the auditory evoked fields of 10 young autistic children (M = 6.2 years, range = 4.2- 8.6) and 63 neurotypical children (M = 6.1 years, range = 3.0- 9.8) as they listened to a roving auditory oddball paradigm. For each participant, we subtracted the evoked responses to the standard from the deviant pure tones to calculate the mismatch field MMF: an electrophysiological component that is widely interpreted as a neural signature of predictive coding. We found no significant differences between the two groups MMF amplitudes, p > .05. An exploratory analysis indicated larger MMF amplitudes in most of the autistic children compared to their average-age-matched neurotypical counterparts, p < .05. We interpret these findings as preliminary evidence in support of the inflexibly high prior and sensory precision account, and against the inflexibly low prior-relative-to-sensory precision accounts of autistic perception. HighlightsO_LIWe used paediatric MEG to compare autistic and neurotypical MMFs amplitudes. C_LIO_LIExploratory case-cohort analyses revealed mostly larger MMFs in autistic cases. C_LIO_LILarger MMFs support the notion of precise, inflexible prediction errors in autism. C_LI

neuroscience↗

bioRxiv2019

Co-Increasing Neuronal Noise and Beta Power in the Developing Brain

Accumulating evidence across species indicates that brain oscillations are superimposed upon an aperiodic 1/f - like power spectrum. Maturational changes in neuronal oscillations have not been assessed in tandem with this underlying aperiodic spectrum. The current study uncovers co-maturation of the aperiodic component alongside the periodic components (oscillations) in spontaneous magnetoencephalography (MEG) data. Beamformer-reconstructed MEG time-series allowed a direct comparison of power in the source domain between 24 children (8.0 {+/-} 2.5 years, 17 males) and 24 adults (40.6 {+/-} 17.4 years, 16 males). Our results suggest that the redistribution of oscillatory power from lower to higher frequencies that is observed in childhood does not hold once the age-related changes in the aperiodic signal are controlled for. When estimating both the periodic and aperiodic components, we found that power increases with age in the beta band only, and that the 1/f signal is flattened in adults compared to children. These results suggest a pattern of co-maturing beta oscillatory power with the aperiodic 1/f signal in typical childhood development.

neuroscience↗

bioRxiv2019

Towards an individualised neural assessment of receptive language in children

PurposeWe aimed to develop a non-invasive neural test of language comprehension to use with non-speaking children for whom standard behavioural testing is unreliable (e.g., minimally-verbal autism). Our aims were three-fold. First, we sought to establish the sensitivity of two auditory paradigms to elicit neural responses in individual neurotypical children. Second, we aimed to validate the use of a portable and accessible electroencephalography (EEG) system, by comparing its recordings to those of a research-grade system. Third, in light of substantial inter-individual variability in individuals neural responses, we assessed whether multivariate decoding methods could improve sensitivity. MethodsWe tested the sensitivity of two child-friendly covert N400 paradigms. Thirty-one typically developing children listened to identical spoken words that were either strongly predicted by the preceding context or violated lexical-semantic expectations. Context was given by a cue word (Experiment 1) or sentence frame (Experiment 2) and participants either made an overall judgement on word relatedness or counted lexical-semantic violations. We measured EEG concurrently from a research-grade system, Neuroscans SynAmps2, and an adapted gaming system, Emotivs EPOC+. ResultsWe found substantial inter-individual variability in the timing and topology of N400-like effects. For both paradigms and EEG systems, traditional N400 effects at the expected sensors and time points were statistically significant in around 50% of individuals. Using multivariate analyses, detection rate increased to 88% of individuals for the research-grade system in the sentences paradigm, illustrating the robustness of this method in the face of inter-individual variations in topography. ConclusionsThere was large inter-individual variability in neural responses, suggesting inter-individual variation in either the cognitive response to lexical-semantic violations, and/or the neural substrate of that response. Around half of our neurotypical participants showed the expected N400 effect at the expected location and time point. A low-cost, accessible EEG system provided comparable data for univariate analysis but was not well suited to multivariate decoding. However, multivariate analyses with a research-grade EEG system increased our detection rate to 88% of individuals. This approach provides a strong foundation to establish a neural index of language comprehension in children with limited communication.

animal behavior and cognition↗

bioRxiv2018

Topological segregation of functional networks increases in developing brains

A growing literature conceptualises human brain development from a network perspective, but it remains unknown how functional brain networks are refined during the preschool years. The extant literature diverges in its characterisation of functional network development, with little agreement between haemodynamic- and electrophysiology-based measures. In children aged from 4 to 12 years, as well as adults, age appropriate magnetoencephalography was used to estimate unbiased network topology, using minimum spanning tree (MST) constructed from phase synchrony between beamformer-reconstructed time-series. During childhood, network topology becomes increasingly segregated, while cortical regions decrease in centrality. We propose a heuristic MST model, in which a clear developmental trajectory for the emergence of complex brain networks is delineated. Our results resolve topological reorganisation of functional networks across temporal and special scales in youth and fill a gap in the literature regarding neurophysiological mechanisms of functional brain maturation during the preschool years.

neuroscience↗